1998 Intelligent impact control in anti-personnel mine

University of WollongongResearch Online

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1998

Intelligent impact control in anti-personnel minedetectionAlireza Mohammad ShahriUniversity of Wollongong

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Recommended CitationShahri, Alireza Mohammad, Intelligent impact control in anti-personnel mine detection, Doctor of Philosophy thesis, School ofElectrical, Computer and Telecommunications Engineering, University of Wollongong, 1998. http://ro.uow.edu.au/theses/1952

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Intelligent Impact Control in Anti-Personnel Mine

Detection

A thesis submitted in fulfilment of the requirements for

the award of the degree

PhD

from

T TNIVERSITY

\ \ 70LLONGONG

By

Alireza Mohammad Shahri

B.Sc. Khajeh Nasir Toosi University, Tehran, Iran, 1985

M.E. (Hons) University of Wollongong, Wollongong, Australia, 1994

SCHOOL OF ELECTRICAL, COMPUTER AND TELECOMMUNICATIONS

ENGINEERING

August 1998

//

DECLARATION

This is to certify that the work presented in this thesis was carried out by the

author in the School of Electrical Computer and Telecommunications

Engineering at the University of Wollongong, and has not been submitted to any

other university or institute.

Alireza M o h a m m a d Shahri

///

To all victims of injustice

Thanks and love to those who sacrificed their life

and wealth to establish God rules on the earth.

Special thanks and love to Imam Hussein (AS) the

extraordinary teacher of freedom and martyrdom

who sacrificed his life and all his family to teach

how should resist against oppressors in the history.

Next, a warm thank to my wife, my son

Mohammaad, and my daughters Hosna and Zoha

who have supported and put up with me throughout

the course patiently.

IV

ACKNOWLEDGMENTS

In the name of God

Praise to God, the Cherisher and the Sustainer of the world. Without the

strengths and blessings from Him, I simply could not come to this stage.

However, whoever is not thankful to people is not thankful to God. Therefore,

some valuable contributions must be acknowledged here.

I would like to thank m y supervisor, Associate Professor Fazel Naghdy for his

invaluable guidance and supervision throughout this research work. In particular

I would like to thank him for his thorough review of m y thesis and published

papers.

I would also wish to thank m y friends Ali Yazdian, Ali Jalilian, Mohsen Kahani,

Peter Vial and Philip Ciufo for their valuable and helpful discussions. The

technical and adminishative supports of the departmental staff especially Carlo

Giusti, Joe Tisiano, Frank Mikk, Brian Webb, Steve Petrou, Tracy O'Keefe and

Maree Fryer are also acknowledged.

M y gratitude also goes to the Ministry of Culture and Higher Education ( M C H E )

of the Islamic Republic of Iran and the Department of Education, Employment

and Training (DEET) of Australia for their assistance with financial support and

postgraduate research scholarship (OPRS).

V

ABSTRACT

According to the International Committee of the Red Cross and Red Crescent

Societies (ICRC), there are approximately 110 million land-mines scattered

around the world in 64 countries. There are also as many mines in the stockpiles

around the world waiting to be deployed. As the result of explosions of mines,

around 2000 people are killed or maimed monthly. The victims are mostly

civilians including w o m e n and children who are trapped by mines after the end of

hostilities.

At present mine clearance mostly takes place manually. Unfortunately, on

average for every 5000 mines cleared one mine clearer is killed. Using the current

approach, it would take more than 1,100 years to clear the mines planted in the

world at a cost of US$33 billion.

The main focus of this thesis is to investigate the feasibility of developing a

robotic arm to detect buried Anti-Personnel (AP) mines in the field and hence to

overcome the above mentioned problems. The hand-prodding manual demining

procedure using a bayonet is simulated using a single degree force sensing

robotic arm.

The robotic device inserts a bayonet into the soil. A strategy to control the

bayonet is developed by modelling the dynamics of the manipulator and

environment, while adapting for variation in the stiffness sensed by the bayonet

when it comes in contact with the mine or any other object in the soil. A n explicit

impact control scheme is applied as the main control scheme, while three

different intelligent control methods are designed to deal with uncertainties and

varying parameters of the environment.

VI

A n analytical object recognition algorithm based on multiple prodding is

developed. A multi-probe mine detection mechanism with the ability to detect a

mine faster than a single-probe mechanism is also proposed.

All the developed algorithms are validated through computer simulation and

experimented work. The intelligent control algorithms have outperformed the

conventional controllers in all of the case studies. They have also produced

performances acceptable for demining operation.

VII

TABLE OF CONTENTS

DECLARATION II ACKNOWLEDGMENTS IV ABSTRACT V TABLE OF CONTENTS VE LIST OF FIGURES X LIST OF TABLES XIV

Chapter 1: Introduction 1 1.1. Introduction 2 1.2. Need for a Robotic Mine Detector 2 1.3. Problem Statement 3 1.4. Approach 4 1.5. Significance and Contribution of the Work 5 1.6. Thesis Aim and Objectives 6 1.7. Overview of Thesis 7 1.8. Publications Relating to Thesis 8

Chapter 2: Background 10 2.1. Introduction 11 2.2 Current Status of A P Land Mine Clearance Activities 11 2.3 A P Land Mine Detection Techniques 12 2.4. Automated Prodding Systems 16 2.5. A Review of Force/Impact Control Methods 27 2.6. Force/impact Control Methods 29 2.7 Summary 35

Chapter 3: Robot Arm and Environment Modelling 36 3.1. Introduction 37 3.2. Soil Dynamics 37 3.3. Dynamics of the Mine 40 3.4. Arm/Sensor model 42 3.4.1. Mathematical Modelling 43 3.4.2. Validation of the Model 55

3.5. A S M O D Modelling 57 3.5.1. Associative Memory Networks (AMN) 57 3.5.2. B-spline Basis Functions 60 3.5.3. One Dimensional B-Spline Model 62 3.5.4. Multi Dimensional B-spline Models 63 3.5.5. The A S M O D Model Presentation 64 3.5.6. A S M O D as a Neuro-Fuzzy Algorithm 67

VIII

3.5.7. A S M O D (Neuro-Fuzzy) Model Construction 70 3.5.8. A S M O D (Neuro-Fuzzy) Model Validation 72

3.6. Conclusion 75

Chapter 4: Design of Proposed Intelligent Impact Control Methods 76 4.1. Introduction 77

4.2. Impact Control 78 4.2.1. Impact Dynamics Model 78

4.2.2. Impact Control Guidelines 80

4.3. Neuro-Fuzzy Control 81 4.3.1. Direct Neuro-fuzzy Adaptive Control 82 4.3.2. Indirect Neuro-fuzzy Control 83

4.4. Design of Adaptive Indirect Neuro-Fuzzy Controller 85 4.4.1. Neuro-fuzzy Controller Design Based on the A S M O D Algorithm 88 4.4.2. Inverse A S M O D Model Construction 88

4.5. Intelligent Impact Control Design 89 4.5.1. Neuro-Fuzzy Adaptive Gain Impact Controller (NFAGC) 90 4.5.1.1. PI Velocity Control Design 90 4.5.1.2 Proportional Force Control Design 91 4.5.1.3. Simulation Scenario 94

4.5.2. Neuro-Fuzzy Impact Controller (NFIC) 97 4.5.2.1. Neuro-Fuzzy Inverse Model Construction 97 4.5.2.2. Feedforward NFIC without Velocity Controller 101 4.5.2.3. Feedforward NFIC with Velocity Controller 103

4.5.3. Neuro-Fuzzy Impact Control and PID Controller (NFIC/PDID) 105 4.5.3.1. P D Force Control Design 107 4.4.3.2. Simulation Results of the NFIC/PD Control Scheme 109 4.4.3.3. Simulation Results of the NFIC/PDPI Control Scheme 111

4.6. Conclusion 112

Chapter 5: Validation of Control Strategies 115 5.1. Introduction.... 116 5.2. Experimental Rig 116 5.2.1. D C Servo Motor Specifications 117 5.2.2. Load Cell Specifications 118 5.2.3. Linear Variable Differential Transformer (LVDT) 120 5.2.3.1. Operation of L V D T 121

5.2.4. Interface Circuits Description 123 5.3. Commissioning of the Experimental Rig 124

5.3.1. Hardware Problems 124 5.3.2. Software Problems 125 5.3.3. Implementation of Digital PID Controller 126

5.4. Validation ...127 5.4.1. Neruo-Fuzzy Adaptive Gain (NFAG) Controller 128

5.4.2. Neuro-Fuzzy Impact Controller (NFIC) 130 5.4.2.1. Feedforward NFIC with PI Velocity Controller 131

IX

5.4.2.2. Feedforward NFIC without Velocity Controller 133

5.4.3. NFIC/PID Results 136 5.4.3.1. Results of the NFIC/PD without Velocity Controller 136 5.4.3.2. Results of the NFIC/PDPI with Velocity Controller 138

5.5. Summary of Results 140 5.6. Conclusion.... 146

Chapter 6: Mine Detection Algorithm 148 6.1. Introduction 149

6.2. Land Mine Recognition Methods 149

6.3. Object Recognition Procedure 151 6.3.1. Feature Extraction of an Unknown Object 152

6.4. Estimation of the Object Radius and Centre Point 153 6.5. Mine Recognition Algorithm 157 6.6. Simulation 160 6.7. Experimental Results 163 6.8. Fuzzy Decision Making 166 6.9. Conclusion 172

Chapter 7: Conclusion and Further Research 173 7.1. Introduction 174

7.2. Summary of the Thesis 174 7.3. Future Research 176

References 178

Appendix A: Matcom Compiler 186 A.l. Introduction 186 A.2. Makefile for Building Standalone Executable Application 186

Appendix B: Algorithm in C++ Programming 188 B.l. Introduction 188 B.2. A Sample Code of the Intelligent Impact Controller 188 B.3. A Sample Code to build an A S M O D model 193

Appendix C: Plastic Cylindrical Anti-Personnel Mines 194

X

LIST OF FIGURES

Figure 2.1: A probe and two plastic A P land mines 12 Figure 2.2: Different methods of land mine detection 13 Figure 2.3: The lab. prototype deminer robot proposed by [Daw98] 17 Figure 2.4: The result of probing (b) and original image of three A P mines and a rock

(a) proposed by [Daw98] 18

Figure 2.5: Robot proposed by Anotoniae to detect buried mines [Ant95] 19 Figure 2.6: Pemex-B robot in grass and in packed 21 Figure 2.7: DETEC-1, the mobile and static unit [Gue97] 22 Figure 2.8: DETEC-2 [Gue97] 22 Figure 2.9: Close-up of probing mechanism [Ska96] 24 Figure 2.10: The prototype of automated prodding system and proposed terrain vehicle

to support the detection unit 25 Figure 2.11: Vehicle mounted mine detector ( V M M D ) [Ham96] 26 Figure 2.12: Explicit force controller block diagrams 28 Figure 2.13: Implicit force controller block diagrams 29 Figure 3.1: Result of dry beach sand (K=1.2 Kgf/m) 38 Figure 3.2: Result of wet beach sand (K=3.8 Kgf/m) 38 Figure 3.3: Result of river sand (2.6 Kgf/m) 39 Figure 3.4: Pottery clay (K =14 Kgf/m) 40 Figure 3.5: End-effector in contact with the environment 42 Figure 3.6: A/P cylindrical plastic mines (Picture Courtesy of Defence Science and

Technology Organisation) 42 Figure 3.7: Stiffness coefficients of the A/P cylinder plastic mine body and fuse (VS

50) 43 Figure 3.8: The dynamic model of the manipulator and end-effector in contact with the

environment 44 Figure 3.9: The open loop force model 47 Figure 3.10: A simple explicit force control with an inner velocity control loop 48

Figure 3.11: Step response and the root locus of the inner velocity loop using a PID controller 49

Figure 3.12: The root locus of the system with a PID velocity controller 50 Figure 3.13: Step response with the proportional force controller (Ke=14000, Kf=0.()25)

50 Figure 3.14: Velocity of the motor for the above step response 51 Figure 3.15: The step response corresponding to pair poles on the imaginary axis 53 Figure 3.16: Unit step response of the total system in 50 seconds. 53 Figure 3.17: The step response for Ke= 14() and Kt=().()25 54 Figure 3.18: Step response of the system with the designed PID controller contacting

the stiff environment (ke=28000 [Kgf/m]) 55 Figure 3.19: Root-locus of the system after removing poles on imaginary axis 55 Figure 3.20: Behaviour of the system using the designed compensator 57

Figure 3.21: Three-layer perceptron 59

Figure 3.22: B-spline type of an A M N s structure 60

XI

Figure 3.23: One-dimensional B-spline basis functions (degree 0, 1 and 2) for the Knot-Vector r\ = (1, 2, 3, 4, 5) 62

Figure 3.24: A two-dimensional B-spline basis functions with degree 1 and 2 in each dimension 63

Figure 3.25: The A S M O D algorithm structure for the example presented in Table 167 Figure 3.26: A typical triangular fuzzy membership function 68

Figure 3.27: Linguistic fuzzy output sets 70 Figure 3.28: A n Example of a Neuro-Fuzzy Model 72 Figure 3.29: Neuro-fuzzy adaptive gain control system 73 Figure 3.30: Open-loop position step response of the drive system 74 Figure 3.31: Simulation Results when stiffness is switching from 10 to 5000 75

Figure 4.1: Impact Dynamic Model 79 Figure 4.2: A general structure of a direct neuro-fuzzy adaptive controller 83 Figure 4.3: Structure of an indirect fuzzy adaptive control system. 84 Figure 4.4: A n online Feedforward Neural Network Model 85 Figure 4.5: Off-line inverse training mechanism 88 Figure 4.6: Neuro-fuzzy adaptive gain controller (NFAG) system 90 Figure 4.7: Step response of the inner velocity loop control 91 Figure 4.8: Gain-Varying Switch Non-linear System 92 Figure 4.9: A S M O D model to estimate the stiffness of the environment 93 Figure 4.10: Neuro-fuzzy adaptive gain controller (NFAG) system 94 Figure 4.11: S I M U L I N K graph of the N F A G control system 95 Figure 4.12: Environment model used in the simulation 95 Figure 4.13: Simulation results of N F A G and proportional force when stiffness changes

from 10 to 2500 [Kgf/m] 96 Figure 4.14: Simulation results of N F A G and proportional force when stiffness changes

from 10 to 5000 [Kgf/m] 96 Figure 4.15: Simulation results of N F A G and proportional force when stiffness changes

" from 10 to 7500 [Kgf/m] 97 Figure 4.16: Off-line inverse training mechanism 98 Figure 4.17: Graphical presentation of Sub-model No. 1 and No. 2 of the constructed

A S M O D model for NFIC controller 99 Figure 4.18: Graphical presentation of Sub-model No. 3 of the constructed A S M O D

model for NFIC controller 100 Figure 4.19: Validation of the constructed model for NFIC control 101

Figure 4.20: Block diagram of a NFIC method 102 Figure 4.21: Simulink graph for feed-forward NFIC 103 Figure 4.22: Simulation result of feedforward NFIC without velocity control loop when

stiffness switches from 10 to 5000 [Kgf/m] 103 Figure 4.23: Simulation result of feedforward NFIC without velocity control loop when

stiffness switches from 10 to 7500 [Kgf/m] 103 Figure 4.24: S I M U L I N K graph of the NFIC controller with PI velocity controller 104 Figure 4.25: Comparison of the simulation results with NFIC and without NFIC when

stiffness is switching from 10 to 2500 [Kgf/m] 104 Figure 4.26: Comparison of the simulation results with NFIC and without NFIC when

stiffness is switching from 10 to 5000 [Kgf/m] 105

xn Figure 4.27: Comparison of the simulation results with NFIC and without NFIC when

stiffness is switching from 10 to 7500 [Kgf/m] 105 Figure 4.28: NFIC controller block diagram 106

Figure 4.29: Root locus for the total system. 107 Figure 4.30: Step response of the PDPI explicit controller for the P D force controller

108 Figure 4.31: Step response of the PDPI explicit force controller 108 Figure 4.32: S I M U L I N K graph of the NFIC/PD control system 109 Figure 4.33: Simulation result of NFIC/PD when stiffness changes from 10 to 5000

[kgf/m] 110

Figure 4.34: Simulation result of NFIC/PD when stiffness changes from 10 to 7500 [kgf/m] 110

Figure 4.35: Simulation result of NFIC/PD when stiffness changes from 10 to 10000

[kgf/m] 110 Figure 4.36: S I M U L I N K graph of the NFIC/PDPI control system 111 Figure 4.37: Simulation result of NFIC/PDPI and PDPI when stiffness changes from 10

to 5000 [kgf/m] 112 Figure 4.38: Simulation result of NFIC/PDPI and PDPI when stiffness changes from 10 " to 7500 [kgf/m] 112

Figure 4.39: Simulation result of NFIC/PDPI and PDPI when stiffness changes from 10 to 10000 [kgf/m] 112

Figure 4.40: Force response comparison of all the proposed methods 114 Figure 5.1: End-effector connected to the force and L V D T sensor 117 Figure 5.2: Unbalanced mode Whetstone bridge 119 Figure 5.3: Cutaway view of the Schaevitz L V D T 121 Figure 5.4: Linear variable differential transformer 122 Figure 5.5: Experimental set up 123 Figure 5.6: Hardware configuration of deminer arm 124 Figure 5.7: Experimental result of N F A G Controller (A) 129 Figure 5.8: Experimental result of N F A G Controller (B) 129 Figure 5.9: Experimental result, of N F A G Controller (C ) 130 Figure 5.10: Experimental result of feedforward NFIC with PI velocity control loop (A)

131 Figure 5.11: Experimental result of feedforward NFIC with PI velocity control loop (B)

132

Figure 5.12: Experimental result of feedforward NFIC with PI velocity control loop (C ) 133

Figure 5.13: Experimental result of feedforward NFIC without velocity control loop (A) 134

Figure 5.14: Experimental result of feedforward NFIC without velocity control loop (B) 135

Figure 5.15: Experimental result of feedforward NFIC without velocity control loop (C ) 135

Figure 5.16: Experimental result of NFIC/PD controller (A) 137 Figure 5.17: Experimental result of NFIC/PD controller (B) 137 Figure 5.18: Experimental result of NFIC/PD controller (C ) 138 Figure 5.19: Experimental result of NFIC/PDPI controller (A) 139

XIII

Figure 5.20: Experimental result of NFIC/PDPI controller (B) 140 Figure 5.21: Experimental result of NFIC/PDPI controller (C ) 140

Figure 5.22: M a x i m u m overshoots of the force step response 143 Figure 5.23: Focus of the previous figure on NFIC/PDPI results 143 Figure 5.24: Comparison of the force range of different conventional and intelligent

methods 144

Figure 5.25: Comparison of the force range of different intelligent methods 144 Figure 5.26: Comparison of standard deviation of the force step response 145 Figure 5.27: Mode values for conventional and intelligent control methods 146

Figure 6.1: A buried anti-personnel mine beneath the soi 150 Figure 6.2: The three-probe robot in contact with a mine 151 Figure 6.3: Top view of the robot probes in contact with the cross section of a

cylindrical object 155 Figure 6.4: Side view of two contact points with a mine 156 Figure 6.5: A P mine recognition algorithm flow chart 159 Figure 6.6: Simulation based on different insertion points and different location for the

third c o ntac t p o i n ts 161 Figure 6.7: Simulation of A P mine recognition by multi-probe robot 162 Figure 6.8: Simulation of A P mine recognition by multi-probe robot 163 Figure 6.9: Objects which are used for experimental results 164 Figure 6.10: The Mamdani Fuzzy Inference System 167 Figure 6.11: Schematic diagram of fuzzy decision making 168 Figure 6.12: Inputs and output membership functions for F D M S 170 Figure 6.13: Outputs of the F D M S for eight objects 172

XIV

LIST OF TABLES

Table 4.1: A S M O D model construction for N F A G C 93 Table 4.2: A S M O D model construction for NFIC 99 Table 5.1: Hitachi D C servo motor specifications 117 Table 5.2: Load cell Specification 118 Table 5.3: Instrumentation amplifier specifications 120 Table 5.4: D C - L V D T specifications 123

Table 5.5: Performance of intelligent impact methods with PI velocity controller 141 Table 5.6: Statistical Comparison of the Intelligent Impact Control Methods 142 Table 6.1: Mine possibility table 153 Table 6.2: Experimental results for mine recognition algorithm 165 Table 6.3: Input and output variables of F D M S system and their characteristics 169 Table 6.4:: Rules of the fuzzy decision making 169 Table 6.5: Input and output data for F D M S 171

INTRODUCTION

Chapter 1 Introduction 2

1.1. Introduction

The main focus of this thesis is to investigate the feasibility of developing a

robotic arm to detect buried Anti-Personnel (AP) mines in the field. The need for

such a device and the problems associated with its development are initially

described in this chapter. The methodologies developed in this work to provide a

solution to some of the problems are then introduced. Finally the structure of the

thesis is presented and a summary of each chapter is given.

1.2. Need for a Robotic Mine Detector

According to the International Committee of the Red Cross and Red Crescent

Societies (ICRC), there are approximately 110 million land-mines scattered

around the world in 64 countries [Intl]. There are also as many mines in the

stockpiles around the world waiting to be deployed. As the result of explosions

of mines, around 2000 people are killed or maimed monthly. The victims are

mostly civilians including women and children who are trapped by mines after

the end of hostilities [Int2]. For every mine cleared, 20 are laid. In 1994, around

100,000 mines were cleared whereas 2 million new mines were planted. Overall,

anti-personnel mines are among the deadliest weapons used in the world today.

The United Nations Secretary-General has stated that "land-mines may be the

most widespread, lethal, and long-lasting form of pollution w e have yet

encountered" [Sta94].

At present mine clearance mostly takes place manually. Unfortunately, on

average, for every 5000 mines cleared, one mine clearer is killed. Using the

current approach, it would take more than 1,100 years to clear the mines planted

in the world at a cost of US$33 billion [Int2]. The hand-prodding technique is

the most reliable method of civilian mine clearance as it has a reliability of more


than 99.8%. A probe is manually insetted into the soil at a 30 degree angle,

approximately every five centimetres. W h e n an object is detected with higher

stiffness compared to the environment stiffness, more examinations are

conducted to identify the shape and size of the object. If the object is determined

to be a potential mine, a mine clearing team is called in to uncover or detonate the

object.

1.3. Problem Statement

In the work conducted in this thesis, the hand-prodding manual demining

procedure using a bayonet is simulated using a single degree force sensing

robotic arm. Four different stages can be identified in the task:

(a) The unconstrained and free motion of the arm towards the soil. At this

stage pure position control of the arm will be sufficient to maintain a stable

movement.

(b) Impact of the arm with the soil. At this stage, the arm encounters a rapid

change in the stiffness of the environment. Impact control is necessary to

prevent the oscillation of the arm, which may result in unstable behaviour of

the arm. Entry of the probe into the ground can be detected by a proximity

sensor.

(c) The movement of the arm in the soil. At this stage, the arm motion is under

physical constraints. In addition, there could be variations in the stiffness of

the environment as the arm comes in to contact with various objects in the

soil.

(d) Detection of an object in the soil and determining whether the object is a

mine. This requires various pattern recognition algorithms.


In this work an in depth study of the above phenomena is carried out. Various

methodologies are also inttoduced and developed to achieve a satisfactory

operation of the arm and a reliable detection of an A P mine.

1.4. Approach

The device inserts a bayonet into the soil. A suategy to control the bayonet is

developed by modelling the dynamics of the manipulator and environment, while

adapting for variation in the stiffness sensed by the bayonet when it comes into

contact with the mine or any other object in the soil. A n explicit impact control

scheme is applied as the main control scheme, while three different intelligent

control methods are designed to deal with uncertainties and varying parameters of

the environment.

Due to the nature of the task and the uncertainties associated with the model of

the system, the primary control methodology applied has been Intelligent Control

(IC). In the study carried out in this project, a neuro-fuzzy contioller is employed

to produce a compliant motion during impact control. Neural networks and fuzzy

systems are trainable dynamical systems which estimate the input-output

functions. Unlike statistical estimators, they estimate a function without a

mathematical model of how outputs depend on inputs. They are referred to as

model-free estimators [Kos92].

The approach adopted in this work is based on the class of (lattice-based)

Associative Memory Networks ( A M N s ) which have the abilities of universal

approximation and local generalisation. Examples of this class of networks

include the Radial Basis Function (RBF) network, the Cerebellar Model

Articulation Controller ( C M A C ) , the Basis (B)-spline network, Adaptive Spline

Modelling of Observation Data ( A S M O D ) and a certain class of fuzzy logic


network. The latter approach is selected as the main approach in this work to

implement the impact control.

Initially, a neuro-fuzzy adaptive gain controller (NFAGC) is designed to adapt

the force gain control according to the estimated environment stiffness. Then, a

feedforward impact controller (NFIC), which is based on the inverse dynamic

model of the aim in contact with the environment, is designed to control the

transient impact force if you move from free motion to constrained motion.

Finally the proposed NFIC, plus a conventional PID controller are employed to

switch from a PID controller to a neuro-fuzzy impact control (NFIC), when an

impact is detected. In this application, the characteristics of the environment

(soil) will change during the mine clearance operation. In other words, there is

uncertainty about the characteristics of the environment and its parameters.

One of the basic issues in intelligent control is to identify the model and the

variation of the system on-line. By introducing the ability of learning into the

control system, a plant becomes more flexible to deal with unpredictable and

complex, real-world environments. Learning, which is an integral part of any

intelligent control system, includes the heart of adaptive neural and fuzzy

modelling and control systems.

1.5. Significance and Contribution of the Work

The work carried out in this study is significant from different points of view. As

mentioned earlier, the A P mines currently cause a great deal of injury and death.

In addition, the current mine clearance methods employed are quite slow and

dangerous for the deminer. This work is a positive step towards the development

of a less dangerous and faster method of mine detection. Hence the project has

significant humanitarian values.

Chapter I Introduction 6

In addition, this work presents an in-depth study of intelligent control of a partly

structured environment. The methods developed for impact control and

constrained motion control of the mine detection arm are generic and can be

applied to similar situations in other applications.

Besides, all the introduced methods are validated through experimental work.

This highlights the effectiveness of the proposed methods in real time

applications.

1.6. Thesis Aim and Objectives

The primary aim of the project can be defined as developing appropriate

methodologies and techniques to mimic the hand-prodding manual demining

procedure using a single degree force sensing robotic arm. This aim has been

pursued by achieving the following objectives:

• A n in depth critical study of the previous work in this area.

• Modelling of the robotic arm and its environment using both mathematical and

intelligent methods.

• Design and development of intelligent impact controllers using neuro-fuzzy

methods.

• Design and development of mine detection algorithms.

• Validation of the developed methods using both computer simulation and

experimental work.

Chapter I Introduction 7 _

1.7. Overview of Thesis

The work carried out is presented in this thesis through seven chapters. The

problem statement, aim and objectives of the work, and a brief introduction to the

A P mine detection arm and major control schemes developed in the work are

provided in Chapter 1. The results of the literature search of the previous work in

the two areas of automatic mine detection and force control techniques is set in

Chapter 2. Chapter 3 is dedicated to the modelling of the robot arm, sensors and

environment. A mathematical model and a neuro-fuzzy model of the robot arm

in contact with the environment are developed and their performance is

compared. The advantages of the neuro-fuzzy model over the mathematical

model is also demonstrated in this chapter through computer simulation.

The intelligent impact control is studied in Chapter 4. An in depth analysis of

impact and the methods employed for its control are presented. Three neuro-

fuzzy impact control techniques developed in this work are then explained and

validated through computer simulation.

Chapter 5 presents the experimental rig used in the project and the experimental

work carried out to validate the impact control methods. In this chapter, the

software and hardware problems encountered in developing the experimental rig

are also described.

The mine detection algorithm developed in the work is described in Chapter 6.

A n analytical object recognition algorithm based on multiple prodding is

presented. A multi-probe mine detection mechanism with the ability to detect an

A P mine faster than a single-probe mechanism is also proposed. The proposed

algorithm is validated through computer simulation and experimental work.

Chapter I Introduction 8

A summary of the work conducted in this project, its outcomes and future work

to enhance the performance of the A P mine detector robot arm are provided in

Chapter 7. This chapter also provides the conclusions reached at in this work.

1.8. Publications Relating to Thesis

A. M. Shahri, F. Naghdy, "Neuro-Fuzzy Adaptive Torque Control of a SCARA

Robot", Proceedings qf the Australian New Zealand Conference on Intelligent

Information Systems (ANZIIS 96), IEEE 96TH8234, pp. 241-244, Adelaide,

South Australia, 18-20 November 1996.

A. M . Shahri, F. Naghdy, "Adaptive Neuro-Fuzzy Compliance Control with the

Ability of Learning", Proceedings of International Conference on Intelligent and

Cognitive Systems (ICICS-96), pp. 74-79, Tehran, Iran, September 23-26, 1996.

A. M . Shahri, F. Naghdy, "Intelligent Compliance Control in Anti-Personnel

Mine Detection", Proceedings of Fourth Annual Conference on Mechatronics

and Machine Vision in Practice (M2vip97), IEEE Computer Society, pp. 84-91,

Toowoomba, Australia, September 23-25 1997.

A. M . Shahri, F. Naghdy, P. Nguyen, "Neuro-Fuzzy Compliance Conttol with the

Ability of Skill Acquisition from Human Experts", Proceedings of First

International Conference on Conventional and Knowledge-Based Engineering

Systems (KES'97), pp. 442-448, Vol 2, IEEE 97TH8250, Adelaide, Australia,

May 21-23 1997.

A. M . Shahri, F. Naghdy, "Anti_Personnel Mine Detection Manipulator",

Proceedings qf the International Conference on Field and Service Robotics

(FSR'97), Australian Robot Association INC., Canberra, Australia 8-10

December 1997.


A. M . Shahri, F. Naghdy, "Mechatronics Approach to Detect Anti-Personnel

Mines", Proceedings qf the International Conference "Detection and

Remediation Technologies for Mines and Minelike Targets III", (SPIE'98), pp.

808-819, 13-17 April, 1998.

A. M . Shahri, F. Naghdy, "Anti-Personnel Mine Detection Algorithm Based on a

Multi-Probe Robotics Arm", To be published in Conference Proceedings oflARP

Workshop on Robotics for Humanitarian Demining, Toulouse, France, 14-15

September, 1998.

A. M . Shahri, F. Naghdy, "Neuro-Fuzzy Impact Control (NFIC) for Anti-

Personnel (AP) Mines Detection", To be published in Conference Proceedings qf

the Fifth International Conference on Control, Automation, Robotics and Vision

(ICARCV98), Singapore, 8-11 December, 1998.

Talaie, Afshad. Shahri, Alireza M. Talaie, Farhad, "Adaptive Spline Modelling of

Observation Data ( A S M O D ) : A Solution to the Problems in Conducting

Polymer-based Sensors", Journal of Synthetic Metals, p 63-67, v 79 n 1 Apr 30

1996.

A. M . Shahri, F. Naghdy, "Anti_Personnel Mine Detection Manipulator", in

"Field & Service Robotics" Springer-Verlag, ISBN: 185230392, July 1998.

A. M . Shahri, F. Naghdy, "Neuro-Fuzzy Compliance Control of Peg-in-Hole

Insertion", Submitted to International Journal of Robotics & Automation (IJRA).

BACKGROUND

Chapter 2 Background }j

2.1. Introduction

In this chapter two distinct reviews of different Anti-Personnel (AP) land mine

detection techniques and different force control strategies will be presented. The

first review will outline research directions currently pursued being in this area

and will highlight the contribution of the work conducted in this thesis. Five

major research works will be reviewed. These works cover the two major

approaches to mine detection: contact and non-contact sensing techniques.

As part of the second review, a review of previous force/impact control research

will be presented in Section 2.5. Then some of the new impact control methods

are discussed. Finally, a summary of this chapter is presented.

2.2 Current Status of AP Land Mine Clearance Activities

Currently, metal detectors are mostly employed as a first step in the demining

process. Metal detectors work by measuring the disturbance of an emitted

electromagnetic field caused by the presence of metallic objects in the soil

[Tsi96]. The hand-prodding technique which is one of the most reliable methods

of mine clearance, is used as the second step to locate the A P mines. A probe is

manually inserted into the soil at a 30 degree angle, approximately every five

centimetres. Figure 2.1: shows a probe and two plastic A P land mines.

Chapter 2 Background J2

_F-__ll

Figure 2.1: A probe and two plastic A P land mines

When an object with higher stiffness compared to the stiffness of the

environment is detected, more examinations are conducted to identify the shape

and size of the object. If the object is determined to be a potential mine, a mine

clearing team is called to uncover or detonate the object as the third and last step

of the demining procedure. The problem associated with this method is that A P

mines are intentionally fabricated with almost no metal parts, except for the

striker pin. Although, it is possible to increase the sensitivity of the metal

detectors to detect very tiny items (a tenth of a gram of metal at a depth of 10

cm), this will considerably increase the rate of the false alarms and therefore lead

to the detection of unwanted small debris and artefacts.

There is a considerable difference between military and humanitarian mine

clearance procedures. In the former case the aim is to make a quick breach in the

minefield usually with a success rate of around 80%, while in the latter approach

the demining process is expected to have a higher success rate of above 99.6%

[Nic96].

2.3 AP Land Mine Detection Techniques

The work carried out so far on A P mine detectors has centred on their sensory

aspects. Very few research groups have attempted to develop a specific mobile

Chapter 2 Background 13

robot to automatically manoeuvre the sensory device on the mine field. The non-

contact sensing techniques have a success rate of up to 9 0 % , which is accurate

enough for military applications but not for civil demining. It should be also

pointed that a mine detected by the non-contact sensing methods ultimately will

be located and cleared by employing a contact sensing technique such as manual

prodding.

The two major categories of mine detection approaches and various techniques

employed in each approach are illustrated in the chart shown in Figure 2.2. The

chart also highlights the specific characteristics of each method including the type

of the sensor, the stage of the development or the maturity of the technique, its

advantages, disadvantages, and cost/complexity.

Sensor

Maturity

Advantage

Mcadvant-gc

Cost

Human Fuiger

Available

Accurate

Reliable

Slow Dangerous

Low ^ J

Forte / Tactile

Near

Accurate

Reliable

Fast

Not Flexible

Medium

Induction Coil

Available

Very Common

Slow Inaccurate

Dangerous

Low

^DogNose ^ Odor Sensor Available

Good for Area Detection

Inaccurate Locating

Nodding is need

Medium

Antenna

Far

Fast

Complex

Heavy-Weight

High j

Infrared Senost

Near

Fast

Soil Dependant

Medium

Combination

Near

Depends on

Serauis "»"l

Complex

Medium/High V _ _ _ J

Figure 2.2: Different methods of land mine detection

2.3.1 Non-Contact Sensing Techniques

The current non-contact sensing methods tested in both laboratories and

landmine fields are:

• Conventional and Advanced Metal Detectors (Impulse M D ) • Infra-Red Sensors (IR)


• Ground Penetrating Radar (GPR)

• Dogs, e.g. Mechem Explosive and Drug Detection Systems(MEDDS)

• Elecuonic Dog's Nose

• Micro Electro Mechanical Systems ( M E M S )

• Bio-Sensors

• Millimetre Wave Radar ( M W R )

• Nuclear Methods and Nuclear Magnetic or Quadruple Resonance ( N M R / N Q R )

• Thermal Neutron Activation (TNA).

In the work conducted by Fritzche and Trinkhaus [Fri95], ground penetrating

radar (GPR) sensors and high sensitivity metal detectors have been used to

identify metal mines. The aim has been to eventually employ the two sensors in

parallel and to fuse the data produced by them to provide a more robust and

reliable detection process. G P R seems to be one of the few technologies feasible

for detection of anti-tank and anti-personnel mines. There is, however, a great

deal of work required to accommodate the technology in an appropriate field

device [Fri95]. The F O A team in Sweden has started a project in this area using

a 0.3-3 G H z G P R system [Chi95]. The Lawrence Livermore National Laboratory

(LLNL) has also developed and patented a new technology, the Micropower

Impulse Radar (MIR) [Bru94].

According to the information available on the World Wide W e b [Aze95], the U S

Army Research has provided funding for Duke University together with five

other institutions, including Caltech, Georgia Tech, Ohio State University and

Stanford University, to explore innovations in mine detection, ranging from a

microelectronic chemical-sniffing "nose", through-the-air ultrasound, to ground-

shaking seismic waves.

In a recent survey conducted by C. Bruschini and B. Gros from Demining

Technology Centre (DeTec), most of the current sensor technologies have been

studied in detail [Bru97]. Although all of these approaches have the capability to


detect A P mines, they generally suffer from various limitations and drawbacks,

which include sensitivity to weather/soil conditions, mine depth, poor

performance in heavy vegetation and wet soil, and by being not applicable in all

conditions. In order to overcome these limitations, researchers [Gue97] and

[Mcm96] are working on fusion techniques to fuse different data obtained from

multiple sensors to provide a more reliable mine detection device, with

considerably less false alarms.

In another critical review James Trevelyan [Tre97] has tried to be more realistic

and study more practical and real problems involved in mine detection methods.

He has particularly addressed the robotics researchers to learn from the land mine

problems and redirect their research toward more practical and worthy

approaches. According to his argument, robots have been tried at great expense,

but without success. H e also suggested that a robot arm was not an appropriate

solution for mine clearance. Research should preferably be directed towards

improving simple and low-cost robotics devices which can provide some useful

improvements in safety and cost-effectiveness.

According to Trevelyan [Tre97] the Pemex-B deminer robot proposed by Nicoud

[Nic95a] lacks a reliable mine detection sensor and the robot proposed by British

company E R A [Dan97], consisting of a robot arm and ground peneuating radar,

is not an appropriate device. He has also criticised all the armoured vehicles

which are claimed to withstand the effect of A P mines as they would set off the

mines by ground pressure or tripwires. All the work earned out on the

automation of the probing process by different research groups, such as Kenneth

Dawson-Howe [Daw97a] and [Daw97b] are blamed for having a non-realistic

assessment of the cost effectiveness of such robots, or do not address the practical


difficulties of dealing with stones, rubbish, roots and other extraneous objects

encountered in real minefields.

2.3.2 Contact Sensing Techniques

Manual prodding and automated prodding are the main techniques pursued in this

category. While some destructive techniques such as mobile mechanical

approaches could have been included, though have not been. In the destructive

methods, very heavy mobile mechanical systems are used to set off the A P mines.

Such an approach is only acceptable in military mine clearance applications not

in civilian mine clearance. As the focus of this research is on civilian mine

clearance, the destructive mechanical approaches are not addressed.

2.4. Automated Prodding Systems

Since all the automated prodding systems imitate manual prodding, they have

many common features. The most important feature of these systems is their

light weight to avoid accidental explosion of the mine if they move over them.

Since these systems are mainly used in underdeveloped parts of the world with

little high technical expertise and knowledge, they should be simple and easy to

maintain and operate. Cost efficiency is another critical issue which should be

considered likewise. There are a few research groups trying to design and

implement semi/full autonomous prodding systems to detect and locate A P

mines. These systems are also still in the laboratory condition test stages. Field

trials are essential to validate the performance and robustness of such devices.

2.4.1 Probot: Autonomous Probing Robot

This research is conducted in the Computer Vision and Robotics Research Group

of the Department of Computer Science at Trinity College Dublin. In this work a

robotics solution to the problem of automatic detection of APLs is proposed.


This solution is based on the physical detection of A P mines using a sharpened

probe in a fashion similar to that employed by human deminers. As the probe is

inserted into the ground (at 30 degrees relative to the horizon in order to avoid

triggering the landmine) the axial force applied to the probe is sensed. The force

information, together with absolute position information, is used to determine the

presence of buried objects [Daw97a]. Figure 2.3 illustrates the implemented

demonstrator system which is capable of scanning and inserting the probe into the

soil in order to locate unknown objects under the ground. The system [Daw98]

can accommodate an array of independent probes and a combination of other

sensors, such as a Metal Detector ( M D ) and Ground Penetrating Radar (GPR) to

speed up the probing task and increase the accuracy of the system.

Figure 2.3: The lab. prototype deminer robot proposed by [Daw98]

The system consists of an X Y table to allow probing over a limited test area. A n

electrical linear actuator drives the probe attached to a force sensor to sense

resistance to the probe. The system provides force and position (from an optical

encoder) data to control the probing task not to exceed a pre-selected threshold

(15 Newtons) and record the depth of insertion. The work conducted on the force

control of the probe does not seem to be considerable. In addition, it is assumed

Chapter 2 Background jg

that the leading edge of an object is probed in order to minimise the risk of

triggering the mine by probing it on the top surface. However, the proposed

object recognition algorithm is based on the data obtained from probing on the

top surface of the objects. Figure 2.4 shows the result of the object recognition

algorithm based on probing a test area consisting of three A P mines and a rock at

the bottom right of the image.

Figure 2.4: The result of probing (b) and original image of three A P mines and a rock (a) proposed by [Daw98]

According to the obtained results, Dawson-Howe claims that there is a significant

potential in the concept of force sensed automated probing. However it seems

that a significant amount of work still needs to be done to develop a system

which is reliable and robust enough to be used in the mine fields. Although this

research work seems very similar to the proposed prodding methods in this thesis,

both force/impact control methods and object recognition methods are totally

different. N o major research is presented on impact control when the robot arm

is approaching a laid mine under the soil, which is very dangerous task. O n the

other hand, the object recognition method presented in this research requires

many contact data points from the top surface of the laid object, which in itself is

a time consuming and dangerous task.

Chapter 2 Background jg

2.4.2 Ground Probing Sensor for Automated Mine Detection

In Zagreb, Croatia, D. Anotoniae [Ant95] from the Faculty of Electrical

Engineering and Computing, Department of Control and Computer Engineering

in Automation, Ministry of Interior, has studied the development of a semi-

autonomous ground probing land-mine detection system. The concept resembles

the process of manual probing by using a teleoperated mobile robot equipped

with an appropriate ground probing sensor. The work so far has focused on the

development of a force detection sensor. The available information neither

reveals the nature of the detection algorithm nor any experimental results on the

performance of the device. The system consists of one linear servomechanism

attached to a 30 cm long needle. Force and position data are used to detect the

depth of a buried object. Anotoniae [Ant95] has proposed a method to determine

the contour of the buried object by analysing the collected data from probing

every 2.5 cm. According to this method, it is necessary to use more probing

around the point where an obstacle is detected. Figure 2.5 illustrates the

proposed semi-automated probing system remotely controlled by a human

operator.

Figure 2.5: Robot proposed by Anotoniae to detect buried mines [Ant95]

In Figure 2.5, two robotics arms are shown whereas the research has only focused

on the arm needed to detect the buried mine. Detection of the touch point is


performed by the force profile analysis. As there is no force measured before

touching the ground, contact with the surface of the soil builds up an increasing

force profile proportional to the depth of the insertion. According to Anotoniae,

the acting force should be below the activating force triggering the mine fuse

which is as low as 10 N for A P landmines. However, how this significant goal is

achieved is not clarified. The object recognition algorithm presented by

Anotoniae, interprets the collected data by converting it to a 256 level grey scale

image. According to this algorithm, the maximum sensing depth corresponds to

white and the sensor reference level is black. A proper threshold level

adjustment is used to enhance the quality of the image. Another algorithm wire

model is presented in this work which is claimed to have the advantage of

exuacting the information of exact position and depth of a buried object. Finally,

Anotoniae has concluded that the proposed system is designed to fill a gap

between the accurate but slow and hazardous manual probing technique, and

various non-contact sensing methods with insufficient accuracy for civil

demining.

2.4.3 Pemex-B, a Low Cost Robot for Searching Anti-Personnel Mines

DeTeC (Demining Technology Center) has developed [Nic95] a semi-

autonomous light weight and low cost robot for A P mine search using a metal

detector sensor. The work has mostly focused on the development of a reduced

scale simple robot. The sensory aspect of the system for mine detection has not

been addressed in detail. Figure 2.6 shows the Pemex-B robot in grass searching

for A P mines and packed in its case.


-_--'-**• *

~ ''/ft-' • ' ??•

. - r'< [ S ^

ft**":'-

• • ? \

f ' *••,

\„ .

' • ^ • ' ; . ,

.., f*l£**"

'= fs^HfaS ' "llf >____ ^^ÎM

MJ ;>*_

S N. i ,r_Jfln

* ^

"'^

Figure 2.6: Pemex-B robot in grass and in packed

It is clear from Figure 2.6 that the robot is low in both cost and weight. This

system is not accurate enough in terms of distinguishing between a mine and

metal debris, which often gives a similar metal detector signal. DeTeC therfore

has developed a new sensory system [Bru96] and [Gue97] to reduce the number

of false alarms. This system, which is shown in Figure 2.7, can be used either by

a human operator or an autonomous robot.

According to Guerne, whose work is concerned with overcoming the

shortcomings of the Pemex-B robot, the most promising sensor combination

includes a metal detector and ground penetrating radar [Gue97]. In the initial

work, an off-the-shelf G P R radar is used assuming that each deminer group has a

metal detector and there is no need to combine M D and GPR. The first prototype

system (DETEC-1) as shown in Figure 2.7, is composed of two main parts: the

mobile and the static unit. The mobile unit is composed of an antenna, a position-

tracking bar and a box containing the radar, interface electronics, a radio modem

and a battery. The static unit where is just the computing and displaying device.


Figure 2.7: DETEC-1, the mobile and static unit [Gue97]

The concept of using two units has proved to be one of the drawbacks of this

system. It is hard for a deminer to trust the decision taken by someone at a safe

distance based on the information received from the static unit. Deminers prefer

to see the data and analyse it themselves rather than someone else far from the

minefield to do it for them. To overcome this problem, DETEC-2 as shown in

Figure 2.8, is integrated in one unit. The major modification in this prototype is

that the G P R antenna to scan the mine field is not carried by hand any more. It is

clear from Figure 2.8 that the G P R antenna has now been located on the top of

the system. The antenna is capable of scanning the front area from left to right

angular motion and back to forward linear motion.

Figure 2.8: DETEC-2 [Gue97]


More details can be found in [Bro98] which reports some results of the DETEC-2

being tested on a real mine field in Cambodia.

2.4.4 A Mechanical Means of Land Mine Detection

This work was initially started as a landmine project for a group of mechanical

students at the University of Alberta, supported by the Defence Research

Establishment Suffield (DRES). Initially a list of minimum design requirements

was given to the students. The best research proposal satisfying the requirements

was then chosen for further work. The final design consisted of an automated,

multiple-prodding device designed to be mounted on the front of a remotely

controlled all terrain vehicle (ARGO).

The detection unit consisted of two sets of probes for Anti-Personnel and Anti-

Tank (AT) mines mounted by springs to sliders attached to the connecting rods.

A slider-crank mechanism is used to move the probes. A motor on the rack

drives two parallel crankshafts, one for the A P probes and the other for the A T

probes. Figure 2.9 shows a close-up of the probes and the crank shaft assembly.

The connecting rods convert the motion of the crank shafts into a reciprocating

motion on the probes.


Figure 2.9: Close-up of probing mechanism [Ska96]

At the back of each probe is an accelerometer, which measures the vibration from

the probe as it strikes an object. The amplitude and frequency of the vibration

signals change according to the object in contact with the probes.

The work has successfully completed the consti-uction of a test stand system,

including a pneumatic driver system, data acquisition and control system.

According to the project report, an automated signal processing and analysis

procedure has also been developed to recognise the unknown buried objects. N o

report is available on the success of the signal processing and other aspects of the

device.

The D R E S implemented test system [Ska96], consists of two pneumatic

cylinders, a steel frame to provide support and adapt for evaluation of different

operating conditions and various instrumentation. The insuumentation includes

an accelerometer, two pressure uansducers, an L V D T , and a rotary

potentiometer. The prodder system is actuated by two pneumatic cylinders to


penetrate probes into the soil. A mechanism is developed to improve object

identification by decoupling the prodder entirely from the insertion cylinder.

This system has the ability to record and analyse the signature vibrations from the

prodder and distinguish between hard and soft objects buried in the ground.

Since soil condition has not been considered in producing the results, a

modification procedure is required to adapt the system to different soil

conditions. Striking an A P mine with the probes has the potential of exploding

the mine. Figure 2.10 shows the implemented prototype detection unit and

proposed terrain used in this system.

Figure 2.10: The prototype of automated prodding system and proposed terrain

vehicle to support the detection unit

As is clear from the picture, the implemented detection unit and its supporter

vehicle are heavy and hence unsuitable for use in an A P landmine field. Using

pneumatic actuation system has made the system fairly complex, heavy and

difficult to maintain.


2.4.5 Vehicle Mounted Mine Detector

The Vehicle Mounted Mine Detector ( V M M D ) is one of the commercially

available mine detection systems, providing remotely controlled capability to

detect landmines in on-road and off-road environments [Ham96]. A photograph

of V M M D during mine detection in a test mine field, is given in Figure 2.11.

Figure 2.11: Vehicle mounted mine detector ( V M M D ) [Ham96]

The Vehicle Mounted Mine Detector which is an example of a multi-sensor

system, detects on/off-road landmines on a commercially available remote control

platform. This system provides deminers with the ability to detect antipersonnel

and antitank mines with minimal metal content using a flexible metal detection

array for close-in detection, and infra-red (IR) and ultraviolet (UV) sensors for

stand off detection. The system also provides the capability to record mine

locations using a Differential Global Positioning System (DGPS). The

demonstrator system (Figure 2.11) was built to conduct testing in 1995. The

vehicle width metal detection unit is commercially available and costs about

$500,000.

Chapter 2 Background 27_

2.5. A Review of Force/Impact Control Methods

Industrial robots have a wide variety of applications in industry. Most of the

robots require control of position in some Cartesian degree of freedom and

control of force in others. Apart from some exceptions such as application of

robots in paint spray or visual servoing, the majority of the robotics applications

require interaction with the environment. Position controlled manipulators are

ideal for tasks, such as pick and place or spot welding with no interaction or

minor interaction with the environment. However in these applications a very

small position error, while the arm is in contact with the stiff environment, can

cause a large reaction forces exerted on the arm. There are other robotics

applications, such as pushing, polishing, assembly, scrapping, grinding, and

twisting in which force control is a vital requirement. In these types of tasks,

there should be at least one degree of freedom force controlled link in addition to

the position control of other links.

Impact phenomena plays an important role in the field of robotics particularly

when the robot is in contact with the environment. The transition between the

conditions of free motion and constrained motion which induces an undesirable

reaction force, is called impact phenomenon. The most common assumption

made in the control of a robotic manipulator is that the robot is either moving in a

free space or is constrained by a known environment.

Robot manipulators and drive systems can experience instability or poor control

performance after collision with a surface. Similarly, during mechanical mine

detection process, an impact force is generated which may cause the robot to

oscillate and hence lose its contact with the environment. This usually results in

an oscillatory motion of the robot as the stiffness of the environment suddenly


changes while the gain of the control system remains constant. This oscillation

can set off an A P mine.

Therefore, force/impact conttol is a vital requirement for a robot under

constrained motion. The control methods used in the constrained motion tasks

can be categorised into passive compliance and active compliance methods. In

this thesis, the active compliance is discussed in details. Active control force

methods can be also categorised into hybrid position/force control and impedance

control.

Hybrid position/force control can be classified into explicit or force-based and

implicit or position-based control methods depending on the method by which

force information is included in the forward control path [Vuk94]. Position-

mode and force-mode are the main two categories of the impedance control

methods. In explicit force conttol [Rai81][Wed88], the reference command is

force whereas in implicit force control [And88] it is position. This implies that

the primary feedback signal in explicit force control scheme is force, while in

implicit control is position. Figures 2.12 and 2.13 show general block diagrams

of explicit and implicit force control schemes.

^Q-Explicit Force Controller

Robots Manipulator

m >

Figure 2.12: Explicit force controller block diagrams


?Q-Implicit Force

Controller

x c

•7TT Position Controller

Robots

Manipulator

X F mm »

Figure 2.13: Implicit force controller block diagrams

There are different control strategies proposed by different researchers for

explicit force-based control, such as simple proportional control to different

subsets of the PID controllers (i.e. I, PI, PD, PID). A n extensive review and

analysis of all these strategies can be found in [Vol91].

2.6. Force/impact Control Methods

There are a number of different force control methods reported in the literature to

control a manipulator coming in contact with a stiff environment. They include;

active stiffness control [SaI80], maximal active damping [kha86], passive

compliance and damping [Xu88], integral explicit force control [Tou89],

impedance control and proportional explicit force control [Hog85a].

Volpe and Khosla identified three phases in controlling a robot approaching a

stiff environment: free motion, impact transience, and force control [Vol91].

Mandal and Payandeh proposed an approach based on the same idea of three

phase but their emphasis was on experimental result and implementing the impact

control strategies [Man93]. Their results indicate that for very stiff environment,

stable impact control may be achieved at low velocities only, and that for a

compliant system, there is a trade off between approach velocity, compliance,

sampling time and the bandwidth of the robot system.


Tornambe has carried out extensive analytical modelling for one-degree-of-

freedom impact between two bodies [Tor96]. H e proposed a control scheme on

the basis of an observer that is able to asymptotically estimate the impact induced

forces and to allow their asymptotic compensation when the two bodies are in

contact. The proposed control scheme is just supported by a simulation test

which is far from a physical system, particularly in a system with force sensing.

W u and his colleagues proposed a solution to the robust impact control and force

regulation by adding a positive acceleration feedback to the feedback loop

[Wu96]. A switching control suategy is also designed to guarantee the stability

of the impact control. This approach is implemented and the results are compared

with the impedance and hybrid force/position control. Results demonstrates the

advantages of the proposed impact control scheme over the other methods. A

stochastic optimal control approach capable of modelling uncertainties in contact

environment, force sensing, and manipulator dynamics is proposed by Lee and

Chiu [Suk96]. Simulation results have verified that the stochastic optimal control

approach yields a controller optimally robust in terms of performance according

to the statistics of the uncertainties. Walker utilised kinematic redundancy of

robot manipulators to find configurations that minimised the effect of impact at

similar approach velocities [Wal94]. Model predictive control (MPC) proposed

by Carufel and Necsulescu is another impact control scheme which allows for the

control in both conditions, free and contact, without having to deal with

switching control law [Car95]. However it is claimed that M P C can solve the

impact-contact motion control problem, though no real-time implementation is

reported.

Increasingly, new impact control schemes combined with conventional force

control methods are proposed as solutions to the problem of impact control. A


few recent impact control schemes proposed by researchers are discussed in more

details in the following sections.

2.6.1. Impact Control Using Force and Vision Feedback

Since transition from non-contact to contact states should be fast and stable, a

new method using vision feedback in addition to the force feedback is proposed

by Nelson and his colleagues in the Robotics Institute at the Carnegie Mellon

University [Nel95]. In this method both vision and force feedback signals are

considered simultaneously in the connol strategy until the camera lens system is

unable to accurately resolve the location of the end-effector relative to the contact

surface. At this stage the conhol system switches to pure force control. The use

of visual servoing has simplified the force control problem by allowing a low

gain force control while approaching a rigid surface with a high velocity, hence

minimising the impact force and avoiding bounce between the surfaces.

The force conuol portion of the vision/force servoing strategy is based on a

combination of hybrid force/position conhol [Rai81] and damping force control

[Whi85]. The aim of visual servoing is to overcome the problem associated with

guarded motion and pure force conhol strategies upon impact. During a guarded

motion, the surface is approached under position control while the force sensor is

monitored. If the measured force exceeds a threshold, motion is immediately

stopped and a force control strategy is invoked. In visual servoing, when the end-

effector is far from the environment, the end effector is driven at a high speed.

The velocity, however, starts to decrease as the end effector comes closer to the

surface. Hence a low gain force conuoller can achieve a stable uansition to

contact state.

The introduction of visual servoing is quite expensive and requires extensive

computing power to handle real time image processing and control. It, however,


improves the manipulator performance during contact transitions. This approach

is not a feasible solution for the mine detection task.

2.6.2. Jump Impact Control (JIC)

The jump impact control method proposed by Chiu and Sukhan [Chi97] is

derived from stochastic optimal control theory and jump linear system theory

with the goal of creating an impact/force controller which is robust to the

environment dynamics and collision surface location uncertainties. Chiu justifies

the use of word 'jump' in JIC method, because after a collision there is a jump in

the system dynamics. The 'jump' implies that the state space description of the

system dynamics changes abruptly. The manipulator collision dynamics in the

presence of uncertainties can be constructed within the framework of a class of

stochastic random processes called jump linear systems, whose regime transition

rate is state-dependent. The JIC theory, optimally chooses the conhol bandwidth

and approach velocity according to uncertainties in the environment dynamics,

the location of the collision surface, and time delay in the force sensor. The

robustness of the JIC control method has been demonstrated through

experimental work.

2.6.3. Bang-Bang Impact Control

In this approach which is proposed by [Lee96], a nonlinear bang-bang impact

controller is developed to absorb impact forces and to stabilise the system during

impact transient. This control strategy uses a robust impedance/time delay

control algorithm with negative force feedback. During impact transient, the

control input switches from negative force feedback to zero if no force is sensed

due to loss of contact. Therefore, there is no control input when contact is broken

due to bouncing. This alternation of control action repeats until the impact


transient subsides and steady state condition is reached. After impact transient,

proportional-derivative force control is used.

The performance of the bang-bang impact controller is investigated by comparing

it against other impact control methods via computer simulation. It is shown that

overall, the performance is comparable or superior to other techniques of impact

control [Lee96]. It is important to note that simulation results of bang-bang

control shows a 5 0 0 % overshoot of the contact force response upon the impact,

which is totally unacceptable in the mine detection manipulator.

2.6.4. Impact Control Using Positive Acceleration Feedback

A n event-driven switching control strategy based on the positive acceleration

feedback is proposed by Tarn [Tar96] to avoid large impact forces and bouncing

after making contact. In this method the detection of the impact is considered as

an event. It is reported that this new control method is robust with respect to

different environments and there is no need to adjust the gain of the controller in

order to have a stable contact Uansition in an unknown environment. This

method is implemented on a 6-DOF P U M A 560 robot arm with a 6-Axis

force/torque sensor. In the experiment, positions in x, y, and z directions are

given for free space motion. The robot will be driven along z-direction only

when a contact is detected. The impact velocity is chosen to be 0.1 m/sec.

According to the experimental results, the force response for a desired force of 1

[Kg] is stabilised after a relatively long time of several bouncing (about 5 sec)

with an overshoot of about 4 0 % . It should be mentioned that these results are

quite comparable to the results of the bang-bang impact control [Lee96].


2.6.5. Impact Control Using Discontinuous Model-based Adaptive Control

This method which is proposed by Akella et al [Ake94] is based on the concept

of Generalised Dynamical Systems (GDSc), the principle of Orthogonalisation,

the Hertz contact model, and model-based adaptive control. In this method the

controller tunes independently for both position and constrained motion while it

attempts to reduce the forces produced during contact with the environment. This

method is digitally simulated for a planar, direct drive robot. The contact force

response to a step of 10 [N] (desired force) shows around 6 0 0 % overshoot and

0.125 [sec] settling time with no bounce. It is reasonable to assume that the

performance of the controller will deteriorate significantly in a physical system

due to various noise signals present in the system, particularly the one produced

by the force sensor.

2.6.6. Impact Control Inspired by Human Reflex

A reflex mechanism that emulates human reflex is the main core of the proposed

impact control by W e n g and Young [Wen96]. Human reflex, which requires no

conscious effort, responds to external stimuli without any delay. The reflex

mechanism basically consists of a series of pre-programmed motion commands.

After detecting an unexpected impact, the reflex mechanism is triggered to issue

appropriate motion commands for impact control. After a smooth contact

transition, the control of the plant is returned to the original controller. The

implemented control system includes three main modules; the impedance

controller, the impact control command derivation module and the impact control

command generalisation module.

The performance of the proposed method is investigated by simulation based on a

single-joint robot manipulator when the robot collides with different types of

environment at high speed. The performance of the impact conuol scheme is


compared with the performance of the system with impedance control alone. The

simulation results indicates that the impact conhol scheme has clearly

outperformed the impedance controller.

2.7 Summary

In this chapter a review of mine detection research and methods was presented. It

was shown that this area is currently active and quite promising to provide

solutions for automatic land mines detection, particularly A P land mines. The

emphasis of this study was mainly on contact mine detection methods whereas

non-contact methods were also discussed. Finally, five implemented mine

detection projects illustrating the features of different systems and methodologies

were also reviewed in more details. This study reveals further study and research

are required before a more practical and realistic automatic deminer is developed.

As impact control is one of the major issues in automatic mine detection methods

a review of different force/impact control methods were also reviewed.

ROBOT ARM AND ENVIRONMENT MODELLING

Chapter 3 Robot Arm and Environment Modelling 37

3.1. Introduction

Modelling is one of the important aspects of developing a robotic arm,

particularly if it is in contact with an unknown environment. As the focus of this

thesis is on designing a control system for a demining robot arm, it is necessary to

derive a complete model of the arm, the sensor used in the arm, and the

environment in contact with the arm. Moreover, as the behaviour of a robotic

arm in contact with the environment is fairly complex, non-linear and to some

extent uncertain, a heuristic method is also employed as an alternative approach

to model the system.

In this chapter various aspects of the mechanical deminer and the environment

are modelled. It is assumed that the environment in contact with the arm consists

mainly of soil and A P mines.

3.2. Soil Dynamics

Soil behaviour in response to the insertion of the bayonet is modelled here. A

series of experiments with three different types of soils were carried out. In each

experiment a sharp bayonet was inserted into the soil at a 30 degree angle relative

to the horizon, while the axial force and linear translation data were measured

and logged. Initially, the shear strength of different types of soil was studied to

acquire some understanding about the prodding process. The shear strength (rj/)

of a specific soil at a point on a particular plane is defined by Coulomb as a linear

function of the normal stress icf ) on the plane at the same point.

x\f =c+Gf tan(|) (3.1)

Where V and '(J)' are the Cohesion and the angle of shearing resistance which are

the parameters of the shear strength [Cra94]. For such a study, generally, clay


and sand are chosen since the cohesion 'c* in sand and the angle of shearing

resistance '(J)' in clay, are negligible. As sand is granular, its shear resistance

comes from the friction between the sand grains. As clay is not granular and the

size of its particles is very small, the shear resistance of the clay is the result of

the inter-planular forces between surfaces of the clay. Therefore, sand (beach

and river) and pottery clay were chosen in this study as the two major different

types of soil to be used to analyse their behaviour in the process of prodding.

The measured force against the insertion depth for dry beach sand, wet beach

sand, river sand, and pottery clay are illustrated in Figures 3.1 to 3.4.

I 8 o u.

1 0 0

9 0

S 0

7 0

CO

5 O

4 li

-

u 1 A

,\ 20

A / , '

* 0 W.V f• 0

P r v B « a c t

8 V [1 IS If. (. C c

S a n d

1U1) [m m ]

1 2 0 1 4 V 1 CO

" -

1 8 0

Figure 3.1: Result of dry beach sand (K=1.2 Kgf/m)

3 5 0

3 0 0

2 5 0

% R 2 0 0

1 5 0

1 0 0

6 0

C

-

"

-A/nA s

W *< E< M C !

0 [i l l l t ' l d l

i. «nd

10 0 [m m ]

1 S •0

Figure 3.2: Result of wet beach sand (K=3.8 Kgf/m)


!

1 2 0

1 00

8 0

no

40

2 0

0

.20

• 40 I

h iv.r

"

"

A r.,, LL.Lril JU. ILL Millv

mmm^^ww . fr i i 2 0 4 o f. y flu

ti i»littt*

: ;• h d

k JWw 1 V

inrH^T ÂPT V

IP 10 0 1 2 " 1 4 0 1 r. (J

. ( m m J

-

-

-

-

1 8 0

Figure 3.3: Result of river sand (2.6 Kgf/m)

3 _ o o t-. - — . — T — . .. . -y —r— •• ••• - t '• i • , , , , , , . . , .

3 0 0 0 -

2 S 0 0 - /"^

«r 2 0 0 0 - jS

» l i O O • /

10 0 0 - /

b o 0 - y

O 2 0 4 1) fc (> H » !(ld 1 _ fi 1 * 0 16ft

I1 i • t * r»«: f | m m )

Figure 3.4: Pottery clay (K =14 Kgf/m)

According to these results, each type of soil has a constant stiffness and the

measured force is partly linear relative to the depth of insertion. It should be

mentioned that because of the triangular shape of the bayonet the force profile at

the insertion point is not increasing fast. Therefore the reaction force generated

in the soil as the result of bayonet insertion can be approximated by the simple

relationship F = K*X, where F is the measured force and X is the relative

distance between the initial bayonet position and the current position. The

surface of the soil is where the force starts to increase. Hence the insertion depth

can be obtained from the distance travelled by the bayonet multiplied by

sin(30°). The parameter K represents the stiffness of the soil, which varies

according to the type of the soil. The stiffness of each type of soil can be derived

from the slope of its corresponding graph. For example, the stiffness of the

pottery clay in response to the bayonet insertion is about 14 [kg/m].


The experiments also show that K has a bigger value for wet soil than dry soil as

it is obvious from the comparison of Figures 3.1 and 3.2. Generally there is no

specific pure soil in nature, but it is possible to predict that the stiffness of a

mixture of different types of soils would not be bigger than the stiffness of the

pottery clay which has the maximum possible Cohesion (about 70 [KN/mA2]).

When the bayonet enters into the soil, there is an axial force sensed by the force

sensor which can be formulated by;

F„ = Ka(Xa-X,) (3.2)

where ^represents the stiffness of the soil having a range of 1-20 [Kgf/m], Xes

and Xs are the soil surface and the end-effector positions respectively.

3.3. Dynamics of the Mine

As soon as the bayonet comes in contact with the mine, the measured reaction

force will increase (impact stage). A relationship similar to (3.3) exists between

the generated force and the stiffness of the mine:

F =K (X -X ) (3.3) em * emv em m' . v '

where X and X,„ are the mine surface and the end-effector positions em tn A

respectively, Kem is the mine stiffness in the range of 1000 - 5000 [Kgf/m],

depending on the material used in the body of the mine. After contact with the

mine, the total force measured by the sensor will be:

F =F +F (3.4) e e.i em

The interaction between the bayonet, soil and mine is illustrated in Figure 3.5.


M D R Robot

_____

Q xs Mine

Soil

DC Drive

Force Sensor

em

__/ Mine

m

Figure 3.5: End-effector in contact with the environment

Due to uncertainties in Kes and Kem, the stiffness of the soil and the body of the

mine, it is quite difficult to design an accurate and stable conventional conhol

system for this task.

In this study two different types of Anti-Personnel (AP) mines, M 1 4 and VS50 as

illustrated in Figure 3.6, are considered. Both mines are plastic and have a

cylindrical shape. Due to their mostly non-metal construction, it is very difficult

to detect them through conventional metal detectors. The VS-50 model weighs

less than 0.2 kg, while the M-14 model weighs less than 0.1 kg.

Figure 3.6: A/P cylindrical plastic mines (Picture Courtesy of Defence Science

and Technology Organisation)


The stiffness coefficients of the two mines were measured experimentally and the

results are illustrated in Figure 3.7. The graph (a) shows the relationship between

the reaction force and the penetration depth of the bayonet for the body of the

mine whereas the diagram (b) illustrates such relationship for the fuse of the

mine. The gradients of these curves define the stiffness coefficients.

a)

VS 50 A/P Mine Stiffness

4000

Distance [mm]

b)

./

i % i % i

Figure 3.7: Stiffness coefficients of the A/P cylinder plastic mine body and fuse

(VS 50)

The stiffness coefficient of the body of V S 50 is about 1200 [Kgf/m] which

means the stiffness coefficient of this type of mine is in the order of a few

thousand. The material used for the fuse of the A/P mines is softer than its body.

In addition, the fuse is assembled with a spring which has a lower stiffness

coefficient compared to the material used in the body of the mine. The

experimental results indicate that the stiffness of the fuse is about 10 [Kgf/m].

Therefore, this study will focus on demining various A/P plastic mines with

stiffness coefficients of up to 5000 [Kgf/m].

3.4. Arm/Sensor model

In this section three different methods are applied to model the arm and the

sensor in contact with the environment. Firstly, a mathematical method is

presented which has proved to be unsatisfactory. Then an experimental approach

is reviewed which uses an input/output data set to derive a simplified transfer


function representing the whole system. Finally a neuro-fuzzy modelling

algorithm is described which is employed to derive an intelligent model for the

whole system.

3.4.1. Mathematical Modelling

Developing an accurate mathematical model to represent the mechanical arm and

its interaction with the environment has proved to be a difficult task. In order to

simplify the modelling process, a number of non-linear and complex parameters

of the experimental rig (Figure 3.8), such as friction and backlash, are ignored in

the model. Studying and including every single of these parameters in the

mathematical model in order to design an appropriate controller could be a P h D

thesis. But the aim of this thesis is to focus on intelligent control methods.

Sliding Part

Force Sensor

f Motor 3

fa \Jr,* G(s)

T muffli

Environment

A

x-,

u< > K T

H_>SI_

4

K

KE

1 B+SJT G2

w • » HH

— i

ke

-<

nil bs nh Dynamic M(«lel irfUw Anii/Sensor/Eiiviroiihienf

Figure 3.8: The dynamic model of the manipulator and end-effector

As it can be seen in Figure 3.8, the stiffness and viscous damping coefficients of

the force sensor are considered in the dynamics of the robot. In the following

equations the term *,(*, -x2) describes the stiffness force component detected

by a force sensor and the term k,xy describes the contact force between the end-

effector and the environment.


The term ^(JCJ -X2) also describes the damping component of the force sensor.

mlxl+bs{xl -x2) + ks(xl -x2) = F (3.5)

m2Jc2+^(i:2 -ij) + fcT(jc2 -x{) + kex3 =0 (3.6)

T-Fr, xx - v, , x2 = v2 , v, =o)r (3.7)

where

mi mass of manipulator [kg]

m2 mass of end-effector [kg]

*! position of manipulator (sliding part) [m]

x2 position of end-effector (sensor shaft) [m]

x, deformation of environment in contact with end-effector [m]

manipulator velocity [m/s]

end-effector velocity [mis]

co angular velocity of motor [rad/s]

bs damping coefficient of force sensor [N-s/m]

ks stiffness coefficient of force sensor [N/m]

ke stiffness coefficient of environment [N/m]

F force applied to manipulator [N]

T torque needed to produce force F [NM]

r radius of motor/reduction gear tm]

The torque equation can be written as

Te = JTw + B(o + T (3-8)

l3

Vl

V2


where

B armature viscous damping coefficient [NM-s/rad]

JT load inertia of the servo motor [NM-s ]

Using Equations (3.7) and (3.8) and substituting them in Equations (3.5) and (3.6

) we obtain

v,= K [Te-Bevl + rbsv2-rkx(xl-x2)] (3.9)

v2 = V [b^v.-b^v^+k^Xy-x^-k^] (3.10) / ^v

J B where Je - — + rm{ and Be = — I - rbs

r r

In order to find the transfer function of the total system, first the transfer function

G/s) is obtained. The input and output of G}(s) are the velocity error and the

output torque generated by the motor respectively. For the velocity control loop,

it is assumed that the end-effector is moving in free space and is not in contact

with the environment. Therefore, the terms in Equations (3.9) and (3.10)

resulting from the contact between the bayonet and the environment have no

effect. Using the mechanical model presented in Figure 3.8, the transfer function

of the dc motor (GM (s)) can be represented as;

G(S) = LS?±= KT (3.11)

l W ejs) Ra+SLa

where KT is the torque constant and KE is the back E M F constant of the motor.

The transfer function G2(s) which relates the motor torque to the angular

velocity of the motor can be obtained as follows:

G, (,)_«___) = _ L _ (3.12) 2 T(s) B + SJT


G A / ( , ) = ^M(£1___S U)G2is)

GMis) =

Ur(s) I+KEG^G^S)

AT

LaJS2 + (LaB + RJ)S + iRaB + KTKE)

(3.13)

(3.14)

Figure 3.9 illustrates the block diagram of the dc motor transfer function GM (s),

including the sensor and the arm dynamics. Assuming that the end-effector is

rigid and the arm is moving in free space for a length equal to the difference of

the X3-X2, it is possible to find the resultant force from F = Ke*x2. In this block

diagram KL is the constant to convert the angular velocity of the dc motor to the

linear translation of the sliding part.

u r

— J * OJ.)

D C Motor

WK ?

Velocity of Velocity of End-effector Sliding Part

\

V(K)

}

s.

G(s) s

;nsor & A

V2W

rm

1/s

End-effector Position

X(s) 2 K

Force F >

Figure 3.9: The open loop force model

The transfer function Gs(s) relates the manipulator velocity to the end-effector

velocity.

VJs) bsS + Ke Gsis) = _ r2"

VAs) m2S2+bllS + Kjl + Ke

(3.15)

Finally the open loop transfer function of the whole system including the motor,

manipulator, sensor, and the environment, relating the reference velocity to the

measured force is:

GMFis) = Fs (s) KeKL{\ - G, (.0]G, (s)G2 (s)

Ur(s) S[\ + KEGx(s)G2(s)} (3.16)

Chapter 3 Robot Arm and Environment Modelling

GMF(s) = KeKLKTibsS + Ke) Ft(s) __

Uris) Sia4S4 + A , 5 3 +a2S

2 + «,S + «0)

47

(3.17)

where

fl4 = LaJTm2

a3 = Ltt/rfcv + m2iRaJT + L,,fl),

a2 = LaJTiKs + Ke) + bs(L<lB + RaJT) + in2(RaB + KTKE)

«,=(*,+ Ke)(LaB + RaJT) + bx(RaB + KTKE)

a0 = iKs + Ke)iRaB + KTKE)

In order to study and analyse the effect of the stiff environment on the overall

behaviour of the system, first a classical PID controller is designed for the

velocity control of the dc servo motor as the inner loop of a general explicit force

control structure (Figure 3.10).

19 K -KV*PID

_A

D C Servo Motor

$. k/s Sensor and_J_^ Environmei it

Figure 3.10: A simple explicit force control with an inner velocity control loop

The PID controller is designed to produce minimum steady state error (Velocity

error constant K v = 40), a fast response and an acceptable overshoot (less than

20%) for a unit step response. The step response and the root locus of the inner

velocity control loop of the dc servo system with the designed controller

(kp=10000; kd=326.6; ki=60000) are illustrated in Figure 3.11. It is clear from the


step response, there is less than 2 0 % overshoot (£ « 05) and an acceptable steady-

state error (e„(0.05).

A

m

P I

1

u

a

•

1

OB

0*

I) 2

°

j

j J

J

/ ' '

005 01 U15 0 2 0 25 o:< IK* I 4

9

A

1

2U

U

r '"

•lo*

" -

•41 10

-.

-_

•W -20

^'

-10

ij

1

0 Be (J AWJ,

10 20 SO 40

Figure 3.11: Step response and the root locus of the inner velocity loop using a

PID controller

The root locus of the system illustrates that the closed-loop system of the

compensated system is stable for all the gains. Since the parameters of the

environment have no effect on the motor transfer function, the PID controller is

assumed to have constant parameters for different types of environment.

The force proportional controller K f in Figure 3.10 is designed to be 0.025. As

the output of the force controller provides the set point for the inner-loop, it

should be in the range of the maximum velocity of the motor, which is 0.027

[m/sec]. By including the inner velocity control loop in the total system with the

force proportional control gain of 0.025, the root locus of the system will be as

shown in Figure 3.12. It is assumed that the experimental rig is in contact with an

environment having a stiffness of Ke= 14000 [Kgf/M].


1 m a

g

A X 1

•

400

200

•200

-400

-600 10

\

^ /

40 -10

i ^ - — — -

j - - " " " " ' o 10

Real Axis

"~" 20

-

-

30

Figure 3.12: The root locus of the system with a PID velocity controller

The root locus has complex conjugate poles from which loci moves towards the

right hand of the s-plane. Since the environment in this model is represented by a

constant gain, a stiff environment makes the total system unstable. The system

has complex conjugate poles on the imaginary axis. In order to investigate the

effect of these poles on the system behaviour in more detail, the step response of

the total system with a proportional gain of (Kf=0.025) in the outer force control

loop (Figure 3.13 ) is considered. Figure 3.14 also illustrates the corresponding

velocity for the step response of the total system.

p i i t u 0.8 d

0.2

0.2 0.3 0 4 0.5 0.6 Time (sees)

Figure 3.13: Step response with the proportional force controller (K_=14000,

Kf=0.025)


A m

P 0.02- A

1 t u 0.015;

d e

0.01

0.005

0

-0.005

/ \

-I \ j I j 1

\

" V 0 0.1

^ 0.2

- — — •

0.3 Time (sees)

0.4 O.S

-

0.6

Figure 3.14: Velocity of the motor for the above step response

According to the obtained unit step response, the transient response is fairly fast

with an acceptable overshoot and a settling time of about 0.2 seconds which

represents the response of a stable system. This result is in conflict with the root

locus of the overall system as the closed loop poles for all values of feedback

gains are shown to be in the right half of the S-plane. Hence, the unit step

response should have been oscillatory and unstable.

More investigation is carried out to identify the reasons for such behaviour. The

total transfer function of the system is in the order of six. The total closed-loop

transfer function of the system can be written as:

C(s) K(s + z, )(s + z2)...(s + zj

R(s) (s + PiXs + p^.-is + p,,)

For a unit step input (Ris) = V ) the response of the system will be:

(3.18)

C(s) = a- + i^ (3.19) s j=ls + Pj

in which a} is the residue for the pole at .v = -p}.

As the poles of C(s) can be both real and complex conjugate, it is better to rewrite

3.19 in the following form:


(3.20) S j^xS + Pj k S2 -r-2^(0fc.f+0)A

2

or in the time domain as

q r

c(t) = a + Ysaie~P}t + X V ^ ° * ' cosco* V-Wt 7=1 *=1

r ________

+ X ^ e " ^ 0 * ' sincoA. jl-tft , > 0 (3.21)

Equations (3.20) and (3.21) show that the unit step response of the full system

can be found by the supeiposition of several first and second order step

responses. Using this method, the total closed-loop transfer function of the arm

in contact with the environment is divided into two first order and two second

order transfer functions. The transfer function producing the complex poles on

the imaginary axis at ± 577.4/ is shown below:

__ - 2597 xlO"7(.y + 4.441 xlO'16) + 8.925 x!0~3 3

lm"s-polex ~ s2 + 8.882 x 10"16 s + 3.334 x 105

By comparing Equation (3.22) with Equation (3.20) the parameters of the

components corresponding to poles on imaginary axis will be;

bk = -2597 x 10~7, ck = 1546 x 10

-5,

co, = 577.4, %k = 7.691 x 10"w (3.23)

Since the damping ratio is very small (£,k = 0 ) , the exponential terms in (3.21)

can be assumed as unity. Therefore, the total step response for the transfer

function (3.22) would be a sinusoidal function with the amplitude and frequency

given in (3.23). The step response of this transfer function is illustrated in Figure

3.15.


, X10

A m

P I I I u d e

-6 - J i _ - J 1 — , 1 i i i _

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 Time (sees)

Figure 3.15: The step response corresponding to pair poles on the imaginary axis

The oscillation of the response due to the imaginary poles seems to be fairly

small compared with the amplitude of the overall response. The amplitude of the

response, however, tends to increase in time, which results in an undesirable

steady state condition. Figure 3.16 illustrates the increasing oscillatory unit step

response of the system after 50 sees.

Figure 3.16: Unit step response of the total system in 50 seconds.

Chapter 3 Robot Ann and Environment Modelling 53

It is clear that the step response in contact with a stiff environment is oscillatory

but with a slowly increasing amplitude. The step response is going to exit from

the limit band of +/-5% after about 40 seconds, which is a long time for a fast

response system. Figure 3.17 shows the step response of the system in contact

with a reasonably soft environment (K_=140) for 10f' and 2*106 seconds. The

step response for 10' seconds (about 12 days) is a satisfactory response but after

that time it starts to increase its oscillatory amplitude, which is not a main issue

for practical applications.

A

»' 1

I 1

u 08 d

or

0*

u;

% 2 t 4 5 f. TrfTi* | » « &)

7 " V 1

«. Hi

0

A

t>

•1

V 2

1

Utf

Oi

Vi

L'l

1 o; 0 4 VH. O B 1 Tirn» (<,*< *

1 2 \ 4 i r.

__,* ^ ^ B ) j

1 6 2

xld

Figure 3.17: The step response for Kd=140 and Kt=0.025

It should be mentioned that in the above results, the stiffness of the environment

is assumed to be constant at 14000 [kgf/m]. In practice, however, such stiffness

can have large variations that will affect the performance of the overall system.

For example, by doubling the stiffness from 14000 to 28000, the step response of

the compensated system has a completely different response (Figure 3.18)

compared to the step response shown in Figure 3.16. It is obvious that the system

would be totally unstable when it comes in contact with this environment. The

reaction force generated is also quite high which can either damage the arm or

trigger the mine.


800 A m p 600 1 1 400 t U d 200 e

0

-200

-400

•600

i 1 i i i

1

i

l

III

111 IM i

0 5 10 15 20 25 30 35 40 45 50 Time (sees) j

Figure 3.18: Step response of the system with the designed PID controller

contacting the stiff environment (ke=28000 [Kgf/m])

By removing the poles on imaginary axis from the total transfer function of the

system, the root-locus of the remaining system is shown in Figure 3.19. This

represents a stable system for all the gain values.

1 m a 9

A X 1 s

6

4

2

t>

-2

-4

-6

xlrf — i —

- /

-

-6

- -i

-4 -2 0 Real Axis

2 1

4

-

6

Xlrf

Figure 3.19: Root-locus of the system after removing poles on imaginary axis


3.4.2. Validation of the Model

The developed model and its controllers are employed to control the experimental

rig. The proportional force controller and PID velocity controller are

implemented using a digital control approach with a sampling interval of T=0.01

[sec]. The following algorithm was used to implement the digital PID

controllers:

/)(_) = A T , + M ( £ ± l ) + £ t ( £ z l ) (3.24) 2 z-1 T z

Where Kp, Ki and Kp are the controller and T is the sampling period of the digital

control loop. Comparing the above PID controller structure with the standard

form of a second order pulse transfer function

results in

a0 = KP+^KIT + KD

a^-Kp+±K{T-^KD

ai=-^KD

bx=-\

b2=0

The result of the experimental work, using the above coefficients, is shown in

Figure 3.20. The output velocity of the inner control loop is quite oscillatory and

saturated between the maximum and minimum velocity of the motor. It is quite

clear that the motion of the bayonet is oscillatory rather than a smooth movement

along a straight line. Moreover, this oscillatory motion has slowed down the

response as it has taken the bayonet 20 seconds to travel a distance of about 10

centimetres, which theoretically should not be less than 2 seconds at the


maximum speed of the motor. Hence, the derived model is not an accurate

representation of the overall system.

Overall, the designed PID controller based on the derived model is completely

oscillatory and out of control even when the arm is moving in free space. This

could be due to ignoring a number of important system parameters, such as

friction, vibration, and backlash, in the model of the system.

Figure 3.20: Behaviour of the system using the designed compensator

Another source of non-linearity and oscillation of the experimental rig could be

the physical limits on the motor velocity which adds a saturation non-linear type

behaviour to the system.

Overall, the derived mathematical model has proved to be unsatisfactory and not

a feasible model to be used in the development of the conhol algorithm. It was

therefore decided to employ a heuristic method known as Adaptive Spline

Modelling of Observation Data ( A S M O D ) [Kav92], to extract a model based on

the input/output data available from the system. The advantage of such a model

is that it can easily accommodate variation in the structure of the system and its


parameters. Hence the modelling approach will be more generic for the

application in hand.

3.5. ASMOD Modelling

The Adaptive Spline Modelling of Observation Data (ASMOD) which is

proposed by [Kav92] is a type of Associative Memory Neural Networks (AMN).

A S M O D is an automatic, off-line construction algorithm that builds a neuro-

fuzzy approximation model of a process from the information contained in a

representative set of training pairs. The approximation is constructed by

iteratively refining the current model. It has been successfully implemented in a

wide range of applications [Car94][Bro94] and [Wey95].

A S M O D uses B-Splines to represent general non-linear and coupled

dependencies in a multi-variable observation data [Kav92]. Since A S M O D

construction is based on a B-spline network, in the following sections A M N s , as

a general class of artificial neural networks, and B-spline networks, in particular,

are discussed.

3.5.1. Associative Memory Networks (AMN)

A M N networks have been applied mostly in functional approximation tasks and

on-line/off-line non-linear adaptive modelling and conhol. The A M N s structure

has many similarities to the classical perceptron neural network, proposed by

Rosenblatt [Ros61]. Therefore, it is useful to include a brief inhoduction to

perceptron network first. The perceptron is the simplest form of a neural network

used for the classification of a special type of pattern, said to be linearly

separable. The perceptron network is an adaptive pattern classification network

that uses a linear discrimination function in order to distinguish between different


classes. The structure of Rosenblatt's network, which has three layers, is

illustrated in Figure 3.21.

Figure 3.21: Three-layer perceptron

The input vector of the perception network is an analogue value while the output

is binary. The association cells (hidden layer) links the input cells to the output

node by a sparse set of random connections and the weight on each connection is

set to learn the network. If the summed input to an association cell exceeds 0, the

output of this node is 1, otherwise it is 0. The association layer is fully connected

to the output node through a set of weights. The output node sums the weighted

contribution from each association cell and this provides a network output of 1,

when the summed quantity is greater than 0, and 0 otherwise. The binary output

classifies a two decision class problem. For a greater than two decision class

problem, the output layer should have more than one cell and the adjustable

weight vector would become a matrix (one vector for each cell). W h e n there are

m output cells in a Perceptron, its behaviour is identical to m single-output

networks which have the same structure, although the form of each output

mapping is different [Bro94].


The structure of an A M N can be presented by a three layer feed-forward network

including input layer, basis function (hidden layer), and output layer. Figure

3.22 illustrates a B-spline type of a three layer A M N structure.

Coeficient Vector

Figure 3.22: B-spline type of an A M N s structure

The input layer of an A M N is a normalised input space. The shape and size of

the basis functions in the hidden layer represent the main structure and

complexity of A M N s . The output of the basis functions, bt generally is in the

range of 0 to 1. W h e n the output is zero, the associated basis function and the

corresponding coefficient (weight) do not contribute to the output of the network.

In the process of learning the basis functions, their locations (knot) in input space

and corresponding coefficients are adapted to minimise the output error. One of

the differences between A M N s and Perceptron networks is that there is no

thresholding unit in A M N s . The output of the A M N is a linear continuous

combination of the outputs of the basis functions. Therefore, the output of an

A M N , is dependent on the basis functions, /?,U) and their corresponding

coefficients, ct. The output can be represented by:

? = _£>,(*) (3.26)


It is clear from (3.26) that the learning process of an A M N network is a simple

linear optimisation problem. There are several different types of A M N networks

such as, B-spline basis functions, Radial Basis Functions (RBF), Cerebellar

Model Articulation Controller ( C M A C ) and Fuzzy networks. There will be more

detailed discussion on B-spline basis functions will be presented in the

forthcoming sections.

3.5.2. B-spline Basis Functions

B-spline is mostly used in graphical applications as a basic surface fitting element

[Cox90]. As the output of B-spline basis function networks is smooth due to

shape of the B-spline basis functions, there is considerable attraction in using this

class of neural network in nonlinear function approximation, particularly in

modelling and conhol of nonlinear systems. The degree of the B-spline function

determines the shape of the B-spline basis function. A set of B-spline basis

functions is known by a set of knots (vector knots) on the input domain and a set

of coefficients defining the percentage contribution of each basis function to the

output. One dimensional B-spline basis functions of degrees zero (constant), one

(linear) and two (quadratic) are illustrated in Figure 3.23. As can be seen from

the figure, for an input between second and third knots (rj2 and q 3) there are one,

two and three B-spline basis functions contributing to define the output for basis

functions of degrees zero to two accordingly. O n each interval, the basis function

is a polynomial of degree P, and at the knots the polynomials are joined

smoothly. A basis function of degree P, is laid in P+l intervals [Bro94]. It is

important to mention that on each interval, P+l B-spline basis functions and their

corresponding coefficients contribute to the output.


1 _

0.8.

0.6 _

0.4 _

0.2 _

0

Constant B-spline (p = 0)

1

Cl 1 J

0.8 _

0.(i _

0.4 _

0.2 _

1 1 n

1 2 3 4 5

Input Knots

Linear B-spliiie (j> = 1)

c c n L2

AA / V\ /A\ / / \ \

1 2 3 4 Input Knots

Quadratic B-spline (j) = 2)

l j

0.8 _

0.(i _

0.4 _

0.2^

1 0

c c c 1 u2 UJ r\ . \ /->

' Y y \ J \..- i ,-\\ vi \ i

5 1 2 3 4 5 | Input Knots

Figure 3.23: One-dimensional B-spline basis functions (degree 0, 1 and 2) for

the Knot-Vector TJ = (1, 2, 3, 4, 5)

Considering this figure and Equation (3.26), which gives the output of a B-spline

basis function network, it is quite clear that the higher basis functions with

smoother basis function shape lead to a smoother networks output accordingly.

In this diagram, the horizontal axis corresponds to the input space with five knots,

while the vertical axis defines the normalised contribution of each supporting

basis functions for a given input. For example, the output of a linear basis

function for an input located at the second knot (rj = 2) is composed of the

contribution of two linear basis functions and their corresponding coefficients,

which is equivalent to 1 x C, + Ox C2 = C,.

A multi-dimensional B-spline basis functions can be constructed as the tensor

products of one-dimensional basis functions. Therefore, all the properties of the

one-dimensional B-spline basis functions are extended to the multi-dimensional

basis function. The shape of the one-dimensional basis functions determines the

smoothness of the multi-dimensional basis functions and the network output. It is

possible to have different degrees of basis function in each dimension. Figure

3.24 illustrates an example of a two-dimensional B-spline basis function with a

basis function of degree 1 (linear) and 2 (quadratic) in each dimension.


Figure 3.24: A two-dimensional B-spline basis functions with degree 1 and 2 in

each dimension

It is natural to initially define the one-dimensional B-spline basis functions and

then generalise it for higher dimensions.

3.5.3. One Dimensional B-Spline Model

The one-dimensional B-spline basis function of degree P has a support that is

P+l intervals wide. That is, for each input to the network, there are P+l

contributing basis functions used in determining the output of the basis functions.

For example, in Figure 3.23 there are one, two and three basis function supports

for any input between knots 2 and 3 of the basis function from degree zero to

three accordingly. Therefore, the degree of the polynomial approximation

specifies the size of the support of the one-dimensional basis functions. A

general one-dimensional B-spline model is formed as a linear combination of a

set of basis functions, b(x) = (/>_(»,&_(*)> ,bL_{(x))T, and can be expressed by

L-\

I i=0

s(x) = ^cibi(x) = c1b(x) (3.27)

The coefficient vector C=(c 0, c,, ...., cL_x)T represents the set of parameters that

are to be estimated when fitting the model to the data, and the number of

parameters L represents the number of degrees of freedom in the model [Wey95].


The set of basis functions in a B-spline model is uniquely defined by a knot

vector, i - {x0,tp X L + P ) » consisting of an ordered sequence of real values.

Each basis function is a composition of a set of piece wise polynomials of a given

degree p, each defined over disjoint sub-intervals of the input space. The knot

sequence forms a partitioning of the input domain into L+p disjoint and normally

non-uniformly sized intervals, corresponding to the polynomial segments of the

basis functions. The basis function of degree zero (p = 0) can be defined as:

<fr,o(*) = 1> t, < X <T,,, 4,0 ' ,+1 (3.28)

0, otherwise

The following recursive algorithm can be then applied to construct the basis

functions of higher degrees (p = 1, 2, )

h,.M=-^-W>+-^-^ w.<*> <3-29) lj+P Lj li+/'+i L;+i

As is clear from (3.28) and (3.29), the basis function is a piecewise polynomial of

degree P, so the output (3.27) of the B-spline network is also a piecewise

polynomials of degree P.

3.5.4. Multi Dimensional B-spline Models

As mentioned, multi-dimensional B-spline model is formed by tensor product of

one dimensional basis functions, ie:

n-l

n 1=0

«**)=rp*<*/> (330)

where b{ (x{) are the one-dimensional basis functions.

For example, two one-dimensional models with functions bm(x0),

m = (0,l,....L0-l) and />„(*,), n = (0X....Ll -1) respectively will form a two-

dimensional tensor product model with the basis functions defined as:


bm,fM0fxl) = bmix0)bll{xl)

The L()Li two-dimensional basis functions are completely defined by the knot

vectors for the two input dimensions.

The number of basis functions of degree P, defined on an axis with L, internal

knots is (L- + Pt +1). So the total memory requirements for a multi-dimensional

B-spline network is given by:

/V = n(I, + /?+l) (3.31) 1=1

where n defines the size of the input space.

It is clear from (3.31), the size of a multi-dimensional B-spline network is

exponentially dependent on the size of the input space, which would indicate that

multi-dimensional B-spline networks are suitable for low dimensional modelling

tasks. But this problem is overcome by A S M O D algorithm which models a high

dimensional input space as a sum of several low dimensional sub-models.

3.5.5. The ASMOD Model Presentation

In the A S M O D algorithm the output of a model with multi-input variable is

modelled as a sum of several low dimensional sub-models, where each sub-model

depends only on a small subset of the input variables. The decomposition of the

high dimensional input space into low dimensional additive sub-spaces makes the

model transparent to the user, and at the same time the complexity (number of

parameters/coefficients) of the model is dramatically reduced. Another attraction

of the A S M O D algorithm is that the derived model could be represented as a set

of fuzzy rules (linguistic rules) which define the relationship between input

variables and their dependencies or independencies. This makes the model more

transparent to the user [Bos95].


The number of basis functions and hence the number of parameters in the model,

grow exponentially with the dimensions of the input space. If there is an internal

correlation between the input variables one or more of the variables can be

eliminated from the input vector, which will reduce the size of the model. These

possibilities for model size reduction are utilised in the A S M O D modelling

scheme by modelling the data as a sum of several low dimensional models using

only the input variables, which are shown to contribute towards the target values.

Smaller the size of a model, less memory and processing time are required, which

leads to faster on-line learning.

An ASMOD model is constructed as a sum of U one-dimensional and/or multi

dimensional B-spline models, {suixu)}, expressed by

m(*) = |>_U_) (3.32) M=0

Xu represents low dimensional input vectors with disjunct subsets of variables in

the model input vector x. One input variable can hence appear in only one of the

sub-model input vectors, and some of the variables may not be included at all. In

order to make it clearer, an A S M O D model of a system with five input variables

is shown in Table 1.

Table 1: An example of A S M O D model.

Sub-model 1

Sub-model 2

Sub-model 3

Sub-model 4

Input Variables

3 1

2

5 4

Spline-degree

2 2

2

1 1

In this model there are three one-dimensional sub-models for the input variables

3, 5, and 4, and one two-dimensional sub-model for the input variables 1 and 2.

As can be seen, the degrees for B-spline basis functions are different. Figure


3.25 shows the basic structure of the A S M O D model of the example presented in

Table 1.

x,—

x.—

** —

x,—

x,—

/xxxxxxxxx

/X>OOOOOOC\

yxxxxxxxxx y>c*XYXyxyx^

VXvXvXyXvXy

s _ \

jC_v—>

7

Figure 3.25: The A S M O D algorithm structure for the example presented in

Table 1

In order to see how ASMOD can be mapped into a neural network architecture,

(3.32) can be expanded with an expression for each sub-model of (3.27);

w-l M-1 L-\

«(*) = _>_ X„ (*_) = _£_>_ Cihi (•*«) (3.33)

«=o M=() 1=0

which results in:

mix) = y£crb,.(x) (3.34)

The sum is taken over all the basis functions and the corresponding coefficients

in the model. Equation (3.34) compared to Equation (3.26) corresponds exactly to

the transfer function of a three layered feed-forward neural network, with one

linear output node and a separate node in the non-linear hidden layer for each of

the B-spline basis functions in the model. The coefficients (weights), location

and the shape of the transfer functions of the hidden layer are implicitly

determined by the model structure as given by the knot vectors.


3.5.6. A S M O D as a Neuro-Fuzzy Algorithm

In the literature, the B-spline basis function networks in general and A S M O D in

particular, are categorised as neuro-fuzzy networks/algorithms. In this section, it

will be made clear why A S M O D is a neuro-fuzzy type network. It is also

important to know the benefits of combined neural networks and fuzzy networks.

The complimentary characteristics of neural networks and fuzzy systems [Bos95]

have authorised the positive attributes of both approaches to be combined. The

combined system is known as a neuro-fuzzy system since it embodies the well

established modelling and learning capabilities of neural network with the

transparent knowledge representation of a fuzzy system.

In particular, it has been shown that B-spline networks and certain forms of fuzzy

systems are equivalent [Bro94]. In order to see the similarity between a fuzzy

and the B-spline networks, the fuzzy set and fuzzy algorithms are defined first.

A fuzzy set A is defined on a universe of discourse X (Figure 3.26), and the

membership function \iA:X —> [0,1] assigns to each element xe X a real number

p.A(;c)in the interval [0, 1] which is:

A = {(x,\LAx))\xeX) (3.35)

•H-W

-4 -3 - 2 - 1 0 1 2 Universe of Discourse

3 4 x

V

Figure 3.26: A typical triangular fuzzy membership function


A fuzzy algorithm is normally presented by a set of rules using fuzzy (vague)

terms (A*) such as small, medium or large to map the input variables to the

output variables. A fuzzy algorithm is composed of fuzzy rules of the form:

IF ixx is A[) AND AND (JC„ is A,;) THEN (y is Bj) (C(/) € [0,1]

where confidence or coefficient Cy determines the contribution of each rule

(membership function) to the output. A fuzzy rule maps the antecedent, formed

by the intersection (AND) of n linguistic statements ixk is A\), to the consequent

formed by a single linguistic statement (v is B J ) . Therefore a set of fuzzy rules

presenting a system could be in the form of:

r..: IFiXisA^THENiyisB') (cê.O, 1]

where ri} represents the ij'h fuzzy rule and A' is the i'h multi-dimensional fuzzy set

formed from the fuzzy intersection of the individual one-dimensional fuzzy set.

If the fuzzy algorithm has n input and m output variables, it can be presented as m

fuzzy algorithms each with n inputs and one output [Bro94].

B-spline basis functions can be used as the fuzzy membership functions by

defining the degree of the basis functions and the knot vectors. The knot vectors

determine the size of the knots interval and hence define the width of each fuzzy

set on the original input space. For example, if a basis function of degree one

iP-1), is used to define the rih one-dimensional fuzzy set membership function

u^ (•), a knot vector including P+2 knots (rj;I_2 !)„_, T)„) should be defined and the

n'h membership function could be found using (3.28) and (3.29).

Hi (•) = *„,(*)

As is shown in Figure 3.23, this basis function has the popular triangular shape

used in many fuzzy logic implementations. It is clear how B-spline basis


functions can be used to represent the fuzzy membership functions which

implement the fuzzy linguistic terms.

Fuzzy rules can be extracted from B-spline basis function networks (ASMOD

model), first, by assigning a fuzzy linguistic label to each of the input one-

dimensional basis functions. The product operator which combines the one-

dimensional basis functions represents a fuzzy conjunction ( A N D ) and addition is

used as the fuzzy disjunction (OR) operator. Therefore, by ORing the sub

models basis functions and ANDing of the one-dimensional input sets in a multi

dimensional sub-model, the antecedent of the fuzzy rules can be produced. Then,

an evenly distributed fuzzy membership function, including five or seven basis

functions, can be used as the output fuzzy sets (Figure 3.27). Finally, all the

possible input variables should be fired to find the A S M O D model output in

order to associate them to the linguistic fuzzy output sets. It is quite clear that a

coefficient would be obtained for each rule in order to establish a correct

mapping from the input space to the output space. More detailed explanation of

extracting rules from the A S M O D model can be found in [Bos95].

^(y)

l

0.92 '

0.21 0

Neg. Neg. Pos. Pos. Large Small Zero Small Large

y

Figure 3.27: Linguistic fuzzy output sets

For example, the multi-dimensional B-spline network illustrated in Figure 3.25

can be interpreted as a set of fuzzy rules as below;


r]2: IF (x3 is very large)

A N D IF (x, is very small) O R (x2 is very large)

A N D IF (x5 is small)

A N D IF ix4 is medium) T H E N (y is Pos. large) (c]2)

where ry refers to the y* fuzzy rule and c.. is the coefficient associated with each

rule. It should be mentioned that the presented rules are just an example for each

sub-model in Figure 3.25. Depending on the number of basis functions and

knots vector, it is possible to derive more rules.

The interpreted fuzzy linguistic rules for this model make the unknown system

more transparent, particularly when the knowledge about the control of the

system cannot be easily obtained from the human operator. By constructing an

A S M O D model from the data obtained from a system controlled by an expert

operator, it is possible to convert the A S M O D model to its equivalent linguistic

fuzzy rule. This similarity between A S M O D models and Fuzzy models could be

also useful in integrating fuzzy linguistic rules into A S M O D model. The fuzzy

linguistic rules defined by a human operator for a system can be added as prior

knowledge to the A S M O D model.

3.5.7. ASMOD (Neuro-Fuzzy) Model Construction

The method of constructing an A S M O D model from the training data is

described in detail in this section. The training data is produced while the arm is

energised by a square wave command to insert the end-effector into the soil using

the experimental rig set up. The first step to model the total system is to acquire

input/output data and use it to construct an A S M O D model. The data was

obtained by inserting the end-effector into different types of soil and environment

including the body and fuse of a mine. The contact force, travel distance, and the

arm velocity were recorded as the output of the system during the experiments.


As the second step in the modelling task, data were combined randomly and then

divided into two sets as "train" and "test" data. A n A S M O D model can be

constructed either according to the desired initialisation set up including the

number of sub-models, internal knots, and the type of B-splines or leaving all

initial conditions as empty data. Then, the A S M O D algorithm builds a B-spline

representation for the model, which automatically is adapted to the structure and

dependencies in the data. In this thesis, several different topologies were

examined in order to find the best A S M O D model with the best performance of

the model. The A S M O D algorithm evaluates the performance of the constructed

model based on the normalised root mean square error between the model output

and system output. Details of this work will be discussed in Chapter 4.

As an example, the structure of an A S M O D model constructed for a neuro-fuzzy

adaptive gain controller is illustrated in Figure 3.28.

1 G r 0.9

a 0£ d e 0.7

0.6

0.5

0.4

0.3

02

0.1

0 — -0.4

V \ / \ /

\ / \ /

v x A / \ / \

/ \ -0.2

Submodel no. 1

\ A \ A i

\ A / \ / \ /

V V A A A A / \ / \

/ \ / \

/ V 0 02 dx [mm]

A \ \ / \ /

V A A / \

/ \

)l. 0.4

/ /

\ . 0.6

1 G r 0.9

> e 0.7

0.6

0.5

0.4

0.3

0.2

0.1

r \ \

'

' /

V

/' \

[

Submodel no. 2

A A /\ \ \ \

\ \ \ WW \ V V V

A A h A /x I /\ /

/ \ /

VV V -500 0 500 1000 1500

<«m

i / /

/ /

\

\

\

,\ 2000 2500

Figure 3.28: A n Example of a Neuro-Fuzzy Model

From this Figure, it can be seen that this model has two sub-models for the

variation of the distance and force as inputs. There are also three internal knots

for the linear B-splines (P = 1) in each sub-model.


3.5.8. A S M O D (Neuro-Fuzzy) Model Validation

Validation is one of the important steps in learning design procedures in neural

modelling. It is important to have a performance index to evaluate the neural

network performance with real-world data. One of the simplest but not reliable

methods [Bro94] is reproducing the training data using the constructed model. It

is, however, more realistic and accurate to split the input/output data into training

and testing sets, to evaluate the constructed model by the test data. This method

is used here to validate A S M O D model as part of the overall control system. The

constructed model which is actually an inverse model of the environment is

validated in an adaptive gain control loop method. The output of the A S M O D

model is used to adapt the gain of the proportional force controller illustrated in

Figure 3.29.

^

)-> K ^

ivj

/

i

ASMOD Model

PI Controllei

-•

4 1

Robot Ann

[Speed

Dis. K e

Force

Figure 3.29: Neuro-fuzzy adaptive gain control system

A proportional force controller with the gain coefficient Kf is designed to drive

the arm in free space. The total gain of the outer (force) loop is obtained by

multiplying a constant proportional gain with the adaptive gain KA produced by

the A S M O D model:

KT = Kf*KA (3.36)


It should be mentioned that the transfer function of the motor is obtained,

experimentally, using the general form of the position transfer function of a dc

servo motor [Oga97] given as:

GiS) = K pos Xis)

Eannis) SiTS + l) (3.37)

In this transfer function Kpos and Tm are the gain and time constant of the dc

servo motor. Figure 3.30 illustrates a few seconds of the open-loop position step

response of the system which is used to find the time constant of the system.

60 D 1 s 50

P

' 40 a c e 30 m e n 20 t

[mm] 1 ' 10

0 (

Step response of the system

\ \ \ \ \-—jf

yr : : : :

•

•• j -

) 02 0.4 0.6 0.8 1 1.2 1.4 1

T = 0.07 sec T i m e [secl

6

Figure 3.30: Open-loop position step response of the drive system

The gain of the system is also obtained from the closed-loop velocity step

response of the system (Equation 3.38). The final (steady state) speed of the

system corresponds to the gain of the system.

VU) = Kwl (338) £„„„(•*) T„,S+l

Based on the experimental results, the velocity transfer function of the

manipulator can be represented by 3.39:

0.003 G„,JS) =

5(0.075 +1) (3.39)


This transfer function is used in order to design a PI controller as the inner

velocity control loop, as shown in Figure 3.29.

The performance of the controller is validated through computer simulation. In

order to simulate the proposed control system using SIMULINK, it is assumed

that an A P mine is located one centimetre under the ground with a stiffness of 10

[Kgf/m]. The stiffness of an object expected to collide with the arm is assumed

to be 5000 [Kgf/m] for simulation result given in Figure 3.31. The desired force

is assumed to be 1 [kgf]. The environment is modelled as a switch in which gain

changes between two constant values. The force and velocity of the arm end-

effector which are under control are presented as the simulation results.

._ 1(r SnaiMNm Rmut <i NFAG wtwvi SHIt*** SwiMms itt«i 10 to 5000 SirmtaUii Rwsi* ti NFAG «*i«l SlWriros Swikrlws Inwn 10 hi 2500

[Kff»]

i r - — - - i — 1 1 1 i i i i 1.5

Figure 3.31: Simulation Results when stiffness is switching from 10 to 5000

As is clear, when the arm end-effector comes in contact with the stiff object, the

resulting force increases suddenly, which is controlled by applying the adaptive

gain introduced by the proposed controller. In the worst case, when the arm end-

effector is reaching from soil to the mine with a stiff object (Ke = 5000), there is

an approximately 2 0 % overshoot.

According to the simulation results, the constructed A S M O D model which

defines the adaptive gain is capable of overcoming the problem of non-linearity

of the system when it collides with a stiff environment. Whereas, using the


proportional gain controller designed to drive the motor in free space, the

behaviour of the control system is completely oscillatory and out of control when

the arm collides with a stiff environment.

3.6. Conclusion

In this chapter, the robotic arm and its environment were modelled using

mathematical and heuristic methods. The mathematical model proved to be an

insufficient model to represent the dynamics of the system. This was caused by

the fact that some complex and non-linear properties of the system, such as,

backlash, friction, vibration and saturation were not included in the model. The

validation results proved that the PID controller response was oscillatory while its

simulation response was, at least, a satisfactory response.

The heuristic model was based on a neuro-fuzzy algorithm known as Adaptive

Spline Modelling of Observation Data ( A S M O D ) . This model produced more

satisfactory results. It was also shown that an A S M O D model was very similar to

a conventional fuzzy model providing a better understanding of an unknown

system. The neuro-fuzzy models could be used in a model based control strategy,

which will be discussed in more detail in Chapters 4 and 5.

Chapter 4

DESIGN OF PROPOSED INTELLIGENT IMPACT

CONTROL METHODS

Chapter 4 Design of Proposed Intelligent Impact Control Methods 77

4.1. Introduction

Recent progress in intelligent control using neural network, genetic algorithms

and fuzzy logic control has provided feasible solutions for modelling, and control

of complex nonlinear systems. Such systems have traditionally been studied

using adaptive and optimal control techniques mainly after they have become

linearised. Linearising a system, however, does not always reflect the true

dynamics and physical properties of a system [Har94].

As part of this new trend in intelligent control, a powerful paradigm known as

"neuro-fuzzy" has been developed which is a combination of neural network and

fuzzy logic. The main strength of the Neuro-fuzzy control method is the fact that

it benefits from the properties of both of the paradigms that it has emerged from

(eg., learning and fuzzy linguistic rules). The method has been extensively used

in many applications [Bro94], [Oma96] and [Sha97a] to model, identify, and

control nonlinear complex systems.

As discussed in Chapter 3, A S M O D algorithm which is employed in this thesis,

is primarily a neural network modeling and control method. Due to the similarity

of the B-spline basis functions used in the A S M O D algorithm and the fuzzy

membership functions, and also the possibility of integrating fuzzy rules into the

A S M O D algorithm, this method is generally referred to as a neuro-fuzzy

modelling and control method.

In this chapter, initially the concept of impact control is introduced. Then a

simplified model of the impact and its sources are discussed. Based on the

described control schemes and the results obtained from impact analysis, some

guidelines are derived for the design of the control methods used in this work.

Subsequently, the basics of the neuro-fuzzy control and different intelligent

Chapter 4 Design qf Proposed Intelligent Impact Control Methods 78_

control structures are presented. Then, the procedure employed in the design of

the three proposed impact control schemes is presented. Finally the performance

of the developed algorithms will be studied and compared against each other.

4.2. Impact Control

As it is discussed in previous chapter, impact phenomena is an important issue in

the field of robotics particularly when the robot is approaching a stiff

environment. The transitions between the conditions of free motion and

constrained motion which induces an undesirable reaction force, is called impact

phenomenon. In order to design an impact controller the impact dynamics should

be studied. The impact dynamics model is presented in next section which helps

to draw some guidelines for impact control particularly for the A P mine detection

task.

4.2.1. Impact Dynamics Model

When an end-effector comes into contact with the environment, the induced

impact force acts for a short period of time. The classical methods of impact

analysis are based on the laws of the conservation of momentum and energy

[Gol60]. These laws can be used to study the changes in velocity of each body

involved in the collision before and after the impact, and exchange of energy

during the impact. The impact collision is equivalent to one half oscillation of a

lumped parameter spring-mass-damper system, which is shown in Figure 4.1.


Figure 4.1: Impact Dynamic Model

The two bodies with masses of M} and M2, move respectively at velocities V} and

V_. The measure of the energy loss, as given by the coefficient of restitution (e),

could be assumed to be equal to one, according to the law of conservation of

energy [Tor96]. The impact momentum is:

jF-dt = M^n -Vn)-M2(V2l-V22)

Mi Mi (4.1)

where

F is the impact force,

e is the restitution coefficient which is assumed to be one,

Vn, and V]2 denote the velocity of mass (M_) before and after impact

respectively,

and i is the duration of impact.

Since the duration of impact is short, the impact could be assumed as a pulse of

magnitude J and duration h as given by [Tou89]:


Jh = )Fdt = ie + l) M^ (Vn-V21) (4.2) o (Ml + M2)

where h should be chosen less than x (eg h=0.5x).

In a particular mechanical system approaching an environment, the. masses Mj

and M_ are constant. Hence according to Equation (4.2), the magnitude of the

pulse generated by the impact force is proportional to the velocity of the masses.

In this study, the mechanical arm is the only moving mass as the environment is

stationary. Therefore, the single dominant factor in this relationship is the

velocity of the moving arm. This implies that a faster moving arm results in

shorter duration impacts, which in turn makes the task of the control algorithm

more difficult. The duration of an impact also dictates the maximum sampling

time in the computer control system. It is obvious that the mass of a mechanical

arm should be minimised to obtain less impact force for the same arm velocity.

Considering the original required specifications of the mechanical mine detection

arm, it is more desirable to have a faster insertion of the bayonet into the soil.

This may induce larger impact forces with shorter duration. Therefore, it is

crucial to have an impact control scheme without delay, capable of coping with

the unexpected stiffness variation while minimising the induced impact force. On

the other hand, it is necessary to maintain the velocity as high as possible to

reduce the time required to detect an A P mine.

4.2.2. Impact Control Guidelines

Considering the reviewed impact control methods, the dynamic model of impact

and the characteristics of a robotics deminer, the following guidelines for a

proper impact control method are suggested in this work:


• It is desirable to move the arm as fast as possible in order to speed up the mine

detection process.

• It is desirable not to have more than 2 0 % or, in the worst case, 4 0 % overshoot

as it may set off the mine.

• It is not possible to slow down the arm before contact phase using a vision

system [Nel95] or a proximity sensor in conjunction with the force sensor

[AU92]. Therefore, the speed of the free motion and constrained motion will

be the same, unless the impact control mechanism can reduce the velocity to

minimise the induced impact force.

• The control algorithm should cope with the unknown parameters of the

environment.

• The mass of the aim end-effector should be minimised to reduce the impact

force.

4.3. Neuro-Fuzzy Control

In recent years, intelligent control in general and neuro-fuzzy control in

particular, have been quite inspiring paradigms for real time control applications

[Oma96]. Intelligent control methods normally have properties such as learning

ability, flexibility, robustness, and nonlinearity, which make them quite attractive

in complex systems.

Neuro-control is a scheme based on artificial neural networks which imitate the

biological brain information processing mechanism. Neural networks with the

ability to learn from input-output non-linear functions are employed as good

candidates to solve complex non-linear control problems [Asa90], [Moo93],

[Was93] and [Kav92]. Neurones are basically non-linear elements and hence,

neural networks are basically non-linear systems which can be used to learn and

solve non-linear control problems, usually too difficult for traditional and


conventional conhol methods to handle. The neuro-controllers can realise non

linear control algorithms, and are robust to noise, complexities, and variations in

the plant. According to Omato et al [Oma96], the neural networks are ideal in

process control as they have the following properties:

• Ability to learn any function provided there is enough training data.

• Ability to m a p non-linearly.

• Ability to control under a wide range of uncertainties.

• Possibility of implementing Neural Networks on a Parallel computing

platform.

Neuro-fuzzy control has also been applied to force/impact control, which is a

non-linear complex controller [Ara96], [Bog93], [Kig95], [Kim95], [Mas96],

[Shi96], [Suh94b], [Suk96], [Tar97] and [Yon93].

A neuro-control structure usually has a learning and adaptive mechanism to adapt

to variations in the process parameters. Depending on the applied structure for

learning and control mechanism, there are two major types of intelligent

(adaptive) control structures; direct and indirect adaptive control. There have

been references to other types of structures in the literature [Oma96], but they

eventually fit into one of the two major approaches.

4.3.1. Direct Neuro-fuzzy Adaptive Control

The learning mechanism in this neuro-fuzzy controller adapts itself according to

the performance of the plant. The learning mechanism behaves as a critic,

evaluating the current state of the plant and adapting the control signal through

the performance index and the model [Bro94]. By measuring the output of the

plant and comparing it with the desired set point, error and change (derivative) in

eiror may be calculated. The performance index is estimated based on the error

Chapter 4 Design of Proposed Intelligent Impact Control Methods 83_

and change in error, and is used to adapt the model and controller accordingly. A

general structure of the direct neuro-fuzzy adaptive controller is illustrated in

Figure4.2.

Desired Output +/-

/

V.

"V

— *

Performance Index

]

? e

.-earning mechanism y

Neuro-£ugzy Cpfffrollei

& u

~\

Plant Model

// J

Output Plant

Feedback Loop

y

Figure 4.2: A general structure of a direct neuro-fuzzy adaptive controller

In this approach, the parameters of the controller are directly adjusted to reduce

the output eiTor between the plant and the reference model. The parameters of the

plant are estimated according to the analysis of error. The controller is then

chosen based on the assumption that the estimated parameters represent the true

values of the plant parameters. In brief, in this type of controller, the neuro-fuzzy

system is used directly as the controller.

4.3.2. Indirect Neuro-fuzzy Control

In an indirect adaptive fuzzy controller, a neuro-fuzzy system is used as the

model of the plant to design the controller [Wan93]. The parameters of the plant

are estimated and the controller is chosen assuming that the estimated parameters

represent the true values of the plant parameters. In this type of neuro-fuzzy

controller, a separate adaptive neuro-fuzzy model is constructed to model the

dynamics of the plant. A design procedure is then used to calculate the control

signal.

Chapter 4 Design qf Proposed Intelligent Impact Control Methods 84

The architecture of an indirect neuro-fuzzy adaptive controller is illustrated in

Figure 4.3. There are two feedback loops in this structure; the negative feedback

controller loop and the model/controller adaptive loop. The model/controller

adaptive loop uses input/output observation data to adapt the process model and

adapt the controller accordingly. While, the controller loop uses just the output

observation to compare with the desired output and send the difference (error

signal) to the controller. In this architecture, a neuro-fuzzy model is used to

model the unknown dynamics of the plant, and predict the current state of the

plant from the previous values of state and conhol signal.

Predicted Current State (PCrS)

Controller Design

__k_ Desired Current State (PCrS) Menro-Fuzzy

Controller

Fuzzy/Neural Model

Previous State (PS)

(CnS),

Control Signal

Plant Output

Feedback Loop

Figure 4.3: Structure of an indirect fuzzy adaptive control system.

The information that should be provided for the controller could be a desired next

state, in which a reference model or desired trajectory must also be included in

the architecture, or it could be a step input and the structure of the controller must

contain information about the desired response of the system [Bro94]. The time

constant of the model/control adaptive loop should be longer than the time

constant of the conhol loop, which is determined by the sampling time of the

output data. The model should be adapted at a reasonable rate in order not to

miss any changes in the system dynamics. O n the other hand, it should not be

adapted at a rate to include measurement noise in the model. Therefore, it is


necessary to have a slower adapting rate compared to the sampling rate used for

the controller. It is worthwhile to mention that, confusingly, some researchers

[Oma96] and [Hun92] refer to this method as direct inverse control.

4.4. Design of Adaptive Indirect Neuro-Fuzzy Controller

In a neuro-fuzzy conhol system the model of the plant is constructed. This model

should relate the deterministic input signals (previous state values and control

signals) to the deterministic output (current state value). Figure 4.4 illustrates an

on line feedforward learning network.

u(t)

Control Signal

?

Feedforward

Plant Model

Previous State

Plant

y(t)

Predicted Current State

y(t)

Figure 4.4: A n online Feedforward Neural Network Model

A neuro-fuzzy model forms a nonlinear mapping if the input signals are

representative and rich enough (meaning that data includes all dynamics of the

system), otherwise it is not able to store the data and learn the unknown

functions. In a given neuro-fuzzy model which has learnt to associate the past

states and control signals with the current state, the control design procedure aims

to produce a controller that can calculate the control signal necessary for the plant

to achieve the desired dynamic response. According to the structure shown in

Figure 4.3, the controller implements the following relationship;


DCrS iPSx DCrS)-^ CnS (4.3) PS Controller CnS

Model PCrS

where PS, DCrS and CnS represent Previous State, Desired Current State and

Control Signal respectively.

The neuro-fuzzy plant model maps the following relationship;

i PS x CnS) -> PCrS (4.4) ~

where PCrS represents Predicted Current State.

Comparison of (4.3) and (4.4), indicates that the controller function is an inverse

model of the plant model [Bro94]. This implies that the trained inverse neuro-

fuzzy model could be configured directly to control the plant. Therefore, the total

transfer function across the controller-plant would be a unity transfer function,

which maps the desired output to the real output. Using the inverse model as the

main block in the neuro-control approach is one of the most widely applied

schemes. Miller et al. provides a number of examples of this method mainly in

robotics applications in his book, Neural Network for Control [Mil90]. This

approach has a long history in the traditional self-tuning control schemes [Ast95],

where the control output to the plant can be obtained from the inverse

mathematical model of the plant by giving the desired plant output. According to

Omatu [Oma96], another reason for the popularity of this approach is its

simplicity.

It is important to mention that this method is highly dependent on an accurate

inverse model of the plant. The role of the neuro-fuzzy modelling system is to

update the plant model on any possible discrepancy between the desired output

and the actual output (online learning). Meanwhile, the control feedback signal

tries to compensate for the model error.


The assumption is that the neuro-fuzzy model approximates the input-output

mapping of the system being modelled precisely. In the real world, however,

factors such as modelling error, dynamical changes or measurement noise, will

induce a discrepancy eyit) between the estimated output, yit) and the system

output yit).

eyit) = yit)-yit) (4.5)

Mean Square Enor (MSE) performance function, given below, is the most

common criterion used to improve the performance of a neuro-fuzzy model.

J = Eie2yit) = ~fje2yit) = îyit)-y(t))

2 (4-6)

where E is the Expectation function. This criterion puts a higher emphasis on the

modelling error in the region of the input domain with high probability of

observation compared to the region of low probability [Kav92].

The learning mechanism in a neuro-fuzzy modelling technique is used to

minimise the error eyit) in Equation (4.5). Considering Equation (3.23), which

defines the output of an B-spline network model, the coefficient vector crcan be

adjusted to minimise the enor. The output of the neuro-fuzzy model is linear

with respect to the coefficients as shown in Chapter 3. Hence linear optimisation

techniques, such as Error Correction (EC), Gradient Descent (GD), and

Stochastic Approximation, can be used to optimise the coefficients of the B-

spline basis functions to improve the performance of the model. Kavli [Kav92]

has used the stochastic approximation version of the L M S (Least Mean Square)

rule as a learning technique for the A S M O D algorithm. The stochastic

approximation scheme is a general iterative algorithm for finding the parameter

vector, cr, minimising enor in the function given by (4.6). It is important to note


that in stochastic approximation, which is capable of filtering out the

measurement and modelling noise, the learning rate is slowly reduced through

time, which results in a faster convergence rate [Bro94].

4.4.1. Neuro-fuzzy Controller Design Based on the ASMOD Algorithm

The A S M O D algorithm has been applied to a wide variety of modelling

problems; robot actuator modelling, metallurgic process modelling, water content

estimation and redundant functional approximation [Kav92]. It has been bench-

marked against multi-layer perceptron, radial basis function and partial least

square networks, and appeals to perform at least as well as the best of the other

models [Bro94].

To design a neuro-fuzzy intelligent conhol based on A S M O D algorithm, an

inverse neuro-fuzzy model of the plant should be constructed first. Then, based

on the constructed model, several conhol structures can be presented. In the next

section the A S M O D model construction is discussed.

4.4.2. Inverse ASMOD Model Construction

In order to construct an A S M O D neuro-fuzzy model of the robot arm used in the

mine detection task, it is necessary to generate sufficient input/output data pairs

to train the model off-line as shown in Figure 4.5.

Q

InVei Neurdfuzzy Motel

Training Mechanism

Robot Ann

Figure 4.5: Off-line inverse training mechanism


As mentioned before, the aim of the training mechanism is to minimise the M S E

error between the output of the constructed inverse model and the required signal

to drive the robot. A set of experiments should be performed to measure the

position/velocity and force of the arm with the associated applied signal to the

robot arm, as the input/output pairs. In order to have a complete set of data

covering the universe discourse, it is necessary to organise as many different

situations as possible to experience various induced forces from environments

with different characteristics. This is vital to the model generalisation. As there

are two different types of models used in the proposed control schemes, the

procedure used in the construction of each model will be described in the

following sections.

4.5. Intelligent Impact Control Design

In this section three intelligent impact conhol methods are studied. There could

be different control configurations depending on the model construction and the

structure of the controller applied to the robot arm. Generally, the conhol

structure for all the proposed methods is inspired from the explicit force control

method in which an internal velocity or position control loop is placed inside the

outer force loop control. The first method presented here is based on a simple

gain scheduling technique. W h e n the arm comes in contact with different types of

environments, it experiences different gains which leads the controller to adapt

the gain of the force control loop accordingly. This method is called Neuro-

Fuzzy Adaptive Gain controller ( N F A G ) and estimates the changes in

environment stiffness. In the second method, a neuro-fuzzy inverse dynamic

model of the robot arm in contact with the environment is applied to control the

impact force of the arm searching for A P mines. This method is called Neuro-

Fuzzy Impact Control (NFIC). Finally, another intelligent control method,


combining a conventional P D force controller and the previous (NFIC) method

using the A S M O D model is presented. In the following sections these three

control configuration are discussed in greater detail.

4.5.1. Neuro-Fuzzy Adaptive Gain Impact Controller ( N F A G C )

The Neuro-Fuzzy Adaptive Gain Controller ( N F A G C ) technique is proposed to

produce a compliant motion and maintain the force applied to the end-effector

within the desired boundary. As mentioned before, the general structure of the

proposed impact controller is based on the explicit force control. Therefore, two

control loops need to be designed. It is logical to start the design from the inner

velocity control loop and then proceed to the outer force control loop as shown in

Figure 4.6.

ASMOD Model

-^CH^ PI

Controllei Robot Arm

Dis. K

Speed

Force

Figure 4.6: Neuro-fuzzy adaptive gain controller (NFAG) system

4.5.1.1. PI Velocity Control Design

In order to design a conventional velocity PI controller for the dc servo motor

connected to the arm end-effector, a simplified transfer function of the system is

derived experimentally which was discussed in previous chapter, Section 3.5.8.

The velocity transfer function of the manipulator can be represented by (4.7).

0.003 G™(S) =

S(0.07S + 1) (4.7)


This transfer function is used to design a PI controller (Kp=334, Ki=19047), as

the inner velocity control loop. This PI controller is designed to meet the

standard requirements of a closed-loop control system. Figure 4.7 illustrates the

step response of the inner velocity control loop which has acceptable transient

parameters (fast rise time compared to the time constant and 2 0 % overshoot), as

well as good steady state response.

1.2 A m

? 1 I I 1 U O.B d e

0.6

0.4

0.2

0

'

0.05 0.1

1 1

0.15 0.2 Time (sees)

—

0.25

'

0.3

>

0.35

-

-

-

0.4

Figure 4.7: Step response of the inner velocity loop control

This PI velocity controller is used for comparison with the proposed impact

control methods for all the configurations.

4.5.1.2 Proportional Force Control Design

In order to design an adaptive gain controller to adapt to the stiffness variation of

the environment (Figure 4.8), the proportional gain of the force controller should

switch from an initial value of Kfl to Kf2 conesponding to Keland Ke2 which

are two different environment stiffness. This requires an estimation of the

stiffness of the environment on-line and the gain of the force controller (KT) to

adapt accordingly.


•

*___,_

s

K /

Figure 4.8: Gain-Varying Switch Non-linear System

For this purpose, the Adaptive Spline Modelling of Observation Data (ASMOD),

[Kav92] is used.

Given that the ASMOD model can model the non-linear gains accurately, it can

be used as the gain scheduling method in conjunction with the conventional

control. The use of a conventional control algorithm and an A S M O D neuro-

fuzzy system design will reduce the computational burden, and permits analysis

of the performance of the closed-loop system using conventional techniques.

The first step to modelling the total system is to acquire training (input/output)

data and use it to construct the A S M O D model. The training data is produced

while the robot arm is energised by a square wave command to insert the end-

effector into the soil using the experimental rig. The data was obtained by

inserting the end-effector into different types of soil and colliding with different

types of environment, including the body and fuse of A P mines. The contact

force, the travelled distance, and the arm velocity were recorded as the output of

the system during the experiments. As the second step of the modelling task, the

derived data was combined randomly and then divided into two sets of "training"

and "testing" data. Then, the A S M O D algorithm using an iterative method,

builds the model which automatically is adapted to the structure and

dependencies in the data. The best model with minimum enor was finally

chosen. The best model could be selected based on different performance

criteria, such as Root Mean Square enor (RMS), Structural Risk Minimisation


(SRM), Cross Validation, Akaike's Final Predictor Enor (FPE), Akaike's

Information Criterion (AIC), and the Minimum Description Length ( M D L )

criterion.

The model constructed based on the structural risk minimisation criterion, has a

coefficient vector of 9 coefficients and the structure of the model is presented in

Table 4.1.

Table 4.1: A S M O D model construction for N F A G C .

Sub-model 1

Sub-model 2

Input Variable

l(dx)

2(df)

Intenal knots

3 3

Spline-degree

1 1

As is clear from this Table, this model has two sub-models for displacement and

force derivatives, as input variables. There are also three internal knots for the

linear B-splines (P = 1) in each sub-model. The output of this model is the

stiffness of the environment in contact with the robot arm.

There is another way to represent the A S M O D model, which is the graphical

illustration of the B-spline basis functions for each input variable. Figure 4.9

represents the structure of the constructed A S M O D model given in Table 4.1.

Submodel no. 1 Submodel no. 2

2000 2500

Figure 4.9: A S M O D model to estimate the stiffness of the environment

The output of this model should be applied to the closed-loop force control of the

robot. Before using the adaptive gain controller, a simple proportional gain


iKf - 0.01) is designed for the robot arm motion in free space. In the process of

designing this gain iKf), it is considered to avoid saturation of the motor

velocity. Without this assumption, the response of the inner velocity control loop

could be easily saturated. This also adds another nonlinear dynamic to the

control system, which should be avoided. W h e n the robot arm is in contact with

an environment with an unknown stiffness, the A S M O D model estimates the

stiffness and then the gain of the controller changes according to the estimated

stiffness.

Kf*^V

•K.T

/

j

ASMuu Model

PI Controllei

-*

4 1

Robot Ann

Speed

Dis." K

Figure 4.10: Neuro-fuzzy adaptive gain controller (NFAG) system

The total gain of the force loop is obtained by multiplying a constant proportional

gain with the adaptive gain KA produced by the A S M O D model.

KT=Kf*KA (4.8)

4.5.1.3. Simulation Scenario

The performance of the N F A G controller is validated through computer

simulation. In the simulation earned out using S I M U L I N K (Figures 4.11 and

4.12), it is assumed that a stiff object, such as an A P mine is located one

centimetre below the ground. The ground is modelled as pure soil with stiffness

of 10 [Kgf/m]. W h e n the arm approaches the mine, the stiffness of the

environment changes suddenly from 10 to the stiffness of the mine.


>p Inptrt

Load Model

Step Inpi

Sum Kf

Kt -•0.01

Ka

Product PI 0.003

0.07S+1

Speed Position

Sumi PID ControllerVransferFcni Sa,ura,ion Transfer F *,! 'Subsystem2

Adaptive Gain A S M O D MODEL

Environment Fs

Subsystem

Figure 4.11: S I M U L I N K graph of the N F A G control system

0.01

mine locat < >i

0.01

mine location

eu Sum

in 1 I r\. Switch 1 out 1

Figure 4.12: Environment model used in the simulation

The stiffness of the mine is assumed to be 2500, 5000 and 7500 [Kgf/m] for three

different cases as illustrated in Figures 4.13 to 4.15. The desired force, which is

the minimum safe force, not detonating the mine, is set to 1 [Kgf]. The

environment is modelled as a switch in which gain changes between two constant

values (Figure 4.12). In each case, the speed of the robot arm and the axial force

are compared with the results of a conventional controller without N F A G .

According to the results, when the end-effector comes into contact with the mine,

the resulting impact force increases suddenly, which is controlled by adjusting the

gain. In the worst case, when the end-effector approaches the mine (stiffness of

7500) from the soil, an overshoot of about 1 0 0 % occurs.


' * 1 1 1 1 i i I i _ 1 5 ~ 25 3 0 US 1 15 2 25

Tim* (M. cor id) Tim» (ttecor.d)

Figure 4.13: Simulation results of N F A G and proportional force when stiffness

changes from 10 to 2500 [Kgf/m]

As can be seen from Figure 4.13, there is no overshoot on the force response of

the system using N F A G , whereas the force response of the system using the

proportional force controller has experienced 2 0 % overshoot. The response of the

N F A G system is 2 0 % slower due to the applied adaptive gain iKA)

conesponding to the stiffness of the first environment with Ke=10 [Kgf/m].

There would have been no slow down in the N F A G response if the robot arm had

approached the stiff environment from free space. This is because the adaptive

gain of the robot arm in free space is equal to one iKA=l). Hence it can be

concluded that the applied adaptive gain control (NFAG) has improved the

performance of the system.

Figure 4.14: Simulation results of N F A G and proportional force when stiffness changes from 10 to 5000 [Kgf/m]


(K9II 2

F " 0 If. r C 1 4

• 1.2

08

06

02

Simulation ft*_uh «l N F A G wii*n ulillr *AMI .Witcti** lorm 10 to 7500

i 1 il\ ! i :

L ' '

! i

: i

i i

' '

1 j

I • 1 i

i

i

j

: i

t

• i

Stmulntior. (Will I wittumt N F A G wl.on _tilli>*bb twitch** lorm 10 lo 7500

_i il u 11 I 0 Tim* (_*coud) Tim* (.ocorid)

Figure 4.15: Simulation results of N F A G and proportional force when stiffness changes from 10 to 7500 [Kgf/m]

Considering the results when stiffness switches from 10 to 5000 [kgf/m], the

response of the system with the N F A G system has an overshoot of 4 0 % , which is

not ideal for mine detection task. However, this result is still superior to the

performance of the conhol system without N F A G , which has a force step

response with an overshoot of 7 0 % and a long settling time.

When the stiffness switches from 10 to 7500 [Kgf/m], the system response with

the N F A G controller does not meet the desired requirements, though it is much

superior to the system response without N F A G .

4.5.2. Neuro-Fuzzy Impact Controller (NFIC)

This method is based on the indirect adaptive neuro-fuzzy conhol structure which

was explained in Section 4.2. In this method, an A S M O D model is used as an

inverse model of the plant. It was shown that such a model can be used as a

neuro-fuzzy controller.

4.5.2.1. Neuro-Fuzzy Inverse Model Construction

In order to implement the adaptive neuro-fuzzy inverse model of the robot arm

used in the mine detection task, it is necessary to generate sufficient input/output

data pairs to train the model off-line shown in Figure 4.16.


Inverse Neurofuzzy M</del

K-

u

c^ TrainiiwMechanism

rLTLTL

*9 u Robot

Arm

0 V

-»F. in

Figure 4.16: Off-line inverse training mechanism

A series of experiments were carried out to measure the velocity and contact

force of the ami in response to the input signal applied to it. The input signal was

a square wave. Various types of soil and objects were used in the experiments in

order to obtain a rich input/output data. The obtained data is divided into two sets

of training data and validation data to test the conectness of the produced model.

The model has four inputs including velocity, force and their previous values

[Vk, Vk_{, Fk Fk_l]. The output is the difference between the desired square

wave iF(l) and the measured force (F„()

U = Fd-Fm

The ASMOD algorithm constructs the model based on minimising the enor

between the model estimation and applied input to the robot arm. As mentioned

before, the optimisation technique used by Kavli [Kav92] is a stochastic

approximation version of the L M S rule, where the learning rate is slowly reduced

through time. Generally, in L M S learning technique, the performance function

given in (4.10) should be minimised.

euit) = u(t)-u(t) (4.9)

Chapter 4 Design qf Proposed Intelligent Impact Control Methods 99_

J = Eie;it) = ±jê2uit) = ±-îuit)-uit))2 (4.10)

The construction of the A S M O D model in this section was terminated at (rmse

=0.1192). The structure of the constructed model which has 34 coefficients is

shown in Table 4.2.

Table *

Sub-model 1 Sub-model 2 Sub-model 3

1.2: A S M O D model construction for NFIC

Input Variable 4 f(k-l)

2 v(k-l)

1 v(k)

3 f(k)

Intenal knots 4

4 4 2

Spline-degree 1 1

1 i 1

This model has two one dimensional sub-models (1 and 2) and one two

dimensional sub-model with Vk and Fk as its input variables. All B-splines basis

functions used in the model are degree one (P=l). The graphical B-spline basis

function representations on the input domain are illustrated in Figures 4.17 and

4.18.

Figure 4.17: Graphical presentation of Sub-model No. 1 and No. 2 of the constructed A S M O D model for NFIC controller


Submodel no. 3

Var. 3, F(k) -0.04

Var. 1,V(k)

Figure 4.18: Graphical presentation of Sub-model No. 3 of the constructed A S M O D model for NFIC controller

The test data was applied to the constructed model. Figure 4.19 illustrates the

comparison of the model output («) and the input applied to the system (w). In

this figure solid line represents the test data and the dotted line represents the

output estimated by the model. It is obvious that the model is capable of

estimating the dynamics of the robot aim with an acceptable accuracy.


(Kgf) 1.5

0.5

-0.5

-1

Comparison between model output and system input ~ i 1 1 1 i 1 — - r —

— Test data Output ••• Model Output

>M Wvi i — An

0 100 200 300 400 500 600 700 800 900 1000 Time [msec]

Figure 4.19: Validation of the constructed model for NFIC control

4.5.2.2. Feedforward NFIC without Velocity Controller

The configuration of this control scheme is shown in Figure 4.20. There are two

controllers in this mode. A Feed-Forward Controller (FFC) which is the main

controller and Feed-Back Controller (FBC) which stabilises the closed-loop

system based on the error signal between the desired output signal and the robot

arm output. In this method, the F B C can be either a conventional PID controller

or a neural network controller. In the proposed method F B C is simply a

proportional controller while FFC is designed based on the inverse dynamics of

the robot arm. The input/output mapping of the robot arm will be as follows, if

the feedback control loop is not considered;

y = farmiu) (4.11)

The neuro-fuzzy model estimates the inverse dynamics of the robot arm:

U = fa~rl(y) (4.12)

where f'^ denotes the inverse mapping of fam.


For the reference signal (r), the plant output, which is the outcome of controllers

and the robot arm in series, would be:

> = / « « , ( « ) = /.,«,(/„«('•)) = / (4.13)

This confirms the concept of using an inverse model as the controller, given in

Section 4.4.

NFIC FFC

(V2-* FBC _*£> Robot Arm

Force

Figure 4.20: Block diagram of a NFIC method

Based on the constructed model explained in Section 4.4.2.1 and the proposed

control scheme, a computer simulation using S I M U L I N K was earned out. The

block diagram of the simulated scheme is shown in Figure 4.21. In this

simulation the environment is simulated as a varying stiffness from very soft (10

kgf/m) to very stiff (ke=5000 and 7500 [kgf/m]). The simulation results are

based on the comparison of the step response of the system with and without the

NFIC controller.


^

NFIC Controller -,

H3—H Delayl

Delay2

uhsygtom Load Model

Step Input $Th$~ 0.003

0.07S+1

Speed 3ositioa

Sum Gainl Servo Motot SaturatiopTransfer Fcn1

Environmen Fs

Subsystem2

Figure 4.21: Simulink graph for feed-forward NFIC

F

r

c

Feedforward NFIC without velocity controller (K» 1-10 and ke2-S000)

! 1 A 1 I | ) v ; ;

___ __J_I____H ] ]

Result witlinui NFIC nnd velocity controller (Kel-10 and ke 2-5000)

I 15 Time (second)

Figure 4.22: Simulation result of feedforward NFIC without velocity control loop when stiffness switches from 10 to 5000 [Kgf/m]

(Kgi)

o

1

•

00

Feedforward NFIC without velocity controller (Kel-10 arid ke2-7P0U)

____]__Z--H

1 !

If i i !

\ \

I NFIC nnd velocity conl poller (Kol-10 «nd k»2-7SOO)

Time (vecond) T.m» (second)

Figure 4.23: Simulation result of feedforward NFIC without velocity control loop when stiffness switches from 10 to 7500 [Kgf/m]

4.5.2.3. Feedforward NFIC with Velocity Controller

In this method, the NFIC controller is used as the explicit force controller, the

same as in the previous method (Section 4.5.2.1), in conjunction with the PI


velocity inner control loop. The proportional gain iKf= 0.01) which was

designed for the N F A G control scheme is also used in this method. By adding

the PI velocity inner control loop, a better performance than the feedforward

NFIC without velocity control is expected.

The simulation scenarios were chosen as before (Section 4.5.1.3) in order to

compare the results. The S I M U L I N K block diagram used for this method is

shown in Figure 4.24. The simulation results are presented in Figures 4.25 to

4.27.

Fd

Step Inpi t

NFIC Controller

*{__§> Delayl Subsystem

<ifc Delay2

Load Model ] Sum2 :£"+ -•0X31>—r>|T

Sum K-I r ^ — •M Cum

PI 0.003

0.07s+1

S >eed

Sum1 Controller Motor

Inner velocity control loop

Saturatior

Environment

Position Subsystem2

Fs

Figure 4.24: S I M U L I N K graph of the NFIC controller with PI velocity controller

SiiM-Mimi A M M * nf NFIC WIMMI riiftiiMt* MMIIJIBS tnttit in In ?&H1 SkmiWinh HHNII* wrlln - il NFIC wtiMi statu HSS switches (otm 10 to 2500

(K.J,

F 1.2-

(K.pO

Tmto (wfctaicj)

l\./.^,L

1.5 -i. Tune (second)

Figure 4.25: Comparison of the simulation results with NFIC and without NFIC when stiffness is switching from 10 to 2500 [Kgf/m]


According to the results obtained for all the three situations, it is obvious that the

performance of the force response deteriorates when the robot arm approaches a

stiff object.

Simvlritinri result ol NFIC WIIPN «Hltot>b!, CIKHIQIH, form 11) to 5UIH.) Sinnilatnii f4.-s.nll witrnml NFIC wtifri .trill.«*& witches lorm 10 to 5000

(Kg I)

1 I

2.„_ 1 i

i Ii i i

j i___J

\

!


Simulation Ptcwilt ol NFIC when eft Hue** t-wilrl.es ti.rm 10 h- 7«".i 8mnil.itK.fi FtnMili wiilitHii N F A G when .tillnetsB .wilclies lorm 10 lc> 7500

X---1-'

\:lLj\^

j

| {SM]

"" ]_. r__=___-i: u t" W.u.L LMJ t u Time tt->Tc>ii_)


The result for impact with an object with stiffness of 7500 [Kgf/m] has an

overshoot of 4 0 % , which is acceptable for mine detection. The performance of

the NFIC control scheme is clearly superior to the system response without the

NFIC controller.

4.5.3. Neuro-Fuzzy Impact Control and PID Controller (NFIC/PDPI)

This conhol scheme is based on a combination of NFIC and conventional PID

controller. The NFIC controller will be activated when an impact is detected.

Otherwise the P D force controller is active. The NFIC/PDPI control scheme

http://f4.-s.nll

http://t-wilrl.es

http://8mnil.itK.fi


which is illustrated in Figure 4.28 is designed based on a neuro-fuzzy ( A S M O D )

inverse dynamic model of the robot arm in contact with the environment.

Fd o^~^ + i i \ /£

. /

P D Force Controller

NFIC Controller tr " L _

Impact

Detector

PI Speed Controller

*

Robot Ann

Speed

Dis. Environment

Force

W

Figure 4.28: NFIC controller block diagram

This conttol method is inspired from the human reflection in collision with an

environment. There is good fusion of force, tactile, sound and vision sensory

systems in a human being to detect a collision and consequently reflect

immediately to minimise possible injuries. In this approach, a fast reaction is

produced by minimising the impact force. The system is stabilised by reducing

the oscillatory behaviour of the conventional impact control methods. A n impact

detector mechanism is designed to add the NFIC control signal to the normal P D

force control system. The impact detector is designed based on the force

derivative to detect a sudden increase/decrease in the force profile and to activate

the NFIC controller when a force threshold is reached. Therefore, it is important

to define a valid threshold to minimise or avoid false detection by the impact

detection mechanism. The minimum value of this threshold could be defined by

the maximum value of the noise present during force measurement. O n the other

hand, the response of the system can become oscillatory if a very low threshold is

selected.


4.5.3.1. PD Force Control Design

In order to design an explicit force controller, the robot arm with the previously

designed PI velocity controller as its inner loop is considered. A P D force

controller which is a good candidate for an explicit force controller [Vol91] is

designed to stabilise the system response. The root locus of the whole system,

before and after introducing the P D force controller is shown in Figure 4.29. It is

clear that the system response without P D force controller is unstable for gains

more than one.

I H K > [ i i ' r

•100 -80 -BO -411 -M ' 4 0 HI M '***«> -150 -««> -SO 0 50 100 ISO 200 FlnalA^ R_IA*i

Figure 4.29: Root locus for the total system.

The stabilised system will have a faster step response if the zero of the PD is

located in the most far left of the real axis. Figure 4.30 illustrates the step

response for the designed P D force controller (Kp=0.01 Kd=0.0001) which has a

zero atz=-100.

11 • i

,,N [____]


Figure 4.30: Step response of the PDPI explicit controller for the P D force controller

This Figure illustrates the force step response of the PDPI controller when the

robot arm comes into contact with two different types of environment iKel=50

and Ke2=5000). As is clear the P D should be designed to produce an acceptable

step response for a wide range of stiffness. If it is desirable to have a faster

response when the robot arm is in contact with a soft environment, the P D

controller can be designed ieg. Kp=0.3 Kd-0.0001) to have a settling time of less

than one second.

Figure 4.31: Step response of the PDPI explicit force controller

Figure 4.31 illustrates the step response of the new PDPI (PD force and PI

velocity) controller for two different types of environment (Kel=50 and

Ke2-5000). The system is unstable for the suffer environment. Therefore, it is

necessary to find a trade off between stability of the system when in contact with


a stiff environment and the speed of the response when in contact with a soft

environment or in free space.

4.4.3.2. Simulation Results of the NFIC/PD Control Scheme

In this section two versions of the new control scheme are simulated. The first

version is a combination of NFIC and a P D force control without using the PI

velocity control (NFIC/PD) while the second version includes the PI velocity

conhol (NFIC/PDPI). Figure 4.32 illustrates the S I M U L I N K block diagram of

the NFIC/PD method. Impact detection control which receives the measured

force and its previous value, detects a sudden change in the induced force. A

sudden change in force profile could be due to transition from free space to a stiff

environment or just caused by noise. In the following simulation results, the

threshold value for the impact detection is set to 0.2 [Kg], which is more than the

noise level observed in a physical system.

Fd

ED Step

^

•>!_____—•

NFIC Controller

Delay2

Delayl Subsystem

->r—' PID

Sum PD

Load Model ]

c,,m.i Gainl

0.003 0.07S+1

5peec Position

Environment

Motor Saturation |ntegrator l - — - ^

Wf__Z]-Deiay3

Impact Dete

Subsystem3

Fs

Constant 1

Figure 4.32: S I M U L I N K graph of the NFIC/PD control system

The simulation results for the NFIC/PD control are illustrated in Figures 4.33 and

4.34. According to these results, which are compared individually with the P D

force control without NFIC control, there is a quite robust force response to

variations in the environment stiffness. The conventional controller has shown a


considerable variation in its force response (overshoots of 80%, 100% and

120%). On the contrary, the overshoot has remained constant for the NFIC/PD

conhol scheme in all situations.

StmulnitoTi t-wilt i>t NFK:/F'[i ci-MnJIf i {K»1 = 10 tirid K » 2 c 5 0 W )

1*90 .1 F'[) (irf.inJlHi (Kol-Ui and Ke.'=SWO)

Time (_4tri>hd)

Figure 4.33: Simulation result of NFIC/PD when stiffness changes from 10 to 5000 [kgf/m]

SMnidnlinti ir_ul1 of NFtdJ'Ei r<-ili<>llei (K^l-lii ;,r.rl K»2-7f.i

(Kgt)

"* '

J -

Mn...l -, .--.I ..1 f[ic<..iuJli.( ( K H 1 = K » n.d K*-?=7SUUt

! ; ' ' i f ! . 1

j -J

1 : ; ft • !

.1 ! : • ; ! ;|/ ; . v ; j

; . ; ;


I ('[i n.iiliollcf (K(-1 = Uiiiiid K* 2= 10000)

Tim*. |t>ftitid) TKNt. (•>-(.«.(])



The oscillation is due to the adaptation mechanism which sometimes takes too

long and hence introduces some noise into the system performance.

4.4.3.3. Simulation Results of the NFIC/PDPI Control Scheme

In order to see the effects of the designed PI velocity controller as the inner

control loop and investigate the performance of the NFIC/PDPI control scheme,

in this section the PI velocity controller is added to the control method presented

in the previous section. The simulation block diagram is given in Figure 4.36.

The NFIC controller is activated when a sudden change in the measured force is

detected. The threshold level for impact detection is set to 0.2 [Kg]. Simulation

results for the three different situations when the environment stiffness varies

from 10 to 5000, 7500 and 10000 [Kgf/m], compared with the system with no

NFIC controller, are illustrated in Figures 4.37 to 4.39. Again, it is quite clear

that in each situation, results of NFIC/PDPI control scheme give a better

performance than the PDPI controller without NFIC.

r4d

U Step

^

NFIC Controller —,

>{____}-H Delay 1 Subsystem

— K T i

Delay2

Load Model

PID

Sum PD PID

0.003

0.07S+1 Sum3 Sum1 PI Motor

Saturation

*__§_]-Delay3

Impact Dete.

Speej Posi,i,p

_&-prHE illiration § I I —

Environment —*i

Transfer Fcn$ubsystem2

.Subsystems

0 Constant 1

Figure 4.36: S I M U L I N K graph of the NFIC/PDPI control system

In the NFIC/PDPI control scheme, there are some oscillations in the response

after impact detection. But in all situations, the overshoot does not go above 2 0 % ,

regardless of the environment stiffness.


Simulation mull PI NFIC/PDPI controller (K.lglQ and k«2«50001

[Kgi]

SimulalKHi i»_utl ol PDPI «mUoll»( {K«1=10 and k»2=SOO0]

Tim* (wcond) Tim* (wcond)

Figure 4.37: Simulation result of NFIC/PDPI and PDPI when stiffness changes from 10 to 5000 [kgf/m]

Simulation result ol NFlOVPDPI controller (K»1 = 10 and k_2=75(K>)

4 - 4 4 j i j

[Kgn 1 8

» 1.2

U8

Simulation result ol PDPI c rmlrollar (Ke1=10 and ke2=7500)

i A i

I )•••

I I-

I — f -

|M—H j i

I r . i


Simulation result ol NFIC/PDPI controller {Ke1=1U find ke2=100W)

(Kgl)

Time (»ec(rnd)

(Kql) 1 8

F

Simulalion rpsuli el PDPI controller (K*1=10 and ke2=i0OO0)

c

0

1 r- — •• I . , ,.,

| \ \ |

1 ;....

_™»=n.-4:-.--r;.....:4:..

, A |

t [ A / 1 A -III/

i 1

Time (second)


4.6. Conclusion

In this chapter the design procedure and simulation results for three intelligent

impact control schemes have been presented. A neuro-fuzzy adaptive gain


controller ( N F A G ) produced a better performance than a fixed gain proportional

force controller.

A neuro-fuzzy impact controller (NFIC) was also designed to control the impact

phenomenon using an intelligent controller based on the inverse dynamic model

of the robot arm in contact with the environment. The performance of the

controller was validated through computer simulation. The designed controllers

have produced encouraging results. The controller maintains the stability of the

system after a change in the stiffness of the environment.

The proposed neuro-fuzzy controller is applied through two different control

schemes including the NFIC and the NFIC/PDPI controller. Both controllers

have proved to be robust to the variations of stiffness in the environment and

have outperformed similar systems with conventional controllers.


Ke varies from 10 tO 5000

(D

(2a)

(2b)

IKot) !

Simulation Re.utl ol N F A G whan fltflnais iwrlohai la m tO lo SOCIO

! f

_ i — ^ — '

•--- -

• ! - — -

flerweid NFIC without velocity controller (Kei-io end ke2-6000]

• —

- ----

;

(\ : v

1

Sunulaliei, itsuK ol NFIC oil •n (litlnass chanoag tori ID lo 6000

i

i

A IW Hyp y

1 i

---~~---q___f----

Simulation ra.ult ol NFIC/F'O conliollar (Kol-10 end KoZ-SOOO)

(3a)

Simulation raiuH ol NFHJ/PDÎconHollar (,Ket-tO and hog-SOOQ)

!

I \i 1 1 •

1 !

1 •ll/ 1 •

__ 1 ;

i •

(3b) 1 If.. » I • • < '.!•« '

Ke varies from 10 tO 7500

Minuliiu.n fcaiull ol N F A G whan stillnaai rwitctiee lorm 10 to 7600

IKgl)

F t :

T" : d

(Kg')

.

0

Faadloiward NFli: trill • ulv«

—.,^7-h:.::-:;::r T_....

ociiy controller (Kol.to ond koZ-75O0)

I [

1 | \T~

i

i

i-

Tr.na(socond)

.-. - F-.c mt ol NFli: when stillness switches term tOlo 7fi00

i

f '1 P : ;

Time (fecond]

..,.,1,1,.... result ol NFIUff'D controller (Kel-10 end Ke_-76001

Time (second]

.SHIM Mi or, re:ull ol NF icff'Of'i contro

\l

Am ™._iLnl

! .

tar (K*I*10 andke£-7600)

i A

\j-\/^^-T-

'.

Figure 4.40: Force response comparison of all the proposed methods

VALIDATION OF CONTROL

STRATEGIES

Chapter 5 Validation of Control Strategies 116

5.1. Introduction

In this chapter the experimental work conducted to validate the proposed

intelligent control system is described. Initially, the structure and details of the

experimental rig will be explained. This experimental rig is the same as the one

used in Chapter 4 to validate the simulation results. Afterwards some hardware

and software problems encountered during the implementation of the demining

robot will be discussed. The experimental results of the intelligent impact control

methods are compared with the conventional impact control schemes to illustrate

the validity of the developed system. The obtained results will be finally analysed

and some concluding remarks will be made.

5.2. Experimental Rig

At this stage the work is focused on a single axis force controlled arm to drive the

bayonet as its end-effector. The robotic aim imitates the operation of a human

de-miner using the hand prodding approach. As shown in Figure 5.1, the device

consists of a bayonet which is attached to a dc servo motor (Hitachi) via a load

cell (force sensor) and a linear distance sensor (Linear Variable Differential

Transformer-LVDT) to measure the linear translation. As the bayonet is inserted

into the soil, the generated reaction force is measured by the force sensor.

A normal bolt and a long screw, assembled as a lead-screw mechanism with a

considerable amount of backlash and static friction, is used to convert the rotation

of the motor to a linear translation. The arm is assembled with a 30 degree angle

to the ground to induce half (sin(30°)xFv) of the axial force to the object in

contact with the bayonet. In the following sections, specifications for the dc-

servo motor and sensors are given in more detail.


Figure 5.1: End-effector connected to the force and L V D T sensor

5.2.1. D C Servo Motor Specifications

The specification of the Hitachi dc servo motor, including the rotational speed

transducer which is used to drive the end-effector of deminer arm, is given in

Table 5.1.

Table 5.1: Hitachi D C servo motor specifications

Input Voltage Current Max. Speed Torque Output Power Pulse/Rotation

24 [VDC] 2.2 [A] 1880 [RPM] 0.212 [Nm] 33 [W] 640

The accompanying rotational speed transducer is an incremental shaft encoder

which simply uses one set of opto-electrical sensors. The incremental shaft

encoder consists of a disc in which a series of holes or marks are engraved

around the periphery. A light source, usually a light emitting diode, is positioned

on one side of the disc and a photosensitive transistor on the other. Rotation of

the disc generates a series of light pulses which are registered as a square pulse

Chapter 5 Validation qf Control Strategies 118

train output from the photosensitive transistor. By using a mechanism consisting

of two light sources, the direction of the rotation can be detected. This is not

included in the Hitachi dc servo motor. Therefore, alternatively, the applied

voltage to the motor or current taken by the motor is measured to identify the

direction of motor rotation. The output waveform, which is a train of pulses, is

counted over a constant period of time to convert the number of pulses to

rotational speed of the motor.

5.2.2. Load Cell Specifications

The force sensor used in the experimental rig is a load cell with specifications as

given in Table 5.2.

Table 5.2: Load cell Specification

Model Rated Load Output Voltage Non-linearity

XTRAN-K4 25 Newton 2.843 mV/V at rated load ! +/-0.01%

Load cells which are normally used as force, pressure, or torque transducers are

based on a strain measurement, which is essentially a displacement. The physical

variable to be measured ultimately causes, by design, a deflection in an elastic

member. Direct measurement of the strain associated with the deflection can

then be related back to the physical variable through calibration. Strain sensitive

sensors are incorporated into a Wheatstone bridge circuit.

The Wheatstone bridge consists of four resistors, a voltage source and usually a

high impedance instrument to record the bridge output voltage. W h e n the bridge

is balanced, no current is drawn through the voltmeter. The sensing element,

which is normally a strain gauge, constitutes one arm of the bridge while the

other three anus have constant resistance equal to the sensing element at no load


condition. The bridge normally operates in an unbalanced mode where the

voltmeter reading is used to quantify the changing conditions at the sensing

element, which is the result of changes in the measured physical variable. Figure

5.2 shows a normal bridge configuration in unbalanced mode.

Figure 5.2: Unbalanced mode Wheatstone bridge

It is assumed that the bridge is initially in balance, with all elements having a

resistance R. The sensing element is then subjected to a condition which causes

its resistance to change to (R + AR). The bridge thus becomes unbalanced and a

potential difference v is generated and registered on the voltmeter. Given that the

voltmeter has nearly infinite resistance, there will be a zero current drawn

through the voltmeter. The current drawn through A and C is V/2R, while that

through B and D is V/ilR + AR). The potential seen at x will be:

(V - RV/2R) = V/2

The potential seen at y is;

V-ViR + AR)/(2R + AR)

The resultant potential difference (x-y) is:


_V AR 1 V ~ 2 R i2 + AR/R)

If R))AR, the potential difference is given approximately by:

VAR

Equation (5.1) gives a linear relation between the potential difference v and the

changes in the strain gauge resistance AR, which is the only variable in the

equation. It should be noted that an instrumentation amplifier, including a

differential amplifier with high input impedance, should be used to avoid loading

the bridge. In the experimental rig, an off-the-shelf strain gauge amplifier with

the given specifications (Table 5.3) is used to amplify the potential difference v.

Table 5.3: Instrumentation amplifier specifications

Supply Voltage Input offset voltage Input offset voltage/supply

Input impedance Input noise voltage

Band width (unity gain) Output current Common mode rejection ratio Maximum bridge supply current Power dissipation

+1-2 to +/-20 V dc 1 1 m V max 5p:V/V

>2.5MQmin 1 |j,Vp.pmax

400 kHz 5 mA > 100 dB 12 mA 0.5 W

5.2.3. Linear Variable Differential Transformer (LVDT)

The L V D T is widely used as a measurement and control sensor wherever

displacements of a few micro-inches to several feet need to be measured directly.

Such a sensor also can be used to measure other physical quantities, such as force

and pressure, after being converted to linear displacement. The L V D T is

extremely accurate, can operate in extreme conditions and produce repeatable


measurements. Hence it is the ideal transducer element for thousands of

industrial, military and aerospace applications. There is no physical contact

between the movable core and coil structure, which means that the L V D T is a

friction-less device. This gives the L V D T essentially infinite mechanical life,

which is a good characteristic for deminer arm expected to repeatedly insert the

bayonet into the soil.

5.2.3.1. Operation of LVDT

The L V D T is an electromechanical device that produces an electrical output

proportional to the displacement of a separate movable core. It consists of a

primary coil and two secondary coils, symmetrically spaced on a cylindrical form.

A free-moving rod-shaped magnetic core inside the coil assembly provides a path

for the magnetic flux linking the coils. A cross section of the L V D T and a

diagram of its operating characteristics are shown in Figures 5.3 and 5.4.

jtumless .ttf.l housing arid end lids

COIL

Vacuum and pressure

impregnation with high grade electrical varnish

High permeability ,., -ri' nickel-iron core

CORE

Figure 5.3: Cutaway view of the Schaevitz L V D T


Output voltage

•Cyj •

kjlflJUlfiJLr -vMUJUL/-Core

rarnrrini'JOTnin mm mm \

Input voltage

Secondary coils

Primary coil

Figure 5.4: Linear variable differential transformer

When the primary coil is energised by an external AC source, voltages are

induced in two secondary coils. The secondary coils are connected in series

having voltages with opposite polarity. Therefore, the net output of the transducer

is the difference between these voltages, which is zero when the core is at the

centre or null position. W h e n the core is moved from the null position, the

induced voltage in the coil toward which the core is moved increases, while the

induced voltage in the opposite coil decreases. This action produces a

differential voltage output that varies linearly with changes in the core position.

The phase of this output voltage changes abruptly by 180 degree as the core is

moved from one side of the null point to the other.

The D C - L V D T maintains all the desired characteristics of the A C L V D T , with

the simplicity of D C operation. It consists of two integral parts: the AC-operated

L V D T and a carrier-signal-conducting module. The application of specific

integrated circuits permits a high-density packaging of all the necessary

electronics, the L V D T , and the core in one housing. The unit can operate from a

single source such as a battery, while virtually any D C meter can be used for

readout. Specifications of the D C - L V D T used in the arm is given in Table 5.4.


Table 5.4: D C - L V D T specifications

Model Input Voltage Range Scale Factor Null Voltage A C Ripple Linearity

Lucas Schaevitz DC-operated (5000 DC-E) +/-15 V D C (nominal), +/- 20 m A +/- 5.0" 2.025 Volts D C / Inches 0.00003 Volts D C Less than 25 m V rms 0.17 %

The experimental rig and its associated electronics are shown in Figure 5.5. In

this figure the robot arm is inserting the bayonet into the soil to detect a laid mine

beneath the soil.

Figure 5.5: Experimental set up

5.2.4. Interface Circuits Description

A block diagram given in Figure 5.6 illustrates the interface circuits between the

sensors and the control computer, including the data acquisition board (PC-30C)

and the designed electronic circuits driving the dc servo motor.


r__3

oojtr 1200 _Hr

Pentium

j

< '

Data Acquisition PC30C

l

D C Motor PWM/Driver

> D C Servo Motor +

End-effector

t

r -\ Electronic Circuits Interface

M

Shaft Encoder

I\I\

Sensor

' 1 0 LVDT 7

Bayonet i

Figure 5.6: Hardware configuration of deminer arm

The interface card PC-30C supports 16 analog input channels with a 100 kHz

A/D converter, 24 digital I/O lines and four D/A converters connected to the

analog output channels. The interface components include the force sensor

amplifier, P W M servo motor driver, and a digital circuit measuring the speed of

the motor by counting the pulses from the shaft encoder.

5.3. Commissioning of the Experimental Rig

There were a number of difficult problems encountered during commissioning of

the hardware and software of the experimental rig.

5.3.1. Hardware Problems

As the constructed experimental rig was the first prototype deminer robotics arm

developed at Wollongong, a number of problems regarding the mechanical

design and manufacturing inaccuracies were encountered. Some examples were

varying friction of the mechanism according to the position of the end-effector,

backlash and vibration of the arm. In the mechanical structure of the deminer, a

normal bolt and a long screw are assembled as a lead-screw mechanism with

considerable amount of backlash. On the other hand the screw which is


connected to the end-effector, is off-centred relative to the motor shaft. Hence, a

considerable amount of static friction, which varies according to the position of

the bolt, affects the performance of the control system. Impact control can

perform satisfactory when the system has an accurate positioning mechanism

with no backlash. The backlash involved with the experimental rig, is quite high

and can significantly deteriorate the performance of the impact controller. For

this reason and because of the noise involved with the force sensor, the step

response of the deminer sometimes demonstrates another transient response after

reaching a steady state.

The experimental results clearly show a considerable amount of unwanted noise

in the force signal, which is due to the vibration of the arm when the bayonet is

inserted into the soil. Another problem encountered is the unpredictable and

variable static friction which effects the steady state enor of the system.

5.3.2. Software Problems

The major software problem encountered in this thesis was the conversion of

M A T L A B M files to C programming language running in a D O S environment.

The A S M O D algorithm employed in this work, was initially developed by T o m

Kavli [Kav92] in a M A T L A B environment. There was no problem in using M

files in off-line training as time was not so critical. However, they are too slow

for on line estimation of the model. They needed to be converted to a more

efficient programming language. Since the data acquisition software was written

in C, it was decided to convert the A S M O D M files to C.

At the first attempt, the converter toolbox provided by M A T L A B was used. The

code produced by this toolbox runs only in the Microsoft Windows environment

[Mat96a] which is not very efficient due to the operating system overheads. The

second package used to convert the M files was a C + + compiler D J G P P


supported by M A T C O M [Mat96b] for the D O S environment. This converter

proved quite successful and the generated code ran on D O S with no problem. As

this compiler was not supported by the data acquisition board (PC30) library, all

the drivers for the board had to be modified for the D J G P P compiler.

The generated code produced an execution time of 10 [msec] for each iteration of

the program without updating the A S M O D model on a Pentium 200MHz. But

when it was attempted to update the A S M O D model on-line, the execution time

for each loop increased to about 50 [msec], which is five times more than the

sampling period (T=10 [msec]) used in the digital controller.

Therefore, it was decided to update the A S M O D model only after one complete

procedure of probe insertion by the collected input/output data rather than

updating the A S M O D model on-line. It was, however, realised that there was no

considerable difference between the updated model and the original one. The

reason was that in consUuction of the model, different types of soil were taken

into account.

5.3.3. Implementation of Digital PID Controller

Digital P (proportional), PI (proportional + integral) and P D (proportional +

derivative) controllers were used in conjunction with the intelligent controllers in

the experimental work. The sampling time used for the digital controllers was

the same as the sampling time used in the S I M U L I N K simulation (T = 0.01

Seconds). The following algorithm was used to implement the digital PID

controllers:

w_*,+M(i±l)+£fci=!, 2 z-1 T z


where KP, Kj and KD are the controller and T is the sampling period of the digital

control loop. Comparing the above PID controller structure with the standard

form of a second order pulse transfer function

a0+aiZ_ 1 + a2z~

2

Diz)= l + b}z~

l+b,z

the parameters will be obtained as follows:

a, = KP+^KtT + jKD

ai=-KP+±K}T-jKD />,=-l

a2=~KD b2=0

In order to obtain an execution time for each loop of the digital controller equal

to the selected sampling time, a special C + + function provided with the D J G P P

compiler was used. Using this function, it was possible to measure the execution

time in microseconds. The difference between the sampling time and the

execution time can then be delayed sufficiently to produce the desired sampling

time for each loop of the program.

5.4. Validation

In order to validate the proposed impact control methods described in Chapter 4,

a series of experiments were conducted to locate a VS-50 A P mine laid under the

garden soil. For each control method, three different sets of results based on the

variation of the garden soil (environment) are presented. This was achieved by

mixing the garden soil with other ingredients such as sand, clay, and small stones.

Moreover, the soil which was used for the first set of results (labelled 'A') was

dried loose soil, while for the second set of results (labelled 'B') it was the same


but compressed and for the third set of results, water was added to provide a

different environment. The experimental results for each control method has

been presented in the same order as the simulation results presented in Chapter 4.

In order to illustrate the effectiveness of the intelligent controllers, their

performances have been compared against a system using a conventional

controller.

The results of the experimental work are given by a set of diagrams which

illustrate the measured force during the insertion of the robot arm into the soil

and the impact stage when the arm has reached the body of an A P mine. In each

diagram, the results with the intelligent methods are shown in black lines while

the results with pure conventional controllers are in faded black.

5.4.1. Neruo-Fuzzy Adaptive Gain (NFAG) Controller

The N F A G technique based on the adaptive gain (Section 4.5.1) was

implemented in real time using a P C Pentium 200MHz. Figures 5.7, 5.8 and 5.9

compare the performance of the N F A G algorithm against a proportional force

controller. The PI velocity controller is the same as the one presented in Chapter

4, Section 4.5.1.1. The arm becomes stationary when it comes into contact with a

stiff environment. Accordingly, the resulting static friction causes some

problems such as steady state error or stick/slip phenomena.

Chapter 5

2.5

To. __ ® 1.5

8 u.

0.5

Validation of Control Strategies

Practical Result ol N F A G controller

™

ii UjaJ

•

rn.

1 1 1 i ! !

With NFAG

Without NFAG

r~

j i

;

—

j

j

^FT?f fflSF'^f^F' roi " r" vIP Tin"" ™f 7

1 ! L.

T "f' IT ! 1

6 8 10 12 Time[s]

14 16 18 20

Figure 5.7: Experimental result of N F A G Controller (A)

129


2.5

® 1.5 o LL

0.5

i J Jl AM ll

j

\l

| I f j I

i i I i

> _ - • »

}-,

I j j

With NFAG

Without NFAG —

r"'" •

w||# rfTt j__kniM«_J_y4

l

e 10 12 Time[s]

14 16 18 20

Figure 5.8: Experimental result of N F A G Controller (B)

Figures 5.7, 5.8 and 5.9 clearly illustrate that the NFAG control method produces

a better performance than the conventional proportional controller. One of the

most significant differences is the overshoot percentage of the fixed proportional

controller which is about 300%.


3.5

2.5

s a* 1.5

0.5

-0.5


Uk&to* S f c f c * * ^

1. 1

1 1 1

I :

With NFAG

Without NFAG

m^WBmmmsam I'tfn

V

JEL^L." ,TIH \nVf n » ilMI'P

4 6 8 10 Time[s]

12 14 16

Figure 5.9: Experimental result of N F A G Controller (C )

Overall the NFAG algorithm produced a smoother response and a smaller

overshoot compared to the conventional controller which had an oscillatory

response. The slower response of the N F A G algorithm was its only shortcoming,

which was also obvious in computer simulation. The slow response is due to the

inverse dependence of the adaptive gain, KA generated by the neuro-fuzzy

model, on the environment stiffness. Hence, the total gain of the control loop is

smaller than the gain of the fixed proportional controller. This was the case only

when the arm was in contact with the environment, whereas the response of the

arm in free space motion is the same as the fixed proportional controller.

5.4.2. Neuro-Fuzzy Impact Controller (NFIC)

As explained in Chapter 4, Section 4.5.2, this method was based on an indirect

adaptive neuro-fuzzy control structure in which the derived A S M O D model is

used as an inverse model of the plant.


5.4.2.1. Feedforward N F I C with PI Velocity Controller

The control block diagram for this control method is presented in Chapter 4,

Section 4.5.2.2. The experimental results for three different situations based on

the soil variation (dry (A), dry compact (B) and wet (C)) are given in Figures 5.10

,5.11 and 5.12. From these figures, it can be seen that the performance of the

impact controller with NFIC is superior to the system without the NFIC

controller. The overshoot for the NFIC controller has a maximum value of 2 0 %

compared to 2 5 0 % overshoot for the controller without NFIC.

Practical Result o( feedforward NFIC with PI Velocity controller

I

!

i

1 1 w rn

W*fH V f

+~""T '

\

1 li W

- j

With NFIC

Without NFIC

I

i i ... . A i I I 1 1 1- 1 '

0 2 4 6 8 10 12 14 16 Tlme[s]

Figure 5.10: Experimental result of feedforward NFIC with PI velocity control

loop (A)


Practical Result ol leedlorward NFIC with PI Velocity controller

U*-U nHA

rfP Ik 4A..+.I

Ul TT ru ]\T '

j AjirT

flr 1

I ~*

f

• " '

i»fc*n»i^M

With NFIC

Without N F I C

i i

0 2 4 6 8 10 12 14 16


loop (B)

The stick/slip phenomena can be clearly seen in Figures 5.10 and 5.12 without

the NFIC. For example, in Figure 5.10, the step response has jumped to a level

which is more than the desired step level. It is, however, stuck to that level trying

to compensate for the error (stick). After a while, due to the integration of error

signal in the PI velocity controller, the motor overcomes the static friction and

attempts to return to the desired level (slip). But, again, it is stuck to another

level less than the desired step. This stick/slip can happen a few times until it

reaches to the desired step level.


Practical Result ol leedlorward NFIC with PI Velocity controller

1 :

With NFIC

Without NFIC

lijAt lAAl i-U*.--ti-M

|ii- (> #

m^l ll( ft

rH

r

M

w

frW;.*».-...-i-

ffi

0 2 4 6 8 10 12 14 16


loop ( C )

It is also clear that the performance of the control system without the NFIC

controller is very poor. This is contrary to the results produced in the computer

simulation (Figures 4.25 to 4.27). The reason for this difference between the

practical and simulation results could be the effect of non-linearities such as

backlash, and friction not taken into account in the conventional controller and

also the noise produced by the force sensor and other elements of the system.

5.4.2.2. Feedforward NFIC without Velocity Controller

In order to implement this control method the same block diagram given in

Figure 4.21 is used. The experimental results are also based on the comparison

of the step response of the system with NFIC against the system without it.

Figures 5.13, 5.14 and 5.15 illustrate the results of the NFIC impact controller

without the velocity conhol loop.


2.5 Practical Result ol leedlorward NFIC without Velocity controller

B __ "aT S> o LL

With NFIC

Witliout NFIC

Figure 5.13: Experimental result of feedforward NFIC without velocity control

loop (A)

An obvious result from the feedforward NFIC without the PI velocity controller

is that there is no compensation for the steady state enor in the force step

response. This means that the feedforward NFIC is not able to overcome the

static friction at zero velocity, when the arm is in contact with a stiff object.

However, for the purpose of A P mine detection, this poor performance is not

significant and can be ignored. The obtained results indicate that the feedforward

NFIC has produced a better step response with less overshoot and oscillation.

Chapter 5 Validation of Control Strategies

2.5

•â^n>m' -**• - *f*

With NFIC

Witliout NFIC

8 10 Time[s]

12 14 16

135


loop (B)

2.5 Practical Result of leedlorward NFIC without Velocity controller

1 1

--K r.

With NFIC

Without NFIC

14 16


loop (C)

According to the stiffness of the soil, the force profile of the NFIC has different

levels of overshoot in the range of 10-30% which is acceptable for mine


detection. At the same time the control system without the NFIC has produced

overshoots of about 2 5 0 % and long settling times of up to 3 seconds shown in

Figure 5.13.

5.4.3. NFIC/PID Results

This control method, as explained in Chapter 4, is inspired by the human reaction

in collision with an environment. In this approach, a fast reaction is produced by

minimising the impact force and stabilising the system by reducing the oscillatory

behaviour of the conventional impact control methods. A n impact detector

mechanism is designed to add the NFIC control signal to the normal PI control

system.

Therefore, it is vital to have a proper impact detection mechanism to activate the

NFIC impact controller to reduce the total driving signal of the arm driver (servo

motor). As is mentioned before, the force derivative is used to detect an impact

when the arm is approaching a stiff object to detect a sudden increase/decrease in

the force profile. Due to the noise produced by the force sensor and vibration of

the robot arm, an optimum threshold should be defined to prevent a false impact

detection.

In the next two following sections experimental results for the NFIC/PD and

NFIC/PDPI control methods are presented.

5.4.3.1. Results of the NFIC/PD without Velocity Controller

In this control method which is based on the block diagram given in Figure 4.33,

a force P D controller is designed to implement an explicit P D force controller

without the PI velocity controller. The detail of the P D force controller is given

in Chapter 4. This controller is implemented based on the same algorithm

explained in section 5.3.3. The performance of this algorithm is compared with


the P D force controller without the N F I C controller in three different situations

based on the environment variation (soil). Figures 5.16, 5.17 and 5.18 illustrate

the step response of this control method.

2.5

.1.5 O) __ a? LL

0.5

Practical Result ol NFIC/PD controller

!

|

i

\r—*~ r l

With NFIC

Without NFIC —

6 8 10 Time[s]

12 14 16

Figure 5.16: Experimental result of N F I C / P D controller (A)

2

1.8

1.6

1.4

_J2

"• 1

a o LL

0.8 0.6

0.4

0.2

0 (

Practical Result of NFIC/PD controller

1 ! !

1 Ij

ill

JJHI i. L JP

m™^

1 1/ w r.: T t" . / \ fj^-K^vJHl W \

i i

•*-**'_'_,

With NFIC

Without NFIC

T i • 1 1 i i ! 1 i ' i ) 2 4 6 8 10 12 14

Time[s]

....

1 6

Figure 5.17: Experimental result of N F I C / P D controller (B)


Practical Result of NFIC/PD controller

1 !

liL-ii Brr*

jl_..__uJ_,

lP "I™ IT T

nl

ip

l«J L

T

0^J u F f

- • * •

!

1

IKAw-•• .••* w—~™

v:^:;::,r r -----1

i

With NFIC

WitlioutNFIC

i j

0 2 4 6 8 10 12 14 16 Time[s]

Figure 5.18: Experimental result of NFIC/PD controller (C )

Since there is no integral term in the controller, the response has a steady state

error. Similar to the simulation results, the overshoot of the NFIC/PD method is

fairly low (5-20%) compared to the overshoot of the conventional P D force

controller which varies between 70-120%. Another problem associated with the

conventional P D force controller, which was not shown in the simulation results,

is the high oscillation of the response which results in a longer settling time.

5.4.3.2. Results of the NFIC/PDPI with Velocity Controller

In this controller (Figure 4.37), the force P D controller is implemented in

conjunction with the previous PI velocity controller. W h e n there is no

considerable impact with a stiff environment, the conventional P D force

controller (outer loop) and PI velocity controller (inner loop) are the main

controllers of the system. However, after impact detection, the NFIC impact

controller is activated to minimise the impact force. The results of the

NFIC/PIPD impact control method are compared with a PIPD controller for three

different situations in Figures 5.19, 5.20 and 5.21. It is clear from these results,


that the NFIC impact controller improves the performance of the PIPD controller

when an impact is detected. The overshoot of the NFIC/PDPI controller has

proved to be one-third of the conventional PDPI controller. It is also clear that

the settling time for the NFIC/PDPI is shorter while the PDPI method presents

more oscillatory response and accordingly, a longer settling time.

1.2

B°-8 __

o u.0.6

0.4

0.2

Practical Result ol NFIC/PDPI controller

Wt

1 1 BilLl JiJhlitLiU

1 1 i

J

_ #

IP

- "I ~ - T" • 1

t. ! J _

\ ' 1 1

. M ^-^vvw-^wlV^|f

i

With NFIC

Without N F I C

i i . . .-8

Timefs] 10 12 14 16

Figure 5.19: Experimental result of NFIC/PDPI controller (A)

Chapter 5 Validation of Control Strategies

Practical Result ol NFIC/PDPI controller

1.2

.U^M»**»

With NFIC

Without NFIC

14 16

Figure 5.20: Experimental result of NFIC/PDPI controller (B)

Practical Result of N F I C / P D P I controller 1.2

)f1«t 'itMJ><il 'i 'I "< |U*'VI»I.'«I '.'-

With NFIC

Without NFIC

12 14

Figure 5.21: Experimental result of NFIC/PDPI controller (C)

140

5.5. S u m m a r y of Results

In order to provide an overview of the performance of the intelligent controllers

produced in the experimental work, the results, in conjunction with the PI

Chapter 5 Validation of Control. Strategies 141

velocity controller, are summarised in Table 5.5. It has been shown that the

performance of the method with no PI velocity controller was not acceptable.

Hence this method is excluded in the following analysis.

In the first instance, a simple comparison based on the unit step response of the

system in terms of overshoot, settling time and steady state enor is carried out

and presented in Table 5.5. Since the overshoot percentage is the most critical

characteristic in the mine detection process, it can be concluded that the

performance of the NFIC/PDPI is superior to other intelligent methods.

Table 5.5: Performance of intelligent impact methods with PI velocity controller

Method

NFAG

NFIC

NFIC/PDPI

Overshoot %

20-25

5-25

5-10

Settling Time (Sec.)

8-12

6-7

8-11

Steady State Error

0.005 - 0.025

0.0 - 0.005

0.0

In the second step, a statistical analysis of the obtained results was carried out.

The statistical characteristics calculated include Maximum, Minimum, Range,

Mode, and Standard Deviation as tabulated in Table 5.6. The data used in the

analysis are the parameters of the force step response measured after reaching the

desired output (1 Kgf). The analysis is performed for three intelligent methods of

N F A G , NFIC and NFIC/PD applied in three different conditions of A, B, C

which were explained in Section 5.4. In this table M a x and Min refer

respectively to the maximum overshoot and minimum undershoot of the force

step profile. Minimum undershoot in this context refers to the minimum value of

the force profile when oscillation takes place. Range is the difference between

the M a x and the Min, indicating the largest variation in the obtained force profile.


The smaller the Range, the better is the performance of the impact controller as it

guarantees a safer prodding.

Table 5.6: Statistical Comparison of the Intelligent Impact Control Methods

Max

Min

Range

Mode

STDEV

NFAG

A 1.04

0.935

0.105

0.975

0.0125

B 1.18

0.88

0.3

1.005

0.0418

C 1.155

0.99

0.165

1.015

0.0227

NFIC

A 1.18

0.89

0.29

1

0.0250

B 1.43

0.285

1.145

1.005

0.0421

c 1.14

0.915

0.225

1

0.0168

NFIC/PD

A 1.105

0.95

0.155

1

0.0227

B 1.09

0.95

0.14

1

0.0207

c 1.075

0.97

0.105

1

0.0094

The performance of three intelligent impact control methods are compared with

the conventional methods in Figures 5.22-5.27. These figures clearly illustrate

the superiority of the intelligent control methods over the conventional methods.

The maximum overshoots of the conventional and intelligent conhol methods are

compared in Figure 5.22. It is obvious that the NFIC/PIPD method has produced

the smallest overshoot. Another important conclusion is that the overshoots for

three different conditions A, B, and C are the same for the NFIC/PD control

method. While, for PDPI, there was about a 2 0 % variance in the overshoots of

the three conditions (Figure 5.23). This proves the robustness of the NFIC/PD

control to variation in the parameters of the environment.


Maximum Oveshoot

35!

arvfaxjrt

mWBK Corw

Figure 5.22: M a x i m u m overshoots of the force step response

Comparison between NFIC/PDPI and PDPI

0)

> O

1.4

1.2

1

0.8

0.6

0.4

0.2

0

• Maxjnt

__ Max_Conv

NFIOPD_A NFlC/PD_B NFlC/PD_C

Figure 5.23: Focus of the previous figure on NFIC/PDPI results

Another important issue which could be crucial in mine detection task is the

extent of variation of the force profile. The smaller the variation, the more

reliable will be the prodding process. Generally, after the impact of the bayonet

with a stiff object, some unwanted oscillations take place on the force profile. A n

effective impact controller, however, can cancel or reduce the oscillations to a

satisfactory level. Figure 5.24 shows a considerable difference between the

force profile of the intelligent and conventional impact controllers. It is obvious

again that the force profile of NFIC/PD has experienced less variation than the


others and hence this method is more satisfactory. Figure 5.25 shows the same

comparison only for the intelligent impact conhol methods.

Force R a n g e

Q Rangejnl

_1 Range_Corw

Figure 5.24: Comparison of the force range of different conventional and

intelligent methods

1.2

1

0.8

0.6

0.4

0.2

M. m

i

< U-

z z

CD <

Force Range

<

< i

o Lt z

CO I

o U-

z

° 9 Lt Br z g

o CL

g u.

£L D Q-O LS. Z

p Range_lnt

Figure 5.25: Comparison of the force range of different intelligent methods

This comparison proves that variation in the range of the force amplitude for the

three conditions A, B, and C using NFIC/PD method is less than 20%.


The range on its own can be misleading as two different data sets could have the

same range, but differ greatly in variation of their amplitudes. Therefore, it is

important to consider the standard deviation, a (square root of the variance, a2),

which is a measure of the spread or variability of a set of data. The unit of a is as

the observed data, whereas the unit of variance (a2) is the square of the unit of

the measurement which is usually not used directly as a descriptive measure.

The standard deviations are compared in Figure 5.26. For intelligent methods, the

standard deviation of the force amplitude is less than 0.03 [Kgf] or 30 [gf] which

is mainly due to the measurement noise.

Standard Deviation

rjSTDEVJnt

0STDEV Corv

Figure 5.26: Comparison of standard deviation of the force step response

Figure 5.25 illustrates again the superiority of the intelligent impact control

methods over the conventional methods.

Because of the presence of noise in all the results shown in Figures 5.7 to 5.21, it

is difficult to define the accurate final output level and accordingly the steady

state error of the force response. In order to clear the results from the impact of

noise, the "Mode" of the force amplitudes for different methods are compared in

Chapter 5 Validation of Control. Strategies 146

Figure 5.27. The "Mode" of a data set provides the most commonly occurring

value when the data is restricted to specific values.

Mode

Drvbdejnt

O Mxle_Corv

Figure 5.27: M o d e values for conventional and intelligent control methods

According to this figure which illustrates the most commonly occurred value of

the force step response for conventional and intelligent methods it is feasible to

assume that these values are the final values for the force step response.

Therefore, based on these values the steady state enor of each control method

could be determined.

5.6. Conclusion

In this chapter the experimental rig used in the work has been described. The

problems encountered in commissioning the hardware and software of the rig

were also explained.

All the intelligent impact conhol methods presented in Chapter 4 were

implemented on the experimental rig and their performance were compared

against conventional control methods. It was clearly shown that the intelligent

methods produced better results.


The NFIC/PDPI impact control method has the best performance compared to the

other two intelligent methods in terms of the range of the force variation and

percentage overshoot. These are critical parameters in the mine detection process.

The intelligent control methods have also produced better robustness and stability

to variation in the environment.

Chapter 6

MINE DETECTION

ALGORITHM

Chapter 6 Mine Detection Algorithm 149

6.1. Introduction

In the previous chapters a single-probe mechanical aim was studied for detection

of a buried object beneath the surface of the ground. During demining, after the

first encounter with an object, further prodding is needed to ensure that the

detected object is an A P mine.

In this chapter the development of a mine recognition algorithm based on

multiple prodding is described. The algorithm requires three points of a detected

object in order to estimate its features and to determine whether it is a mine. The

proposed algorithm is validated through computer simulation and experimental

work.

In addition, the design of a multi-probe mine detection mechanism is proposed in

this chapter. Such a device will detect a mine faster than a single-probe

mechanism.

6.2. Land Mine Recognition Methods

As mentioned in Chapter 2, hand prodding is the most reliable method of mine

detection and clearing. A probe is manually inserted into the soil at a 30 degree

angle, approximately every five centimetres. When a stiff object is detected,

more probing is conducted to identify the shape and size of the object. If the

object is determined to be a potential mine, a mine clearing team is called to

uncover or detonate the object.

Using this method one square metre of land is cleared in approximately four

minutes. This process of land mine detection is automated in this work. The

focus of the research is only on plastic A P mines, which are mostly cylindrical.


Figure 6.1: A buried anti-personnel mine beneath the soil

At least three known points on a cylindrical object are needed to estimate its

geometrical properties. Hence, three consecutive probe insertions are carried out

for each iteration of the mine detection algorithm.

The distance between consecutive probing, "W", is determined based on the type

of A P mines used in the field to ensure that no land mine is missed in between the

probes. The diameter of a plastic A P mine is in the range of 5 to 10 cm (D =

5-10 [cm]) [Min96]. Hence the value of W should be chosen to be

W < D12 [cm] to ensure that the three contact points are located on the body of

the mine. This distance should not be more than 2 cm ( W = 2 [cm]) for the

smallest mine.

A n alternative approach to multiple probing is to use a multi-probe device. Figure

6.2 illustrates a three-probe mechanism in contact with an A P mine. This

diagram also illustrates the ideal configuration of the three consecutive insertions.

Another advantage of a multi-probe robot for A P mine detection compared to a

single-probe is the ability to detect whether the detected object is electrically

conductive. In a plastic mine, the resistance measured between two probes will be

quite large. Wet soil, on the other hand, has a resistance lower than a plastic

mine, but higher than a conductive object.


c

p, 1

Robot Arm

I 4 W [_

_ """""" F

Mine

D

p

•

"S

J

Figure 6.2: The three-probe robot in contact with a mine

Generally, it is possible to identify the shape of an object by moving the probe

along the contour of the object. This, however, is not possible for an object

buried under soil.

Another constraint in the mine recognition process is that the gripper attached to

the robot end-effector cannot be a jaw type gripper to grasp the mine from two

opposite sides.

6.3. Object Recognition Procedure

The task of mine detection consists of two phases. In the first phase, three

contact points are established on the mine either through consecutive probing or

using a multi-probe device. In the second phase, the geometrical features of the

object are derived using the information provided by the three contact points.

The problem associated with the first phase is force/impact control of the arm

which was addressed in the previous chapters. In the feature extraction process,

the most important geometrical parameters of an object to be measured are the


radius of the base and centre point location. With this information, it is possible

to determine whether or not the unknown object is of cylindrical shape.

An analytical approach to calculate the radius and centre point of an unknown

curvature based on three contact points on the boundaries is presented in Section

6.4. But, initially, a clear generic definition of an unknown 3-D object is given.

A 3 D object is defined as a combination of well-defined natural quadric surfaces

in the object reference frame, satisfying the properties of regular sets (r-sets)

[Req82] [Hah94].

6.3.1. Feature Extraction of an Unknown Object

Assuming that all three probes are in contact with the outer surface of an object,

there could be three possibilities of contact:

• Contact points are located in a planar surface which means they are in a

straight line

• Contact points are located in a concave curved surface

• Contact points are located in a convex curved surface.

For the last two cases the surface could be sphere, cylinder, cone or other

irregular curved surfaces. Since the plastic A P mine surfaces are just limited to a

cylindrical surface, a cylinder which can be represented by the lengths of the long

axis, radius and centre axis point of the base circle are the targets of this work.

Therefore, the main criteria for differentiating among different objects is the

radius and centre point location of the cylinder base circle. These values could be

used to distinguish among concave, convex, or plane surfaces. For a plane

surface the radius would be infinity while for a curved surface (concave/convex)

it is not. To distinguish between a concave from convex curved surface, the

following conditions could be examined:


P + p if —-z-^ > P2 then it is a Concave curvature

p + P if * 3 < P2 then it is a Convex curvature

in which Px, P2 and P3 represent the probes contact points.

Alternatively, if the estimated centre point of an unknown curvature is behind the

contact points, the curvature surface is a concave curvature, which cannot be an

A P mine.

Therefore, the centre point location defines the type of the curved surface

(concave or convex) and the radius value defines whether the object is in the

range of available A P mines (2-5 [cm]). The following table categorises the type

of surfaces according to the criteria.

Condition

Oxv < P XV

Surface TyPe

Plane. Concave

O w > P XV Convex

Table 6.1: Mine possibility table

Condition

0 < R < oo Cm 5 < R < 2 Cm 2 < R < 5 Cm

Mine Possibility

No

No

No

Yes

Object Material

-

-

Conductive

Non-conductive

Mine Possibility \

No

No !

; No

No !

><J 6.4. Estimation of the Object Radius and Centre Point

In the A P mine recognition algorithm, it is essential to estimate the curvature

radius of the object in contact with the probes and decide whether it is concave or

convex. Using the information provided by the three contact points, the following

procedure is used to estimate the radius of the circular cross section of a cylinder,

as shown in Figure 6.3. It can be easily seen that


(6.1)

where in triangle P^Py the angle 3 is

2 . "> ~> 1 1 , 1 '

A _i b +c -a

and fl = Vw2+4 b = ^W2+d;, c = V4W2-r-4

where W is the distance between the insertion points in the consecutive insertion

of the probes or the distance between probes in a multi-probe arm. The

parameters d12 , dt3 ,and d2.< are the distances between contact points:

d12=P,-P2, </13 = />-/>, </_., = /_-/>,

The following Equations (6.2) and (6.3) could be used to locate the centre of the

circle iOx , Oy) by a vector R = iR,d)with the amplitude of R and the angle

d = 0 - 90° relative to the first probe contact:

X0 = Rcosd + X p (6.2)

Y0 = R sin d + YP (6.3)

where

0=4+1+5

? _,.<r+<-2-/V 1 = cos (- 2«c

4 = tan -i W (Li 12

) 5 = cos '(-2Rc

) = cos_ 1(—) 2R


Backward-Fo •ward Move (BF)

Left-Right Move (LR)

Probe X

Figure 6.3: Top view of the robot probes in contact with the cross section of a

cylindrical object

It is assumed so far that the mine is buried perpendicular to the ground. It is also

possible to investigate whether the cylindrical object is perpendicular to the

ground level which is important to know in mine removal. In order to find the

angle of the cylinder axis (3 in Figure 6.4), w e should have at least two vertical

contact locations on the surface of the mine. After locating a set of horizontal

contact points by three probes, the robot arm should move backward/forward

(BF) before inserting the probes into the ground to obtain another set of contact

points. It is sufficient to obtain only two contact points for each probe. Figure

6.4 shows the two contact points for one of the probes on the surface of the mine.


Ground Level

Figure 6.4: Side view of two contact points with a mine

The angle of the cylinder axis (3) using law of Sines in the hLd12 triangle is

estimated as follows:

d\2 sin(l) sin(90"-3) sin(3 + 2)

since (1) = 30 then

h = lA dn

cos(3) (6.4)

Using the law of Cosines gives:

Is +h2 -dn cos(90° - 3) =

2hL (6.5)

Substituting (6.4) into (6.5) and simplifying the result will produce:

Asin2(jc) + flsinU) + c = 0 (6.6)

where

A = AL 4L4+4<2-8L

24

dn B = -

KL3-4L(/2

c/f2


C = 4L2 - 3d22 and x = 2(3) or

5 x

3 = - (6.7)

It is clear from Equation (6.6), that the angle of the cylinder axis can be

calculated in terms of the difference between the penetration length of the probes

idj2) in two locations and the distance (L) between the two insertion locations.

6.5. Mine Recognition Algorithm

In this section a mine recognition algorithm is presented using the obtained

contact points and the analytical method presented in the previous section. This

algorithm which is not a finalised algorithm for mine detection is illustrated in

Figure 6.5 in the form of a flow chart. In this algorithm, which is based on three

contact points with any unknown object beneath the soil, it is vital to wait until

the insertion process of the probes is completed. The radius and centre point of

the circular intersection of a detected object is calculated according to (6.1), (6.2)

and (6.3). If three contact points can not be detected even by inserting the

maximum length of the probe, the arm should be moved to left or right. For

simplicity, it is assumed that mines are cylindrical and laid perpendicular to the

ground. The first important comparison is to check whether the calculated radius

is bigger than the maximum radius for the A P mines [Min96] which is 5 [cm].

W h e n all the three contact points are on a planar surface, the radius should be

theoretically infinity or a very large value. If the calculated radius matches that

of an A P mine (2-5 [cm]), the next step is to find out the convexity or concavity

of the curvature surface.

For a concave curvature, the centre point will locate behind all the three contact

points. It implies that Ya (the distance of the centre point from the X_axis) of the

Chapter 6 Mine Detection Algorithm 15$

center point will be negative relative to the initial location of the first probe. O n

the other hand, if the curvature surface is convex and the radius is significantly

larger than 5 [cm] or smaller than 2 [cm], the object is cylindrical but it is not an

A P mine.

Otherwise, more investigation will be necessary to decide whether the detected

object is possibly an A P mine or not. In this situation, the robot arm is moved to

left or right before the probes are inserted to obtain another set of contact points.

The above mentioned procedure will be then repeated. If the new contact points

will also produce a valid radius for a potential A P mine, the next step is to check

how similar is the new estimated radius and centre point to the previous ones.

If the estimated values in the two occasions are identical, the detected object is a

mine. Using a multi-probe arm may help at this point to determine whether the

detected object is electrically conductive.


(v Start )

<*)

Insert Probs 4

No

Move Robot Forward

\ No y YPS

3Contacts?V5-.; Maximum? —L5=-

Register a Planar Surface

YesT

| Yes T

Calculate trie

Radius & Centre

1 T N R»?

V^T

U —

1 1 T

Move Probes Forward —•, Insert Probes

1

\ No \ R » ? y

No |

— » - 3 Contacts?

Y Calculate the | Radius & Centre

. Yes

/

S x Yes Oy<PiP2IP,? — •

Register a I Concave Surfacef

j Move Robot "^1 Forward -•(A)

No

T < R < t, •> N ° J Register a big I Convex Surface

Move Robot •i Forward — • . A :

Yes

Register a Possible Cilyndrical

Object

Move Probes Lett/Right

No (R&C).- = (R&C)?

Yes

Note: At this point several

methods could be

carried out, such as

Statistc method and

F D M S method

Register a Possible Mine

Move Robots Forward

Figure 6.5: A P mine recognition algorithm flow chart


It is suggested to obtain two more sets of three contact points and estimate their

corresponding radii and centre points to determine the probability of having a

mine through statistical methods. Alternatively, using the fuzzy decision making

approach, it is also possible to derive the degree of certainty for the object to be a

mine. Both of these methods will be discussed in the following sections.

For simplicity, the possibility of encountering a non-vertical cylindrical object is

not considered in this algorithm. But a non-vertical cylindrical object can be

recognised at the end of the algorithm after registering a possible A P mine. This

helps to establish the angle of the cylinder axis (Equation (6.7)) which is

important for the stage of mine clearance or removal. As mentioned before,

probes are inserted at a 30 degree angle to the ground to induce half of the axial

force to the mine in contact with the probes. But if the mine is laid down with a

60 degree angle to the ground and probes are touching the top surface of the

mine, the total amount of axial force would be induced to the mine.

6.6. Simulation

A numerical simulation is carried out to illustrate how an A P mine can be

detected according to the developed algorithm. The contact points of the probe

with a circular object and four non-circular objects are used in the method. The

scenario is illustrated in Figure 6.6. The contact points of three probes for five

sets of insertions with all the objects are shown. The contact points with the

cylindrical object are located on the circle and the points for one insertion are

labelled by />, P2, and P3. For non-circular objects, the first two contact points

are the same as for the circular object ( P{ and P21 while the third contact Points

have an offset of (-1, +1, -2, +2) from /> (Figure 6.6). In this simulation the

radius and centre point of a cylindrical object with radius R = 4 [cm], located at

(X = 4 and Y = 10) is estimated for seven different insertions at a distance of 2


[mm] from each other. Results for the circular and non-circular objects are

shown in Figures 6.7 and 6.8 by 'o', '*' and V respectively. The estimated

radii and centre points for the circular object are fairly constant in different

insertions.

Probes Movement

1....7

1. . . .7

X

Figure 6.6: Simulation based on different insertion points and different location

for the third contact points

Chapter 6 Mine Detection A Igorithm

(Cm) 10|-

Y

;> a X 8

(Cm) 4.5

4

3.5

3

2.5

2

1.5

1

0.5

0

* o <?••

~ T~ Position ol the located object

_.T. *..

* * * _ * : ^ « + :+ + : +

-I 1 1 L.

•or-rj-

* ; +1 +"••;•_••

0.5 1 1.5 2 2.5 3 3.5 4 4.5 X-axis (Cm)

Radius ol the located object

:0 0

!+ +2

3 4 Iteration

162

Figure 6.7: Simulation of A P mine recognition by multi-probe robot

Such results, however vary significantly for the third contact points in different

insertions. The radius varies from 2 to 50 [cm]. The varied centre points obtained

for the different insertions indicates that the object under study had no circular

curvature.

Chapter 6

(Cm)

Y

60

50

40

30

20

10

Mine Detection Algorithm

Centre Position ol the located objecl i i -j

++i+

+ *

: * :

o

* +

-1 -2

10 15 X-Axis (Cm)

20 25

163

60 (Cm)

50

40

30

20

(

0 •

f

*

> Cf

2

Radius of the located objecl

! ! '

: f •

t

; 4 •. : +

*f* 4* 4* 3 4 5

Iteration

i

*

+ 0

6

o * -t-

0

-2

c >

r

Figure 6.8: Simulation of A P mine recognition by multi-probe robot

Such analysis clearly indicates whether a detected object is cylindrical or not.

6.7. Experimental Results

In order to investigate the accuracy and validity of the mine detection/recognition

algorithm, a set of experiments were canned out. Since the implemented single-

probe arm had no horizontal movement to insert its probe at different points, the

buried mine and soil container are moved horizontally on the X-axis (Figure 6.3)


by the steps of 2 cm (W= 2 [cm]) at a time to produce a similar effect. Eight

different sets of data are collected for different objects as shown in Figure 6.9.

The measurement for each object has been repeated three times for better results.

The first object is a cylindrical shape with a radius of 3.5 [cm]. Application of

the algorithm to this object indicates that it had a radius in the range of 3.33 to

3.39 [cm].

Figure 6.9: Objects which are used for experimental results

The second and third objects are two real A P mines (Ml4 and VS50) with the

radius of 2.78 [cm] and 3.8 [cm] which are detected as cylindrical objects with

radii in the range of 2.65-2.74 [cm] and 3.31-3.46 [cm]. Due to some corners on

the body of the VS50 mine (object 3), the estimation error of radius for this

object is high compared to others.

Three different stones with unknown shapes were also employed in the

experiment (Objects 4, 5, and 7). The size of stones were chosen to be in the

range of A P mines to test the accuracy of the detection algorithm. A rectangular


box (box of 10 floppy disks) is also used as a planar surface (Object 6). For this

object, two of the three contact points ( P, and P2) are on one side of the box and

the third (P3) is on the other side. Object 8 is the same box when all three

contact points are on one side of the box. This means that the contact points are

located on a planar surface which should produce a radius of infinity. The

detection algorithm has estimated a large radius of 173 c m for this object. The

area covered by such an object is 1000 (1198) times bigger than the area of the

biggest available A P mine. The experimental results for eight different objects

are summarised in Table 6.2.

Table 6.2: Experimental results for mine recognition algorithm

Object

Objl(Dl)

cylindrical

disc

Obj2(Ml)

AP mine

M14

Obj3(M2)

AP mine

VS50

Obj4(Sl)

unknown

stone

Obj5(S2)

unknown

stone

Obj6(B2)

rectangular

box

Obj7(S3)

unknown

stone

Obj8(Bl)

rectangular

box

PI 6.72

6.3 7.12

5.74

6.18

5.48

5.31

5.04

5.57

3.54

3.31

5.49

4.93

7.68

4.33

4.07

3.78

4.25

5.41

5.68

6.1 7.18

7.43

6.88

P2 5.753

5.726

5.806

5.067

5.08

5.132

4.655

4.677

4.707

2.892

2.956

3.098

3.156

4.36

3.22

3.624

4.67

3.098

5.738

5.66

5.489

5.978

6.153

5.661

P3 6.132

6.483

5.987

6.264

5.741

7.003

5.347

5.714

5.138

3.569

3.821

3.04

2.898

2.99

2.95

7.793

8.735

7.196

6.384

6.169

5.975

4.744

4.969

4.393

R 3.3958

3.344

3.337

2.6545

2.74

2.677

3.3008

3.3104

3.46

3.354

3.66

3.135

3.986

7.425

5.8185

2.8075

4.96

2.475

13.832

7.9215

3.9225

173.24

67.52

138.96

X 2.3968

1.8782

2.7132

1.775

2.2052

1.4282

1.9777

1.5671

2.3037

1.9791

1.6129

3.0861

3.4934

7.14

3.7667

1.5651

0.9597

1.8994

1.2391

1.0629

2.1062

89.965

37.391

70.368

Y 2.4055

2.7672

1.9431

1.9744

1.6313

2.2646

2.6427

2.916

2.5818

2.7084

3.2857

0.5532

1.9199

2.0388

4.435

2.3299

-4.867

1.6101

-13.78

7.85

3.3091

148.05

56.221

113.2

SDK 0.032

SDY 0.413

SDR*SDY

75.373

P% 100

0.044 0.317 71.173 94.429

0.089 0.178 62.933 83.496

0.264 1.44 2.8829 3.8243

1.721 1.419 0.4095 0.5428

1.349 3.964 0.187 0.2476

4.985 11.4 0.0176 0.0228

53.94 46.36 0.0004 6E-08

Different sources of enor such as noise, measurement errors and backlash have

caused discrepancies between the obtained results even for a homogeneous

cylindrical object in each iteration. These discrepancies are more pronounced for


non-cylindrical objects. Variation of the estimated radius for each can be

measured by the standard deviation (SD) of the calculated values. The

measurements show that the S D is quite small for a cylindrical object compared

to others. Hence the inverse of the S D is used as an index of how cylindrical is

an object. The larger the index, the higher the probability that the object is

cylindrical.

For a more reliable index, the SD of the estimated centre position is multiplied by

the standard deviation of the radius before the value is inverted to produce the

index as shown in the ten1'1 column of Table 6.2. According to the estimated

values, the index varies between 0.0004 for the worst case (the planar surface) to

75.37, for best case which is a homogeneous cylindrical disk. This range is

mapped linearly to a value between 0-100 and used as the probability that the

object can be a cylindrical object. For the A P mines the probabilities are 9 4 %

and 8 3 % while for the three stones the probabilities are 0.02%, 0.54% and 3.8%.

6.8. Fuzzy Decision Making

As an alternative approach, a fuzzy decision making algorithm has been

investigated for determining whether an object is a mine or not. This algorithm

provides the degree of certainty that an object is a mine. The fuzzy inference

systems have been successfully applied in various fields such as automatic

control, data classification, computer vision, decision analysis and expert

systems.

The basic structure of a fuzzy inference mechanism consists of three conceptual

components: a rule base, which contains a selection of fuzzy rules, a database

which defines the membership functions used in the fuzzy rules, and a reasoning

mechanism, which performs the inference procedure on the rules to produce an


output or a conclusion. In this approach, the Mamdani Fuzzy Model [Mam75] is

employed.

A typical fuzzy rule in a Mamdani fuzzy model, consists of antecedents and

consequent:

if x is A and y is B, then z is C iw)

where A and B are fuzzy sets in the antecedent, and C is a fuzzy set in the

consequent and w is the weight of the rule which could be between 0 and 1. The

structure of a two-rule fuzzy inference system using min and max for fuzzy A N D

and O R operators and Bell-shaped membership functions is illustrated in Figure

6.10.

Inputl Input2

1(1) 1(2) max

Figure 6.10: The Mamdani Fuzzy Inference System

Chapter 6 Mine Detection A Igorithm 168

The most frequently defuzification strategy is the Centroid of Area (COA), which

is defined as:

Z-COA ~~

\z\LcU)zdz

j \ir(z)dz

where Uc(z) is the aggregated output membership function.

The developed fuzzy decision making is a Multi-Input-Single-Output (MISO)

system. The mine database [Min96] and experimental data are employed to set

up the rules and to interpret the membership functions. The input variables are

estimated Radius of an object (R), the distance of the estimated centre point from

initial position of the first probe (Y), and differences of R and Y compared to the

previous values (DR and D Y ) . The output variable is Percentage of mine

Certainty (POC). Figure 6.12 shows the schematic diagram of the fuzzy decision

making.

Radius (R)

Y.distanee (Y) _

Difference of R ( D R 1

Difference of Y (DY)_

INPUTS

FUZZY DECISIONMAKING

SYSTEM

FDMS

AF Mine Percentage of

Certainty (POC)

OUTPUT

Figure 6.11: Schematic diagram of fuzzy decision making

The range and categories of the input and output variables are described in Table

6.3. The membership functions can be tuned for a better system performance.

More experimental data and knowledge acquired from the A P mine experts may

result in a better performance by the fuzzy decision making.


Table 6.3: Input and output variables of F D M S system and their characteristics

Input

Output

Variables Radius (R)

Y_distance (Y)

Difference of R (DR)

Difference of Y (DY)

AP Mine Certainty

Range 0-100

-100-100

0-100

0 -100

0 -100

Categories

Small Good, Big

Neg, good, VBig

Snui, Med, Big

Snui, Med, Big

Vlow, low, Medium, High, Vhigh

A trapezoidal shape is chosen to represent the membership functions of the input

and output variables as shown in Figure 6.12. The knowledge-base of the system

is a collection of intuitive rules modelling the relationship between input/output

variables. They have been developed based on the observations made in the

experimental work. The rule-base is listed in Table 6.4

Table 6.4: Rules of the fuzzy decision making

Rule 1. If (R is Small) and (Y is Neg) then (POC is Vlow) (1)

Rule 2. If (R is Small) and (Y is Too_Big) then (POC is Vlow) (1)

Rule 3. If (R is Good) and (Y is Too_Big) then (POC is low) (1)

Rule 4. If (R is Big) and (Y is Too_Big) then (POC is Vlow) (1)

Rule 5. If (R is Big) and (Y is Good) then (P(X' is low) (1)

Rule 6. If (R is Big) and (Y is Neg) then (POC is Vlow) (1)

Rule 7. If (R is Good) and (Y is Neg) then (POC is Vlow)(l)

Rule 8. If (R is Good) and (Y is Good) and (DR is Sma) and (DY is Sma) then (POC is vHigh) (1)

Rule 9. If (R is Good) and (Y is Good) and (DR is Sma) and (DY is Med) then (POC is High) (1)

Rule 10. If (R is Good) and (Y is Good) and (DR is Sma) and (DY is Big) then (POC is Medium) (1)

Rule 11. If (R is Good) and (Y is Good) and (DR is Med) and (DY is Sma) then (POC is High) (1)

Rule 12. If (R is Good) and (Y is Good) and (DR is Med) and (DY is Med) then (POC is Medium) (1)

Rule 13. If (R is Good) and (Y is Good) and (DR is Med) and (DY is Big) then (POC is low) (1)

Rule 14. If (R is Good) and (Y is Good) and (DR is Big) and (DY is Sma) then (POC is Medium) (1)

Rule 15. If (R is Good) and (Y is Good) and (DR is Big) and (DY is Med) then (POC is low) (1)

Rule 16. If (R is Good) and (Y is Good) and (DR is Big) and (DY is Big) then (POC is Vlow) (1)

Chapter 6

Mem. Degree 1

0.6

0.4

0.2

M e m Degree 1

M e m D e gf e e

Mine Detection A I go ri thin

0.5 I 15 2 5 DR

0.5 1 1.5 2 2.5 DY

Mem D.gr. i

10 20 30 40 50 60 70 P rob

Mem. D egre e 1

0 8

0 6

0 4

0.2

0

N " ' 1 T....-.,...

w

\ \

•

y •

•

,_ ,_

SO 100

Figure 6.12: Inputs and output membership functions for F D M S

170


The experimental data presented in Table 6.2 were used to verify the fuzzy

decision making algorithm. In the algorithm, the radius, Y coordinate of the

centre point and their differences relative to another set of insertion points are

used. Hence two sets of data are obtained from three sets of insertion data for

each object. The two sets of data and relevant outputs generated by the algorithm

are tabulated in Table 6.5

Table 6.5: Input and output data for F D M S

Objects

D,

Mi

M2

Si

s2

B2

s3

B,

Iteration

1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2

Ri

3.3958

3.3958

2.6545

2.6545

3.3008

3.3008

3.354

3.354

3.986

3.986

2.8075

2.8075

13.832

13.832

173.24

173.24

Y,

2.4055

2.4055

1.9744

1.9744

2.6427

2.6427

2.7084

2.7084

1.9199

1.9199

2.3299

2.3299

-13.78

-13.78

148.05

148.05

R2

(In_D 3.344

3.337

2.74

2.677

3.3104

3.46

3.66

3.135

7.425

5.8185

4.96

2.475

7.9215

3.9225

67.5195

138.96

Y 2

(In_2)

2.7672

1.9431

1.6313

2.2646

2.916

2.5818

3.2857

0.5532

2.0388

4.435

-4.8667

1.6101

7.85

3.3091

56.2213

113.202

IR2-R1I (In_3)

0.0518

0.007

0.0855

0.063

0.0096

0.1496

0.306

0.525

3.439

1.6065

2.1525

2.485

5.91

3.999

105.72

71.4405

IY2-Y_I (In_4)

0.3617

0.4624

0.3431

0.2902

0.2733

0.0609

0.5773 2.1552

0.1189

2.5151

7.1966

0.7198

21.6263

17.0854

91.8269

34.8463

POC% (Out)

93.5945

93 93.6897

93.9231

93.9231

93.231

62.5

37.0527

25 29.7622

6.0769

25 25 6.0769 j

6.0769

6.0769

As can be seen, the Percentage of Certainty (POC) for the first three objects

which are cylindrical is about 9 3 % while for other objects P O C are mostly in the

range of 6 to 3 7 % . Considering that all the cylindrical A P mines are mostly made

of plastic material, the material of the object could also be considered as an input

variable of the F D M S . Consequently, the percentage of mine certainty (POC) of

the first object (D,) which was a metallic disc, would be very low. Figure 6.13

illustrates the output results of the F D M S system for different objects.


AP Mine Percentage of Certainty

100

80

S* 60 -O 2 40

20

I a No, 1

| D No. 2 [

D1 M1 M2 S1 S2 B2 S3

Objects

B1

Figure 6.13: Outputs of the F D M S for eight objects

The POC for cylindrical objects (D,, M, and M2) are mostly the same for the two

sets of measurements. While the results for the other objects vary depending on

the position of three contact points on the object. The results obtained in this

method are very similar to those that were obtained in the first method.

6.9. Conclusion

In this chapter, A P mine detection/recognition methods and an algorithm based

on three contact points between the probe and the mine were studied. The three

contact points can be produced either in three consecutive insertions of the probe

or by one insertion of a multi-probe device. It was shown that the minimum gap

between three insertions or the fingers of a multi-probe device was 2 [cm] for the

A P mines used in this work.

T w o methods were presented to identify the geometrical shape of a detected

object and hence determine whether it is a mine or not. The first method was

based on the statistical characteristics of the features obtained from an object in

multiple insertion. In the second method a fuzzy decision making algorithm

estimated the percentage of certainty that a detected object was a cylindrical

mine. Both methods were validated through computer simulation and

experimental work.

Chapter 7

CONCLUSION AND FURTHER RESEARCH

Chapter 7 Conclusion and Further Research 174

7.1. Introduction

The outcomes of the work are summarised in this chapter. Although the

emphasis of this project has been on mine detection, the methodologies

developed can be applied to other applications. In this chapter, such generic

aspects of the work will be highlighted. Based on the obtained results, some

conclusions will be drawn and some directions for further work will be proposed.

7.2. Summary of the Thesis

The development of an automatic anti-personnel mine detector robot has been

reported in this thesis. The device mimics the hand-prodding de-mining

procedure by inserting a bayonet into the soil and measuring the generated force.

Detection and clearance of A P mines to a 99.6% accuracy rate is a very time

consuming and dangerous task. Therefore, the proposed robotics prodding

approach has the potential to replace the manual prodding to increase the

detection rate while preventing the accidental loss of life.

As a first step, a thorough review of the previous work in this area was carried

out. In particular, the contact and non-contact methods employed by other

research groups for detection of a mine were studied. This review confirmed that

A P mine detection is an active and promising research area, requiring a great

deal of research and development for a satisfactory outcome.

The properties of the soil and the objects encountered during prodding are

unknown prior to the operation. Hence the demining robot will be operating in

an unstructured or semi-structured environment. In addition, there is a sudden

change in the stiffness of the environment as the robot enters the soil or

encounters an object. This will create an impact on the manipulator.


Due to these varying conditions, conventional mathematical modelling and

control of the robot and its environment will not produce a satisfactory outcome.

Consequently, the overall system was modelled using a neuro-fuzzy method

called Adaptive Spline Modelling of Observation Data ( A S M O D ) . Force/impact

conhol was introduced to maintain system stability during sudden changes in the

stiffness of the environment.

A study of the impact phenomenon provided some guidelines on the design of

the robot and its control system. It was shown that the sampling time for the

digital controller should be as short as possible to minimise the effect of the

impact force. On the other hand, the sampling time should be long enough to

allow the complete execution of the control algorithm. The mass of the robot

contributes significantly to the magnitude of the impact force. Hence, it is

important to design the robot as light as possible.

A number of intelligent force/impact control algorithms were developed in the

study. The structure of all these algorithms was based on the explicit force

control, including a velocity control loop inside an external force/impact conhol

loop. The intelligent control schemes for the prodding arm were based on the

inverse dynamic model of the robot arm in contact with the environment.

Overall, two main intelligent control schemes the Neuro-Fuzzy Adaptive Gain

( N F A G ) and the Neuro-Fuzzy Impact Control (NFIC), including some sub-

driven control schemes from the NFIC, were developed and validated.

The developed models were validated through both computer simulation and

experimental work. The intelligent control algorithms outperformed the

conventional controllers in all of the case studies. They also produced

performances acceptable for demining operation.


Finally, an algorithm was developed to distinguish an A P mine from other types

of objects. The algorithm is based on three contact points obtained from an

object either through three consecutive insertions of the probe or one insertion of

a multi-probe device. The degree of certainty for a detected object to be a mine

was determined through two methods using statistical and fuzzy approaches.

The performance of the algorithm was validated through computer simulation

and experimental work.

7.3. Future Research

The work earned out in this study can be considered as the first step towards the

development of a realistic and practical automatic robotic deminer. Further work

is required in different areas to complete the work. They will include:

• Development of a multi-probe arm. This will enhance both the reliability and

speed of the demining process.

• Further work on the object recognition algorithm for a more robust and

reliable method.

• Enhancement of the neuro-fuzzy model based on the input/output data

collected from the field operation of the prototype deminer. This approach

was successfully used for the peg-in-hole insertion task [Sha97].

• Proper design and development of the mechanical and electronic hardware of

the deminer for optimum field performance.

• Design and development of a reliable computing platform suitable for the field

implementation of the control algorithm.


• Design and development of a mobile robot to navigate the deminer arm in the

field. Simplicity of the design, low cost, and low weight are important criteria

to consider. In addition, the weight distribution on the feet or wheels should

be studied to minimise the resultant force on each foot or wheel to avoid

triggering the mine. The mobile robot should also have proper drive systems

for navigation on various types of ground, including flat and bumpy areas,

covered by soft, sticky, hard soil (sand, mud, rock), and vegetation.

Alternatively, different types of driving mechanism may be designed for

different types of surfaces.

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Appendix A:

MATCOM COMPILER

A.l. Introduction

In this part an explanation on using MATCOM compiler which enables to create

a standalone program using M A T L A B .m files will be provided. It should be

mentioned that DJGPP is selected as the C++ compiler and D O S as the platform

to run the executable files. As there are different M A T L A B functions which

should be called inside the C++ program and there are also some functions

supporting the PC30-C board, the best way of building standalone executable

application is to use a makefile which is given below.

A.2. Makefile for Building Standalone Executable Application

In the following makefile the output is co32cl.exe which can be run in DOS

environment.

#

# M A T C O M makefile for DJGPP : Target GG32

#

all: co32cl.exe mapdata.cpp: mapdata.m

d:/matcom/matcom mapdata.m mapdata.cpp -mat2cpp -nologo

mapdata.o: mapdata.cpp gcc -c -Id:/matcom/ -w -L./ -I./ -o mapdata.o mapdata.cpp

fndKVInd.cpp: fndKVInd.m d:/matcom/matcom fndKVInd.m fndKVInd.cpp -mat2cpp -nologo

fndKVInd.o: fndKVInd.cpp gcc -c -Id:/matcom/ -w -]_./ A.I -o fndKVInd.o 1'ndKVInd.cpp

computeB.cpp: computeB.m d:/matcom/matcom computeB.m computeB.cpp -mat2cpp -nologo

Appendix A 187

computeB.o: computeB.cpp

gcc -c -Id:/matcom/ -w -L./ -I./ -o computeB.o computeB.cpp cmpBasis.cpp: cmpBasis.m

d:/matcom/matcom cmpBasis.m cmpBasis.cpp -mat2cpp -nologo cmpBasis.o: cmpBasis.cpp

gcc -c -Id:/matcom/ -w -L./ -I./ -o cmpBasis.o cmpBasis.cpp cmpSplBs.cpp: cmpSplBs.m

d:/matcom/mateom cmpSplBs.m cmpSplBs.cpp -mat2cpp -nologo

cmpSplBs.o: cmpSplBs.cpp

gcc -c -Id:/matcom/ -w -L./ -I./ -o cmpSplBs.o cmpSplBs.cpp

tmult.cpp: tmult.m

d:/matcom/matcom tmult.m tmult.cpp -mat2cpp -nologo

tmult.o: tmult.cpp

gcc -c -Id:/matcom/ -w -L./ -I./ -o tmult.o tmult.cpp

djpc30s.o: djpc3()s.cpp gcc -c -Id:/matcom/ -w -L./ -I./ -o djpc30s.o djpc30s.cpp

djpc30d.o: djpc3()d.cpp gcc -c -Id:/matcom/ -w -L./ -I./ -o djpc.3()d.o djpc30d.cpp

g_co32cl.o: g_co32cl.cpp gcc -c -Id:/matcom/ -w -L./ -I./ -o g_co32cl.o g_co32cl.cpp

go32cl.exe: mapdata.o fndKVInd.o computeB.o cmpBasis.o cmpSplBs.o tmult.o djpc30s.o djpc30d.o

g_co32cl.o gcc -Ld:/matcom/ -w -L./ -I./ -o co32cl.exe («>co32cl.rsp -s -lmlihO -lmlibl -Igpp -lm -lpc

Appendix B:

ALGORITHM IN C++ PROGRAMMING

B.l. Introduction

As it is mentioned C++ is used as the software programming language for

implementing the intelligent impact control methods. In this appendix an

example of the main code used to implement the intelligent controllers is

presented in B.2. A sample MATLAB code is also given in section B.3 for

building a neuro-fuzzy (ASMOD) model based on the experimental data.

B.2. A Sample Code of the Intelligent Impact Controller

#ifdef m_type // m_type is the type used in MATCOM compiler

#undef m_type #endif #define m_type float

#include "train.h" // train.h is a C++ format of train.m #include "mapdata.h" // mapdata.h is a C + + formal of mapdata.m

#define data_30 // PC30-C board initialisation

#deflne T R U E 1 #define F A L S E 0

#define badd 0x700

M<float> xmod("xmod",0,0); M<float> ymod("ymod",0,0);

M<float> xmodel("xmodel",0,0); M<float> ymodel("ymodel",0,0):

M<float> traces("traces",0,0); M<tloat> est("est",(),0);

load(•,mod32cl^&n()Sm_,&knVcct()rs_,&nl)Kn()ts_,&degrees_,&iI)Dinl_,&inV;trs_,\

&c_,&initKn_,&initNo_,&traces_): //was mod42cl

xmodel=zeros(l,4); // there are four inputs v(k), v(k-l), f(k) and f(k-l)

ymodel=zeros(l,l);

xmod=zeros(HX),4);

Appendix B

ymod=zeros(100,l);

signed int d_a[20000];

FILE *fp_in; /* pointer to a tile */

unsigned int vout, vsign=0; // zero is for forward direction float varm=0.0, varm 1=0.0;

float xl.vl, outfc;

float x.f.finit;

float df,mf,mfl,yf,temp;

float vel, freq, errorv, errorvl=0.0, errorv2=0.0. xinit;

float ke, kf, errorf,errorf_l=0, vpwm, Deltaf=0.0; //outfc=0.0.vl,xl int N=660, counter= 100;

float refrence =1.0, refrence_l=0.0, r=().()12, vl_l=0, f_l=0, x_l=0,f_2, dx; float aO=428.235, al=-237.765, a2=0.0, hl= -1.0; //digitized PI controller

float aa0=0.02, aal=-0.03, aa2=0.01, bbl= -1.0: float farm, farm 1=0.0, errorfl=0.0, errorf2=0.0;

float outtPI=0.0, outfPI_l=0.0. sampling_freq=100.0:

uclock_t end_timel, end_time2, end_time21, initjime, startjime;

float delai, loop_time, exec_time, progjime, ymodel_l=0; int dela, ind;

unsigned int omega,count;

char inf[20]; register int i,j;

float m, lim; int tl=0;

U /*** Declarations for openning the data file ********/

char f_name[40]: /* buffer for file name */

char file; clrscr(); printf("\n\r (Press E S C to exit) " ) ;

printf("\n D o you want to save data in a file '.' [y/n]:"):

file = getcharQ;

if (file =='y' II file == 'Y') { printf(" Enter file name :\t");

scanf("%s",f_name); fp_in = fopen(f_name,"w"): /* Open the tile */

if(fp_in==NULL) {

printf("\n Can not open the file" );

exit(l);

} else printf("\n FILE O P E N E D \n");

} printf("Press any key to start: \n"); getch();

base_30 = badd; if (diag()) printf("\n PC-30 fault."):

else if (type_30 < found_39) printf("\n PC-3U, PC-30B, PC-30C, PC-30D or PC-30 P G required."):

else { init(); // for 2khz sampling period which is 1msec for each sample

Appendix B 190

ad_prescaler(5); // set the prescaler of the A D C ad_clock(4); // set the clock rate of the A D C for (i = 0; i < 5; i++) set_gain(i, 0);

cntr_cfg(0); // set the counter mode

1

ad_in(5, &d_a[l]); // position sensor // initial position sensor

xinit=((-(d_a[l] - 4()95)/16.25)-6.523077-l/16.25)/1000; // 16.25 is the calibration coefficient [m] ad_in(l, &d_a[0]); // force sensor

// initial force sensor

finit=(d_a[0]-2048)*5 -40; // 5 is the calibration coefficient [grf]

f_l=finit*0.001;

// Timer Initialisation

init_time = uclockQ;

count = 30000 - cntr_read(); cntr_write(30000); // load the counter

end_time2 = uclock();

end_time21 =end_time2;

while(l){ if(kbhit()) { if (getchQ = '\xlB') /* check for E S C character */

break;

1 III****** Getting Data (begining of the main i o o p ) * * * * * * * * * * * * * * * / counter =counter +1; ad_in(l, &d_a[0]); // force sensor ad_in(5, &d_a[l]); // position sensor

f=(d_a[0]-2048)*5-finit: x=(-(d_a[l] - 40lA5)/16.25)-6.523077: // 16.25 is the calibration coefficient

count = 30000 - cntr_read(); cntr_write(30000); // initialize counter for next loop

if(count >= 200 ) count = count_l; freq=(count*sampling_freq*2.0)/N: // [Hz]. N=f>60 , *2 is for nip-Hup

if (freq > = 33) freq=32.0; // 32 is max speed at 12 volt vel=freq*0.00175: //Linear Velocity [m/sec] pitch=1.75 [mm]

i///***************+ Control Procedure *************

xl=((x/1000)-xinit): // manipulator position [m]

// FILTER T O C A N C E L F O R C E N O I S E mf=(t71(XM).())-(-().«6yy*mn); // it was (-O.o065;|:mfl):

yf=0.1331 *mfl; //it was 0.3(J35*mfl: // [kgl'J

mfl=mf; // in order to define the direction of speed we should check the

// voltage of the motor drive.

if(varm>=0) vl=vel;

else vl=-vel;

xmodel.r(l)=vl;

xmodel.r(2)=vl_l;

errorf=(refrence- (f*().(K)l))*l.():

xmodel.r(3)=errorf* 1.0;

xmodel.r(4)=errorf_l * 1.0:

// without force filter // this is to be used in model as controller

ymodel=mapdat a( xmode 1):

Appendix B jgj

xmodel.r(3)=yl; // tnis is t0 be used in atlapting the motlel xmodel.r(4)=l_l; // tnis is t0 b e used in .ldapti„g the m o d e l

ind=counter%100; if(ind==0)ind=100;

xmod(ind,c_p)=xmodel(1.0,c_p); ymod(ind,c_p)=ymodel .r( 1);

kl=0.04; // this is according to the simulink simulation lim=0.35;

Deltaf=fabs((f*0.001 )-f_l);

//if ((((f*0.001)-f_l) >= lim)ll(((f*0.<X)l)-f_l) <= -lim)){ if(Deltaf>=lim)(

farm = (aaO*errorf+aal*em)rfl-bbl*farml+aa2*errorf2): //output of the controller errorf2=errorf 1;

errorfl=errorf; //previous state farml=farm;

outfc=(-ymodel.r(l )*2.()/l .0)+farm: //outfc=farm; //without NFIC

1 //else if((((f*0.001)-f_l) < lim)ll(((f*0.<X>l)-f_l) > -lim)){ elseif(Deltaf<lim){

farm = (aa0*errorf+aal*errorfl-bbl*farml+aa2*errorf2); //output of the controller errorf2=errorf 1:

errorfl=errorf; // previous state farml=farm;

outfc=farm:

//outfc=(errorf * kf); // Pcontroller

} ymodel_l=y model.r(l):

//// ********** Soeed control loon *****************

errorv=(outfc-vl); //error of speed control //error of speed control loop varm = aO*errorv+al*errorvl-bl*varml+a2*errorv2: //output of the controller

errorv2=errorvl;

errorvl=errorv; //previous state

varml=varm;

x_l=xl;

vl_l=vl;

refrence_l =refrence:

errorf_l=errorf;

count_l = count; if(varm > 12) varm=12; // limit to +/-24 volt

if(varm < -12) varm=-12; varml=varm; // it is important to have this after saturation which is actual voltage apply to motor

v p w m = l+(-0.1042*(fabs(varm)-24.0)); //map to the p w m voltage

// f_l=yf; // yf is after filer (it was f/1000;)

f_l = f*0.001; //without filter

if (x <= 4) vpwm=3.5; // to stop the motor

if (f>2500) (vpwm=3.5; break:} // to stop the motor

if (f<-1000){vpwm=3.5; break:} // to stop the motor vout = vpwm*4()l».6; // to send the absolute value to D A C

Appendix B 192

if (varm < 0) vsign = 128*16; // to send the direction (1 = backward) else vsign = 0; // direction (0 = forward) da_out(l, vout); //0 to 10 calibrated

da_out(3, vsign); //should shift left 4 bits

da_out(2, vsign); //should shift left 4 bits end_timel =uclock();

exec_time = ((end_timel-end_time2)*1000.0)/l 103150;

if(exec_time < 10) delai=10.0 - exec_time;

else

delai = 0.0;

dela= (int)(delai+0.5);

delay(dela);

if (counter == 10000) break;

end_time2 = udockQ; loop_time=(end_time2-end_time21 )*1000.0/1193150.0; if (counter > 110) sampling_freq=l(X)0.0/loop_time;

fprintf(fp_in, "%t\t%f\t %f\t %t\t %t\\ '7< An".end_time2* 1000.0/1 lir3150,xl.vl,f/1000,yf,ymodel.r(l));

end_time2 l=end_time2; end_time2 = uclockQ;

prog_time =(end_time2-init_time)/l 193150.0; printf("Totaol Execution Time = %i [Sec] \t No. of Iteration = %d\n".prog_time. counter-KX));

//savemodl("mod32cln"); //save("m(xl42cln",&n()Sm_,&knVecti)rs_,&n()Knc)ts_,&degrees_.&inDim_.&iiiV;irs_,\

// &c_,&initKn_,&initNo_.&traces_);

fclose(fp_in); while(l){ // to return the knife to its home position v p w m = 2 ; // to make it slower it should be 2.5:

vsign = 128*16; // backward direction

ad_in(l, &d_a[0]): ad_in(5, &d_a[l]); // position sensor

x=(-(d_a[l] - 4095)/16.25)-6.523077; f=(d_a[0]-2048)*5: // 5 is the calib. coeff. new scnsor[grf]

if(x<=3.5){ vpwm=3.5; //to stop the motor

vout = vpwm*40%/10;

da_out(l, vout): //() to 10 calibrated

da_out(3, vsign):

da_out(2, vsign);

exit(0):

vout = vpwm*409.6: // convert to send to DAC da_out(l,vout): //() to 10 calibrated

da_out(3, vsign); // should shift left 4 hits xpwm=2.5:

1 return 0;

Appendix B 193

B.3. A Sample Code to build an A S M O D model

ASMOD software which is written using 25 MATLAB functions can be used as

a main.m in MATLAB environment to build an ASMOD model. A sample code

for building a model is given below.

% main.m

load mine_test.dat; % load test data

load mine_train.dat; % load train data

xTest = [mine_test(:,l) mine_test(:,2) mine_test(:,3) mine_test(:,4)]; yTest = mine_test(:,5);

xTrain = [mine_train(:,l) mine_train(:,2) mine_train(:,3) mine_train(:,4)];

yTrain = mine_train(:,5);

degree=2; % the same spline degree (quadratic) is used for all variables %degree=[2 2 2 1 1 1 1 1 1 1 ] ; % individual spline degrees can be used

resolution=0; % the initial resolution (number of internal knots)

%resolution=[l 1 0 0 0 0 0 0 0 0]; % individual init. resol. can be used

initSubmodels = []; % Initialize as an empty model

% initSubmodels = [2 1 2; 1 3 0; 1 4 0; 1 5 ()]; % Initialize with % one 2 dimensional of variables 1 and 2, and three onedimensional

% submodels of variables 3, 4 and 5 respectively

xmin = D;xmax = []; % Automatic determination of input domain. % xmin = [000 0 0 0 0 0 0 0]; % Setting of input domain lower limits. % x m a x = [l 1 1 1 1 1 1 1 1 1]; % Setting of input domain upper limits.

iniAsmod(xTrain, degree, resolution, xmin, xmax.initSubmodels):

[traces, est] = train(xTrain, yTrain,xTest. yTest, 'SRM', 25);

%plot(traces(:,8), traces(:, 1:7)) [rmse, nrmse, ev, mae, maxe] = testModl(xTest, yTest)

saveModl( 'mine_model 1');

Appendix C:

PLASTIC CYLINDRICAL ANTI-PERSONNEL MINES

No.

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

Type

APNMAE TI

APNMM14

ARGES SPM75

AUPS

BM/85

BOOBYTRAPPED

CP-X.01

CP-X.02

DM-11

DM-39

FAMA

FMK-1

GYATA-64

K-2

LBA TYPE-A

LBA TYPE-B

LORY

M14

M412

MAI68

MAI75

MAI-GR1

MAI-GR2

MAP II

MAPP78 F-2

MAUS

MAUS PRACTICE

MAUS/1

MBV-78-A2

MD-82-B

MGP.30

MI AP DV56

MI AP DV59

MIAPDV61

MI AP DV M63

MIAPDVPIGX61

MI AP DV X59

MI AP ID 51

MN-79

NO.6

Weight

[KK1 0.42

0.10

6.00

0.30

2.00

0.39

0.085

0.078

0.231 -

0.086

0.2509

0.45

4.00

-

-

0.25

0.099

0.13

0.23

0.33

0.11

0.11

0.50

0.205

0.275

0.277

0.267

0.15

0.128

0.08-0.125

0.165

0.13

0.125

0.1

0.115

0.085

0.09

0.099

0.227

Diameter/Hight Min/Max [mm]

85 / 95

50 / 43

125/255

102-74/36

120/200

125/65

71/37

78/32

82 / 33.5

100/40

71/38

82-80/46.7

142- 106/61

100/225

108/33

108/33

115/40

56 / 40

88/32

78 / 68

95/61

75 / 45

70/45

113/83

-

89 / 46

89 / 46

89 / 46

53/ 130

55 / 55

90/40

70/78

62 / 55

35 / 270

35 / 270

54 / 274

60 / 32

70 / 52

56/40

40/200

Actuation

Force [KgfJ

17

9- 16

5 - 10 -

12- 13/6

7 - 30

14 - 36

14-36

5

-P1

30 -

7.5 -

-

-

15

9 - 16

5 - 20

15-32

5 - 25

-

-

10- 14

-

10

10

10.3

2-5

4-5

15

-

5

-

-

18

18

14-24

9.0 - 16.0

22.0

Burial Depth

Max/Min [cm] -

-

-

4.0 / 0.0 -

13.0/0.0 -

-

-

-

-

-

10.0/0.0 j

20.0/13.0

6.0 / 3.0

6.0 / 3.0 !

6.0 / 0.0

-

-

-

-

-

-

-

I

4.0 / 0.0 !

4.0 / 0.0

4.0/0.0

-

-

-

- / 8.0

-/0.0

-

-

-

-

50 / 0.0

-

1 Pressure Release

Appendix C

No.

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

61

62

63

64

65

66

67

68

69

70

71

72

73

74

75

76

77

78

79

80

81

82

83

84

85

86

87

Type

NR22

NR22C1

P5

P-4-A

P-4-B

P-40

P2MK2

P4MK1

PATVAG 59

PATVAG M3

PM79

PMA-2

PMA-3

PMN

PMN-2

PMN-4

PMP71

POMD-1

PP MI-SR

PP MI-SR II

PPM-2

PRBBAC H-28

PRB M35

PRB M409

R2M1

R2M2

T-AB-1

T/79

TM-100

TRUPPMINA 10

TYPE 58

TYPE 63

TYPE 68

TYPE 72

TYPE 72A

TYPE 72 B

TYPE 72C

U/I

U/ITH(AP.l)

U/I TH (AP.2)

UNKNOWN

UNKNOWN

VALMARA 69

VAR/40

VSAPFM1

VS-MK2-EL

ZAPS

Weight

[Kgi

0.085

0.13

0.13

0.21

0.171

2.00

0.14

0.095

-

0.095

0.28

0.135

0.183

0.55

0.417

0.3

1.0- 1.5

2.7

3.2

3.2

0.39

0.165

0.158

0.183

0.13

0.128

0.125

0.19

-

0.231

0.55

1.335

2.8

0.14

1.00

0.15

0.28

0.013

0.13 -

0.206

0.13

3.3

0.1

3.5

0.2

1.7

Diameter/Hight Min/Max [mm]

70 / 52

62 / 55

88/32

72 / 55

73 / 43

90/215

70 / 38

72/45

71.9/52.8

79/18

88.0/48.0

68.0/61 & 3 0

103/36

112/56

50.9- 121.2 / 52.3

46 / 95

175/115

112/135

102 / 152

102 / 152

81.8- 124.7/63

90 / 28

63.5/39.0

82/28

69 / 57

69 / 57

60/61

-/45

33/ 107

80.5-79.8/33.5

112 / 56

140/ 190

135/109

78.5 / 38.5

64/123

78.5 / 38.5

78.5 / 38.5

44.5/15.0

100/40

80/40

81 / 35.5

28/ 150

130- 107/205

78/45

130/190

89.5 / 35

90/210

Actuation

Force [Kgf]

5.0 -25.0

5.0

5.0 - 20.0 _

10.0- 14.0

7.0- 13.0

10.0

10.0

5.0

15.0

5.0 - 25.0

5.0- 15.0

3.0- 15.0

8.0-25.0

15.0

2.0- 15.0

6.0

-T2

4 - 8 / 3 - 6

4 - 8 / 3 - 6

12.5

9- 19

9- 14

8 - 30

1 -6

3-7

15 -

Varies

10

-

-

7-20

5-7P

7 - 20 P

3-7P

2.5 - 25 P

2.5 P

-P

-P

5 - 20 P

-P

10 P

12-13

6.3 T

-P

C3P

Burial Depth

Max/Min [cm] _

.

_

:

_

_,

_,

_

10.0/0.0

5.0 / 0.0 j

9.0 / 0.0

8.0/0.0

25.0 / 0.0 _

.

9.0/4.0 !

.

20.0 / 0.0

20.0 / 0.0 |

10.0/0.0 -

-

-

-

-

-

-

3.3/0.3

3.0 / 0.0

25.0 / 0.0 -

-

8.0 / 0.0

18.0/0.0

8.0/0.0 :

8.0 / 0.0

2.0 / 0.0

-

-

12.0/0.0

19.0/0.0

17.0/12.0

-

-

2.0 / 0.0

-

2 Trip Wire 3 Command Initiation

Documents

1998 Intelligent impact control in anti-personnel mine