Asymptotically Optimal Sampling-Based Motion Planning Methods

Authors: Jonathan D. Gammell, Marlin P. Strub

Published: 2020 (Journal Paper)

Source: Annual Review of Control, Robotics, and Autonomous Systems

arXiv: 2009.10484

DOI: 10.1146/annurev-control-061920-093753

Summary

Abstract

Motion planning is a fundamental problem in autonomous robotics that requires finding a path to a specified goal that avoids obstacles and takes into account a robot's limitations and constraints. It is often desirable for this path to also optimize a cost function, such as path length. Formal path-quality guarantees for continuously valued search spaces are an active area of research interest. Recent results have proven that some sampling-based planning methods probabilistically converge toward the optimal solution as computational effort approaches infinity. This article summarizes the assumptions behind these popular asymptotically optimal techniques and provides an introduction to the significant ongoing research on this topic.

Links

Primary

Paper arxiv.org

Standard

arXiv Abstract 2009.10484 arXiv PDF 2009.10484 arXiv HTML 2009.10484 DOI 10.1146/annurev-control-061920-093753

Alternate

annualreviews.org annualreviews.org

Tags

• Survey

• Robotics

• Motion planning

• Robot motion planning

• Sampling-based planning

• Optimal motion planning

• Asymptotically optimal