Dynamic Agent Grouping ECBS: Scaling Windowed Multi-Agent Path Finding with Completeness Guarantees
Tiannan Zhang, Rishi Veerapaneni, Shao-Hung Chan, Jiaoyang Li, and Maxim Likhachev.
AAAI Conference on Artificial Intelligence (AAAI), pages 29911–29920, 2026.
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Abstract
Multi-Agent Path Finding (MAPF) is the problem of finding a set of collision-free paths for a team of agents. Although several MAPF methods that solve full-horizon MAPF have completeness guarantees, very few MAPF methods that plan partial paths have completeness guarantees. Recent work introduced the Windowed Complete MAPF (WinC-MAPF) framework, which shows how windowed optimal MAPF solvers (e.g., SS-CBS) can use heuristic updates and disjoint agent groups to maintain completeness even when planning partial paths. A core limitation of WinC-MAPF is that it requires optimal MAPF solvers. Our main contribution is to extend WinC-MAPF by showing how we can use a bounded suboptimal solver while maintaining completeness. In particular, we design Dynamic Agent Grouping ECBS (DAG-ECBS) which dynamically creates and plans agent groups while maintaining that each agent group solution is bounded suboptimal. We prove how DAG-ECBS can maintain completeness in the WinC-MAPF framework and can improve scalability compared to windowed ECBS which does not have completeness guarantees. More broadly, our work serves as a blueprint for designing more MAPF methods that can use the WinC-MAPF framework.