Optimizing Space Utilization for More Effective Multi-Robot Path Planning
This addresses congestion issues in multi-robot systems for applications like logistics or warehousing, but it is incremental as it builds on existing decoupled planners.
The paper tackled congestion in multi-robot path planning by introducing Space Utilization Optimization (SU-I) as a heuristic, resulting in dramatic reductions in computation time and sizable optimality gains in diverse scenarios.
We perform a systematic exploration of the principle of Space Utilization Optimization (SUO) as a heuristic for planning better individual paths in a decoupled multi-robot path planner, with applications to both one-shot and life-long multi-robot path planning problems. We show that the decentralized heuristic set, SU-I, preserves single path optimality and significantly reduces congestion that naturally happens when many paths are planned without coordination. Integration of SU-I into complete planners brings dramatic reductions in computation time due to the significantly reduced number of conflicts and leads to sizable solution optimality gains in diverse evaluation scenarios with medium and large maps, for both one-shot and life-long problem settings.