PRISM: Complete Online Decentralized Multi-Agent Pathfinding with Rapid Information Sharing using Motion Constraints

2025-05-12

AI Summary

This work addresses the multi-task multi-agent pathfinding (MT-MAPF) problem in large-scale dynamic environments. Methodologically, it introduces a decentralized cooperative framework featuring a novel motion-constrained packet mechanism for asynchronous broadcast—enabling global situational awareness and strong coordination without direct inter-agent communication. The framework integrates local conflict resolution, online incremental replanning, and formal deadlock-avoidance guarantees to ensure both safety and real-time responsiveness. Experimental results demonstrate that, compared to CBS, the approach supports 3.4× more agents; achieves 2.5× higher task throughput than TPTS in narrow-passage scenarios; attains solution quality comparable to state-of-the-art methods; exhibits significantly improved computational efficiency; and maintains robustness under low network connectivity.

Technology Category

• Multiagent Systems: Multiagent Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• 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

We introduce PRISM (Pathfinding with Rapid Information Sharing using Motion Constraints), a decentralized algorithm designed to address the multi-task multi-agent pathfinding (MT-MAPF) problem. PRISM enables large teams of agents to concurrently plan safe and efficient paths for multiple tasks while avoiding collisions. It employs a rapid communication strategy that uses information packets to exchange motion constraint information, enhancing cooperative pathfinding and situational awareness, even in scenarios without direct communication. We prove that PRISM resolves and avoids all deadlock scenarios when possible, a critical challenge in decentralized pathfinding. Empirically, we evaluate PRISM across five environments and 25 random scenarios, benchmarking it against the centralized Conflict-Based Search (CBS) and the decentralized Token Passing with Task Swaps (TPTS) algorithms. PRISM demonstrates scalability and solution quality, supporting 3.4 times more agents than CBS and handling up to 2.5 times more tasks in narrow passage environments than TPTS. Additionally, PRISM matches CBS in solution quality while achieving faster computation times, even under low-connectivity conditions. Its decentralized design reduces the computational burden on individual agents, making it scalable for large environments. These results confirm PRISM's robustness, scalability, and effectiveness in complex and dynamic pathfinding scenarios.

Problem

• Decentralized multi-agent pathfinding for multiple tasks
• Ensures collision-free paths with rapid information sharing
• Resolves deadlocks and scales efficiently in large environments

Innovation

• Decentralized multi-agent pathfinding with rapid communication
• Motion constraints for collision avoidance and deadlock resolution
• Scalable solution outperforming CBS and TPTS algorithms