Planning Algorithms

by Steven M. LaValle

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.

Genres: Engineering, Artificial Intelligence, Computer Science, Technology, Textbooks, Computation, Programming

837 pages, Kindle Edition

First published May 30, 2002

Reviews

[Kostiantyn Shevchenko · Rating 5 out of 5]

This is an original textbook-grade course gathering algorithms, techniques and math related to all aspects of planning useful in wide variety of applied engineering and computer science fields.
My personal focus was also on Chapter 5, which serves as a concise and effective introduction to low-discrepancy sampling theory and features results immediately useful particularly for low-dimensional hyperparameter optimization and quasi-Monte Carlo integration.

[Andrew]

An omnibus book. The first chapter is a lot of fun because it motivates the subject well from both a pure and applied standpoint -- these are problems that are both interesting in their own right and useful. And with the material covered, it's sort of amazing how much we can do in the field -- there's so much that goes into solving the most fundamental questions, like localizing a robot.

The sources that I recognized at the end of chapters were generally good, so I feel comfortable drawing recommendations for further reading from the sources I didn't.