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Estimation, Search, and Planning (ESP) Research Group

A Survey of Asymptotically Optimal Sampling-based Motion Planning Methods

Jonathan was invited to survey the popular field of asymptotically optimal sampling-based motion planning algorithms in the Annual Review of Control, Robotics, and Autonomous Systems. The paper will be published in 2021, but you can read a preprint of it on arXiv now.

Authors
  1. J. D. Gammell
  2. M. P. Strub
Title
A survey of asymptotically optimal sampling-based motion planning methods
Publication
Journal
Annual Review of Control, Robotics, and Autonomous Systems
Volume
4
Number
1
Date
Notes
Invited, To Appear
PDFs
PDF
Google Scholar
Google Scholar

Abstract

Motion planning is a fundamental problem in autonomous robotics. It requires finding a path to a specified goal that avoids obstacles and obeys 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 towards the optimal solution as computational effort approaches infinity. This survey summarizes the assumptions behind these popular asymptotically optimal techniques and provides an introduction to the significant ongoing research on this topic.