{"ID":2889945,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.20293","arxiv_id":"2507.20293","title":"Decentralized Uncertainty-Aware Multi-Agent Collision Avoidance with Model Predictive Path Integral","abstract":"Decentralized multi-agent navigation under uncertainty is a complex task that arises in numerous robotic applications. It requires collision avoidance strategies that account for both kinematic constraints, sensing and action execution noise. In this paper, we propose a novel approach that integrates the Model Predictive Path Integral (MPPI) with a probabilistic adaptation of Optimal Reciprocal Collision Avoidance. Our method ensures safe and efficient multi-agent navigation by incorporating probabilistic safety constraints directly into the MPPI sampling process via a Second-Order Cone Programming formulation. This approach enables agents to operate independently using local noisy observations while maintaining safety guarantees. We validate our algorithm through extensive simulations with differential-drive robots and benchmark it against state-of-the-art methods, including ORCA-DD and B-UAVC. Results demonstrate that our approach outperforms them while achieving high success rates, even in densely populated environments. Additionally, validation in the Gazebo simulator confirms its practical applicability to robotic platforms. A source code is available at http://github.com/PathPlanning/MPPI-Collision-Avoidance.","short_abstract":"Decentralized multi-agent navigation under uncertainty is a complex task that arises in numerous robotic applications. It requires collision avoidance strategies that account for both kinematic constraints, sensing and action execution noise. In this paper, we propose a novel approach that integrates the Model Predicti...","url_abs":"https://arxiv.org/abs/2507.20293","url_pdf":"https://arxiv.org/pdf/2507.20293v2","authors":"[\"Stepan Dergachev\",\"Konstantin Yakovlev\"]","published":"2025-07-27T14:15:02Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false,"code_links":[{"ID":611724,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2889945,"paper_url":"https://arxiv.org/abs/2507.20293","paper_title":"Decentralized Uncertainty-Aware Multi-Agent Collision Avoidance with Model Predictive Path Integral","repo_url":"https://github.com/PathPlanning/MPPI-Collision-Avoidance","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}