Dynamic Agent Grouping ECBS: Scaling Windowed Multi-Agent Path Finding with Completeness Guarantees

2025-09-18

AI Summary

To address the challenge in windowed multi-agent path finding (WinC-MAPF) where existing partial-path planners struggle to balance completeness and scalability, this paper pioneers the integration of bounded-suboptimal search—specifically Enhanced Conflict-Based Search (ECBS)—into the WinC-MAPF framework. We propose a dynamic agent grouping mechanism and an adaptive heuristic update strategy: agents are partitioned into collaborative subgroups within each sliding window, and bounded-suboptimal paths are jointly computed for each subgroup. This ensures global completeness while substantially improving computational efficiency. Experimental results on large-scale benchmarks demonstrate that our approach outperforms SS-CBS in solution quality and runtime, and surpasses windowed ECBS (without completeness guarantees) in success rate, solution suboptimality, and execution time. The method thus establishes new state-of-the-art trade-offs among efficiency, robustness, and scalability in WinC-MAPF.

Technology Category

• Multiagent Systems: Multiagent Planning
• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Search

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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 which 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 (Veerapaneni et al. 2024). A core limitation of WinC-MAPF is that they required 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. DAG-ECBS shows improved scalability compared to SS-CBS and can outperform windowed ECBS without completeness guarantees. More broadly, our work serves as a blueprint for designing more MAPF methods that can use the WinC-MAPF framework.

Problem

• Extending WinC-MAPF framework to use bounded suboptimal solvers
• Designing DAG-ECBS for dynamic agent grouping with completeness guarantees
• Improving scalability in windowed multi-agent path finding problems

Innovation

• Dynamic agent grouping with bounded suboptimal solver
• Extends WinC-MAPF framework with completeness guarantees
• DAG-ECBS enables scalable multi-agent path planning