Planning Algorithms by Steven M. LaValle, The text is written primarily for computer science and engineering students at the advanced-undergraduate or beginning-graduate level. it's also meant as an introduction to recent techniques for researchers and developers in robotics, artificial intelligence, and control theory. it's expected that the presentation here would be of interest to those operating in different areas like computational biology (drug design, protein folding), virtual prototyping, manufacturing, video game development, and computer graphics.


I Introductory Material

1 Introduction
2 Discrete Planning

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

III Decision-Theoretic Planning

9 Basic Decision Theory
10 Sequential Decision Theory
11 Sensors and Information Spaces
12 Planning Under Sensing Uncertainty

IV Planning Under Differential Constraints

13 Differential Models
14 Sampling-Based Planning Under Differential Constraints
15 System Theory and Analytical Techniques