Journal article
A survey of asymptotically optimal sampling-based motion planning methods
- 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.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
-
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(Preview, Accepted manuscript, pdf, 557.0KB, Terms of use)
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- Publisher copy:
- 10.1146/annurev-control-061920-093753
Authors
- Publisher:
- Annuals Reviews
- Journal:
- Annual Review of Control, Robotics, and Autonomous Systems More from this journal
- Volume:
- 4
- Issue:
- 2021
- Pages:
- 1-25
- Publication date:
- 2021-01-12
- Acceptance date:
- 2020-09-22
- DOI:
- EISSN:
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2573-5144
- Language:
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English
- Keywords:
- Pubs id:
-
1133311
- Local pid:
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pubs:1133311
- Deposit date:
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2020-09-23
- ARK identifier:
Terms of use
- Copyright holder:
- Annual Reviews.
- Copyright date:
- 2021
- Rights statement:
- © 2021 by Annual Reviews. All rights reserved
- Notes:
- This is the accepted manuscript version of the article. The final version is available from Annual Reviews at: https://doi.org/10.1146/annurev-control-061920-093753
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