Journal article
Multi-Agent Path Finding with Temporal Jump Point Search
Shuli Hu, Daniel D Harabor, Graeme Gange, Peter J Stuckey, Nathan R Sturtevant
Proceedings of the International Conference on Automated Planning and Scheduling | Association for the Advancement of Artificial Intelligence (AAAI) | Published : 2022
Open access
Abstract
Temporal Jump Point Search (JPST) is a recently introduced algorithm for grid-optimal pathfinding among dynamic temporal obstacles. In this work we consider JPST as a low-level planner in Multi-Agent Path Finding (MAPF). We investigate how the canonical ordering of JPST can negatively impact MAPF performance and we consider several strategies which allow us to overcome these limitations. Experiments show our new CBS/JPST approach can substantially improve on CBS/SIPP, a contemporary and leading method from the area.