Scalable Multi-Robot Motion Planning Using Workspace Guidance-Informed Hypergraphs
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866914139380645888 |
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| author | McBeth, Courtney Motes, James Ngui, Isaac Morales, Marco Amato, Nancy M. |
| author_facet | McBeth, Courtney Motes, James Ngui, Isaac Morales, Marco Amato, Nancy M. |
| contents | In this work, we propose a method for multiple mobile robot motion planning that efficiently plans for robot teams up to 128 robots (an order of magnitude larger than existing state-of-the-art methods) in congested settings with narrow passages in the environment. We achieve this improvement in scalability by extending the state-of-the-art Decomposable State Space Hypergraph (DaSH) multi-robot planning framework to support mobile robot motion planning in congested environments. This is a problem that DaSH cannot be directly applied to because it lacks a highly structured, easily discretizable task space and features kinodynamic constraints. We accomplish this by exploiting knowledge about the workspace topology to limit exploration of the planning space and through modifying DaSH's conflict resolution scheme. This guidance captures when coordination between robots is necessary, allowing us to decompose the intractably large multi-robot search space while limiting risk of inter-robot conflicts by composing relevant robot groups together while planning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_10176 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Scalable Multi-Robot Motion Planning Using Workspace Guidance-Informed Hypergraphs McBeth, Courtney Motes, James Ngui, Isaac Morales, Marco Amato, Nancy M. Robotics Multiagent Systems In this work, we propose a method for multiple mobile robot motion planning that efficiently plans for robot teams up to 128 robots (an order of magnitude larger than existing state-of-the-art methods) in congested settings with narrow passages in the environment. We achieve this improvement in scalability by extending the state-of-the-art Decomposable State Space Hypergraph (DaSH) multi-robot planning framework to support mobile robot motion planning in congested environments. This is a problem that DaSH cannot be directly applied to because it lacks a highly structured, easily discretizable task space and features kinodynamic constraints. We accomplish this by exploiting knowledge about the workspace topology to limit exploration of the planning space and through modifying DaSH's conflict resolution scheme. This guidance captures when coordination between robots is necessary, allowing us to decompose the intractably large multi-robot search space while limiting risk of inter-robot conflicts by composing relevant robot groups together while planning. |
| title | Scalable Multi-Robot Motion Planning Using Workspace Guidance-Informed Hypergraphs |
| topic | Robotics Multiagent Systems |
| url | https://arxiv.org/abs/2311.10176 |