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Acta Aeronautica et Astronautica Sinica ›› 2026, Vol. 47 ›› Issue (2): 332075.doi: 10.7527/S1000-6893.2025.32075

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

Task allocation algorithm for UAV swarm based on temporal coupling analysis

Haoyu WANG1, Zexu ZHANG1(), Shan WEN1, Jinlong LIU1, Beixiao ZHU2, Weimin BAO3   

  1. 1.School of Astronautics,Harbin Institute of Technology,Harbin 150001,China
    2.School of Information Science and Technology,ShanghaiTech University,Shanghai 201210,China
    3.Corporation Science and Technology Committee,China Aerospace Science and Technology,Beijing 100048,China
  • Received:2025-04-03 Revised:2025-04-17 Accepted:2025-05-13 Online:2025-05-28 Published:2025-05-27
  • Contact: Zexu ZHANG E-mail:zexuzhang@hit.edu.cn
  • Supported by:
    Aeronautical Science Foundation of China(2024Z023077001)

Abstract:

With the increasing scale of UAV swarm and the growing complexity of mission scenarios, designing efficient task allocation algorithms has become a significant challenge for swarm applications. To address the allocation failures and conflicts caused by cross-queue influences in temporal task allocation for UAV swarm, a Temporal Coupling Analysis-based Task Allocation method (TCATA) is proposed. Firstly, a task allocation model considering UAV payloads, task requirements, and task temporal constraints is established. The impact of task temporal coupling constraints under the market mechanism is analyzed from two aspects: performance function design and performance validity. Next, a local adjustment set is constructed for each UAV within the swarm, and a global adjustment set is obtained through communication consensus. Then, based on the performance magnitude and conflict relationships of each adjustment, the global adjustment scheme decision problem is modeled as a maximum weighted clique problem and solved by a greedy algorithm by each UAV. Finally, the executors of the tasks are determined and the global task assignment result is updated. Simulation experiments demonstrate that in solving temporal task assignment problems with hundreds of UAVs and tasks considering communication delays, TCATA significantly outperforms distributed genetic algorithms in both efficiency and performance metrics. Compared with the CBBA-TCC and CNP algorithm, TCATA achieves marginally superior performance while reducing executing time and iteration number by more than 50%, validating its effectiveness in large-scale sequential task allocation.

Key words: unmanned aerial vehicles swarm, temporal task coupling constraint, distributed task allocation, maximum weighted clique, market mechanism

CLC Number: