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Multi-agent path finding (MAPF) is an active area in artificial intelligence,which has many real-world applications such as warehouse management, trafficcontrol, robotics, etc. Recently, M* and its variants have greatly improved theability to solve the MAPF problem. Although subdimensional expansion used inthose approaches significantly decreases the dimensionality of the joint searchspace and reduces the branching factor, they do not make full use of thepossible non-uniqueness of the optimal path of each agent. As a result, theupdating of the collision sets may bring a large number of redundantcomputation. In this paper, the idea of bypass is introduced intosubdimensional expansion to reduce the redundant computation. Specifically, wepropose the BPM* algorithm, which is an implementation of subdimensionalexpansion with bypass in M*. In the experiments, we show that BPM* outperformsthe state-of-the-art in solving several MAPF benchmark problems.

Multi-Agent Path Finding Based on Subdimensional Expansion with Bypass