Computational Principles of Mobile Robotics

An advanced undergraduate/graduate text, emphasizing computation and algorithms for locomotion, sensing, and reasoning in mobile robots.

Gregory Dudek (Author), Michael Jenkin (Author)

9780521692120, Cambridge University Press

Paperback, published 26 July 2010

406 pages, 243 b/w illus. 6 tables 96 exercises
25.1 x 17.8 x 2.3 cm, 0.7 kg

'This book is an indispensable tool for any - both pre-university and university - course on mobile robotics. It covers the main topics related to any mobile robotic system, including hardware (locomotion and communication), sensors, navigation, path planning, mapping, and localization.' Ramon Gonzalez Sanchez, University of Almeria

This textbook for advanced undergraduates and graduate students emphasizes algorithms for a range of strategies for locomotion, sensing, and reasoning. It concentrates on wheeled and legged mobile robots but discusses a variety of other propulsion systems. This edition includes advances in robotics and intelligent machines over the ten years prior to publication, 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. Researchers as well as students in the field of mobile robotics will appreciate this comprehensive treatment of state-of-the-art methods and key technologies.

1. Overview and motivation
2. Fundamental problems
Part I. Locomotion and Perception: 3. Mobile robot hardware
4. Non-visual sensors and algorithms
5. Visual sensors and algorithms
Part II. Representation and Planning: 6. Representing and reasoning about space
7. System control
8. Pose maintenance and localization
9. Mapping and related tasks
10. Robot collectives
11. Robots in practice
12. The future of mobile robotics
Appendix A. Probability and statistics
Appendix B. Linear systems, matrices and filtering
Appendix C. Markov models.

Subject Areas: Artificial intelligence [UYQ], Robotics [TJFM1]