{"ID":2861802,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.00425","arxiv_id":"2510.00425","title":"Conflict-Based Search as a Protocol: A Multi-Agent Motion Planning Protocol for Heterogeneous Agents, Solvers, and Independent Tasks","abstract":"Imagine the future construction site, hospital, or office with dozens of robots bought from different manufacturers. How can we enable these different robots to effectively move in a shared environment, given that each robot may have its own independent motion planning system? This work shows how we can get efficient collision-free movements between algorithmically heterogeneous agents by using Conflict-Based Search (Sharon et al. 2015) as a protocol. At its core, the CBS Protocol requires one specific single-agent motion planning API; finding a collision-free path that satisfies certain space-time constraints. Given such an API, CBS uses a central planner to find collision-free paths - independent of how the API is implemented. We demonstrate how this protocol enables multi-agent motion planning for a heterogeneous team of agents completing independent tasks with a variety of single-agent planners including: Heuristic Search (e.g., A*), Sampling Based Search (e.g., RRT), Optimization (e.g., Direct Collocation), Diffusion, and Reinforcement Learning.","short_abstract":"Imagine the future construction site, hospital, or office with dozens of robots bought from different manufacturers. How can we enable these different robots to effectively move in a shared environment, given that each robot may have its own independent motion planning system? This work shows how we can get efficient c...","url_abs":"https://arxiv.org/abs/2510.00425","url_pdf":"https://arxiv.org/pdf/2510.00425v2","authors":"[\"Rishi Veerapaneni\",\"Alvin Tang\",\"Haodong He\",\"Sophia Zhao\",\"Viraj Shah\",\"Yidai Cen\",\"Ziteng Ji\",\"Gabriel Olin\",\"Jon Arrizabalaga\",\"Yorai Shaoul\",\"Jiaoyang Li\",\"Maxim Likhachev\"]","published":"2025-10-01T02:07:18Z","proceeding":"cs.MA","tasks":"[\"cs.MA\",\"cs.RO\"]","methods":"[\"Reinforcement Learning\",\"Diffusion Model\"]","has_code":false}