Asymptotically optimal sampling-based motion planning methods
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- Publication Date
- 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.
- Publication Details
- Type
- Journal Paper
- Journal
- Annual Review of Control, Robotics, and Autonomous Systems
- Volume
- 4
- Number
- 1
- Pages
- 295–318
- Digital Object Identifier
- 10.1146/annurev-control-061920-093753
- arXiv Identifier
- 2009.10484 [cs.RO]
- Notes
- Invited
- Manuscript
- Open-Access PDF
- http://www.annualreviews.org/eprint/TC4SXD67SIZDB8AQ5PBH/full/10.1146/annurev-control-061920-093753
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- BibTeX Entry
@article{gammell_arcras21,
author = {Jonathan D Gammell and Marlin P Strub},
title = {Asymptotically optimal sampling-based motion planning methods},
journal = {Annual Review of Control, Robotics, and Autonomous Systems},
year = {2021},
volume = {4},
number = {1},
pages = {295--318},
month = may,
doi = {10.1146/annurev-control-061920-093753},
}