Divide-and-Conquer CBS Design for Multi-Agent Path Finding in Large-scale Scenario

Abstract

Multi-Agent Path Finding (MAPF) problem is intensively studied in theoretical computer science, robotics and so on. The key for MAPF problem is to plan a conflict-free path for each agent with different start and goal positions, and to minimize the cost of the paths. Conflict-Based Search (CBS) is one of the optimal algorithms, which can ensure that the optimal solution is obtained, in the case of small-scale maps and low number of agents. However, in many real-world multi-agent systems, the scale of the map is very large and the number of agents is more. In most cases, CBS is not applicable any more. Therefore, we develop Divide and Conquer CBS, called DC-CBS algorithm which divides large-scale map and the original problem into several smaller ones. For each subproblem, we use the CBS to get the optimal solution. The experimental results show that for the large-scale scenario, the DC-CBS algorithm can get the result more quickly and plan the paths for agents successfully. As for small-scale situation, the performance of DC-CBS is almost as good as that for CBS.