Planning Algorithms

by Steven M. LaValle

In mathematics, logic, computer science and related disciplines, an algorithm (from Greek and Latin, dixit algorithmus and this in turn from the Persian mathematician Al-Juarismi1) is a prewritten set of well-defined, ordered and finite instructions or rules that It allows an activity to be carried out by means of successive steps that do not generate doubts to those who must perform said activity.2 Given an initial state and an entry, following the successive steps, a final state is reached and a solution is obtained. Algorithms are the object of study of the algorithm.

Written for developers and engineers with interests in artificial intelligence, robotics, or control theory, this is the only book in its category that integrates resources from numerous sources to teach everything related to computational biology, digital graphics, manufacturing, aerospace applications and medicine.

One of the classic manuals in Computer Science edited by the prestigious University of Cambridge.

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

1447 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.