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.
DOI: 10.1109/IROS60139.2025.11246543
Скачать статью (PDF) или читать онлайн на IEEE Xplore (англ., требуется регистрация): https://ieeexplore.ieee.org/document/11246543
Скачать препринт (PDF) или читать онлайн на arXiv.org (англ.): https://arxiv.org/html/2507.20293v1
Публикации ВШЭ: https://publications.hse.ru/chapters/1134113558
Stepan Dergachev and Konstantin Yakovlev. Decentralized Uncertainty-Aware Multi-Agent Collision Avoidance With Model Predictive Path Integral // In the Proceedings of the 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2025, Hangzhou, China, pp. 12456–12463.