Simultaneous Computation with Multiple Prioritizations in Multi-Agent Motion Planning

This paper addresses the computational challenges of multi-agent path finding (MAPF) in large networks by proposing a method that allows agents to compute with multiple prioritizations simultaneously. The approach is general and does not rely on domain-specific knowledge, achieving near-optimal prioritization with minimal additional computation time. It is evaluated in a multi-agent motion planning (MAMP) context with a receding horizon, demonstrating real-time capability in a road network experiment.