Accelerating Multi-agent Path Finding Based on Roadmap Construction in Non-grid Environment

Conflict-based search (CBS) is one of the widely used algorithms to solve multi-agent path finding (MAPF) problems. Though it combines optimality and completeness as a search algorithm, the time it takes to search for a solution grows exponentially as the number of agents gradually increases, and thus does not scale well. In this paper, a learning-based graph transformer is utilized as the heuristic function to accelerate planning in non-gird environment with roadmap generation. To solve unconnected graph problems when performing low-level path planning search in the CBS framework, triangulation method is introduced to obtain an optimized path with less nodes and edges. Simulation shows that the learning-based search method trained on roadmaps generated by constrained delaunay triangulation (CDT) leads to better solutions and a much greater success rate.