Cooperative Multi-Agent Path Finding: Beyond Path Planning and Collision Avoidance

TL;DR

This paper introduces the Co-MAPF problem, extending classical MAPF to include cooperative behavior among agents, and proposes an optimal solution algorithm with empirical validation on benchmark problems.

Contribution

It formalizes the Co-MAPF problem and develops Co-CBS, a novel CBS-based algorithm that incorporates cooperation planning for multi-agent path finding.

Findings

Co-CBS solves Co-MAPF optimally on various benchmarks.

The algorithm effectively integrates cooperation into path planning.

Empirical results demonstrate improved performance over baseline methods.

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 the other agents in the group. This extension naturally models many real-world applications, where groups of agents are required to collaborate in order 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. Finally, we present empirical results on several MAPF benchmarks demonstrating our algorithm's properties.