Planning Algorithms

A coherent source for applications including robotics, computational biology, graphics, manufacturing, aerospace applications and medicine.

Steven M. LaValle (Author)

9780521862059, Cambridge University Press

Hardback, published 29 May 2006

844 pages, 304 exercises
26.2 x 18.2 x 4.3 cm, 1.59 kg

' … this book really is monumental and well-written piece of work, and although few will have cause to read more than a fraction of its content, at its price it deserves to find its way onto the bookshelves of many of us, as well as being recommended to our students.' ScienceDirect

Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning, but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the 'configuration spaces' of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. This text and reference is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

Part I. Introductory Material: 1. Introduction
2. Discrete planning
Part II. Motion Planning: 3. Geometric representations and transformations
4. The configuration space
5. Sampling-based motion planning
6. Combinatorial motion planning
7. Extensions of basic motion planning
8. Feedback motion planning
Part III. Decision-Theoretic Planning: 9. Basic decision theory
10. Sequential decision theory
11. Information spaces
12. Planning under sensing uncertainty
Part IV. Planning Under Differential Constraints: 13. Differential models
14. Sampling-based planning under differential constraints
15. System theory and analytical techniques.

Subject Areas: Systems analysis & design [UYD], Algorithms & data structures [UMB], Graphical & digital media applications [UG]