🤖 AI Summary
This work addresses the challenges of dynamic obstacles, partial observability, and agent coordination in dynamic multi-agent pathfinding (D-MAPF) by proposing the A** algorithm. A** introduces a novel template mechanism that decouples offline geometric path generation from online spatiotemporal replanning. By precomputing a diverse set of candidate paths and dynamically reconnecting them during execution, A** efficiently handles environmental changes and perception limitations within a unified simulation framework. The authors evaluate A** against six baseline algorithms—including Dijkstra, D* Lite, Space-Time A*, WHCA*, and M*—demonstrating that A** significantly improves path quality and adaptability in dynamic, partially observable scenarios, thereby validating its effectiveness in complex multi-agent systems.
📝 Abstract
This paper presents a systematic study of pathfinding algorithms in the context of Dynamic Multi-Agent Pathfinding (D-MAPF), a setting that combines dynamic obstacles, partial observability, and inter-agent conflicts. We evaluate six representative algorithms: Dijkstra, D* Lite, Space-Time A*, WHCA*, M*, and a novel method denoted as A** within a unified simulation framework. The proposed A** algorithm introduces a template-based approach that decouples offline geometric path generation from online temporal adaptation. By precomputing multiple diverse candidate paths and dynamically reconnecting to them using space-time planning, A** improves solution quality in environments with frequent changes and limited sensing