发表机构
National Institute of Advanced Industrial Science and Technology (AIST)（国立产业技术综合研究所（AIST））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于局部观测和标记多伯努利滤波的无通信多机器人任务分配框架，通过贪婪拍卖策略实现分布式协调，蒙特卡洛仿真验证了其在杂波和不确定性下的有效性。
AI 中文摘要
本文提出了一种仅基于局部观测的无通信多机器人任务分配框架。在本研究中，任务被定义为到达目标位置。每个机器人使用标记多伯努利（LMB）滤波器估计邻近机器人的位置，并通过基于贪婪拍卖的策略独立分配任务。通过在执行过程中持续更新状态估计并重新分配任务，所提出的方法实现了无需显式通信的分布式协调。蒙特卡洛仿真表明，所提出的方法能够在无机器人间通信的情况下实现有效的协作任务分配，同时对测量杂波和观测不确定性保持鲁棒性。
英文摘要
This paper proposes a communication-free multi-robot task allocation framework based solely on local observations. In this study, tasks are defined as reaching target locations. Each robot estimates the positions of neighboring robots using a Labeled Multi-Bernoulli (LMB) filter and independently assigns tasks through a greedy auction-based strategy. By continuously updating state estimates and reallocating tasks during execution, the proposed method enables decentralized coordination without explicit communication. Monte Carlo simulations demonstrate that the proposed method enables effective cooperative task allocation without inter-robot communication while remaining robust to measurement clutter and observation uncertainty.
CommentsThis paper has been submitted to IEEE ICRA 2027 for possible publication
