Publication

N. Greshler,, O. Gordon, O. Salzman and N. Shimkin. Cooperative Multi-Agent Path Finding: Beyond Path Planning and Collision Avoidance. In Proceedings of the International Symposium on Multi-Robot and Multi-Agent Systems (MRS), pages 20-28, 2021.

Abstract: We introduce the Cooperative Multi-Agent Path Finding (Co-MAPF) problem, an extension to the classical MAPF problem, where cooperative behavior is incorporated. In this setting, a group of autonomous agents operate in a shared environment and have to complete cooperative tasks while avoiding collisions with each other. This extension naturally models many real-world applications, where groups of agents must work together to complete a given task. To this end, we formalize the Co-MAPF problem and introduce Cooperative Conflict-Based Search (Co-CBS), a CBS-based algorithm for solving the problem optimally for a wide set of Co-MAPF problems. Co-CBS uses a cooperation-planning module integrated into CBS such that cooperation planning is decoupled from path planning, while ensuring that paths obtained are optimal. Finally, we present empirical results on several MAPF benchmarks demonstrating our algorithm's properties.

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