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This paper introduces certificate trajectories and fleet budgets to improve the scalability and solution quality of closed-loop Multi-Agent Path Finding (MAPF) algorithms. By filtering updates based on certificate improvement and enabling budget-limited factorization, the approach mitigates the shortsightedness of finite-horizon planning. Experiments demonstrate that Certificate-Driven Conflict-Based Search (CDCBS) achieves more consistent solution quality than ACCBS, especially in dense environments, while reducing effective group size through factorization.
Guaranteeing completeness in closed-loop multi-agent path finding doesn't require sacrificing scalability: just add certificates to your trajectories.
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.
