信息网络安全 ›› 2024, Vol. 24 ›› Issue (2): 217-228.doi: 10.3969/j.issn.1671-1122.2024.02.005

• 理论研究 • 上一篇    下一篇

面向异构复合任务的无人集群动态重叠联盟任务分配方法

姚昌华1, 程田圆1(), 屈毓锛2, 苏婷3   

  1. 1.南京信息工程大学电子与信息工程学院,南京 210044
    2.南京航空航天大学电子信息工程学院,南京211106
    3.海南大学信息与通信工程学院,海口 570228
  • 收稿日期:2023-06-21 出版日期:2024-02-10 发布日期:2024-03-06
  • 通讯作者: 程田圆 E-mail:3383774180@qq.com
  • 作者简介:姚昌华(1983—),男,重庆,教授,博士,主要研究方向为智能无人集群和电磁频谱对抗|程田圆(1999—),女,山东,硕士研究生,主要研究方向为智能无人集群|屈毓锛(1987—),男,湖北,副研究员,博士,CCF会员,主要研究方向为移动边缘计算、边缘人工智能、无人机协同智能及其应用|苏婷(1985—),女,安徽,副教授,博士,主要研究方向为信号处理、无线通信
  • 基金资助:
    国家自然科学基金(61971439);国家自然科学基金(U22B2002);江苏省自然科学基金(BK20191329)

A Task Allocation Method for Unmanned Clusters Based on Dynamic Overlapping Coalition Toward Heterogeneous Composite Tasks

YAO Changhua1, CHENG Tianyuan1(), QU Yuben2, SU Ting3   

  1. 1. School of Electronics & Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
    2. College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
    3. School of Information and Communication Engineering, Hainan University, Haikou 570228, China
  • Received:2023-06-21 Online:2024-02-10 Published:2024-03-06
  • Contact: CHENG Tianyuan E-mail:3383774180@qq.com

摘要:

智能无人集群由于其平台特性的多样性,具备较好的资源调配空间和功能弹性,能够应对复杂多变的任务需求。现有研究大多未考虑任务的异构性和关联性等实际需求,其任务分配方法在需求和资源的适配性、集群协同的动态响应能力上存在不足。文章针对无人集群系统在遂行多目标任务过程中存在的任务分配不均、协同性差和动态适应性差等问题,提出了面向异构复合任务的动态响应重叠联盟任务分配架构。首先,综合考虑多个耦合异构任务的价值、优先级、需求和任务变化带来的影响来构建联盟博弈模型;然后设计算法,分布式协同调度不同无人集群(无人机和无人车)的资源,实现集群异构成员的资源与异构任务合理匹配,并能根据任务变化情况进行高效的动态调整。仿真结果表明,文章提出的算法能够适应动态任务场景,形成稳定高效的任务联盟和资源分配方案,提高了无人集群遂行多样化异构任务的系统收益和成功率,实现了无人集群系统在动态条件下的协同任务分配优化。

关键词: 无人集群, 智能决策, 重叠联盟博弈, 动态任务分配

Abstract:

Due to the diversity of platform characteristics, intelligent unmanned clusters have good resource allocation space and functional flexibility, and can respond to complex and ever-changing task requirements. However, most existing researches have not considered practical needs such as the heterogeneity and correlation of tasks, and their task allocation methods still lack adaptability to the requirements and resources, as well as dynamic response capabilities for cluster collaboration. This paper proposed a dynamic response overlapping coalition task allocation architecture for unmanned cluster systems to address problems such as uneven task allocation, poor collaboration, and weak dynamic adaptability in the process of performing multi-objective tasks. The architecture considered the value, priority, and requirements of multiple coupled heterogeneous tasks, as well as the impact of task changes, to construct a coalition game model and designed a coalition formation algorithm. It distributed and coordinated the resources of different cluster members, including unmanned aerial vehicles and unmanned ground vehicles, to achieve a reasonable match between heterogeneous member resources and heterogeneous tasks. Furthermore, it can efficiently perform dynamic adjustments according to changes in tasks. Simulation results show that the proposed algorithm can adapt to dynamic task scenarios, form stable and efficient task coalitions and resource allocation results, improve the system benefits and success rate of unmanned clusters performing diverse heterogeneous tasks, and achieve collaborative task allocation optimization under dynamic conditions.

Key words: unmanned clusters, intelligent decision-making, overlapping coalition formation game, dynamic task assignment

中图分类号: