系统工程与电子技术 ›› 2026, Vol. 48 ›› Issue (1): 185-197.doi: 10.12305/j.issn.1001-506X.2026.01.17

• 系统工程 • 上一篇    下一篇

多智能体系统故障临机处理下的快速任务重分配

单晨宇1,2, 李少凡1,2, 齐瑞云1,2,*()   

  1. 1. 南京航空航天大学航空航天结构力学及控制全国重点实验室,江苏 南京 211106
    2. 南京航空航天大学自动化学院,江苏 南京 211106
  • 收稿日期:2023-09-06 出版日期:2026-01-25 发布日期:2026-02-11
  • 通讯作者: 齐瑞云 E-mail:ruiyun.qi@nuaa.edu.cn
  • 作者简介:单晨宇(1998—),男,硕士研究生,主要研究方向为多智能体任务分配、路径规划
    李少凡(1998—),男,硕士研究生,主要研究方向为多智能体任务规划、任务重规划
  • 基金资助:
    国家自然科学基金(62020106003);航空航天结构力学及控制全国重点实验室(南京航空航天大学)自主研究课题(MCAS-I-0225G03)资助课题

Fast task reassignment in multi-agent system emergency fault handling

Chenyu SHAN1,2, Shaofan LI1,2, Ruiyun QI1,2,*()   

  1. 1. National Key Laboratory of Aerospace Structural Mechanics and Control,Nanjing University of Aeronautics and Astronautics,Nanjing 211106, China
    2. College of Automation Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China
  • Received:2023-09-06 Online:2026-01-25 Published:2026-02-11
  • Contact: Ruiyun QI E-mail:ruiyun.qi@nuaa.edu.cn

摘要:

为解决在执行任务过程中部分智能体可能会发生故障,无法完成预定的任务分配方案,需要进行任务重分配的问题,提出一种具有虚拟管理者基于共识的捆绑算法的局部重分配方法。首先,引入基于时序的任务优先级排序方法,提高资源利用率。然后,基于K均值聚类方法,将大规模任务场景划分为多个小规模任务组,减少任务重分配求解时间。最后,仿真实验验证所提方法的有效性。与基于共识的捆绑算法的全局重分配方法进行对比,所提该方法在大规模场景下具有更好的实时性。随着任务规模的扩大,采用该方法进行任务重分配的任务完成率上升。

关键词: 多智能体, 任务重分配, 故障临机处理, 基于共识的捆绑算法

Abstract:

To solve the problem of some intelligent agents failing to complete the predetermined task allocation plan and requiring task reallocation during task execution, a local reallocation method consensus-based bundle algorithm with virtual managers is proposed. Firstly, a time series based task priority sorting method is introduced to improve resource utilization. Secondly, based on the K-means clustering method, the large-scale task scenario is divided into multiple small-scale task groups to reduce the time required for task reallocation and solving. Finally, the effectiveness of the proposed method is verified through simulation experiments. Compared with the global reallocation method based on the consensus bundling algorithm, the proposed method has better real-time performance in large-scale scenarios. As the scale of tasks expands, the completion rate of tasks reassigned using this method increases.

Key words: multi-agent, task reassignment, emergency fault handling, consensus-based bundle algorithm

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