PRISM：利用运动约束实现快速信息共享的完备在线去中心化多智能体路径规划
PRISM: Complete Online Decentralized Multi-Agent Pathfinding with Rapid Information Sharing using Motion Constraints
AI总结：
针对多任务多智能体路径规划中的碰撞、死锁与通信受限问题，本文提出去中心化算法 PRISM，通过运动约束信息包快速共享实现可扩展协作，并在实验中相较 CBS 与 TPTS 展现显著优势。
摘要：
我们提出 PRISM（Pathfinding with Rapid Information Sharing using Motion Constraints，利用运动约束进行快速信息共享的路径规划），这是一种旨在解决多任务多智能体路径规划（MT-MAPF）问题的去中心化算法。PRISM 使大型智能体团队能够同时为多个任务规划安全且高效的路径，同时避免碰撞。它采用快速通信策略，使用信息包交换运动约束信息，即使在没有直接通信的场景中也能增强协作路径规划和态势感知。我们证明，在可能的情况下，PRISM 能够解决并避免所有死锁场景，而这是去中心化路径规划中的一项关键挑战。在实验方面，我们在五个环境和 25 个随机场景中评估 PRISM，并将其与集中式 Conflict-Based Search（CBS，基于冲突的搜索）以及去中心化 Token Passing with Task Swaps（TPTS，带任务交换的令牌传递）算法进行基准比较。PRISM 展现出可扩展性和解的质量：其支持的智能体数量是 CBS 的 3.4 倍，并且在狭窄通道环境中能够处理的任务数量最多可达 TPTS 的 2.5 倍。此外，即使在低连接性条件下，PRISM 在解的质量上也与 CBS 相当，同时实现更快的计算时间。其去中心化设计减轻了单个智能体的计算负担，使其能够扩展到大型环境。这些结果证实了 PRISM 在复杂、动态路径规划场景中的鲁棒性、可扩展性和有效性。
英文摘要：
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.
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