Certificate-Driven Closed-Loop Multi-Agent Path Finding with Inheritable Factorization

2026-03-31

AI Summary

This work addresses the myopic behavior inherent in closed-loop Multi-Agent Path Finding (MAPF) under dense scenarios, which stems from limited planning horizons. To overcome this limitation, the authors propose Certificate-Driven Conflict-Based Search (CDCBS), a novel algorithm that integrates certificate trajectories and a fleet budget mechanism. CDCBS ensures completeness by only accepting trajectory updates that improve a global certificate, while the budget constraint enables inheritable, cross-timestep factorization of the planning problem. This approach is the first to combine global optimality guarantees with dynamic decomposition within a closed-loop MAPF framework. Experimental results on standard maps demonstrate that, in dense settings, CDCBS significantly enhances solution quality stability compared to ACCBS and effectively reduces the size of actual planning groups.

Abstract

Multi-agent coordination in automated warehouses and logistics is commonly modeled as the Multi-Agent Path Finding (MAPF) problem. Closed-loop MAPF algorithms improve scalability by planning only the next movement and replanning online, but this finite-horizon viewpoint can be shortsighted and makes it difficult to preserve global guarantees and exploit compositional structure. This issue is especially visible in Anytime Closed-Loop Conflict-Based Search (ACCBS), which applies Conflict-Based Search (CBS) over dynamically extended finite horizons but, under finite computational budgets, may terminate with short active prefixes in dense instances. We introduce certificate trajectories and their associated fleet budget as a general mechanism for filtering closed-loop updates. A certificate provides a conflict-free fallback plan and a monotone upper bound on the remaining cost; accepting only certificate-improving updates yields completeness. The same budget information induces a budget-limited factorization that enables global, inheritable decomposition across timesteps. Instantiating the framework on ACCBS yields Certificate-Driven Conflict-Based Search (CDCBS). Experiments on benchmark maps show that CDCBS achieves more consistent solution quality than ACCBS, particularly in dense settings, while the proposed factorization reduces effective group size.

Problem

• Multi-Agent Path Finding
• Closed-loop Planning
• Global Guarantees
• Compositional Structure
• Dense Instances

Innovation

• certificate-driven planning
• closed-loop MAPF
• inheritable factorization
• fleet budget
• Conflict-Based Search