Computational Principles of Mobile Robotics

Overview

"This textbook for advanced undergraduates and graduate students emphasizes computation and algorithms for a range of strategies for locomotion, sensing, and reasoning. It concentrates on wheeled and legged mobile robots but also discusses a variety of other propulsion systems. The new edition presents advances in robotics and intelligent machines over the last 10 years, including significant coverage of SLAM (simultaneous localization and mapping) and multi-robot systems. It includes additional mathematical background and an extensive list of

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Former Library book. Shows some signs of wear, and may have some markings on the inside. 100% Money Back Guarantee. Shipped to over one million happy customers. Your purchase ... benefits world literacy! Read more Show Less

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Computational Principles of Mobile Robotics

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Overview

"This textbook for advanced undergraduates and graduate students emphasizes computation and algorithms for a range of strategies for locomotion, sensing, and reasoning. It concentrates on wheeled and legged mobile robots but also discusses a variety of other propulsion systems. The new edition presents advances in robotics and intelligent machines over the last 10 years, including significant coverage of SLAM (simultaneous localization and mapping) and multi-robot systems. It includes additional mathematical background and an extensive list of sample problems. Various mathematical techniques that were assumed in the first edition are now briefly introduced in appendices at the end of the text to make the book more self-contained"-- Provided by publisher.

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Editorial Reviews

Booknews
Canadian computer scientists Dudek (McGill U.) and Jenkin (York U.) examine how existing autonomous robot systems have approached the three tasks of moving, sensing, and reasoning out their environment. They emphasize the computational methods of programming robotics rather than the methods for constructing the hardware. Intended for graduate and advanced undergraduates in robotics. Annotation c. Book News, Inc., Portland, OR (booknews.com)
From the Publisher
"This book is an indispensable tool for any—both pre-university and university—course on mobile robotics. In relation to the first edition, this current one has been sufficiently updated. I recommend this book to researchers—particularly those who study localization or mapping—and doctoral students who are interested in investigating the latest approaches and techniques in the mobile robotics field."
RAMON GONZALEZ SANCHEZ, Computing Reviews

"This work (1st. ed., CH, Nov'00, 38-1584) became an immediate resource for this reviewer for both research and teaching purposes. In the multidisciplinary area of robotics, it is not uncommon to find textbooks that describe the algorithms involved in robotic control, perception, planning, etc., but fail to provide the reader with the critical details necessary for implementation. For the majority of topics, Dudek (McGill Univ., Canada) and Jenkin (York Univ., Canada) present the necessary math to perform the computation and in some cases also provide pseudocode. References are included throughout. The text really shines in its handling of algorithms for perception, sensor fusion, and path planning. The appendixes review probability, statistics, linear systems, filters, and Markov models, but not with enough depth to provide an introduction to someone new to any one of these topics. This work is a bit too advanced for an introductory course on robotics, but it would be a great resource for an intermediate or advanced course on mobile robotics."
R.S. STANSBURY, Embry-Riddle Aeronautical University

"...a great resource for an intermediate or advanced course on mobile robotics." CHOICE

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Product Details

  • ISBN-13: 9780521560214
  • Publisher: Cambridge University Press
  • Publication date: 2/28/2000
  • Pages: 294
  • Product dimensions: 6.97 (w) x 9.96 (h) x 0.91 (d)

Meet the Author

Gregory Dudek is Professor of Computer Science and Director of the School of Computer Science at McGill University. He holds a James McGill Chair and is a member of the Center for Intelligent Machines, and has been co-author of over 150 refereed publications on robotics and computer vision.

Michael Jenkin is a Professor of Computer Science and Engineering at York University. He has co-edited a series of eight books on human and machine vision.

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Table of Contents

Acknowledgments

Preface to the Second Edition

1 Overview and Motivation 1

1.1 From Mechanisms to Computation 4

1.2 Historical Context 5

1.3 Biological Inspiration 11

1.4 Operational Regimes 11

1.5 Operational Modes 11

1.6 A Guide to This Book 12

1.7 Further Reading 13

1.8 Problems 16

2 Fundamental Problems 18

2.1 Path Planning for a Point Robot 19

2.2 Localization for a Point Robot 21

2.3 Sensing for a Point Robot 23

2.4 Mapping for a Point Robot 25

2.5 SLAM for a Point Robot 25

2.6 Looking Forward 26

2.7 Further Reading 27

2.8 Problems 27

Part One Locomotion and Perception 29

3 Mobile Robot Hardware 31

3.1 Locomotion 31

3.2 Off-Board Communication 71

3.3 Processing 75

3.4 Further Reading 76

3.5 Problems 77

4 Non-Visual Sensors and Algorithms 82

4.1 Basic Concepts 82

4.2 Contact Sensors: Bumpers 86

4.3 Inertial Sensors 87

4.4 Infrared Sensors 90

4.5 Sonar 91

4.6 Radar 98

4.7 Laser Rangefinders 98

4.8 Satellite-Based Positioning 100

4.9 Data Fusion 102

4.10 Biological Sensing 118

4.11 Further Reading 120

4.12 Problems 121

5 Visual Sensors and Algorithms 123

5.1 Visual Sensors 124

5.2 Object Appearance and Shading 131

5.3 Signals and Sampling 132

5.4 Image Features and Their Combination 134

5.5 Obtaining Depth 149

5.6 Active Vision 155

5.7 Other Sensors 158

5.8 Biological Vision 162

5.9 Further Reading 163

5.10 Problems 164

Part Two Representation and Planning 165

6 Representing and Reasoning About Space 167

6.1 Representing Space 167

6.2 Representing the Robot 176

6.3 Path Planning for Mobile Robots 179

6.4 Planning for Multiple Robots 208

6.5 Biological Mapping 209

6.6 Further Reading 210

6.7 Problems 210

7 System Control 212

7.1 Horizontal Decomposition 213

7.2 Vertical Decomposition 217

7.3 Hybrid Control Architectures 223

7.4 Middleware 226

7.5 High-Level Control 226

7.6 Alternative Control Formalisms 230

7.7 The Human-Robot Interface 235

7.8 Mobile Robot Software Development as Experimentation 237

7.9 Standard Software Toolkits 237

7.10 Further Reading 238

7.11 Problems 239

8 Pose Maintenance and Localization 240

8.1 Simple Landmark Measurement 241

8.2 Servo Control 249

8.3 Recursive Filtering 250

8.4 Non-Geometric Methods: Perceptual Structure 260

8.5 Correlation-Based Localization 267

8.6 Global Localization 267

8.7 Biological Approaches to Localization 273

8.8 Further Reading 274

8.9 Problems 274

9 Mapping and Related Tasks 276

9.1 Sensorial Maps 278

9.2 Geometric Maps 279

9.3 Topological Maps 287

9.4 Exploration 291

9.5 Further Reading 294

9.6 Problems 294

10 Robot Collectives 295

10.1 Categorizing Collectives 296

10.2 Control Architectures 296

10.3 Collective Communication 299

10.4 Sensing 300

10.5 Planning for Action 301

10.6 Formation Control 302

10.7 Localization 303

10.8 Mapping 304

10.9 Further Reading 305

10.10 Problems 306

11 Robots in Practice 307

11.1 Delivery Robots 307

11.2 Intelligent Vehicles 309

11.3 Robots for Survey and Inspection 314

11.4 Mining Automation 316

11.5 Space Robotics 317

11.6 Autonomous Aircraft 319

11.7 Military Reconnaissance 320

11.8 Bomb/Mine Disposal 320

11.9 Underwater Inspection 322

11.10 Agriculture/Forestry 323

11.11 Aids for the Disabled 325

11.12 Entertainment 326

11.13 Domestic Robots 327

11.14 Further Reading 327

11.15 Problems 328

12 The Future of Mobile Robotics 329

12.1 Locomotion 329

12.2 Sensors 331

12.3 Control 332

12.4 System Integration 332

12.5 Standardization 333

12.6 Future Directions 333

Appendix A Probability and Statistics 335

A.1 Probability 335

A.2 Some Simple Statistics 338

A.3 Further Reading 339

A.4 Problems 339

Appendix B Linear Systems, Matrices, and Filtering 341

B.1 Linear Algebra 341

B.2 Matrix Algebra 341

B.3 Signals and Systems 343

B.4 Fourier Theory 344

B.5 Sampling and the Nyquist Theorem 344

B.6 Further Reading 345

B.7 Problems 345

Appendix C Markov Models 346

C.1 Discrete Markov Process 346

C.2 Hidden Markov Models 348

C.3 Markov Decision Process 349

C.4 POMDP 350

C.5 Further Reading 351

C.6 Problems 351

Bibliography 353

Index 381

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