Greedy Heuristics for Sampling-Based Motion Planning in High-Dimensional State Spaces Article Swipe
Phone Thiha Kyaw
,
Anh Vu Le
,
Lim Yi
,
Prabakaran Veerajagadheswar
,
Mohan Rajesh Elara
,
Dinh Tung Vo
,
Bùi Vũ Minh
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2405.03411
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2405.03411
Informed sampling techniques accelerate the convergence of sampling-based motion planners by biasing sampling toward regions of the state space that are most likely to yield better solutions. However, when the current solution path contains redundant or tortuous segments, the resulting informed subset may remain unnecessarily large, slowing convergence. Our prior work addressed this issue by introducing the greedy informed set, which reduces the sampling region based on the maximum heuristic cost along the current solution path. In this article, we formally characterize the behavior of the greedy informed set within Rapidly-exploring Random Tree (RRT*)-like planners and analyze how greedy sampling affects exploration and asymptotic optimality. We then present Greedy RRT* (G-RRT*), a bi-directional anytime variant of RRT* that leverages the greedy informed set to focus sampling in the most promising regions of the search space. Experiments on abstract planning benchmarks, manipulation tasks from the MotionBenchMaker dataset, and a dual-arm Barrett WAM problem demonstrate that G-RRT* rapidly finds initial solutions and converges asymptotically to optimal paths, outperforming state-of-the-art sampling-based planners.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2405.03411
- https://arxiv.org/pdf/2405.03411
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396787906
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- OpenAlex ID
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https://openalex.org/W4396787906Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2405.03411Digital Object Identifier
- Title
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Greedy Heuristics for Sampling-Based Motion Planning in High-Dimensional State SpacesWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
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2024-05-06Full publication date if available
- Authors
-
Phone Thiha Kyaw, Anh Vu Le, Lim Yi, Prabakaran Veerajagadheswar, Mohan Rajesh Elara, Dinh Tung Vo, Bùi Vũ MinhList of authors in order
- Landing page
-
https://arxiv.org/abs/2405.03411Publisher landing page
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https://arxiv.org/pdf/2405.03411Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2405.03411Direct OA link when available
- Concepts
-
Heuristics, Sampling (signal processing), Computer science, Greedy algorithm, State (computer science), Motion (physics), Motion planning, Mathematical optimization, Artificial intelligence, Theoretical computer science, Mathematics, Algorithm, Computer vision, Robot, Filter (signal processing)Top concepts (fields/topics) attached by OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.dataset, | 145 |
| abstract_inverted_index.dual-arm | 148 |
| abstract_inverted_index.formally | 80 |
| abstract_inverted_index.informed | 40, 58, 87, 121 |
| abstract_inverted_index.planners | 9, 94 |
| abstract_inverted_index.planning | 138 |
| abstract_inverted_index.sampling | 1, 12, 63, 99, 125 |
| abstract_inverted_index.solution | 31, 74 |
| abstract_inverted_index.tortuous | 36 |
| abstract_inverted_index.(G-RRT*), | 110 |
| abstract_inverted_index.addressed | 51 |
| abstract_inverted_index.converges | 160 |
| abstract_inverted_index.heuristic | 69 |
| abstract_inverted_index.leverages | 118 |
| abstract_inverted_index.planners. | 168 |
| abstract_inverted_index.promising | 129 |
| abstract_inverted_index.redundant | 34 |
| abstract_inverted_index.resulting | 39 |
| abstract_inverted_index.segments, | 37 |
| abstract_inverted_index.solutions | 158 |
| abstract_inverted_index.accelerate | 3 |
| abstract_inverted_index.asymptotic | 103 |
| abstract_inverted_index.solutions. | 26 |
| abstract_inverted_index.techniques | 2 |
| abstract_inverted_index.(RRT*)-like | 93 |
| abstract_inverted_index.Experiments | 135 |
| abstract_inverted_index.benchmarks, | 139 |
| abstract_inverted_index.convergence | 5 |
| abstract_inverted_index.demonstrate | 152 |
| abstract_inverted_index.exploration | 101 |
| abstract_inverted_index.introducing | 55 |
| abstract_inverted_index.optimality. | 104 |
| abstract_inverted_index.characterize | 81 |
| abstract_inverted_index.convergence. | 47 |
| abstract_inverted_index.manipulation | 140 |
| abstract_inverted_index.outperforming | 165 |
| abstract_inverted_index.unnecessarily | 44 |
| abstract_inverted_index.asymptotically | 161 |
| abstract_inverted_index.bi-directional | 112 |
| abstract_inverted_index.sampling-based | 7, 167 |
| abstract_inverted_index.MotionBenchMaker | 144 |
| abstract_inverted_index.state-of-the-art | 166 |
| abstract_inverted_index.Rapidly-exploring | 90 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |