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

翻译：多智能体路径规划（MAPF）是大规模机器人及信息物理系统中的基础协调问题，要求多个智能体在有限的计算与通信资源下计算无冲突轨迹。集中式最优求解器虽能提供最优性保证，但其指数级计算复杂度限制了在大规模系统中的可扩展性与实时应用能力。现有去中心化启发式算法虽速度更快，却导致次优解与高成本不均问题。本文提出一种面向协作式MAPF的去中心化协调框架，基于因果机制——一种不可交易的人工信用分，该机制记录智能体过往协作行为并调控未来冲突解决决策。该方法将冲突解决建模为双边协商过程，使智能体能够通过成对重规划解决冲突，同时在有限通信且无全局优先级结构下促进长期公平性。本文在具有运动学方向约束的终身机器人仓库多智能体取送货场景中评估该机制。结果表明：因果机制能均衡智能体间的重规划负担，在保障整体效率的前提下降低服务时间差异。代码地址：https://github.com/DerKevinRiehl/karma_dmapf