计算机科学

计算机科学 ›› 2024, Vol. 51 ›› Issue (5): 242-249.doi: 10.11896/jsjkx.230300159

• 人工智能 • 上一篇    下一篇

基于平均一致协议的分布式自适应多智能体聚集控制

谢光强, 钟必为, 李杨   

  1. 广东工业大学计算机学院 广州 510006
  • 收稿日期:2023-03-20 修回日期:2023-06-14 出版日期:2024-05-15 发布日期:2024-05-08
  • 通讯作者: 李杨(liyang@gdut.edu.cn)
  • 作者简介:(xiegq@gdut.edu.cn)
  • 基金资助:
    国家自然科学基金(62006047);广东省重点领域研发计划项目(2021B0101220004)

Distributed Adaptive Multi-agent Rendezvous Control Based on Average Consensus Protocol

XIE Guangqiang, ZHONG Biwei, LI Yang   

  1. School of Computer Science and Technology,Guangdong University of Technology,Guangzhou 510006,China
  • Received:2023-03-20 Revised:2023-06-14 Online:2024-05-15 Published:2024-05-08
  • About author:XIE Guangqiang,born in 1979,Ph.D,professor,master supervisor,is a member of CCF(No.17290S).His main research interests include multi-agent systems and data mining.
    LI Yang,born in 1980,Ph.D,professor,master supervisor,is a member of CCF(No.23122M).Her main research interests include differential privacy,multi-agent systems and machine lear-ning.
  • Supported by:
    National Natural Science Foundation of China(62006047) and Guangdong Key Areas R&D Program Projects(2021B0101220004).

PDF (PC)

293

摘要： 分布式聚集控制问题是多智能体协同控制中的一个重要问题。由于智能体的可移动性和感知能力有限,传统的分布式聚集算法难以保证连通性,从而聚集成多个簇群。此外,去中心化的大规模聚集控制给获取全局聚集点带来了巨大的挑战。针对连通性保护问题,基于平均一致协议与约束集,提出了一个带有连通性约束的多智能体聚集协议(Multi-Agent Rendezvous Protocol with Connectivity constraints,MARP-CC)。然后针对聚集点无法预测的问题,提出了位置合成(Location Synthesis Strategy,LSS)和位置重定向(Location Redirection Strategy,LRS)两种控制策略。智能体根据当前连通情况,自适应选择最优的控制策略进行迭代。结合这两种控制策略,提出了带连通性约束的分布式自适应多智能体聚集算法(Distributed Adaptive Multi-Agent Rendezvous algorithm with Connectivity Constraints,DAMAR-CC)。对算法的收敛性和连通性进行了分析,并通过大量的仿真说明了DAMAR-CC可以引导智能体稳定地聚集在初始拓扑的几何中心。

关键词: 平均一致, 连通性保持, 多智能体聚集, 约束集

Abstract: Distributed rendezvous control is an important issue in multi-agent collaborative control.Due to the limited mobility and perception capabilities of agents,traditional distributed rendezvous algorithms are difficult to ensure connectivity,thereby aggregating multiple clusters.In addition,decentralized large-scale rendezvous control poses a huge challenge to obtaining global rendezvous points.For the connectivity protection problem,based on the average consensus protocol and constraint set,a multi-agent rendezvous protocol with connectivity constraints(MARP-CC) is proposed.Then,for the rendezvous point unpredictability problem,location synthesis(LSS) and location redirection(LRS) control strategies are proposed.The agent adaptively selects the optimal control strategy for iteration based on the current connectivity situation.Finally,combining these two control strategies,a distributed adaptive multi-agent rendezvous algorithm with connectivity constraints(DAMAR-CC) is proposed.The conver-gence and connectivity analysis of the algorithm are given,and a large number of simulations show that DAMAR-CC can make agents stably rendezvous at the geometric center of the initial topology.

Key words: Average consensus, Connectivity maintenance, Multi-agent rendezvous, Constraint set

中图分类号: 

  • TP249
[1]AFRIN M,JIN J,RAHMAN A,et al.Resource allocation and service provisioning in multi-agent cloud robotics:A comprehensive survey[J].IEEE Communications Surveys & Tutorials,2021,23(2):842-870.
[2]ZHANG D,FENG G,SHI Y,et al.Physical safety and cyber security analysis of multi-agent systems:A survey of recent advances[J].IEEE/CAA Journal of Automatica Sinica,2021,8(2):319-333.
[3]GRONAUER S,DIEPOLD K.Multi-agent deep reinforcement learning:a survey[J].Artificial Intelligence Review,2022,55(2):895-943.
[4]XIONG L,CAO L,LAI J,et al.A review of Multi-agent deep reinforcement Learning based on Value decomposition [J].Computer Science,2022,49(9):172-182.
[5]KE J,XIAO F,YANG H,et al.Learning to delay in ride-sourcing systems:a multi-agent deep reinforcement learning framework[J].IEEE Transactions on Knowledge and Data Enginee-ring,2020,34(5):2280-2292.
[6]LI X,YU Z,LI Z,et al.Group consensus via pinning control for a class of heterogeneous multi-agent systems with input constraints[J].Information Sciences,2021,542:247-262.
[7]HAYDARI A,YILMA Y.Deep reinforcement learning for intelligent transportation systems:A survey[J].IEEE Transactions on Intelligent Transportation Systems,2020,23(1):11-32.
[8]DONG G,LI H,MA H,et al.Finite-time consensus tracking neural network FTC of multi-agent systems[J].IEEE Transactions on Neural Networks and Learning Systems,2020,32(2):653-662.
[9]TANG Y,ZHANG D,SHI P,et al.Event-based formation control for nonlinear multiagent systems under DoS attacks[J].IEEE Transactions on Automatic Control,2020,66(1):452-459.
[10]ZHANG J,ZHANG H,SUN S,et al.Leader-follower consensus control for linear multi-agent systems by fully distributed edge-event-triggered adaptive strategies[J].Information Sciences,2021,555:314-338.
[11]XIE G,CHEN J,LI Y.Hybrid-order network consensus for distributed multi-agent systems[J].Journal of Artificial Intelligence Research,2021,70:389-407.
[12]LI Y,TIAN J,XIE G,et al.A review of research on multi-agent rendezvous problem[J].Computer Application Research,2019,36(6):1609-1613.
[13]BARTSCHI A,BAMPAS E,CHALOPIN J,et al.Near-gathe-ring of energy-constrained mobile agents[J].Theoretical Computer Science,2021,849:35-46.
[14]DONG Y,XU S.Rendezvous with connectivity preservationproblem of linear multiagent systems via parallel event-triggered control strategies[J].IEEE Transactions on Cybernetics,2020,52(5):2725-2734.
[15]LIN J,MORSE A,ANDERSON B.The multi-agent rendezvous problem-the asynchronous case[C]//43rd IEEE Conference on Decision and Control(CDC).BAHAMAS,2004,2:1926-1931.
[16]HUI Q.Finite-time rendezvous algorithms for mobile autono-mous agents[J].IEEE Transactions on Automatic Control,2010,56(1):207-211.
[17]FAN Y,FENG G,WANG Y,et al.Distributed event-triggeredcontrol of multi-agent systems with combinational measurements[J].Automatica,2013,49(2):671-675.
[18]DONG Y,SU Y,LIU Y,et al.An internal model approach formulti-agent rendezvous and connectivity preservation with nonlinear dynamics[J].Automatica,2018,89:300-307.
[19]OZSOYELLER D,OZKASAP O,ALOQAILY M.m-rendez-vous:Multi-agent asynchronous rendezvous search technique[J].Future Generation Computer Systems,2022,126:185-195.
[20]SAMTILLI M,FRANCESCHELLI M,GASPARRI A.Securerendezvous and static containment in multi-agent systems with adversarial intruders[J].Automatica,2022,143:110456.
[21]YU D,DONG W,REN W.Finite Time rendezvous Control for Topologically Connected Multi-Agent Networks [J].Control and Decision,2016,31(4):750-754.
[22]DONG J G.Finite-time connectivity preservation rendezvouswith disturbance rejection[J].Automatica,2016,71:57-61.
[23]XIE G,XU H,LI Y,et al.Fast distributed consensus seeking in large-scale and high-density multi-agent systems with connecti-vity maintenance[J].Information Sciences,2022,608:1010-1028.
[24]OLFATI-SABER R,FAX J A,MURRAY R M.Consensus and cooperation in networked multi-agent systems[J].Proceedings of the IEEE,2007,95(1):215-233.
[25]ANDO H,OASA Y,SUZUKI I,et al.Distributed memorylesspoint convergence algorithm for mobile robots with limited visibility[J].IEEE Transactions on Robotics and Automation,1999,15(5):818-828.
[26]CORTES J,MARTINEZ S,BULLO F.Robust rendezvous for mobile autonomous agents via proximity graphs in arbitrary dimensions[J].IEEE Transactions on Automatic Control,2006,51(8):1289-1298.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
地址：重庆市渝北区洪湖西路18号    邮编：401121  
电话：023-63500828（编辑部） 023-67039612（会议/专题） 023-67039623（财务/发票）
E-mail：jsjkx12@163.com
本系统由北京玛格泰克科技发展有限公司设计开发