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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