UNIVERSITI PUTRA MALAYSIA

WERIA KHAKSAR

FK 2013 13

A HYBRID SAMPLING-BASED PATH PLANNING ALGORITHM FOR MOBILE ROBOT NAVIGATION IN UNKNOWN ENVIRONMENTS

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A HYBRID SAMPLING-BASED PATH PLANNING ALGORITHM FOR

MOBILE ROBOT NAVIGATION IN UNKNOWN ENVIRONMENTS

By

WERIA KHAKSAR

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

in Fulfillment of the Requirements for the Degree of Doctor of Philosophy

January 2013

DEDICATION

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This research is dedicated to my lovely wife, Shahedeh, for her moral support and

encouragement. Without her, I could not even start my study.

I also want to dedicate this thesis to my father, mother and brothers for their

supports.

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

of the requirement for the degree of Doctor of Philosophy

A HYBRID SAMPLING-BASED PATH PLANNING ALGORITHM FOR

MOBILE ROBOT NAVIGATION IN UNKNOWN ENVIRONMENTS

By

WERIA KHAKSAR

January 2013

Chairman: Associate Professor Tang Sai Hong, PhD

Faculty: Engineering

The motion planning problem poses the question of how a robot can move from an

initial to a final position. Sampling-based motion planning is a class of randomized

path planning algorithms with proven completeness. These algorithms generate

paths using random numbers and perform efficiently in guiding the robot in known

environments. There are a variety of algorithms in this class with different

objectives, advantages and drawbacks. The existing drawbacks of current sampling-

based planners can be categorized into five categories including non-optimality of

the resulted paths, instability of the results in different runs of the planner, high

running time requirements in some problems, failure in difficult environments such

as narrow passages and Bug traps, and inability to plan in unknown environments.

Although several extensions of the sampling-based algorithms have been proposed

for solving each drawback, but the lack of a randomized planner that overcomes all

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abovementioned inefficiencies in a single package is evident and makes the

sampling-based path planning less effective for certain purposes.

In this research, a sampling-based path planning algorithm was proposed which

employs several heuristic and intelligent techniques to improve the performance of

the planner in terms of quality of the resulted paths, runtimes of the planner, stability

of the results in different executions, ability to solve difficult problems effectively

and capability of planning in unknown environment.

First, a sensor-based path planner was designed which incorporates the heuristic

rules of tabu search technique to handle uncertainty and lack of information about

the environment and to prevent trapping in local minima which is quite common in

online planning. This planner considers the points on the vision range of the robot’s

sensory system as the sampling area and uses the tabu search rules to evaluate the

generated samples and select the most promising ones.

Secondly, a fuzzy logic controller (FLC) was constructed for evaluating the

generated samples in order to make the planner behavior close to the human manner

and solve the planning queries in difficult environments.

Afterward, a genetic algorithm-based optimization framework was designed to

improve the interpretability and accuracy of the proposed fuzzy-tabu controller by

optimizing the parameters of the FLC and also some of the planner’s parameters in

order to improve the quality of the generated paths and runtimes of the planner and

also to decrease the variation of the results in different runs of the planner. The

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genetic optimizer also evaluates the fuzzy rules and selects those rules that directly

affect the performance of the planner and ignores irrelevant and erroneous fuzzy

rules.

Finally, an adaptive neuro-fuzzy inference system (ANFIS) was designed which

constructs and optimizes a fuzzy logic controller using a given dataset of

input/output variables in order to increase the optimality and stability rates of the

proposed path planning algorithm. The simulation and comparison results indicate

the superiority of the proposed algorithm. The proposed Tabu-based path planner

successfully guides the robot in unknown environments without trapping in

encountered local minima. The designed fuzzy-Tabu controller effectively solves the

path planning queries in difficult environments like mazes, narrow passages and Bug

traps without any failure. After optimizing the proposed fuzzy model by means of

genetic algorithm, the resulted planner produces shorter paths in shorter runtimes

with limited variations in results of different runs of the planner. Finally, the

proposed ANFIS-generated FLC successfully improves the optimality and stability

of the proposed planner. The average runtime was less than 4 seconds while the

optimality of the generated paths was more than 95% with less than 0.1 standard

deviations for path length and runtime.

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Abstrak tesis yang dikemukakan kepada Senat Universiti Putra Malaysia sebagai

memenuhi keperluan untuk ijazah Doktor Falsafah

ALGORITMA PERANCANGAN PATH HIBRID BERASASKAN

PENSAMPELAN UNTUK ROBOT MOBILE NAVIGATION DALAM

PERSEKITARAN TIDAK DIKETAHUI

Oleh

WERIA KHAKSAR

January 2013

Pengerusi: Tang Sai Hong, PhD

Fakulti: Kejuruteraan

Masalah perancangan gerakan menimbulkan persoalan bagaimana robot boleh

bergerak dari awal untuk kedudukan akhir. Berasaskan Persampelan perancangan

gerakan adalah kelas algoritma perancangan laluan rawak dengan kesempurnaan

terbukti. Algoritma ini menjana laluan menggunakan nombor rawak dan

melaksanakan cekap dalam membimbing robot dalam persekitaran yang dikenali.

Terdapat pelbagai algoritma di dalam kelas ini dengan objektif kelebihan, dan

kelemahan yang berbeza. Kelemahan yang sedia ada perancang berasaskan

persampelan semasa boleh dikategorikan kepada lima kategori termasuk bukan

optimaliti-laluan menyebabkan ketidakstabilan keputusan dalam berjalan berbeza

perancang, tinggi keperluan masa berjalan dalam beberapa masalah, kegagalan

dalam persekitaran yang sukar seperti yang sempit petikan dan perangkap Bug, dan

ketidakupayaan untuk merancang dalam persekitaran yang tidak diketahui.

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Walaupun sambungan beberapa algoritma berasaskan persampelan telah

dicadangkan untuk menyelesaikan setiap kelemahan, tetapi kekurangan perancang

rawak yang mengatasi semua ketidakcekapan tersebut di atas dalam pakej tunggal

adalah jelas dan membuat perancangan pensampelan berasaskan jalan kurang

berkesan untuk tujuan tertentu.

Dalam kajian ini, kami mencadangkan jalan perancangan berasaskan pensampelan

algoritma yang menggunakan heuristik dan beberapa teknik pintar untuk

meningkatkan prestasi perancang dari segi kualiti laluan menyebabkan, runtimes

perancang, kestabilan keputusan hukuman yang berbeza, keupayaan untuk

menyelesaikan masalah yang sukar berkesan dan keupayaan perancangan dalam

persekitaran yang tidak diketahui.

Pertama, seorang perancang laluan berasaskan penderia telah direka yang

menggabungkan kaedah-kaedah yang heuristik teknik gelintaran tabu untuk

menangani ketidaktentuan dan kekurangan maklumat tentang alam sekitar dan untuk

mengelakkan memerangkap di minima tempatan yang agak biasa dalam

perancangan dalam talian. Perancang ini menganggap mata pada julat penglihatan

sistem deria robot sebagai kawasan persampelan dan menggunakan peraturan

gelintaran tabu untuk menilai sampel yang dijana dan pilih yang paling cerah.

Kedua, pengawal logik kabur (FLC) telah dibina untuk menilai sampel dijana untuk

membuat tingkah laku perancang berhampiran dengan cara manusia dan

menyelesaikan pertanyaan perancangan dalam persekitaran yang sukar.

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Selepas itu, satu genetik-berasaskan algoritma pengoptimuman rangka kerja telah

direka untuk memperbaiki yang interpretability dan ketepatan yang pengawal yang

dicadangkan kabur-tabu dengan mengoptimumkan parameter FLC itu dan juga

beberapa parameter jururancang untuk meningkatkan kualiti di laluan runtimes dan

dijana perancang dan juga untuk mengurangkan perubahan keputusan dalam

berjalan berbeza perancang. Pengoptimasi genetik juga menilai peraturan kabur dan

memilih kaedah-kaedah yang secara langsung menjejaskan prestasi perancang dan

mengabaikan peraturan kabur tidak relevan dan salah.

Akhirnya, penyesuaian kabur neuro inferens sistem (ANFIS) telah direka yang

membina dan mengoptimumkan pengawal logik kabur menggunakan dataset

diberikan pembolehubah input / output untuk meningkatkan kadar optimaliti dan

kestabilan algoritma perancangan laluan yang dicadangkan.

Keputusan simulasi dan perbandingan menunjukkan keunggulan algoritma

dicadangkan. Perancang laluan yang dicadangkan Tabu berasaskan berjaya

membimbing robot dalam persekitaran yang tidak diketahui tanpa memerangkap

dihadapi minima tempatan. Yang direka kabur-Tabu pengawal berkesan

menyelesaikan laluan merancang pertanyaan dalam persekitaran yang sukar seperti

mazes, laluan sempit dan perangkap Bug tanpa sebarang kegagalan. Selepas

mengoptimumkan model kabur yang dicadangkan melalui algoritma genetik,

perancang menyebabkan menghasilkan laluan yang lebih pendek dalam runtimes

pendek dengan variasi yang terhad dalam keputusan berjalan yang berbeza

perancang. Akhirnya, cadangan ANFIS dijana FLC berjaya meningkatkan optimaliti

dan kestabilan perancang yang dicadangkan. Runtime purata adalah kurang dari 4

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saat manakala keoptimum laluan yang dijana adalah lebih daripada 95% dengan

kurang daripada 0.1 sisihan piawai bagi panjang jalan dan runtime.

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ACKNOWLEDGEMENTS

I would like to thank Assoc. Prof. Dr. Tang Sai Hong, my supervisor, for the

professional, scientific, and personal guidance he has generously given me through

this long journey.

My gratitude also goes to the member of my supervisory committee, Prof. Napsiah

BT. Ismail and Assoc. Prof. Dr. Mohd Khairol Anuar B. Mohd Ariffin for their

expertise and important contributions, and useful suggestions and helpful comments

and personal mentor.

I also wish to express my appreciation to the memebrs of the Functional Devices

Laboratory (FDL) at institute of advanced technology (ITMA) Assoc. Prof. Dr.

Mohd Nizar Hamidon, Mrs. Juraina Md Yusof, Mr. Mohd. Wafi Azimin, and Mr.

Md. Ali Rani for their cooperation and supports during the last three years.

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APPROVAL SHEET 1

I certify that an Examination Committee has met on date of viva voce to conduct the

final examination of name of student on his (or her) degree thesis entitled "Title of

thesis" in accordance with Universiti Pertanian Malaysia (Higher Degree) Act 1980

and Universiti Pertanian Malaysia (Higher Degree) Regulations 1981. The

Committee recommends that the student be awarded the (Name of relevant degree).

Members of the Examination Committee were as follows:

Rosnah bt. Mohd. Yusuff, PhD

Professor

Faculty of Engineering

Universiti Putra Malaysia

(Chairman)

Shamsuddin b. Sulaiman, PhD

Professor

Faculty of Engineering

Universiti Putra Malaysia

(Internal Examiner)

B.T. Hang Tuah b. Baharudin, PhD

Senior Lecturer

Faculty of Engineering

Universiti Putra Malaysia

(Internal Examiner)

Marcelo H. Ang Jr., PhD

Associate Professor

Department of Mechanical Engineering

National University of Singapore

Singapore

(External Examiner)

SEOW HENG FONG,

PhD

Professor and Deputy Dean

School of Graduate Studies

Universiti Putra Malaysia

Date: 12-March-2013

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

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

The members of the Supervisory Committee were as follows:

Tang Sai Hong, PhD

Associate Professor

Faculty of Engineering

Universiti Putra Malaysia

(Chairman)

Napsiah BT. Ismail, PhD

Professor

Faculty of Engineering

Universiti Putra Malaysia

(Member)

Mohd Khairol Anuar b. Mohd Ariffin, PhD

Associate Professor

Faculty of Engineering

Universiti Putra Malaysia

(Member)

BUJANG BIN KIM HUAT,

PhD

Professor and Dean

School of Graduate Studies

Universiti Putra Malaysia

Date: 30 April 2013

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I declare that the thesis is my original work except for quotations and citations

which have been duly acknowledged. I also declare that it has not been previously,

and is not concurrently, submitted for any other degree at Universiti Putra Malaysia

or at any other institution.

(Signature)

WERIA KHAKSAR

Date:18 January 2013

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TABLE OF CONTENTS

Page

DEDICATION ii

ABSTRACT iii

ABSTRAK vi

ACKNOWLEDGEMENTS x

APPROVAL xi

DECLARATION xiii

LIST OF TABLES xvii

LIST OF FIGURES xix

CHAPTER

1 INTRODUCTION

1.1 Background of the Study 1

1.2 Problem Statement 9

1.3 Research Objectives 11

1.4 Scope of the Study 12

1.5 Thesis Outline

15

2 LITERATURE REVIEW

2.1 Introduction to Robotics 17

2.2 Robotics Motion Planning 29

2.3 The Complexity of Motion Planning 31

2.4 Motion Planning Algorithms 34

2.5 Robot Navigation in Unknown Environments 57

2.6 Sampling-Based Motion Planning 61

2.6.1 Roadmap-Based Algorithms 62

2.6.2 Tree-Based Algorithms 66

2.6.3 Current Issues in Sampling-Based Algorithms 69

2.7 Summary

81

3 RESEARCH METHODOLOGY

3.1 The Overview of the research Methodology 83

3.2 Evaluation Methods 87

3.2.1 Programming Software 88

3.2.2 Test Environments 91

3.2.3 Comparing with Optimum Solution 97

3.2.4 Comparing with Other Algorithms 99

3.3 Planning in Unknown Environments in Low Running Time 100

3.4 Planning in Difficult Environments 102

3.5 Improving Optimality and Stability 103

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3.5.1 Genetic Algorithm-Based Optimization Framework 103

3.5.2 Adaptive Neuro-Fuzzy Inference System 104

3.6 Summary of the Research Methodology

105

4 RESULTS AND DISCUSSION

4.1 Sampling-Based Tabu Search 107

4.1.1 Sampling Procedure 109

4.1.2 Tabu Search Procedure 111

4.1.2.1 Local Search 112

4.1.2.2 Intensification 116

4.1.2.3 Diversification 118

4.1.3 The Proposed Planner Outline 121

4.1.4 Simulation Studies 122

4.1.5 Summary of the Proposed Planner 138

4.2 Fuzzy-Tabu Controller 140

4.2.1 Construction of the Fuzzy Controller 141

4.2.2 Fuzzy Rules 146

4.2.3 The Proposed Planner Outline 149

4.2.4 Simulation Studies 152

4.2.5 Fuzzy Variables Analysis 159

4.2.6 Summary of the Proposed fuzzy-Tabu Controller 165

4.3 Genetic Algorithm-Based Optimization Framework 167

4.3.1 Genetic Optimization Procedure 167

4.3.2 Tuning the Fuzzy Membership Functions 168

4.3.3 Rule Selection 170

4.3.4 Path Planner Parameters 171

4.3.5 The Fitness Functions 172

4.3.6 Simulation Studies 178

4.3.7 Summary of the Genetic-Based Optimized FLC 185

4.4 Adaptive Neuro-Fuzzy Inference System (ANFIS) 185

4.4.1 Characteristics of the Proposed ANFIS 185

4.4.2 Dataset Generation 188

4.4.3 Simulation Studies 198

4.4.4 Summary of the Proposed ANFIS 203

4.5 Comparison Studies 205

4.6 Improvement Progress of the Proposed Algorithms 212

4.7 Summary of the Results

221

5 CONCLUSION AND RECOMMENDATION

5.1 Conclusion 228

5.2 Thesis Contribution 227

5.3 Recommendations

229

REFERENCES 231

APPENDICES

A Original Algorithms of the used planners in comparison studies 247

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B Optimum Solutions in Test Environments 257

C Simulation Results for Other Studied Algorithms in Test

Environments

260

D Simulation Results for the Proposed Algorithms in Test

Environments

287

BIODATA OF STUDENT 320

LIST OF PUBLICATIONS 321