P4: Pruning and Prediction-based Priority Planning

In recent years, multi-agent path finding (MAPF) has attracted widespread attention in the fields of artificial intelligence and robotics. Its main goal is to find paths for multiple agents, each having specified start and end locations on a grid, without collisions, while minimizing the total travel time. In this work, we present a new algorithm called P4 (Pruning and Prediction-based Priority Planning), designed to accommodate large numbers of agents with enhanced scalability. The P4 method combines three key components: Point-to-Point (PnP) algorithm, dynamic window approach, and path direction prediction. In this way we reduce the search space and increase the speed of the computation. Our experiments show that P4 consistently achieves shorter execution times and produces solutions that are close to optimal. For example, for 200 agents and real map orz900d, P4 is 4× faster than optimal algorithm CBSH while the sum of delays is within 15% of optimal. The P4 method outperforms other existing suboptimal methods in both performance and solution quality. We also show that our approach exceeds existing methods in success rate under time constraints. As time limit is increased from 0.1 to 100 seconds, success rate of P4 increases from 50% to 100%. On the other hand, the success rate for alternative sub-optimal methods is less than that of P4.