Principles of Robot Motion: Theory, Algorithms, and Implementations (Intelligent Robotics and Autonomous Agents) reflects the great advances that have taken place in the last ten years, including sensor-based planning, probabalistic planning, localization and mapping, and motion planning for dynamic and nonholonomic systems. Its presentation makes the mathematical underpinnings of robot motion accessible to students of computer science and engineering, relating low-level implementation details to high-level algorithmic concepts. New mathematical concepts are introduced in an intuitive manner on an as-needed basis.
Hardcover, 560 pages
- Chapters 2-6 treat geometric motion planning approaches
- Chapter 7 covers probabilistic methods for geometric planning
- Chapters 8-9 cover probabilistic methods mainly for localization
- Chapters 10-12 cover dynamic mechanical systems
- Graph Theory
About the Authors
The broad range of robot motion applications is reflected in the broad spectrum of expertise represented by the seven co-authors, each of whom worked on all of the twelve chapters to create a fully integrated text:
- Howie Choset is Associate Professor in the Robotics Institute at Carnegie Mellon University.
- Kevin M. Lynch is Associate Professor in the Mechanical Engineering Department, Northwestern University.
- Seth Hutchinson is Professor in the Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign.
- George Kantor is Project Scientist in the Center for the Foundations of Robotics, Robotics Institute, Carnegie Mellon University.
- Wolfram Burgard is Associate Professor and Head of the Autonomous Intelligent Systems Research Laboratory in the Department of Computer Science at the University of Freiburg.
- Lydia E. Kavraki is Professor of Computer Science and Bioengineering, Rice University.
- Sebastian Thrun is Associate Professor in the Computer Science Department at Stanford University and Director of the Stanford Artificial Intelligence Laboratory.