{"ID":2859585,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.06436","arxiv_id":"2510.06436","title":"R3R: Decentralized Multi-Agent Collision Avoidance with Infinite-Horizon Safety","abstract":"Existing decentralized methods for multi-agent motion planning lack formal, infinite-horizon safety guarantees, especially for communication-constrained systems. We present R3R which, to our knowledge, is the first decentralized and asynchronous framework for multi-agent motion planning under range-limited communication constraints with infinite-horizon safety guarantees for systems of nonlinear agents. R3R's novelty lies in combining our gatekeeper safety framework with a geometric constraint termed R-Boundedness, which together establish a formal link between an agent's communication radius and its ability to plan safely. We constrain trajectories to lie within a fixed planning radius, determined by a function of the agent's communication radius. This enables trajectories to be certified as provably safe for all time using only local information. Our algorithm is fully asynchronous, and ensures the forward invariance of these guarantees even in time-varying networks where agents asynchronously join and replan. We evaluate our approach in simulations of up to 128 Dubins vehicles, validating our theoretical safety guarantees in dense, obstacle-rich scenarios. We further show that R3R's computational complexity scales with local agent density rather than problem size, providing a practical solution for scalable and provably safe multi-agent systems.","short_abstract":"Existing decentralized methods for multi-agent motion planning lack formal, infinite-horizon safety guarantees, especially for communication-constrained systems. We present R3R which, to our knowledge, is the first decentralized and asynchronous framework for multi-agent motion planning under range-limited communicatio...","url_abs":"https://arxiv.org/abs/2510.06436","url_pdf":"https://arxiv.org/pdf/2510.06436v2","authors":"[\"Thomas Marshall Vielmetti\",\"Devansh R. Agrawal\",\"Dimitra Panagou\"]","published":"2025-10-07T20:13:49Z","proceeding":"cs.MA","tasks":"[\"cs.MA\"]","methods":"[]","has_code":false}