TRACS: A Geometry-Aware Framework for Scalable Multi-Agent Path Finding in Warehouses

Abstract

Large scale warehouse automation relies on efficient multi agent path finding (MAPF) to coordinate thousands of robots in structured environments. Existing MAPF algorithms primarily improve conflict resolution while representing warehouses as generic navigation graphs, overlooking their inherent geometric structure and traffic patterns. This paper presents TRACS (Traffic aware Routing and Aisle Coordination System), a geometry aware planning framework that exploits warehouse layout to simplify planning rather than introducing another conflict-resolution algorithm. TRACS constructs a directed routing graph with alternating one way aisles that eliminates head on and edge swap conflicts by design, decoupling spatial routing from temporal traffic coordination. Independent hybrid graph grid routing is combined with lightweight edge based scheduling to avoid joint space time search while ensuring collision free execution. Experimental evaluation on warehouse benchmarks against representative priority based, iterative repair, and search based MAPF planners shows that TRACS consistently achieves a 100% empirical success rate while substantially improving planning scalability. On fixed scene benchmarks with up to 1000 robots, TRACS reduces planning time by up to 14.7X while maintaining competitive makespan, lower flowtime, and near optimal path quality. Under a fixed 10 minute planning budget, TRACS routes up to 5120 robots, roughly twice the largest fleet reached by the strongest baselines, while sustaining a 100% success rate, demonstrating the effectiveness of exploiting warehouse geometry for scalable robotic warehouse systems.