A Survey of Asymptotically Optimal Sampling-based Motion Planning Methods
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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.
- Publication
- Journal
- Annual Review of Control, Robotics, and Autonomous Systems
- Volume
- 4
- Number
- 1
- Pages
- 295–318
- Date
- Notes
- Invited
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 survey summarizes the assumptions behind these popular asymptotically optimal techniques and provides an introduction to the significant ongoing research on this topic.