A Multimodal Stochastic Planning Approach for Navigation and Multi-Robot Coordination
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914052427481088 |
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| author | Gonzales, Mark Oh, Ethan Moore, Joseph |
| author_facet | Gonzales, Mark Oh, Ethan Moore, Joseph |
| contents | In this paper, we present a receding-horizon, sampling-based planner capable of reasoning over multimodal policy distributions. By using the cross-entropy method to optimize a multimodal policy under a common cost function, our approach increases robustness against local minima and promotes effective exploration of the solution space. We show that our approach naturally extends to multi-robot collision-free planning, enables agents to share diverse candidate policies to avoid deadlocks, and allows teams to minimize a global objective without incurring the computational complexity of centralized optimization. Numerical simulations demonstrate that employing multiple modes significantly improves success rates in trap environments and in multi-robot collision avoidance. Hardware experiments further validate the approach's real-time feasibility and practical performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_19168 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Multimodal Stochastic Planning Approach for Navigation and Multi-Robot Coordination Gonzales, Mark Oh, Ethan Moore, Joseph Robotics In this paper, we present a receding-horizon, sampling-based planner capable of reasoning over multimodal policy distributions. By using the cross-entropy method to optimize a multimodal policy under a common cost function, our approach increases robustness against local minima and promotes effective exploration of the solution space. We show that our approach naturally extends to multi-robot collision-free planning, enables agents to share diverse candidate policies to avoid deadlocks, and allows teams to minimize a global objective without incurring the computational complexity of centralized optimization. Numerical simulations demonstrate that employing multiple modes significantly improves success rates in trap environments and in multi-robot collision avoidance. Hardware experiments further validate the approach's real-time feasibility and practical performance. |
| title | A Multimodal Stochastic Planning Approach for Navigation and Multi-Robot Coordination |
| topic | Robotics |
| url | https://arxiv.org/abs/2509.19168 |