Asymptotically Optimal Sampling-based Planners

TL;DR

This paper discusses the theoretical foundations, analysis, and practical applications of asymptotically optimal sampling-based motion planning algorithms that improve robot path planning over time.

Contribution

It provides a comprehensive overview of the theory, origins, and extensions of asymptotically optimal sampling-based motion planners.

Findings

Theoretical proof of asymptotic optimality.

Analysis of practical performance and extensions.

Applications in complex robot motion planning tasks.

Abstract

An asymptotically optimal sampling-based planner employs sampling to solve robot motion planning problems and returns paths with a cost that converges to the optimal solution cost, as the number of samples approaches infinity. This comprehensive article covers the theoretical characteristics of asymptotic optimality of motion planning algorithms, and traces its origins, analysis models, practical performance, extensions, and applications.