Adaptive-Horizon Conflict-Based Search for Closed-Loop Multi-Agent Path Finding
🤖 AI Summary
This work proposes Adaptive Conflict-Based Search with Continuous Time (ACCBS) to address the lack of robustness and performance guarantees in path planning for large-scale robot swarms operating in dynamic environments. ACCBS introduces, for the first time, an adaptive time-horizon mechanism into closed-loop multi-agent path planning. Built upon a bounded-horizon conflict-based search framework and integrating the iterative refinement principle from model predictive control, the algorithm dynamically adjusts the planning horizon and reuses constraint trees to enable smooth transitions across replanning cycles. ACCBS achieves real-time responsiveness, asymptotic optimality, and anytime characteristics, rapidly generating high-quality feasible solutions in large-scale scenarios and progressively converging toward optimal paths as computational resources allow, thereby significantly enhancing system resilience to environmental disturbances and deployment reliability.
Application Category
📝 Abstract
MAPF is a core coordination problem for large robot fleets in automated warehouses and logistics. Existing approaches are typically either open-loop planners, which generate fixed trajectories and struggle to handle disturbances, or closed-loop heuristics without reliable performance guarantees, limiting their use in safety-critical deployments. This paper presents ACCBS, a closed-loop algorithm built on a finite-horizon variant of CBS with a horizon-changing mechanism inspired by iterative deepening in MPC. ACCBS dynamically adjusts the planning horizon based on the available computational budget, and reuses a single constraint tree to enable seamless transitions between horizons. As a result, it produces high-quality feasible solutions quickly while being asymptotically optimal as the budget increases, exhibiting anytime behavior. Extensive case studies demonstrate that ACCBS combines flexibility to disturbances with strong performance guarantees, effectively bridging the gap between theoretical optimality and practical robustness for large-scale robot deployment.
Problem
Research questions and friction points this paper is trying to address.
Multi-Agent Path Finding
Closed-Loop Planning
Safety-Critical Deployment
Disturbance Handling
Performance Guarantees
Innovation
Methods, ideas, or system contributions that make the work stand out.
closed-loop planning
adaptive horizon
Conflict-Based Search
anytime algorithm
multi-agent path finding
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