LayeredMAPF: a decomposition of MAPF instance to reduce solving costs

TL;DR

LayeredMAPF introduces a decomposition framework that breaks down large MAPF problems into smaller subproblems, enabling existing algorithms to solve complex multi-agent pathfinding tasks more efficiently with minimal loss of solvability.

Contribution

The paper presents a novel decomposition approach for MAPF that is compatible with all MAPF algorithms, significantly reducing solving costs and memory usage while maintaining high solution feasibility.

Findings

Decomposition completes in under 1 second on average.

Reduces memory and time costs for multiple MAPF algorithms.

Loss of solvability is estimated to be less than 1%.

Abstract

Multi-agent pathfinding (MAPF) holds significant utility within autonomous systems, however, the calculation and memory space required for multi-agent path finding (MAPF) grows exponentially as the number of agents increases. This often results in some MAPF instances being unsolvable under limited computational resources and memory space, thereby limiting the application of MAPF in complex scenarios. Hence, we propose a decomposition approach for MAPF instances, which breaks down instances involving a large number of agents into multiple isolated subproblems involving fewer agents. Moreover, we present a framework to enable general MAPF algorithms to solve each subproblem independently and merge their solutions into one conflict-free final solution, and avoid loss of solvability as much as possible. Unlike existing works that propose isolated methods aimed at reducing the time cost of MAPF, our method is applicable to all MAPF methods. In our results, we apply decomposition to multiple state-of-the-art MAPF methods using a classic MAPF benchmark\footnote{https://movingai.com/benchmarks/mapf.html}. The decomposition of MAPF instances is completed on average within 1s, and its application to seven MAPF methods reduces the memory usage or time cost significantly, particularly for serial methods. Based on massive experiments, we speculate the possibilty about loss of solvability caused by our method is $<$ 1\%. To facilitate further research within the community, we have made the source code of the proposed algorithm publicly available\footnote{https://github.com/JoeYao-bit/LayeredMAPF/tree/minimize\_dependence}.