Extended Version: Multi-Robot Motion Planning with Cooperative Localization
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915233808777216 |
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| author | Theurkauf, Anne Ahmed, Nisar Lahijanian, Morteza |
| author_facet | Theurkauf, Anne Ahmed, Nisar Lahijanian, Morteza |
| contents | We consider the uncertain multi-robot motion planning (MRMP) problem with cooperative localization (CL-MRMP), under both motion and measurement noise, where each robot can act as a sensor for its nearby teammates. We formalize CL-MRMP as a chance-constrained motion planning problem, and propose a safety-guaranteed algorithm that explicitly accounts for robot-robot correlations. Our approach extends a sampling-based planner to solve CL-MRMP while preserving probabilistic completeness. To improve efficiency, we introduce novel biasing techniques. We evaluate our method across diverse benchmarks, demonstrating its effectiveness in generating motion plans, with significant performance gains from biasing strategies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_06429 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Extended Version: Multi-Robot Motion Planning with Cooperative Localization Theurkauf, Anne Ahmed, Nisar Lahijanian, Morteza Robotics Multiagent Systems We consider the uncertain multi-robot motion planning (MRMP) problem with cooperative localization (CL-MRMP), under both motion and measurement noise, where each robot can act as a sensor for its nearby teammates. We formalize CL-MRMP as a chance-constrained motion planning problem, and propose a safety-guaranteed algorithm that explicitly accounts for robot-robot correlations. Our approach extends a sampling-based planner to solve CL-MRMP while preserving probabilistic completeness. To improve efficiency, we introduce novel biasing techniques. We evaluate our method across diverse benchmarks, demonstrating its effectiveness in generating motion plans, with significant performance gains from biasing strategies. |
| title | Extended Version: Multi-Robot Motion Planning with Cooperative Localization |
| topic | Robotics Multiagent Systems |
| url | https://arxiv.org/abs/2504.06429 |