Planning Algorithms

Erscheinungsjahr 2006
ISBN: 978-0-521-86205-9
Verlag: Cambridge University Press

Buch, Englisch, 842 Seiten, Format (B × H): 183 mm x 260 mm, Gewicht: 1751 g

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.

Autoren/Hrsg.

LaValle, Steven M.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Informatik Mathematik für Informatiker

Inhaltsverzeichnis

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.

Über Autor(innen)

LaValle, Steven M.
Steven M. LaValle is Associate Professor of Computer Science at the University of Illinois at Urbana-Champaign. He has worked in motion planning and robotics for over a decade and is a leading researcher who has published dozens of articles in the field. He is the main developer of the Rapidly-exploring Random Tree (RRT) algorithm, which has been used in numerous research labs and industrial products around the world. He has taught material on which the book is based at Stanford University, Iowa State University, the Tec de Monterrey, and the University of Illinois.