基于 Karma 机制的去中心化协作多智能体路径寻找
Karma Mechanisms for Decentralised, Cooperative Multi Agent Path Finding
AI总结：
本文提出基于 Karma 机制的去中心化协作 MAPF 框架，通过非交易性信用机制平衡智能体的重规划努力，减少服务时间差异，提升整体效率。
摘要：
多智能体路径寻找（MAPF）是大规模机器人和物理系统中关键的协调问题，多个智能体需在有限计算和通信资源下计算无冲突路径。尽管集中式最优求解器保证了解的最优性，但其指数级计算复杂度限制了大规模系统和实时应用的扩展性。现有去中心化启发式方法虽更快，但导致次优结果和高成本差异。本文提出基于 Karma 机制的去中心化协作 MAPF 框架，Karma 机制是人工非交易性信用，用于记录智能体的过去合作行为，并调节未来冲突解决决策。该方法将冲突解决建模为双边谈判过程，使智能体通过成对重规划解决冲突，同时在有限通信和无全局优先级结构下促进长期公平。机制在具有运动学方向约束的终身机器人仓库多智能体取货送货场景中进行了评估。结果表明，Karma 机制平衡了智能体的重规划努力，减少了服务时间差异，而无需牺牲整体效率。代码：https://github.com/DerKevinRiehl/karma_dmapf
英文摘要：
Multi-Agent Path Finding (MAPF) is a fundamental coordination problem in large-scale robotic and cyber-physical systems, where multiple agents must compute conflict-free trajectories with limited computational and communication resources. While centralised optimal solvers provide guarantees on solution optimality, their exponential computational complexity limits scalability to large-scale systems and real-time applicability. Existing decentralised heuristics are faster, but result in suboptimal outcomes and high cost disparities. This paper proposes a decentralised coordination framework for cooperative MAPF based on Karma mechanisms - artificial, non-tradeable credits that account for agents' past cooperative behaviour and regulate future conflict resolution decisions. The approach formulates conflict resolution as a bilateral negotiation process that enables agents to resolve conflicts through pairwise replanning while promoting long-term fairness under limited communication and without global priority structures. The mechanism is evaluated in a lifelong robotic warehouse multi-agent pickup-and-delivery scenario with kinematic orientation constraints. The results highlight that the Karma mechanism balances replanning effort across agents, reducing disparity in service times without sacrificing overall efficiency. Code: https://github.com/DerKevinRiehl/karma_dmapf
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
