A MILP-Based Solution to Multi-Agent Motion Planning and Collision Avoidance in Constrained Environments
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866918395621933056 |
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| author | Jaitly, Akshay Cline, Jack Farzan, Siavash |
| author_facet | Jaitly, Akshay Cline, Jack Farzan, Siavash |
| contents | We propose a mixed-integer linear program (MILP) for multi-agent motion planning that embeds Polytopic Action-based Motion Planning (PAAMP) into a sequence-then-solve pipeline. Region sequences confine each agent to adjacent convex polytopes, while a big-M hyperplane model enforces inter-agent separation. Collision constraints are applied only to agents sharing or neighboring a region, which reduces binary variables exponentially compared with naive formulations. An L1 path-length-plus-acceleration cost yields smooth trajectories. We prove finite-time convergence and demonstrate on representative multi-agent scenarios with obstacles that our formulation produces collision-free trajectories an order of magnitude faster than an unstructured MILP baseline. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_21982 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A MILP-Based Solution to Multi-Agent Motion Planning and Collision Avoidance in Constrained Environments Jaitly, Akshay Cline, Jack Farzan, Siavash Robotics Systems and Control We propose a mixed-integer linear program (MILP) for multi-agent motion planning that embeds Polytopic Action-based Motion Planning (PAAMP) into a sequence-then-solve pipeline. Region sequences confine each agent to adjacent convex polytopes, while a big-M hyperplane model enforces inter-agent separation. Collision constraints are applied only to agents sharing or neighboring a region, which reduces binary variables exponentially compared with naive formulations. An L1 path-length-plus-acceleration cost yields smooth trajectories. We prove finite-time convergence and demonstrate on representative multi-agent scenarios with obstacles that our formulation produces collision-free trajectories an order of magnitude faster than an unstructured MILP baseline. |
| title | A MILP-Based Solution to Multi-Agent Motion Planning and Collision Avoidance in Constrained Environments |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2506.21982 |