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Acta Aeronautica et Astronautica Sinica ›› 2025, Vol. 46 ›› Issue (16): 331635.doi: 10.7527/S1000-6893.2024.31635

• Electronics and Electrical Engineering and Control • Previous Articles     Next Articles

Self-organized consensus decision-making method for swarm UAV tracking multiple targets

Jiang ZHAO1, Minghao PI1, Bailing TIAN2, Pei CHI3(), Yingxun WANG3   

  1. 1.School of Automation Science and Electrical Engineering,Beihang University,Beijing 100191,China
    2.School of Electrical and Information Engineering,Tianjin University,Tianjin 300072,China
    3.Institute of Unmanned System,Beihang University,Beijing 100191,China
  • Received:2024-12-09 Revised:2025-01-03 Accepted:2025-01-09 Online:2025-08-25 Published:2025-01-16
  • Contact: Pei CHI E-mail:peichi@buaa.edu.cn
  • Supported by:
    Fundamental Research Funds for the Central Universities of China

Abstract:

Distributed Unmanned Aerial Vehicle (UAV) swarms can achieve collaborative tracking and dynamic task allocation through autonomous decision-making and information interaction, demonstrating significant application potential in complex and dynamic target tracking control scenarios. To address poor performance of the single swarm in tracking multiple separating moving targets, a self-organized dynamic decision-making method for UAV swarm based on information consensus is proposed. Firstly, considering the communication distance constraints and communication delays between UAVs, a swarm information consensus algorithm based on the latest timestamp forwarding principle is designed to achieve information consensus among UAVs. Secondly, a swarm collaborative decision-making algorithm based on request-response is proposed, realizing member allocation, decision-making and self-organized grouping. Finally, a target tracking control algorithm based on a self-coordination mechanism is designed, achieving adaptive adjustment of control component weights and self-organized target tracking. Compared to existing research on multi-target tracking, the proposed method can realize dynamic target allocation decision-making during the target tracking process and determining the number of UAVs allocated to each target based on the escape risk of each target. Simulation results show that the proposed method can achieve self-organized grouping of the swarm when targets are separating, with a grouping accuracy rate of above 0.9.

Key words: unmanned aerial vehicle (UAV), multi-target tracking, swarm information consensus, collaborative decision-making, self-organizing

CLC Number: