Improving Collaborative Pathfinding Using Map Abstraction


  • Nathan Sturtevant University of Alberta
  • Michael Buro University of Alberta



In this paper we combine recent pathfinding research on spatial abstractions, partial refinement, and space-time reserva- tions to construct new collaborative pathfinding algorithms. We first present an enhanced version of WHCA* and then show how the ideas from WHCA* can be combined with PRA* to form CPRA*. These algorithms are shown to effectively plan trajectories for many objects simultaneously while avoiding collisions, as the original WHCA* does. These new algorithms are not only faster than WHCA* but also use less memory.




How to Cite

Sturtevant, N., & Buro, M. (2021). Improving Collaborative Pathfinding Using Map Abstraction. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 2(1), 80-85.