Architectures for Controlling an

Intelligent Autonomous Vehicle

By John Stevens

Presented as a Partial Fulfillment of the Requirements for an

MSc. in

Intelligent Knowledge-Based Systems

Department of Computer Science University

of Essex Supervisor: Dr. Sam Steel

Presented September, 1994

Abstract

Surname: Stevens Other Names: John Kenneth

Qualification Sought: MSc in Intelligent Knowledge Based Systems

Title of Project: Architectures for Controlling an Intelligent Autonomous Vehicle

Supervisor: Dr Sam Steel

Artificial intelligence researchers have developed several styles of IA V

architectures, mapping systems, and planners. Considerable debate surrounds the utility of

each. This dissertation contributes to these debates. This document reports on the

experimental testing of three architectures: a reaction-based architecture implemented on

an economical mobile vehicle developed for this project; a subsumption architecture, and a

combined architecture integrating subsumption with a non-linear planner, the latter two of

which were implemented on a University of Essex Marvin vehicle. The results of this

research suggest four conclusions. First, simple, inexpensive mobile vehicles can

successfully perform basic behaviours, and the technology and software for such vehicles

can be rapidly developed and tested. Second, ultrasound and infra-red sensors limit the

ability of a mobile vehicle. Future use of alternative sensors, such as vision, may well

yield improved performance. Third, while sUbsumption architecture does not live up to

some of the claims made by its designers, it can facilitate successful goal completion by an

IA V. Fourth, subsumption architectures can be effectively integrated with non-linear

planners to improve vehicle performance. The final chapter summarises the findings of

this work and explores the potential avenues for future research suggested by these

findings.

ii

Acknowledgements

My first thanks have to go to Kimberly Fisher who has spent many hours copy-editing and correcting English mistakes in my draft texts, assistance rendered necessary by my dyslexia. This special lady also has to be thanked for putting up with me during the production of this report.

I would also like to express my gratitude to Paul Chernett, and Robin Dowling, who provided both technical support and comments to assist the implementation of my ideas on the robots. I extend similar gratitude to Stuart Soltysiak, who also offered advice over many cans of coke and games of pool.

I would like to acknowledge Mr. Malcom Lear's input into the development of the clocking circuitry, PCB board art work, and the small mobile vehicle. Without his technical input this development would have been more difficult to complete.

Finally, of course, I wish to thank my supervisor, who nudged me in constructive directions, while allowing me to do the hard work myself.

iii

Acknowledgement of Examination Regulation 6.5(c)

I declare that any use of, or reference to, the work of others has been

explicitly acknowledged in this dissertation.

John Stevens

iv

Table of Contents

Title Page Abstract Acknowledgements Acknowledgement of Exam Regulation 6.5(c) Table of Contents Table of Figures

1

ii iii iv v vii

1. Introduction 1

2. The Subject Area 2.1 IA V Architectures Process

Control Systems Plan then Execute Architectures Subsumption Architectures Combined Architectures Non Symbolic Methods Self Organising Architectures Summary

2.2 Mapping Systems Geometric Mapping Systems and Dead Reckoning Landmark Mapping Topological Mapping Summary

2.3 Planning Systems Path Planners Non-Liner Planning Hierarcal Plans Planning to Plan

2.4 Attention 2.5 Summary

5 6 6 7 9 12 13 15 16 16 17 17 18 18 19 19 21 22 23 23 23

3. An Implementation of a Reactionary IA V Architecture 3.1. Vehicle Developed 3.2. Software Developed 3.3. Experimental Results 3.4. Future Work 3.5. Summary

24 26 28 29 30 30

v

4. An Implementation of a Subsumption IA V Architecture 32 4.1. Hardware 33

Sensor Details 34 Hardware Modification 35

4.2. Subsumption Software developed 36 Reflex Module 36 Obstacle Avoidance Module 37 Wall Following Module 37 Infra Red Location Module 38 Implementation Details 39

4.3. Experimental Results 39 Comments on the Performance of the Vehicle 41 Comments on the Performance of Subsumption 41

4.4. Future Work 42 4.5. Summary 42

5. Combing Subsumption and Plan then Execute IA V Architectures 43 5.1. Architecture Developed 44 5.2. Implementation Details 47

Mixing 'C' with PROLOG 47 Plan-then-Execute Behaviour 48 Topological Path Planner 48 Subsumption Interface with Topological Path Planner 50

5.2. Experimental Results 50 5.3. Future Work 53 5.4. Summary 53

6. Conclusions 56 6.1 Introduction 56 6.2 Conclusions About Hardware 57 6.3 Conclusions About the Implementation of the Architectures 58 6.4 Closing Notes 60

Bibliography 61

Appendices

a. Mouse PCB board b. Reactionary Software c. Reactionary Test Results d. Marvins Dimensions e. Marvins Memory map f. std_ops.h g. subsumption.c h. Subsumption Test Results i. Wall following algorithms j. comb.c k. top_planner.pl 1. Combination Test result

Al A2 A3 A7 A8 AlO A19 A25 A29 A31 A38 A40

vi

Table of Figures

1.1. Picture of Robots 3 2.1. Diagram of Process Control Architecture 7 2.2. Diagram of Process Control Architecture 7 2.3. Diagram of Plan-then-Execute Architecture 7 2.4. Diagram of Plan-then-Execute Architecture 7 2.5. Brooks' Modular AI Diagram 9 2.6. Brooks' Behaviour Based Control Architecture Diagram 9 2.7. A Subsumption Network 10 2.8. Alice Architecture 13 2.9. Fuzzy Logic Language 14 2.10. Topological Map 18 2.11. Free Space Representation Map 20 2.12. NaT Map 20 2.13. Non-Linear Plan 22 2.14. Hierarchical Plan 22 3.1. Picture of Small Robot 25 3.2. Circuit Diagram for Small Robot 27 3.3. Circuit Diagram for Op-amp 27 3.4. Pseudo Code for Reaction Software 28 3.5. Table of Results of Reaction Software Experiments 29 4.1. Picture of Department Robot 33 4.2. Diagram of Positioning of Ultrasound Sensors 35 4.3. Subsumption Network Diagram 36 4.4. Pseudo Code of Wall Following 38 4.5. Pseudo Code of Subsumption Control Network 39 4.6. Table of Results of Subsumption Experiments 40 5.1. Architecture Diagram of Combined Approach 46 5.2. Pseudo Code for Plan-then-Exicute Behaviour 48 5.3. Topological Map Reprsentations 49 5.4. Topological Map Used in Testing 50 5.5. Table of Results of Combined Architecture Experiments 51 5.6 Architecture Diagram of Possible Future Work 54

Vll

Chapter 1

Introduction

1.0 Introduction

For centuries, people have pondered the possibility of developing machines

capable of independent thought. In the nineteenth century, writers like Robert Lewis

Stevenson founded the literary genre which would develop into today's science fiction - a

genre replete with robotic beings who have reached a level of sentiency and ability which

exceeds human capacities. While the fans of science fiction literature and fihns may

fantasise about interactions with the likes of Data or C3PO, the real life world of artificial

intelligence research is now - and for some time in the future, will remain - far more

mundane. The researchers who undertook the first attempts to write programs to allow

autonomous mobile vehicles to act with a degree of intelligence soon made two

discoveries. First, they learned that artificial intelligence programming must enable the

mobile vehicle to perform basic levels of decision making, such as figuring out how to

navigate differing terrains and learning to avoid obstacles. Second, they learned that

humans must develop complex programs to enable machines to perform even such

seemingly simple tasks.

To make this programming challenge manageable, researchers have split the

levels of decision-making into manageable and understandable modules. Many early

experiments centred on the execution of a single behaviour, such as path planning. By

adopting this simplified approach, computer scientists soon succeeded in expanding the

capacity of Intelligent Autonomous Vehicles (IAVs). This success, however, generated a

new and challenging problem - mobile vehicles cannot cope with independent movement

without constant attention from researchers if they do not possess the ability to integrate

the performance of several behaviours. Until very recently, only a few research groups

demonstrated interest in the design of integrated architectures. The process of organising

behaviours into functional, combined control systems has recently become one of the

major themes of IA V research. This project, investigates such systems through the

implementation of three different control strategies.

Chapter Two offers a brief overview and analysis of the literature on IA V

behaviour control. The major developments in IA V architecture research, discussed in

this chapter in a slightly idealised chronological order, are: 1) early process control

systems; 2) plan-then-execute architectures; 3) reaction-based systems; 4) subsumption

architectures; 5) combined architectures; 6) non-symbolic methods; and 7) selforganising

architectures. This chapter additionally offers discussion of the techniques developed to

facilitate mapping, planning, and attention in mobile vehicles.

The three subsequent chapters discuss the implementation of three of these

architectures and the mobile vehicles on which they were tested. Both of these vehicles

are pictured in Figure 1.1.

Chapter Three records the results of the implementation of a reaction-based

architecture. As such architectures may have a simple design, this chapter details an effort

to develop a minimal architecture to produce a single behaviour from a small mobile

vehicle. The vehicle designed for this experiment (see Figure 1.1) uses a single layer

reactive control system which enables it to move around its environment without

damaging itself. The simplicity of this design permitted rapid development and testing of

the architecture, and also demonstrated that utility of this hardware and software for low-

budget projects.

2

Figure 1.1.

Chapter Four reports on a re-evaluation of subsumption architecture. This architecture

which was developed by Rodney Brooks and detailed in Brooks (1986) was implemented on one

of the departmental Marvin vehicles, Ford, shown in Figure 1.1. Chapter Four begins with a

description of the test vehicle, then proceeds to discussion of modifications made on Ford,

including justification for these changes. The chapter then offers an evaluation of the architecture.

Ford was able to perform behaviours such as ret1ex action: avoid obstacles: follow walls: and

tind infra red beacons.

Chapter Five records the implementation and testing of an architecture combining

subsllmption and plan-then-execute techniques. Ford again served as the base vehicle for these

experiments. The results reponed in Chapter Five indicate the compatibility of these two

architectures. This combination pemlits a vehicle to make lise of the advantages of both systems:

the suitability of reaction-based subsllmption for addressing immediate dewlopments in the

environment: and the capacity of the

plan-then-execute architecture for looking ahead and creating plans to circumvent

problems.

Chapter Six fIrst provides additional comment on the different architectures

presently under investigation in the artificial intelligence community. The experimental

results obtained are then summarised, and possible future applications of these findings

are considered. The text closes with discussion of the directions in which research into

Intelligent Autonomous Vehicle architectures could head in the future.

4

Chapter 2

Related Work

Contents

2.0 Introduction 2.1 IA V Architectures

Process Control Systems Plan-then-Execute Architectures Subsumption Architecture Combined Architectures Non Symbolic Methods Self-Organising Architectures Summary

2.2 Mapping systems Geometric Mapping and Dead Reckoning Landmark Mapping Topological Mapping Summary

2.3 Planning Systems Path Planners Non-Linear Planners Hierarchical Plans Planning to Plan

2.4 Attention 2.5 Summary

5

2.0 Introduction

In this chapter, I detail the major developments in the published research on the

control of autonomous mobile vehicles which I have reviewed to ground my own

architectural experiments detailed later in this dissertation. The first and largest section

contains discussion of the major IA V control architectures described in the literature,

focusing on the connections between the individual modules of the control system. I then

examine the research covering two of the more important modules. The second section of

this chapter details developments in IA V mapping, and the third section offers an

examination of the research on planning. The fourth section contains discussion of IA V

attention.

2.1 IA V Architectures

Initially, the researchers working with IA V s concentrated on functionality of

individual behaviours, such as obstacle avoidance or path planning. Only a few devoted

minimal effort to designing strategies for organising these behaviours into a combined

control system. The following sub-sections of this report provide details of the major

developments in IA V architecture research in chronological order, beginning with

process control systems, then moving on to plan-then-execute architectures, subsumption

architectures, combined architectures, non-symbolic methods, and selforganising

architectures. Members of the IA V community now generally regard architectural design

as one of the most important areas of research on this field.

2.1.1 Process Control Systems

Before the AI community developed an interest in IA V s, industrial technicians

had designed process control architectures for robots. Examples of early process control

architectures can be found in Grover (1987). Academic research consequently began with

examination of these systems, which are generally implemented by a single layer of

process control through which a given input stimulates an output. This structure may be

diagrammed as follows:

6

Sense --_\ / React (Execute)

Figure 2.1

This architecture has been implemented by many people to control IA V s, an example of

which can be found in Miller et. al. (1992), and is shown in Figure 2.2.

Sense for obstacle

--_\ / If obstacle in front, tum left

Figure 2.2

The simplicity of these initial IA V architectures enables a mobile vehicle to

function in a relatively static environment, such as a production line, where it would

perform generally uncomplicated manoeuvres to achieve a single goal. AI researchers

soon found that process control systems did not facilitate more complex behaviour in

dynamic environments, and instigated a search for a more flexible IA V architecture.

2.1.2 Plan-then-Execute Architectures

This search gave rise to a second approach to IA V control, a functionally

decomposed "sense, plan-then-execute" architecture. Much like the process control

systems, plan-then-execute architectures also generally have only a single layer of

abstraction, although there are exceptions. The reader will notice the similarities between

Figure 2.3 and the preceding figures:

Sense --_\ / Plan

--_\ / Execute

Figure 2.3

The similarities likewise appear with the adaptation for a mobile vehicle:

Sense and Interpret the Environment

--_\ / Plan Response(s)

--_\ / Execute

Figure 2.4

7

This architecture converts sensory information into a symbolic internal

representation of the environment, which it stores in the vehicle's memory. The planner

reads this symbolic representation, then generates a list of actions to be executed. This

architecture offers a major advantage - the addition of planners, such as non-liner

planners (described later is this chapter) - which provide the ability to plan several step s

in advance to find a solution to more complex problems.

Researchers who have conducted the most recent work with these architectures,

including Drabble (1993) and Simmons (1990), have concentrated on the execute phase.

B. Drabble and R. Simmons strove to detect deviations from planned reactions to sensory

input, and to determine at which stage of plan execution process to pass control back to

the planner for partial or total re-planning. This endeavour requires the researchers to

anticipate all potential problems before they are encountered by the IA V - a difficult, if

not impossible task. Some IA V s have been programmed to stop while re-planning

occurs. Alternatively, an IAV could be programmed to continuously modify its plans to

achieve the goals of an application so that it could respond to changes in sensory

information. These proposed solutions likewise require a delay while the re-planning

process occurs, causing the modified plan to possibly become obsolete before it is

implemented. This latter feature is a major disadvantage with this type of control

architecture.

The other criticism of this type of architecture is the process of converting sensory

data into internal symbolic notations. Researchers such as Brooks (1991b) argue that

humans do not process sensory information in this manner (I only wish to note the

conectionist V s symbolic argument but not become embroiled in it in any way).

In summary, plan-then-execute architectures improved the performance of IA V s

by introducing the ability to plan many steps in advance to solve problems. Nevertheless,

opponents of these architectures have cited their symbolic approach and the slow speed of

a planner's assessment, execution, reassessment and re-planning

8

process as major flaws. For this reason, some members of the IA V community continued to

look for other possible control systems.

2.1.3 Subsumption Architecture

In the eighties, Rodney Brooks re-kindled interest in the reaction-based method of IA

V control by publishing papers outlining the development of subsumption architecture

(Brooks 1986; Brooks 1990b; updated version in Brooks 1991a). Brooks designed a layered

architecture which enables an IA V to respond to sensory input from

an environment with fixed, simple reactions. Brooks and collaborating researchers

claim to have improved the reaction-based systems by replacing the traditional AI

modularity approach, outlined in Figure 2.5, with a behaviour-based, layered approach, show

in Figure 2.6.

Sensors I

V Perception Manipulate the World

Modelling Build Maps

Planning Sensors ----> Explore ----> Actuators

Task Execution Avoid Hitting Things

Motor Control I V

Actuators

Locomotive

Figure 2.5 Figure 2.6

This layered approach entails no implicit separation of data and computation.

Brooks implements these architectures as "networks of message-passing augmented finite

state machines (AFSMs)". An example of one such network is shown in Figure 2.7. Lower

layers in the network take control of the vehicle's actuators by suppressing higher layers in

some situations. For example, if an IA V moves too close to an obstacle, the obstacle

avoidance procedure takes control and suppresses the output of

9

Find Infra-Red I

Module

Obstacle UltraSo

Avioadance

Sensor

Reflex Module _Bumper

Bars

all higher layers. At any instance, only one behaviour has control of the vehicle's

actuators. This feature could be likened very loosely to attention. Applications of

subsumption have involved the use of both multiple AFSMs per behaviour and multiple

layers. Shared registers facilitate communication between the AFSMs.

Stepper Motors

nfra Sensor

und

Figure 2.7

The designers of subsumption systems contend that their approach offers at least

three advantages over previous models. First, subsumption enables IA V s to

function in uncertain environments. The display of this ability by Genghis and Genghis

II, robotic ant projects developed at MIT (Brooks 1989), attracted the attention of the

popular press (Taylor 1994). While reporters who have highlighted these IA Vs for the

public frequently fail to account for the subtleties and limitations of present AI research,

Genghis and Genghis II are two of the most high profile applications of this architecture.

A second high profile project, the NASA planetary roving vehicle, likewise employs a subsumption-based system to enable vehicles to navigate on

uneven, moon-like terrain (Gatt 1993). The adaptability of subsumption may likely arise

from the reaction-based design of this architecture.

10

The layered approach of subsumption is said to provide the second major

advantage - it facilitates the highest level of integration between the features of the

architectures thus far described. Although Brooks was not the fIrst person to use a layered

approach, he and his collaborators did rekindle interest in this design, and Brooks in

particular expounds on the advantages of the layered approach. He writes:

The behavioural competence of the system is improved by adding more behaviour-specifIc network layers to the existing network. This process is called layering. This is a simplistic and crude analogy to evolutionary development. As with evolution at every stage of the development the systems are tested. Each of the layers is a behaviour-producing piece of network in its own right, although is may implicitly rely on the presence of earlier pieces of network (199la: 1229).

While one might not discuss the functionality of layered approaches with the same bold

confidence as has Brooks, this design nonetheless has provided one of the most successful

avenues for the integration of features of the architectures yet developed and widely tested, though this is not to say that the layered approach necessarily will

feature in the architectures of the future.

The third advantage, according to Brooks, is the absence of an abstracted model

of the world represented by symbolic means. Brooks contends that no human created

abstraction could accurately mirror the actual world in which an IA V must

cope, and hence the elimination of a reliance on symbolic abstraction could permit an IA

V to better assess and respond to its surroundings. Even so, a careful reading of Brook's

own descriptions reveals that even he has not entirely escaped the need for some symbolic

representation, albeit in a lesser quantity than required by some other architectures. The

methods for achieving such behaviours as compilation of maps which Brooks describes (1986; 1991a) would require some form of internal

representation to be shared between behaviours if they are implemented.

Critics of subsumption frequently condemn the occasionally outlandish claims

designers have made about this architecture. Beyond rejecting the excesses of the

subsumption camp, critics also have highlighted four principal shortcomings of this

11

approach. It was noted that the original architecture described by Brooks (1986)

lacked a means of representing time or a time delay, and, second, that there was no

way to combine the inputs or outputs of several behaviours. Brooks rectified these two

problems in an updated version of this architecture (Brooks 1989a and 1990b). In the

revised architecture, Brooks provided flxed duration monostables to deal with time in

each of the individual AFSMs. Brooks redressed the second problem by feeding the

values of the registers within AFSMs "into a local combinatorial circuit to produce

new values for registers or to provide an output message" (1991a: 1229). Additionally,

Brooks introduced a behaviour language to describe systems to be implemented in the

subsumption architecture (1990a). Two of the principal shortcomings, however, remain. Subsumptio

n

architectures still require a predeflned, flxed hierarchy of behaviours which have

either a top down or a bottom up order of control. An inflexible hierarchy may suit

situations where only a single high level task is required, but the hierarchy has to be

reordered for a second high level task. Diverse tasks such as walking or picking up a

pencil require different hierarchies of behaviours. A second yet unanswered criticism

highlights the inability of IA V s controlled by subsumption to conduct whole sale

interpretations of their world at any stage. Not all researchers agree that whole-sale

interpretations are needed, but the critics generally concur that the Brooksian design

does not facilitate a sufflcient level of environmental interpretation. Although the

development of subsumption architectures may have been a good move in the right

direction, the continued problems have encouraged some researchers to look at other

avenues, detailed in the next three sections.

2.1.4 Combined Architectures

Other researchers have attempted to overcome the problems of controlling IA

V s by using architectures which combine both the plan-then-execute and reaction

based approaches to navigation. Lefevre (1994) details one such experimental

architecture designed in Europe, called ALICE. As Figure 2.8 shows, ALICE offers

12

I \ I V I

Navigation / I I \ I

V I Reflexes / I

I \ V

Actuators

the potential for capitalising on the advantages of both earlier systems - the world

interpretation performed in the plan-then-execute architecture, and the adaptability of the

reactive obstacle avoidance system.

' ___________World modelling /\ I I I I

Sensors

Figure 2.8

Gat has developed similar architectures for NASA (1993). Nevertheless, this

combination of approaches retains some disadvantages of both earlier systems, such as a

fIxed order of hierarchical control and the slow speed of sequential world modelling. This

dissertation offers a further possibility by combining a high level non-liner planner with a

subsumption architecture, an approach not widely reported, if reported at all, in the present literature.

2.1.5 Non Symbolic Methods

While many AI researchers may not work with subsumption itself, much

present research involves the modifIcation and adaptation of reaction based control

architectures. These modifIcations may be grouped into two broad categories: non-

symbolic methods and self-organising architectures. This subsection considers the fIrst of

these general groupings, the non-symbolic methods, which include connectionist

approaches and fuzzy logic. Several research groups have experimented with connectionist approaches, that

is, the use of simulated neural networks to endow IA V s with the capacity to engage in

obstacle avoidance. Cliff et. al. (1992) and Gachet et. al. (1993) provide some

13

examples of this work. Connectionist approaches have produced some promising results;

however, the present work tends not to combine more than two types of behaviours.

Secondly, simulated neural networks require extensive design labour, and, as yet, have

only succeeded in achieving results which could also be implemented through less

complicated standard programming techniques.

•

A second major non-symbolic method entails the use of fuzzy logic to produce

and regulate behaviours. Some researchers have tested fuzzy logic to implement single

behaviours. Beom et. al. (1992), for example, used this method for obstacle avoidance,

and Baxter et. al. (1993) applied fuzzy logic to improve IAV navigation. C. Voudouris

expanded on the use of fuzzy logic in his MSc dissertation (1993) to permit individual behaviours to serve as inputs for other behaviours. This modification

enabled Voudouris to design a layered approach possessing the capacity to combine the

outputs of behaviours. An example of this work written in the systems definition language

is shown in Figure 2.9.

rules begin if dmin NEAR then ObsticalAvoidance if

dmin FAR then Goals ObstiacalA voidance begin

if dl==VN and d2==VN and d3==VN and d4==VN then Vleft:=NS and Vright:=NS if dl==VN and d2==NE and d3=NE and d4==VN then Vleft:=PS and Vright:=PS

end Goals begin

if energy <= 20 then go_to_charger if energy> 20 then do_jobs go_to_charger

begin

end do_jobs begin

end end

end

Figure 2.9

14

The further development of fuzzy logic systems, reported in Voudouris et. al. (1994), has

yielded some impressive results. This work has equipped IA V s with the ability to make

smooth transitions between the execution of different behaviours. Nevertheless, fuzzy

logic systems require a predefined and inflexible ordering of behaviours for the

performance of tasks. The Voudouris system in its present design suffers two additional

defects; it lacks both internal mapping systems and the capacity to store memory of an IA

V's previous movements. As a consequence, IA V s on which a fuzzy logic controller is

implemented tend to get stuck in dead ends easily. While both connectionist approaches

and fuzzy logic systems have generated some promising results, non-symbolic methods,

like the other previously described approaches, would need considerable improvement to

facilitate significant advances in IA V research.

2.1.6 Self-Organising Architectures

Self-organising architectures have gained prominence since November 1994,

during the production of this thesis. Researchers developing this method have sought to

empower IA V s to modify their own architectures in response to information gathered

from the environment and stored in memory, as well as to interact with human

programmers, who act as "parents" or "teachers" for the IAVs. Researchers argue that

humans still lack sufficient understanding of their own thought processes to even

approximate a model for an IA V to emulate (Lewin 1994). Instead, these researchers

have sought to equip IA V s with the ability to design and modify their own architectures

(Brooks 1991a). The IAV would gain intelligence through this process of learning.

Two groups working in this area of IA V architectures have gained pUblicity in

both the scientific media and the popular press. The well funded and high-profile Cog's

project of the Artificial Intelligence Laboratory at M.LT. pursues the ambitious objective

of "building brains for bodies" or humanoid upper torsos (Brooks and Stein 1993; Lewin

1994). It is difficult to evaluate the success of this project at present, as the majority of

published references announce the goals rather than detail the results of

15

this work. The second group, at Sussex University, has developed a neural network IA V

control system using genetic algorithms which employ a process of natural

selection to choose the more successful modifications for an architecture. The Sussex

group has generated some potentially promising results in the area of obstacle avoidance,

but, as yet, their work has been implemented only on an IA V simulator and

not on actual lAYs (Cliff et. al. 1992)1. The Sussex group has also experimented with a

simulated vehicle control architecture which uses visual input (Cliff et. ale 1993; and

Harvey et. ale 1994). This latter application has produced very interesting results,

though the visual research falls outside the scope of this thesis. Self-organising architectures

potentially offer one of the ways forward in IA V control, though a great . deal more work

will need to be done which will not necessarily follow on from the two previously described projects.

2.1.7 Summary

As the previous sections indicate, the IA V research community has not reached

a consensus about the direction that future research should take. Each of the methods

discussed, both old and new, has strengths and weaknesses. Different groups favour

different varieties of architectures, and some researchers continue to search for new

methods. After reviewing this literature, I have reached the conclusion that layered

approaches, the flexible ordering of behaviours, and the ability to combine the outputs of

behaviours are the most important features to be included in any architecture.

2.2 Mapping Systems

My thesis research additionally encompasses a second area, the design of

systems which enable IA V s to construct maps of their environment. This next section

covers the major topics in this subject area. Additionally, I discuss the directions in

which I think this research should be going.

IThe Sussex team has recently ported their work onto IAVs, although these results remained unpublished during the production of this document.

16

2.2.1 Geometric Mapping and Dead Reckoning

The maps that were initially used in IA V control systems simulated the way many

scientists believe humans draw maps of the world - using a top down cartographic format.

For some reason, the computing world calls this format a geometric rather than a

cartographic map. An IA V using such a system measures objects in relation to the exact

angle and distance from a specific point. In the early work with geometric maps,

researchers programmed a pre-produced map into the IA V to aid the navigation of the

vehicle. Researchers have subsequently adopted a variety of means to attempt to empower

IA V s to produce their own geometric maps based on their exploration of their

environment.

Experiments soon revealed that the inaccuracy of an IA V's sensors make the task

of mapping a room in this way, let alone a world, extremely difficult. Factors such as

minor inconsistencies in wheel spin have compounded mapping errors as vehicles have

attempted to calculate distances and move to locations recorded on their maps.

The occupancy grid mapping system developed by Elfes (Blfes 1989a; 1989b) is

one practical example of this type of mapping system. This system converts ultra sound

information (which is both noisy and error prone) into a probabilistic map. The system

stores this information in a grid format of probabilistic information which allows the IA V

to determine if a given spot on a grip contains an object or is free. This system still suffers

from positioning problems, as the system uses dead reckoning.

2.2.2 Landmark Mapping

More recently, researchers have been looking for an alternative way for an IA V

to ascertain its position in a world. One possible method of doing this is by using

recognisable objects in the world as landmarks. Currently I have found no papers

reporting on systems which create maps of landmarks, but Saburo & Shigang (1993) have

developed a system to recognise landmarks from visual input. Other researchers,

17

such as Lazanas & Latombe (1993) and Bonasso et. al. (1993), have designed or

implemented systems which navigate either to or between landmarks.

2.2.3 Topological Mapping

Topological maps connect several landmarks together. Zimmer and

Puttkramer provide a description of this form of mapping (1994b), though during my

research, I have not found any articles reporting on its implementation. Figure 2.10 shows an

example of a completed map.

Figure 2.10

2.2.4 Summary

In summary, these three types of mapping systems hold promise for future AI

research. No doubt, further work will be done in this area.

18

2.3 Planning Systems

There are several different behaviours which collectively come under the heading

of planning systems. This section concentrates on two major types of planners: path

planners, which have obvious applications for IA V s; and non-linear planners. This

chapter also covers planning to plans, a method of planning in which the details of the

plan are determined as they are actually required.

2.3.1 Path Planners

Virtually all variants of path planners represent navigable space in a form which,

theoretically at least, allows the planner to come up with a plan of action to reach a goal.

Early path planners tended to represent the layout of a world as a large grid of lateral and

longitudinal lines, called a Manhattan grid, created by the imposition of a grid on a

geometric map. Search algorithms used this grid to find the best route to any given

location. The vehicle would follow the chosen route to achieve its goal. Zimmer &

Puttkamer (1994b) report that Manhattan grids have four major limitations. (I) These grids

limit the IA V to select one of only four possible directions for

movement. As a result, the vehicle follows a choppy path to a goal.

(2) These grids require a very accurate, predefined map of the environment. As a result, the

IA V is unable to handle changes in its environment.

(3) This method requires extensive time to find a suitable path. To redress this problem,

some researchers, such as Scott (1988), have incorporated machine learning into

Manhattan grids so that the search planner remembers well used paths.

(4) The grid has difficulty representing free space.

In the next stage of mapping development, researchers abandoned the simple

imposition of a grid over a geometric map and changed the mapping systems so that they

would more easily facilitate path planning. Brooks (1983) describes one of these

transformed mapping systems, and Kuan (1985) details a refined version. The refined

19

b.

system represents navigable space in world as a series of polygons, such as those shown

in Figure 2.11.

000000000000000000 000000000000000 000000000000 000000000 000000 000

000000000000000000 000000000000000000 000000000000000000 000000000000000000 000000000000000000

00 y

X 000000 0000000000

00000000000000 00000000000000000000000000000000000000000000

0000000000000000000000000000000000000000000000 Figure 2.11

The planner searches for routes through the polygons to find a path to a goal.

D. Miller developed a third system for NASA (1993). Miller's method

represents the world as a field or map of Navigation Templates (NaTs) which indicate

the path to a goal which avoids obstacles. Figure 2.12 provides an example of such a map.

~"':"~~"""~~~~~,""",",,~ ",004 '( .~ y ~"'~~"'''''''>';JP">-~ ::...~ .•. ~~ ..••. ~ ~~ ..•• -'o....a... .••.. , " "C '( "C ,;.c ~ ~~~ ~ ::... ~ .•. ~~.).~...a...~~ ,-' ,~",,",~" ~ '~~>"">~"")/II> .-.. ~~Jo.."A..::"''-''..lt. "'~~~-'o.~~", ~ Y , ,..lI. ...•• ~ ~ ·~~.).~.).~~~~~~~'~~f > .•. >.>~ .•...•...•. D' . N T-.-.. •. ~~ ~ .• .----- .•. ~ .•. lreCUOn s- a *" .-..~),.""'" ......•.. ,...,.p)/ll>>>>,... .lI...~A.."'.lo. .••...•• .lI... •. .lo..lJo.. .•. .lo. lJo..~)o"." ).. .•• ...a... .•..•. _ ,...,_ ___________________________________________••

~~~~">-~ ••• >~ .•. ~~~~~ ~ •••. ~ .•. ". P"Y ~ ""'Y'}ry .,.. """)r ~~ ..•.. ,..,..> ~~ . __ ._- _., _ .•.. .,.. ~ ~.,...,.. ~""'""Ii'7..."-:r..,, ~ ""'~"""''''''''''''''~7777r-r.., .,.. ~ ~ " ~..,.. .,.. '7'."...., ~., ...,., ." .,. ,.."..."..,.~...,..."..., .,., .,..,., .•. ..".,...,...,...,....,...,~...,~..,,~ 7...,. 7.." -r .,...., Y'7' ;r..".T.,~~1'-r.., ~ • .,.,~ 11 ~.~.,r ."".T~.."-r..,,

Figure 2.12

20

Zimmer, Fischer & Puttkamer (1994) report on very similar work completed

using connectionist methods and a Boltzman machine as its basis, though, at its

foundation, this approach is another way of representing the work completed by Miller.

I have not found any instances of path planners being used with topological maps. Some

researchers have used simple search algorithms on these types of maps, though the use of

path planners on topological maps may be tried in future research. Future work will

include efforts to redesign representations of the world which facilitate easier path

planning. Maps developed to date tend to have an obsolescence problem, that is present AI

methods do not equip mobile vehicles to adjust maps at the speed of change in dynamic

environment. Future research will also likely focus on methods for improving the speed of

map adjustment.

2.3.2 Non-Linear Planners

Sacerdoti (1975) fIrst proposed non-linear planners. Tate (1977) later

expanded on the design, and other researchers have since devised further improvements.

This planning technique connects operators together to form a plan which will complete a

goal. Non-Linear planners complete this task with a minimum commitment to a liner

solution. An example of this process is shown in Figure 2.13.

Developed as an extension of linear planning, non-linear planners have been

introduced to overcome such problems such as the Sussman anomaly. The work by Fikes

and Nelssion (1971) on STRIPS is a seminal example. Many researchers in the AI

community now consider non-linear planners useful for general purpose planning.

Non-linear planners offer a major advantage over path planners in that the former

can schedule sequences of tasks. Nevertheless, non-linear planners have difficulty coping

with rapid and unexpected changes during the execution of environmental-based tasks.

21

Initial State'

Operator 1 Goal State

a a c c

b d

Step 1 Step 2

Initial State' Goal State

a

c

b

d

Operator 1

Step 3 a c

Initial State' Goal State

a Operator 2 c

b

b

d

d

Figure 2.l3

2.3.3 Hierarchical Planners

Some planners produce plans which have a hierarchy of abstraction.

Hierarchical planners can expand operators in one level of a plan into more detailed layers.

Sacerdoti (1971) describes this feature for non-linear planners (though other types of planners

may also use a hierarchical structure). An example such expansion follows in Figure 2.14.

Goto London

into

Goto Colchester Train Station Buy Ticket Board Train Travel to London Get off Train

Figure 2.14

This feature has obvious potential uses for mobile vehicles.

22

2.3.4 Planning to Plan

This process of planning to plans encompasses efforts to equip mobile vehicles to decide

how to plan, and when and where to perform planning tasks. In the previously considered

example of making a journey to London, the initial level of planning produced only the

outline shown above. The individual planning events of expanding these goals takes place

at a later stage. Planning to a plan occurs when a step in the plan requires further planning.

Although Munson (1971) completed the initial work on this subject more than two decades

ago, researchers now are returning to this area (Steel 1994).

2.4 Attention

The literature on the current architectures reviewed above describes work which either has

not required the sharing of resources or has been inherently parallel. While attention has

not been implemented as a module, some architectures have produced a form of attention

as a side effect of operation. Some recent work, including Scott (1994), has focused on this

area with a view to using a multi-tasking operating system to share resources. Other

researchers, like Brooks, have tried to focus a vehicle's attention on a process by

introducing "hormone" values which impact the performance of all behaviours. Brooks

claims this process' improves a vehicle's operation by enabling researchers to introduce a

form of attention mechanism into a distributed collection of processing agents.

2.S Summary

The proposal of resolutions to the debates over the most productive strategies for

improving the design of architectures, mapping systems, and planners, as well for

incorporating such features as attention into the programming for intelligent mobile

vehicles, lie far beyond the scope of this project. Three points raised in this chapter will be

considered in more detail. These are Reaction based , Subsumption and Combination

Arcitectures

23

Chapter J

An Implementation of a Reactionary lAY Architecture

Contents

3.0. Introduction 3.1. Vehicle Developed 3.2. Software Developed 3.3. Experimental Results 3.4. Future Work 3.5. Summary

3.0 Introduction

The summer leave taken by a member of staff in August caused a delay in the

implementation of my work on the departmental vehicle, Ford. However, the delay

provided an opportunity for a further extension of my ideas. During two weeks of this

delay, I designed and built a small mobile vehicle, shown in Figure 3.1. This vehicle is

controlled by a single layer reactive control system which enables it to move around its

environment with out damaging itself. The simplicity of this design permits assessment of

whether a reaction-based control architecture could be operated successfully on an

economical machine. The work completed at M.LT. and detailed in Brooks (1989) provided the initial inspiration for the design of an experimental rodent-like vehicle.

24

Nevertheless, there are two major differences between this vehicle and Brooks' work. First,

the MIT vehicle uses a sUbsumption control architecture; and, second, Brooks created a more

complicated, six-legged vehicle. Brooks reported in a 1994 Radio 4 interview that );ASA is

now considering the possibility of deploying an improved version of the .Y1.I.T. vehicles to

prepare ground for the construction of a planetary station on the moon or Mars at some future

time. I sought to determine how successfully this work could be completed using far simpler

designs for both the vehicle and the control architecture. This chapter details the development

of the mobile vehicle, shown in Figure 3.1, then of the control architecture. Finally, I discuss

the results of my experiments, and elaborate on the potential future developments of such a

mobile vehicle.

Figure 3.1

25

3.1 Vehicle Developed

The mobile vehicle illustrated in Figure 3.1 consists of an aluminium base plate

with four 1.5 volt D.C. motors, a 4.5 volt battery pack, and a circuit board which controls

its movement. Two micro switches attached to both the front and rear of the mobile

vehicle provide sensory input. Extensions of these switches enable them to act as

"whiskers" for the vehicle.

The control circuitry, developed in conjunction with Malcom Lear, is based

around the NEC 27** range of EPROMs. The use of an EPROM as the base for a

controller enables a direct mapping between the vehicle's "whisker" readings (which feed

into the EPROM's address lines) and the vehicle's motor control (which feed from the

EPROM's data lines).

Figure 3.2 displays the full circuit diagram for the controller. The important

agents of this circuit arrangement include the following:

a. A clocked buffer (74HTC574 and 74HC14) in the circuit allows the output data lines

to feed back into the input address lines as a way of representing the internal states of

the vehicle. This enables the control architecture to remember the direction the motors

are going when the vehicle makes contact with an obstacle. The additional four data

output lines also can similarly represent additional internal states.

b. The presence of spare quad gates on the clock chip (74HCI4) allows an additional

clock with a delay time of approximately one second to be supplied to address line All

of the EPROM (for use by the software programmer). This clock is reset by the

trailing edge of an inverted pulse from data pin D4 of the EPROM.

c. Four L272N dual power op-amps, connected as shown in Figure 3.2, perform the

directional control of the motors. The direction of the motors is dependent on the

differential between points A and B shown in the diagram. The circuit, in essence, acts

as a bridge.

d. All unused inputs to the EPROM are pulled to Zero when not in

use. The total cost of the completed hardware was under £20.

26

l

~

Ats AI. All

~

AI2 -L2

AI1

AM

OM

011 Ots

012 01. CI() • OIl

012 081 D.

OM

~

GI() CK 11

-L3

c.d.e.f.g.

Figure 3.2

27

3.2 Software Developed

This section describes the software developed for testing the vehicle.

Programming is based on the placing of data in the EPROM's 8 bit memory locations.

This design functions similarly to a large lookup table of all the possible combinations of

inputs. As the end result of the programming is a large table of hexadecimal numbers, I

will describe the developed software in the form of a set of production rules.

A simple reactive control architecture formed the basis for the design of the

test software for this vehicle. This software, shown in Figure 3.4, instructs the vehicle

to constantly move around a room, and to change direction whenever it senses an

obstacle.

IF front_ whiskers NOT pressed AND back_ whiskers NOT pressed THEN go_current_direction

IF front_ whiskers ARE pressed AND back_ whiskers NOT pressed THEN go_backwards

IF front_ whiskers NOT pressed AND back_ whiskers ARE pressed THEN go_forwards

IF front_ whiskers ARE pressed AND back_ whiskers ARE pressed THEN go_spin

Figure 3.4

As the directional output to the motors is constantly fed back into the EPROM,

the software always has knowledge of the direction in which the vehicle is travelling.

This feature instructs the vehicle to proceed in the same direction as long as the

whiskers do not come into contact with an obstacle. This very simple aim did not use

several of the hardware features (such as the slow clock and four additional buffered

internal state bits). These additional features permit the future implementation of a more

complicated program than the one that I have designed for this project.

28

Outcome

1 Movement without problem

2 Movement without problem

3 Movement without problem

4 Movement without problem

5 Movement without problem

6 Movement without problem

7 Got stuck in dead end

8 Movement without problem

9 Movement without problem

10 Movement without problem

Figure 3.5

Appendix B lists the final hexadecimal address map for this code. This address

map is about 10 times the size necessary, as the EPROM selected for this project was in

excess of requirements to allow for future expansion.

3.3 Experimental Results

It is extremely difficult to plot the operational results of a mobile vehicle experiment for

comparison with a simulator experiment, as there is no easy manner of recording the path

that the vehicle takes. For this reason, I evaluated the performance of the vehicle by

running several five minute experiments, and recording which of the following three

outcomes occurred: (a) the vehicle became stuck in a dead end; (b) the vehicle

experienced a mechanical failure; or (c) the vehicle continued move within the room

without a problem. Figure 3.5 below records the outcomes of ten experiments

As expected, the system performed very well, completing its task of wandering

around the room without problems on most occasions. These results could be attributed to

simplicity of the software, hardware, and control architecture. On the one occasion in

which the vehicle experienced an unsuccessful outcome, the vehicle

29

became stuck in a confined area and oscillated between two different reactions. This

oscillation may be an inherent feature of a pure reaction-based architecture. The inclusion

of an internal state in the architecture may possibly cure this problem.

A second drawback of this control architecture is the presence of only one layer of

control. This means that if the vehicle avoids obstacles, it cannot perform other

behaviours, such as achieving a goal, at the same time.

3.4 Future Work

This simplicity of this vehicle is very attractive, and has inspired several ideas for

future development. The first two points below have high priority and are likely to be

completed.

1. The development of a command language and compiler to enable faster and more

efficient production of code for the vehicle

2. Provision of additional sensory abilities, possibly either infra red or light sensors, to

enable the system to have a higher level goal.

3. Investigation of how co-operative behaviour could be achieved between multiple

vehicles to satisfy a goal.

4. Introduction of learning (andlor some form of evaluation) through genetic algorithms

so the vehicle could self describe its own control code or engage in some form of

internal learning.

At present, only two of these vehicles have been constructed. Production of more vehicles

with the enhanced sensory capacity would be necessary to complete the last two goals.

3.5 Summary

This chapter shows how a simple mobile vehicle with a reactive control

architecture can successfully perform a single task, such as obstacle avoidance. This

simple and extremely cheap hardware can be rapidly developed for mobile vehicle

30

experiments. This hardware additionally allows for the possibility of a range of future

developments.

31

Chapter 4

An Implementation of a Subsumptjon lAY Architecture

Contents

4.0. Introduction 4.1. The Hardware Sensor Details

Hardware Modification Details 4.2. Subsumption Software Developed

Reflex Module Obstacle A voidance Module Wall Following Module Find Infra Red Beacon Implementation Details

4.3. Experiments Performed and Their Results Comments of the Performance of the Vehicle Comments on the Performance of Subsumption 4.4. Future Work 4.5. Summary

4.0. Introduction

architecture developed by Brooks (1986) on one of the departmental Marvin vehicles.

This chapter details an implementation and re-evaluation of the subsumption

The chapter first includes a brief deSCription of the Marvin autonomous vehicle, Ford,

used as the base vehicle in both this and the following chapter. This description

32

highlights the vehicle's four types of sensors, and modifications made to the hardware to

improve its sensory input. The second section details an implementation of subsumption

architecture. This section considers the degree to which subsumption enabled Ford to

perform the following four behaviours: reflex action; obstacle avoidance; wall following; and

sensing infra red beacons. The second section additionally details the integration of these

behaviours to form a unified control architecture. The third section lists the experimental

results, and the fourth considers the implications for future work suggested by these results.

Fmally, I summarise the conclusions I have reached from the work in this chapter.

4.1 The Hardware

The Marvin vehicles, developed in the Computer Science Department at the

University of Essex, are mid-sized mobile vehicles built as platforms for a MC68030 based

VME computer system. Figure 4.1 pictures Ford, the vehicle used for the majority of these

experiments.

Figure 4.1

33

Ford is battery powered and has two stepper motors which provide directional

movement. A parallel input/output board provides an interface to most of the vehicle's

sensors, and also contains a clock which can be reset and can create interrupts. These

interrupts are used by the standard operations which are described in more detail in the

section 4.2. The vehicle has a neck to which a third stepper motor that powers a spinning

platform is connected.

4.1.1 Sensor Details

The Essex Marvin vehicles currently have four types of sensory equipment which

read input from the environment:

a) Four bumper bars protrude from the base of the vehicles in a circular pattern. If any of

the bumpers makes contact with an object, the processor interrupts the motion of that

vehicle. These bumper bars effectively and reliably stop vehicle motion should other

sensing systems fail.

b) Four ultra-sound range detectors are positioned in the front quarter of the vehicle.

Each has an arc of approximately 20 degrees of sight, providing a total of around 80

degrees of coverage. The sensors have a sensitivity in which one ultra-sound step

equals 1.66 centimetres.

c) A spinning infra-red pulse detector is positioned on top of each vehicle's neck.

When combined with software, this sensor both differentiates between any number of

infra-red beacons placed in its environment, and detects the angle of each beacon

from the vehicle's centre line. Reading have a potential five degree error.

d) The final sensor, a small camera, feeds input to a frame grabber card. Staff in the

Computer Science Department at Essex developing this sensor had not completed the

design and implementation in time for its inclusion in this project.

The software developed at Essex for the Marvin vehicles relies primarily on input from

the ultra sound sensors for obstacle avoidance and wall recognition. The infra red

bearings facilitate pursuit of higher level goals, such as locating beacons. The cameras

will similarly expand the capacity for task performance in future projects.

34

4.1.2 Hardware Modification

The Marvin vehicles experienced difficulty in consistently avoiding obstacles

during the initial implementation of the subsumption architecture. I speculated that the

vehicles lacked sufficient data from the ultra sound sensors to interpret their

environments, and elected to attach an additional ultra sound controller board to one of the vehicles (Ford). The new board doubled both the number of sensors and the

scope of the input coverage. Figure 4.2 details the positioning of these new sensors, and

Appendix E indicates the address of this board in the vehicle's memory map.

14cm 1 8cm

14cm 8cm 1

13451 12 1 61 1 1 1 11 _____________1, _____________71 1

1

1

1

1

14cm 7cm

22 cm

_____ 8 _________________

( Numbers 1 to 8 are the positions of the ultra sound transmit and receive pairs)

Figure 4.2

This positioning necessitated a ten degree dead area between each pair of ultra

sound sensors to stop interference or cross readings. This gave the vehicle virtually full

coverage of the front half of its operating environment. A single ultra sound

sensor provided some data about the space to its rear. Even with these modifications,

the ultra sound sensors still only provided incomplete information about the

environment. The use of another type of sensor, such as vision, as the primary source

of information, will likely improve the results of future research.

35

Find Infra-Red Jnfr

Module S

~ Obstacle UltraSound

- Avioadance

Sensor

Reflex Module Rumper

Bars

4.2 Subsumption Software Developed

This section details the subsumption architecture produced for this project.

This architecture includes four AFSMs, each of which represents a different behaviour.

These AFSMs operate together in the network shown in Figure 4.3.

Stepper Motors

a ensor

Figure 4.3

The next four sub-sections detail the individual AFSMs, to which I will subsequently

refer as modules.

4.2.1 Reflex Module

Upon receiving an input from one of the vehicle's bumper bars, this module

suppresses all other control of the vehicle's motors and causes the vehicle first to stop,

and then to move approximately 10 em in a direction away from the object with which

it made contact. The software then removes the suppression, enabling other behaviours

to take back control of the motors. This operation positions the vehicle so that it can

rotate freely to any orientation required. This software is implemented in 'C', using a

modified version of the standard bumper interrupt handling routines I.

IThis process has also been implemented using a three layer neural network model, using the PDP software developed by 1. McClelland, et. al. (1989). The PDP approach was not used in the final project as it requires more processing while offering no advantages over the procedural approach.

36

4.2.2 Obstacle A voidance Module

This unit, which gathers input from the ultrasound sensors, avoids obstacles in a

reactive manner. This module suppresses the output of other modules when the vehicle

moves within a close proximity of an object. One example of such a reaction follows:

Input: The vehicle's front-left ultra sound sensor detects an obstacle at a distance of one

metre.

Output: The unit slows the rotation of the vehicle's front-right wheel in correlation to the

distance between the sensor and the object, while keeping the speed of the left

wheel constant, causing the vehicle to veer right.

When the sensors no longer detect a nearby object, the software lifts the suppression, and

control reverts to the higher level unit. This module is currently implemented in 'C' in a

procedural style.

4.2.3 Wall Following Module

I implemented a wall following module as two separate functions. The ftrst

function created an ego-centric map of the vehicle's immediate vicinity. The second

function enabled the vehicle to follow a wall by using this map. Originally, I tested two

different geometric wall recognition routines, developed in conjunction with Steve

Brains, a mathematics graduate (detailed in Appendix I). When Ford operated with these

geometric routines, it successfully followed walls only 50 percent of the time. After

checking the ultra sound readings, measuring the distances to the wall, and calculating the

expected results, I concluded that the inaccuracy and unreliability of ultra sound sensors

contributed to the high number of failures. Instead, I implemented the wall following

module using a far more simple and reactionary method, described in Figure 4.4.

37

IF object IS meduim_distant THEN SUPPRESS higher_level_behaviours tum_parallel_to_object REPEAT

IF going_to_an_object THEN corect_to _parallel_course IF going_away _from_object THEN corect_to_parallel_course IF parallel_to_object THEN go_straight

UNTIL object NOT medium_distant REMOVE_SUPPRESION

ENDIF

Figure 4.4

This third algorithm causes the vehicle to follow the walls of obstacles while

keeping a constant distance away from them. Unlike the previous two algorithms, which

require three correct ultrasound readings at any point in time, this algorithm

requires only two correct readings. Using the third algorithm, Ford successfully followed

walls in 70 percent of trials. This algorithm provided a sufficient degree of

efficiency for the continuation of trials for this project, though it would likely generate

better results if implemented on a vehicle with enhanced sensory capacity.

4.2.4 Find Infra Red Beacon

This module, the highest in the hierarchy of the implemented modules, directs the

vehicle to find a given infra red beacon. This module controls the vehicle if no other

module in the hierarchy is active. The slow scan speed of the infra red sensor slows the

operation of this module. As a result, the current bearings quickly become out of date.

Reducing the speed of the vehicle's movement to one-fifth of its maximum speed allowed

the sensors to hold a reasonable level of accuracy. The speed reduction permitted this

module to perform correctly and guide the vehicle directly and smoothly to its goal.

38

4.2.5 Implementation Details

Each module was initially implemented as a separate unit in an integrated

processes, with operating system priority increasing as the hierarchy was ascended. This

implementation method proved problematic, as the time taken by the operating system's

tasks caused the scheduler to starve important behaviours, such as obstacle avoidance, of processor time - with predictable repercussions.

A second strategy of implementing all of the individual processes in a hierarchy

of IF statements proved more successful. This hierarchy is shown in a section of pseudo-code listed in Figure 4.5.

REPEAT IF obstacle is close THEN aviod_obstacle

ELSE IF obstacle is distant THEN follow _ wall

ELSE IF no_obstacal THEN

follow _infra_red_becon ENDIF

ENDIF ENDIF

UNTIL

Figure 4.5

This second implementation strategy using IF statements generated a more

predictable performance than its predecessor. This approach worked because only a small

amount of processing time was required for each of the modules, although parallel

execution would be required if the processing time of any of these modules was

increased. Appendix G lists the code for the second implementation strategy.

4.3 Experiments Performed and Their Results

Ford was placed in a series of varied environments containing differing sets of

obstacles to ascertain the proficiency with which this mobile vehicle could locate a

39

Outcome

1 Reached goal

2 Reached goal

3 Stuck oscillating between behaviours

4 Reached goal

5 Reached goal

6 Reached goal

7 Reached goal

8 Reached goal

9 Stuck oscilating between behaviours

10 Reached goal

specific infra red beacon. The operation of the subsumption architecture and the test

conditions provided in the laboratory prevented the vehicle from reaching its goal, as the

obstacle avoidance behaviour did not allow direct contact with the beacon. This failure

indicates one of the limitations of this fixed-level architecture. Because of this problem, an

arrival within 50 cm of the goal was considered a success for this experiment. Appendix H

presents hand plotted maps of the paths the vehicle took to achieve results. Experimental

results have been tabulated as a ratio of successes (when the vehicle found the infra red

sensor) and failures. In the case of failure, the reason for the failure was recorded. Figure

4.6 displays the outcome of ten such experiments.

Figure 4.6

The features of Ford's performance during these experiments deserve further

comment. I have split this commentary into two sections, discussion of the performance of

the vehicle, and discussion of the performance of the architecture.

40

4.3.1 Comments on the Performance of the Vehicle

Two difficulties which arose during the experiments conducted for this chapter

resulted from sensory limitations in the present Marvin vehicles.

a. The major problem with the vehicle was the performance the ultra sound system at

detecting obstacles under certain circumstances. These problems mainly occurred

while the vehicle was following a wall and one of the ultra sound sensors failed to

detect the wall, causing erratic behaviour. Also the obstacle avoidance module had

problems.

b. On some occasions, the infra-red scanner failed to detect a beacon and produce an up

to date beacon heading, and the vehicle remained on course for an old infra-red

beacon reading.

These two problems with the sensory abilities of the Marvin vehicles have reinforced my

own conclusion that a combination of bumper bars and computer vision will improve AI

sensing abilities.

4.3.2 Comments on the Performance of Subsumption

The second and more important commentary concerns the performance of the

subsumption architecture. The specific implementation of this architecture for this project

under the conditions available in the Essex Brooker Laboratory may have contributed to

these problems, but they may also in part be symptomatic of this architecture.

a. The IA V tends to oscillate between behaviours, which hampers the smooth

achievement of goals. I feel this is a general fault with the subsumption architecture.

Brooks added a combination feature to the AFSMs in the second version of the

subsumption architecture (Brooks 1991a), which his reported results suggest may

have alleviated this problem.

b. At some times, I wanted the vehicle to avoid obstacles, but at others, I required it to

make contact with an obstacle. The implementation of sUbsumption architecture

41

includes a hierarchy of behaviours, and I found that I could not achieve the two

differing aims. This deficiency arose because the two lowest level behaviours for

obstacle avoidance prevented the vehicle from making contact with an obstacle. I did

not find a way of reordering the layers in the subsumption architecture.

In spite of these two problems, on most occasions, the architecture enabled the integration

of several different behaviours so that an environment-based goal could be achieved.

4.4 Future Work

In the future I would like to further test subsumption architecture to determine of

rigid hierarchies are an inherent feature of subsumption, or if the architecture can be

modified to include a flexible hierarchy. If the latter is possible, I would like to investigate

possible ways of reordering the behaviours, possibly using the output of a non-linear

planner to determine which behaviours should be used where or when in the hierarchy.

Another possible approach would be to use some form of learning function to ascertain

when differing hierarchies of behaviours should be used.

4.5 Summary

In summary, this chapter has shown an implementation of the sUbsumption

architecture on a Marvin mobile vehicle. Although successful, this implementation has

highlighted some minor problems with the sensory abilities of the departmental vehicles.

The other and more major point is that the software implementation of the subsumption

architecture enabled the vehicle to avoid obstacles and achieve its goal of reaching an

infra red sensor on most occasions. The only unanswered question is whether the

subsumption architecture is capable of reordering behaviours. As I have previously stated,

I would like to investigate this question in the future.

42

Chapter 5

Cornbinina : Subsurnption and Plan-then - Execute lAY Architectures

Con t ents

5.0 Introduction 5.1 Architecture Developed 5.2 Implementation Details

Mixing 'C' with PROLOG Plan-then-Execute Behaviour Topological Path Planner Subsumption Interface With Non-Linear Planner

5.3 Experimental Results 5.4 Future Work 5.5 Summary

5.0 Introduction

Several false starts and whole scale redesigns preceded the development of the

architecture which is the subject of this chapter. The fIrst section of the chapter details my

chain of thought and the decisions that I made in designing the architecture, then

provides an exposition of the fInal architecture, which combines subsumption with a plan-

then-execute architecture. The next two sections describe the implementation of this

architecture and the results of testing that implementation. Along with the tabulated

results, I also have discussed the features of the architecture that I observed,

43

and drawn some limited conclusions about them. The fourth section then outlines the

future work which will be performed on this architecture as part of a British Telecom

research project. A summary of this work rounds out this chapter.

5.1 Architecture Developed

My inspiration for the design of this architecture arose from four observations.

First, I have noted that most IA V architectures currently under development fall into one

of two categories:

a. reaction based and sUbsumption architectures, which can cope with rapidly changing

and uncertain environments, but which have trouble reordering priorities of

behaviours to plan strategies for handling different tasks; and,

b. plan-then-execute architectures, which experience difficulty adjusting to change, but

which appear better suited to tasks that require a sequence of events for completion

(such as finding a topological path to a destination or compiling a plan of operations

to travel from Colchester to London).

Second, I noted that the inputs to the behaviour or planning phases of IA V architectures

also generally fall into two categories:

a. inputs from sensory devices attached to the IA V, such as ultrasound, infra-red

beacons, or vision, (one could loosely characterise this sensory input as data collected

in the vehicle's line of sight, though, in comparison to any form of intelligent

biological being, the IA V's immediate sensory input is extremely limited); and,

b. inputs from maps or internal representations exported by the researcher to the IA V or

compiled by the IA V itself from its environment. This input has taken many differing

forms ranging from geometric, inch by inch descriptions of the environment, to

topological, landmark-based maps. I consider the latter to be the more useful way of

representing an operating environment.

44

Third, the actions that an IA V will likely perform likewise cluster into two categories: a.

well-learned or regularly performed tasks, such as moving around a room without hitting

obstacles, picking up and putting down objects, or turning on a tap; and,

b. unfamiliar or unlearned tasks which require the devisement of a plan, such as going

to an unusual destination or cooking a new recipe.

The 'a' point of the third observation includes the tasks best performed by reactionbased or

subsumption architectures, the 'a' type of architectures which rely on the 'a' type of input.

Similarly, the 'b' point of the third observation includes the tasks best performed by plan-

then-execute architectures, the 'b' type of architectures which rely on the 'b' type of input.

Fourth, I noted that no inherent features of either general set of architectures implied that

the two varieties would not be capable of operating jointly, and thus I strove to develop a

combined architecture which would exhibit the strengths and abilities of both the reaction-

based and plan-then-execute architectures.

Figure 5.1 displays the architecture that I developed. This architecture uses

subsumption as a method of controlling the operation of vehicle and of achieving goals

located in the line of sight of the IA V's sensors. To accomplish tasks requiring

observation of elements of the environment located beyond the vehicle's line of sight, the

architecture uses a non-linear planner working on a topological, landmark-based map to

plan paths to achieve its goals.

The subsumption element of this architecture operates similarly to the architecture

described in the pervious chapter. The major difference in operation is from the addition

of the plan-then-execute element which enables the achievement of goals which are

currently out of the vehicle's line of sight. The plan-then-execute element designs a path

for moving between a series of landmarks to enable the vehicle to achieve its overall

objective. The planner passes the ID number of the fIrst beacon to be reached to the find

beacon module of the architecture. The execute phase of the architecture then monitors the

success or failure of reaching this landmark. If the vehicle passes the landmark, the plan-

the-execute module sends the ID number of the next beacon to be found to the find beacon

module. If the vehicle fails to locate a

45

'3

Find Infra-Red _In

Module S

Obstacle UltraSoun

Avoidance

Sensors

Reflex Module .Bumper

~ Bars

landmark, the plan-then-execute behaviour triggers re-planning which uses an

updated version of the topological map. This process then repeats until the final gaol

is reached. In the experiments described below, the IA V used infra-red beacons as

landmarks.

Stepper Motors

Figure 5.1

Plan then Execute Module 1---------- 1 ____________ 1 Topological: 1 Mapping : 1 rE - - _.J r<- -IoE--- : Planner 1 : Module 1 -----r----I ------ __________1

1

All Sensors

1 1- ___1 ____

. 1

1 xecuuon 1

1 r<----------------~-- : Controller 1 -----r----I

V

All Sensors

~ (Infra Red Beacon Number)

fraRed ensor

d

46

E

S.2 Implementation Details

This section details the testing of the combined architecture. The subsumption

element of the architecture uses a modified copy of the work completed for the previous

chapter. These modifications enabled the subsumption element to interface with plan-

then-execute element. Implementing the plan-then-execute element proved more

problematic. I originally had hoped to incorporate a standard plan-then-execute

architecture, including world modelling, topological planning, and plan execution phases;

however, even after the modification of its ultrasound sensors, Ford still encountered

difficulty detecting basic features of its environment. The limitations of Ford's sensors

and the short time available for this research prevented the full implementation of the

second of these modules. Consequently, in these experiments, Ford used a much diluted

adaptation of the world modelling behaviour, drawing information about the world via a

terminal.

The next four subsections detail the implementation. The first of the subsections

describes my findings on the mixing of the programming languages 'C' and PROLOG.

The following three subsections describe the developments and modifications required for

the subsumption interface, the plan-then-execute module, and the topological planner to

produce the combined architecture.

S.2.1 Mixing 'e' with PROLOG

PROLOG is ideally suited for implementing modules which require the use of

redo abilities to find solutions, functions performed by planners. For this reason, I initially

sought to determine if PROLOG could facilitate all, or at least part, of the implementation

of this architecture. Experiments soon suggested that PROLOG on its own did not permit

the full implementation of the combined architecture. In any mobile vehicle application,

the world is constantly changing. As a result, variables which have been assigned in

PROLOG can suddenly become negated, producing inconsistencies. I then sought to

integrate 'C' with PROLOG. The plan-then-execute element of the final architecture uses

'C' to implement the sense and execute phases,

47

then passes the derived information to a topological planner which uses PROLOG.

The planner assumes the information it receives about the state of the world remains

constant during the development of the plan. The planner then passes its plan on to the

execute phase, which again uses 'C' to respond to changes in the environment.

5.2.2 Plan-then-Execute Behaviour

The plan-then-execute behaviour controls the operation of the non-linear planner,

disseminates output to the subsumption architecture, and monitors the achievement of

these steps. If a goal is not achieved, then re-planning occurs. Figure 5.2 outlines this

module in pseudo code. This section of code was implemented in 'C', and Appendix J contains a listing of the program.

initalise_the_description_oCthe_ world create_plan REPEAT

get_next_step_oCplan pass_next_step_to_find_infra_red_module get_responce_from_terminal

IF infra_red_becon_is_reached THEN modify _ world

ELSE modify _ world create_plan ENDIF

UNTIL no_more_steps_in_plan

Figure 5.2

5.2.3 Topological Path Planner

The topological path planner was written in PROLOG. This program is an

adjusted version of the path planning software in the PROLOG to 'C' interface example

programs provided with SICStus PROLOG. The adjustments to the original program

include code to select the shortest path to a given destination, rather than all paths, and

also a modification to enable the input of a limited amount of world state information

about the temporary or permanent blockage of paths on the topological map. The

48

planner draws topological map information from a series of PROLOG clauses which

describe connected points on a topological map provided by the researcher for an

experiment Figure 5.3 shows an example of both the topological map and the PROLOG

clauses used to represent the map. Appendix K lists the program for the planner.

This translates into the PROLOG clauses listed bellow.

connected( 4,5). connected( 6,5). connected(3,4 ). connected( 4,2). connected( 1,2).

Figure 5.3 49

h.5.2.4 SUbsumption Interface with Topological Planner

The subsumption architecture interfaces with the non-linear planner through the find

infra-red beacon behaviour. This function was modified so it accesses a global variable,

which is the infra-red beacon number the vehicle is currently locating. The plan-the-execute

behaviour described in the next section provides this beacon value.

5.3 Experimental Results

In the experiments, Ford was placed in a series of varied environments containing

differing sets of obstacles to ascertain the proficiency with which this mobile vehicle could

locate and navigate between a series of landmarks to achieve a goal. I provided the

topological map planner with a map of the landmarks. Figure 5.4 displays the topological

map used in all experiments.

Figure 5.4

50

Outcome

I Reached goal

2 Reached goal with re-planning

3 Stuck in dead end with no alternate path

4 Reached goal with re-planning

5 Reached goal with re-planning

6 Reached goal

7 Stuck in dead end with no alternate path

8 Reached goal with re-planning

9 Reached goal with re-planning

10 Stuck in dead end with no alternate path

Various obstacles were placed between the landmarks to test whether the

combination of architectures would enable Ford to reach its goals. The tests were

specifically designed to require Ford to use both the high level planning ability of the

plan-then-execute element, and the ability of the subsumption element to deal with minor

problems without a requirement for step by step planning of a solution.

As with the experiments detailed in Chapter Four, the subsumption architecture

and the test conditions provided in the laboratory prevented the vehicle from directly

reaching its goals, as Ford remained unable to both avoid obstacles while also making

contact with goals. As before, I considered an arrival within 50cm of the goal a success.

Experimental results have been tabulated, giving the outcome of successes with re-

planning, successes without re-planning, or failures. In the case of failure, the reason for

failure is also included. Appendix L presents hand-plotted maps of the paths the vehicle

took to achieve results, and also details the initial goal and operators used by the non-

linear planner in each experiment. Figure 5.5 displays the outcome of ten such

experiments.

Figure 5.5

51

Five features of the performance of this combined architecture deserve further

note. Three of these features point up the strengths of this combined architecture. First,

while using this architecture, Ford successfully achieved goals in 70 of the test situations.

When confronted by an obstacle that could be avoided or circumnavigated, Ford used a

combination of obstacle avoidance and wall following techniques to return to the path for

its goal (see Example 1 in Appendix L). When a temporary diversion proved impossible

and the map provided a second route to the goal, re-planning occurred to find that route

(see Example 2 in Appendix L). Second, the ability of the system to plan and re-plan to

achieve a goal gives the this architecture a far greater level of flexibility in achieving

goals than the subsumption architecture. The additional features enable the vehicle to

locate goals which are outside its line of sight. Third, at least in these experimental

conditions, the topological map appeared to effectively represent the environment using

the position of landmarks without a need for resorting to dead reckoning.

Nevertheless, two features of the experimental results highlight deficiencies with

the present construction of the combined architecture which will require adjustment in

future work. First, as a consequence of the inadequate level of sensory input fed to the

processor and the simplicity of current sensory processing techniques, the vehicle

sometimes got stuck in oscillating behavioural loops when it approached near objects,

causing an unnecessary request for re-planning. The other major effect of the sensory

deficiencies is that the vehicle lacks the ability to detect dead ends and to initi ate re-

planning prior to experiencing entrapment. Second, this combined architecture retains a

deficiency of the subsumption architecture tested in Chapter Four. The subsumption

element prevented the reordering of behaviours to enable the IA V to touch its goals,

while still avoiding obstacles en route.

In summary, the combined architecture exhibited a high degree of success in

enabling Ford to achieve goals. The next section details proposed modifications to the

implementation of this architecture which may enable at least partial rectification of the

two remaining deficiencies.

52

5.4 Future Work

Based on the work completed in this chapter, I see three major areas for future

development. First, the results of the experiments with the combined architecture

reinforce the conclusion in the previous chapter that further testing of subsumption should

be conducted to determine if this architecture can be modified to reorder the network of

behaviours to achieve differing goals, such as avoiding obstacles but touching beacons.

Second, both the sensory devices on the base vehicle and the sensory interpretation

software require improvement. Again, as with the results of the previous chapter, these

experiments further support the need for the use of visual input and vision processing,

along with other more reliable and sophisticated sensory techniques, which would enable

the vehicle to recognise landmarks and create its own topological maps without the aid of

pre-programmed maps. Hopefully, this work may also yield a means for IAVs to more

effectively cope with dead ends.

Third, one can experiment with the use a second plan-then-execute architecture as

a still higher level in the hierarchy to produce lists of goals to achieve tasks such as

delivering mail. Figure 5.6 provides a diagram of such an extended architecture. This

extra plan-then-execute architecture would use a non-linear planner, which would give

this higher level of the hierarchy the power and flexibility to achieve a wide range of

goals. I have already implemented the non-linear planner in PROLOG, but have yet to

upgrade the rest of the architecture with the above-specified modifications. Many, if not

all, of the above detailed improvements will be completed in the next year as part of a

collaborative project with British Telecom.

5.5. Summary

In summary, the experiments detailed in this chapter demonstrate the

compatibility of subsumption and a scaled-down version of a plan-then-execute

architecture. This research holds out the possibility that a combined reaction-based

architecture and a fully implemented plan-then-execute architecture may yield yet more

impressive results. Likewise, these experiments suggest that a combination of the

53

I:J

Find Infra-Red Jnfr

Module Se

Obstacle UltraSound

Avoidance Sensors

~ Reflex Module _Bumper __ ~ Bars

Stepper Motors

Plan then Execute Module 2 1---------- I-----~---~ : Non-Linear ~ __________~ Mappmg :.:_ ...•. __ ---

: Planner: ~ _ ~.?~~l~ _____: -----r---- 1

1 ____________1 ____ • 1

: Execution ~ _______________________...•. __ --- 1 Controller : -----r----

V : (Infra Red Beacon Number) 'iJ

: Plan then Execute Module 1 ____ J_____ I ____________~ 1 1M,

: Topological ~ ________~ appmg :.:_ ...•. __ --- : Planner I : Module : -----r----I ----------

I ____1 ____ . I

: Execution ~ ________________________-loE---

I Controller : -----r---- V

: (Infra Red Beacon Number)

Figure 5.6

aRed nsor

54

All Sensors

All Sensors

All Sensors

All Sensors

1

programming languages 'e' and PROLOG can produce an efficient implementation of a

control system. This combined architecture did enable Ford to find beacons, though these

experiments again highlighted the need for improved sensory capacity in the Marvin

vehicles. The future work which is now being funded by British Telecom may likely

facilitate the improvement of both the IA V hardware and this combined architecture to

allow the IA V to perform a more complex series of tasks.

55

Chapter 6

Conclusions

Contents

6.0 Introduction 6.1 Conclusions About Hardware 6.2 Conclusions About the Implementation of the Architectures 6.3 Closing Notes

6.0 Introduction

In comparison with the constructed intelligent beings of Gene Roddenberry's Star

Trek, Isaac Asimov's Robots and Empire series, or other technologically-oriented science

fiction, the intelligent mobile vehicles of today's laboratories are mere rudimentary toys.

Nevertheless, humans have made a remarkable achievement by creating vehicles which can exercise basic levels of judgement. Biological beings no

longer hold an exclusive monopoly in the field of decision-making. The present research

into robotic architectures, mapping systems, and planners - the modules which empower

an IA V to perform basic but essential levels of reasoning now exercised by (relatively)

simple biological species - is enabling computer scientists to compile a foundation of

knowledge which scientists in the future may be able to use in the

56

creation of beings capable of independently contributing to the theoretical foundations of

technological research.

Rather than addressing these lofty speculative issues, this dissertation has

contributed to the practical discussion of the implementation of programs enabling IA V s

to perform simple behaviours by fIrst reviewing the debates in the ArtifIcial Intelligence

literature concerning IA V architectures, mapping systems, planners, and attention; and

then by reporting the results of tests of three different architectures: a reaction-based-

architecture; subsumption; and a combined subsumption/plan-thenexecute architecture

using a topological non-linear path planner. While the limitations of time and resources

associated with MSc research necessarily restrict the scope of conclusions which may be

drawn from this dissertation, the results of this research does suggest some modest

conclusions about IA V hardware and software.

6.1 Conclusions About Hardware

Two different IA V s served as base vehicles for this project, a small and simple

vehicle designed and produced in the course of this research, and Ford, a Marvin vehicle

developed in the Department of Computer Science at the University of Essex. Observation

of the operation of these two vehicles support three conclusions. The simple vehicle

described in Chapter Three required inexpensive hardware easily purchased on a student

budget for construction. This vehicle successfully roamed the research laboratory. The

research detailed in Chapter Three thus fIrst suggests that vehicles capable of performing

simple tasks may be readily designed and produced at a minimal cost. I assigned a series

of more complex tasks to Ford, and found that the ultrasound and infra-red sensors with

which this vehicle is equipped did not provide suffIcient information about the

environment to enable this IA V to interpret its surroundings under some conditions. The

addition of further ultrasound sensors improved the vehicle's performance, but did not

substantially increase Ford's ability to interpret its environment. The research in both

Chapter Four and Chapter Five suggests the second conclusion that the Marvin vehicles

would operate more efficiently

57

if more comprehensive sensors, such as vision, complemented (if not replaced) the

ultrasound and infra-red sensors. Third, the tests conducted for all three implementation

chapters suggested that speed of movement is not necessarily a highly desirable initial

feature for IA V s at the present time. The small vehicle presently navigates rooms at a

speed slightly excessive for convenient observation. In the case of Ford, the maximum

speed could not be permitted as the vehicle's sensors could not accurately feed

information to the subsumption architecture at a rate to permit efficient movement at such

a speed, and since the non-linear planner could not produce plans for goal achievement at

a rate to permit the use of the higher potential speeds.

6.2 Conclusions About the Implementation of the Architectures

The implementation chapters review tests of a simple reaction-based architecture,

then of subsumption, and finally of a combined architecture designed for this project.

Each subsequent architecture facilitated more complex behaviours than its predecessor.

The results of each chapter facilitate little direct comparison, as the base vehicle for the

first implementation differed entirely from the vehicle working with the other two

architectures, and as I implemented each architecture under different test conditions. For

this reason, this section details the general conclusions about the implementation of these

architectures individually.

Chapter Three shows how a simple mobile vehicle with a reactive control

architecture can successfully perform a single task, such as obstacle avoidance.

Nevertheless, the vehicle did not enjoy an unproblematic performance. The reactionbased

architecture developed for this vehicle occasionally restricted this IA V to oscillating

between states, though, as noted in this chapter, the use of an internal state in the

architecture may possibly cure this problem. A second and more significant limitation of

this architecture is the presence of only one layer of control. If the vehicle avoids

obstacles, it cannot perform other behaviours, such as achieving a goal, at the same time.

For this reason, I am one of many researchers who has experimented with other possible

architectures.

58

---------------------------------- ------- ----------

The most sweeping claims made about any architecture appear in both the

academic and popular literature discussing subsumption, an architecture developed by

Rodney Brooks and his team at M.LT. For this reason, Chapter Four reports on the results

of the retesting of the suitability of this architecture for achieving a simple goal, locat ing

infra-red beacons. Generally, this architecture did produce successful results, however, the

findings of the work conducted for this report do not support the entire range of abilities

attributed to subsumption. Some difficulties likewise developed during the

implementation of this architecture. First, some arrangements of dead ends ensnared Ford

into oscillating between behaviours. Second, and in my opinion more importantly, I found

that I could not achieve two different aims using this architecture. In future experiments, I

plan to continue to test subsumption to see if a modification can be found which would

introduce some flexibility into the hierarchy of behaviours required by this architecture. In

spite of these two problems, on most occasions, the architecture enabled the integration of

several different behaviours so that Ford could achieve an environmentally-based goal.

The structure of IA V architectures presently tend to fall into two distinct

categories, as do the primary inputs on which the architectures rely and the types of

actions that an IA V will likely perform. The fIrst category, reaction-based architectures,

tend to use immediate sensory information to perform routine tasks. The second category,

plan-based architectures, use predefined maps or maps generated during an IA V's

exploration of its environment to plan strategies for achieving unfamiliar tasks. Chapter

Five details the implementation of an architecture combining subsumption with a

simplified plan-then-execute architecture using a non-linear planner which I designed to

attempt to capitalise on the strengths of both categories of architectures and to attempt to

mitigate the weaknesses of each. I noted the following five significant observations during

the testing of this combined architecture:

59

a. The architecture successfully achieved goals in 70 of the test situations; b. The ability of this system to plan and re-plan paths for task achievement permitted a

higher level of flexibility than the subsumption architecture implemented on its own. c. Ford successfully located landmarks in the environment using a topological map. d. The limited level of sensory input received by this Marvin vehicle caused problems for

the architecture. e. The programming languages 'C' and PROLOG can be effectively interfaced to enable

the architecture to respond to change, facilitated by 'C', and to plan and re-plan paths to goal achievement, facilitated by PROLOG.

6.3 Closing Notes

While human imaginations may well continue to outstrip our technological

capacity, computer scientists have nonetheless made remarkable achievements in the

study of artificial intelligence over the last few years. The research reported in this

document generally supports the conclusion that the improvement of the flexibility of IA

V architectures may well expand the capacity of thinking vehicles for task performance.

This dissertation has also highlighted the existence of room for combining the features of

the major trends in IA V research. While the evolution of thinking vehicles will not

necessarily parallel biological evolution (Taylor 1994), the process of IA V evolution has

undoubtedly only just begun.

60

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