UNIVERSITI PUTRA MALAYSIA

ANG CHUN KIT

FK 2014 4

DEVELOPMENT OF AN ARTIFICIAL NEURAL NETWORK TOPOLOGY FOR GENERATING THE MOTION OF ROBOTIC MANIPULATOR

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DEVELOPMENT OF AN ARTIFICIAL NEURAL NETWORK TOPOLOGY

FOR GENERATING THE MOTION OF ROBOTIC MANIPULATOR

By

ANG CHUN KIT

Thesis Submitted to the School of Graduate Studies, Universiti Putra Malaysia,

in Fulfillment of the Requirement for the Degree of Doctor of Philosophy.

July 2014

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DEDICATION

I dedicate this thesis to my best companion, Ms. Ong Yien Yien and my family for

their endless support and encouragement

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Abstract of thesis presented to the Senate of Universiti Putra Malaysia in fulfilment

of the requirement for the degree of Doctor of Philosophy

DEVELOPMENT OF AN ARTIFICIAL NEURAL NETWORK TOPOLOGY

FOR GENERATING THE MOTION OF ROBOTIC MANIPULATOR

By

ANG CHUN KIT

July 2014

Chair: Assoc. Prof. Tang Sai Hong, PhD

Faculty: Engineering

Motion planning is an important issue in robot industry. Without an appropriate

motion planning, a robot may be colliding with obstacles or passing through

undesirable points. In order to control the motion of a robot manipulator, a person

has to possess the knowledge of kinematics, dynamics, and trajectory planning.

However, there are two main problems in using conventional methods. Firstly, the

equations are hard to be derived and the calculations are complex. Secondly, the

characteristics of different trajectories are different and there is no mathematical

solution for unknown trajectory. Hence, the first objective in this research is to

simplify the complex calculations in terms of solving kinematics and trajectory

planning issues simultaneously. Another objective of this research was to help in

computing the motion of a manipulator even though the characteristic of the

trajectory is unknown. In order to achieve these research goals, artificial neural

network (ANN) was proposed as a solution.

In the early stage, a virtual manipulator was developed and subjected to different

primitive trajectories. In order to examine the ability of ANN in tracking the motion

of a robot manipulator, a primitive ANN would be used to track the moving path of

the virtual robot manipulator’s end effector in the virtual environment. This ANN

was developed based on the fundamental of back-propagation neural network

(BPNN) topology. The topology of ANN would be modified for reducing the errors

and deviations. Eventually, the developed ANN would be validated through a real

time 5 catalyst robot. Besides, obstacle avoidance planning would be integrated into

the developed ANN. Virtual obstacles would be allocated within the robot’s

workspace randomly and the performances of developed ANN would be observed

through simulation experiments.

The results indicated that ANN possessed ability in tracking the motion of a robot

manipulator in terms of solving kinematics and trajectory planning issues

simultaneously and it was able to compute the motion of a manipulator even though

the characteristic of the trajectory was unknown. Obstacle avoidance planning was

integrated into the architecture of developed ANN for better performances and the

results were satisfactory. With this developed method, a person is able to compute a

safe path for a robot manipulator to avoid obstacles (objects which enclosed in a

sphere).

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Abstrak tesis yang dikemukakan kepada Senat Universiti Putra Malaysia sebagai

memenuhi keperluan untuk ijazah Doktor Falsafah

PEMBANGUNAN DAN PERKEMBANGAN TIRUAN TOPOLOGI

RANGKAIAN NEURAL UNTUK MENJANAKAN PERGERAKAN

MANIPULATOR ROBOTIK

Oleh

ANG CHUN KIT

Julai 2014

Pengerusi: Prof. Madya Tang Sai Hong, PhD

Fakulti: Engineering

Perancangan gerakan merupakan satu isu penting dalam industri robot. Tanpa

perancangan gerakan yang sesuai, robot akan berlanggar dengan halangan atau

melalui laluan yang tidak diingini. Dalam usaha untuk mengawal pergerakan

manipulator robot, seseorang perlu mempunyai pengetahuan dalam kinematik,

dinamik, dan perancangan trajektori. Walau bagaimanapun, terdapat dua masalah

utama dalam menggunakan kaedah konvensional. Pertama, persamaan sukar untuk

diperolehi dan pengiraan adalah kompleks. Kedua, ciri-ciri trajektori yang berlainan

adalah berbeza dan tidak ada apa-apa penyelesaian matematik untuk trajektori yang

mempunyai ciri-ciri yang tidak tentu. Jadi, objektif utama untuk kajian ini ialah

mempermudahkan pengiraan kompleks dari segi kinematik dan menyelesaikan isu-

isu perancangan trajektori secara serentak. Satu lagi objektif kajian ini adalah untuk

membantu dalam pengiraan pergerakan manipulator walaupun ciri-ciri untuk

trajektori tersebut tidak diketahui. Dalam usaha untuk mencapai matlamat kajian ini,

Rangkaian neural tiruan telah dicadangkan sebagai penyelesaian.

Pada peringkat awal, manipulator maya telah dibina dan tertakluk kepada trajektori

primitif yang berbeza. Dalam usaha untuk memeriksa keupayaan rangkaian neural

tiruan dalam mengesan gerakan manipulator robot, satu rangkaian tiruan primitif

akan digunakan untuk mengesan laluan tangan manipulator maya ini dalam

persekitaran maya. Rangkaian neural tiruan ini akan dibinakan berdasarkan asas

rangkaian neural penyebaran belakang. Topologi rangkain neural tiruan akan

diubahsuai untuk mengurangkan kesilapan-kesilapan dan penyelewengan. Akhirnya,

rangkaian neural tiruan yang dibina itu akan disahkan melalui robot sebenar. Selain

itu, perancangan bagi mengelakkan halangan akan disepadukan dalam rangkaian

neural tiruan. Halangan maya akan diletakkan dalam ruang kerja robot secara rawak

dan prestasi rangkaian neural tiruan akan diperhatikan melalui eksperimen simulasi.

Keputusan menunjukkan bahawa kaedah rangkaian neural tiruan baru ini memiliki

keupayaan untuk mengesan gerakan manipulator robot dari segi kinematik dan

menyelesaikan isu-isu perancangan trajektori dengan serentak dan ia mampu mengira

pergerakan manipulator walaupun ciri-ciri trajektori itu tidak diketahui. Perancangan

mengelakkan halangan telah disepadukan ke dalam senibina rangkaian neural tiruan

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untuk prestasi yang lebih baik dan keputusannya adalah memuaskan. Dengan kaedah

ini, seseorang itu dapat mengira laluan yang sesuai untuk manipulator robot untuk

mengelakkan halangan (objek dalam sfera).

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ACKNOWLEDGEMENTS

First of all I would like to express my appreciation to my main supervisor, Assoc.

Prof. Dr. Tang Sai Hong for his guidance, suggestions and advices in my research.

His endless supports and encouragements always motivate me. Besides, I would like

to extend my thanks to my co-supervisors Dr. Syamsiah Binti Mashohor and Assoc.

Prof. Dr. Mohammad Khairol Anuar Bin Mohammad Arrifin as well for their

advices and suggestions in my research.

Other people that I would like to appreciate are my parents and friends that always

support me and willing to lend me their hand when I faced any problem during my

research, especially my best friend Mr. Loo Shing Yan and Ms. Ong Yien Yien.

Finally, for those people who are not mentioned above but have given me a hand, I

would also like to say a word of thanks for their contribution in this research.

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This thesis was submitted to the Senate of Universiti Putra Malaysia and has been

accepted as fulfilment of the requirement for the degree of Doctor of Philosophy.

The members of the Supervisory Committee were as follows:

Tang Sai Hong, PhD Associate Professor

Faculty of Engineering

Universiti Putra Malaysia

(Chairman)

Mohammad Khairol Anuar Bin Mohammad, PhD Associate Professor

Faculty of Engineering

Universiti Putra Malaysia

(Member)

Syamsiah Binti Mashohor, PhD Lecturer

Faculty of Engineering

Universiti Putra Malaysia

(Member)

_______________________________

BUJANG BIN KIM HUAT, PhD Professor and Deputy Dean

School of Graduate Studies

Universiti Putra Malaysia

Date: 9th October 2014

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DECLARATION

Declaration by graduate student

I hereby confirm that:

this thesis is my original work;

quotations, illustrations and citations have been duly referenced;

this thesis has not been submitted previously or concurrently for any other degree

at any other institutions;

intellectual property from the thesis and copyright of thesis are fully-owned by

Universiti Putra Malaysia, as according to the Universiti Putra Malaysia

(Research) Rules 2012;

written permission must be obtained from supervisor and the office of Deputy

Vice-Chancellor (Research and Innovation) before thesis is published (in the form

of written, printed or in electronic form) including books, journals, modules,

proceedings, popular writings, seminar papers, manuscripts, posters, reports,

lecture notes, learning modules or any other materials as stated in the Universiti

Putra Malaysia (Research) Rules 2012;

there is no plagiarism or data falsification/fabrication in the thesis, and scholarly

integrity is upheld as according to the Universiti Putra Malaysia (Graduate Studies)

Rules 2003 (Revision 2012-2013) and the Universiti Putra Malaysia (Research)

Rules 2012. The thesis has undergone plagiarism detection software.

Signature: _______________________ Date: _____________________

Name and Matric No.: ________Ang Chun Kit (GS 27283)____________________

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Declaration by Members of Supervisory Committee

This is to confirm that:

the research conducted and the writing of this thesis was under our supervision;

supervision responsibilities as stated in the Universiti Putra Malaysia (Graduate

Studies) Rules 2003 (Revision 2012-2013) are adhered to.

Signature:

Name of

Chairman of

Supervisory

Committee:

Signature:

Name of

Member of

Supervisory

Committee:

Signature:

Name of

Member of

Supervisory

Committee:

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TABLE OF CONTENTS

Page

DEDICATION

ABSTRACT i

ABSTRAK ii

ACKNOWLEDGEMENTS iv

APPROVAL v

DECLARATION vii

LIST OF TABLES xi

LIST OF FIGURES xii

LIST OF ABBREVIATIONS xvi

CHAPTER

1 INTRODUCTION 1

1.1 Preface 1

1.2 Background of Motion Control 1

1.3 Problem Statement 3

1.4 Research Objectives 4

1.5 Scope of Study 4

1.6 Organization of the Thesis 6

1.7 Summary 6

2 LITERATURE REVIEW 7

2.1 Background 7

2.2 Conventional Approach (Mathematical Model) 8

2.2.1 Kinematics 9

2.2.2 Dynamics 10

2.2.3 Motion Planning 13

2.3 Artificial Intelligences 22

2.4 Obstacle Avoidance Planning 26

2.4.1 Attractive Field 26

2.4.2 Repulsive Field 28

2.4.3 Others 28

2.5 Summary 29

3 METHODOLOGY 31

3.1 Introduction 31

3.2 Conventional Approaches 33

3.2.1 LSPB and Bang-bang Trajectory 33

3.2.2 Acceleration Sine Function Profile Trajectory 35

3.2.3 Quintic Polynomial Trajectory 35

3.2.4 High Order Polynomials 36

3.3 Proposed Method 39

3.3.1 Proposed ANN Method 40

3.3.2 Algorithm 42

3.3.3 Data Collection 44

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3.4 Comparison between ANN and Conventional

Mathematical model 48

3.5 Computation of the Motion for Robot Manipulator

and Obstacle Avoidance Planning 49

3.6 Software and Hardware 52

3.6.1 MATLAB 52

3.6.2 Open Dynamics Engine (ODE) 54

3.6.3 5 Catalyst CRS Robotic Arm 54

3.7 Summary 55

4 RESULTS and DISCUSSION 56

4.1 Introduction 56

4.2 Results in XZ Plane and XYZ Plane (Virtual Environment) 56

4.2.1 Results in XZ Plane Based on

Conventional Approaches 57

4.2.2 ANN Results in XZ Plane 59

4.2.3 ANN Results in XYZ Plane 66

4.3 Hardware Experiments 69

4.3.1 ANN Hardware Results 71

4.3.2 Adapting known trajectories with finalized

ANN topology 75

4.3.3 Comparison to Existing Neural Network models 78

4.4 Obstacle Avoidance Planning 78

4.5 Comparison between Developed Method and

Conventional Approaches 83

4.6 Summary 85

5 CONCLUSIONS AND RECOMMENDATIONS 86

5.1 Summary and Conclusions 86

5.2 Future Works and Recommendations 88

REFERENCES 89

APPENDICES 103

BIODATA OF STUDENT 133

LIST OF PUBLICATIONS 134