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Journal of Projectiles, Rockets, Missiles and Guidance >

2025 , Vol. 45 >Issue 5: 751 - 760

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2025.05.019

Academic article

Research on Multi-UAV Collaborative Scheduling Optimization Algorithm for Power Inspection

  • ZHOU Jiaxing , 1, 2 ,
  • CHEN Yuhao , 1, 2, * ,
  • GAO Dengwei 3 ,
  • DENG Yifan 4 ,
  • LI Qing 5 ,
  • YU Zicheng 1, 2 ,
  • DENG Zhao 1, 2
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  • 1 School of Electrical Engineering and Automation, Xiamen University of Technology, Xiamen 361024,Fujian, China
  • 2 Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control, Xiamen 361024,Fujian, China
  • 3 Xi’an Modern Control Technology Research Institute, Xi’an 710065,Shanxi, China
  • 4 Faculty of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710075,Shanxi, China
  • 5 School of Astronautics, Northwestern Polytechnical University, Xi’an 710072,Shanxi, China

Received date: 2025-01-20

  Online published: 2025-11-28

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Abstract

The use of multi-UAV collaborative scheduling can significantly enhance the efficiency of power line inspections.However,in actual complex environments,the optimization of multi-UAV scheduling for power inspection tasks faces various complex constraints,leading to low solution efficiency and slow convergence of the optimization model.To address these issues,this paper proposes an Adaptive Ant Colony Optimization with Elite Strategy (AACOES).First,an optimization model closely resembling actual UAV inspection scheduling is constructed by comprehensively considering various practical constraints,such as the flight characteristics of homogeneous UAVs,battery endurance,and external wind fields.Second,to overcome the shortcomings of traditional ant colony algorithms in terms of convergence speed and global optimization capability,we introduce an elite strategy and adaptive adjustment factors to optimize the pheromone update rules of the algorithm,thereby effectively enhancing the diversity of the ant population and improving the convergence speed of the algorithm.Finally,through comparative simulation experiments with various advanced algorithms,the effectiveness of the proposed algorithm is validated.The experimental results indicate that under multiple constraint conditions,this method can significantly improve the efficiency and accuracy of multi-UAV collaborative inspections,while also demonstrating advantages in algorithm cost and stability,providing a new technological approach for the field of power line inspections.

Key words： power inspection; multiple unmanned aerial vehicles; ant colony algorithm; collaborative scheduling

Cite this article

ZHOU Jiaxing , CHEN Yuhao , GAO Dengwei , DENG Yifan , LI Qing , YU Zicheng , DENG Zhao . Research on Multi-UAV Collaborative Scheduling Optimization Algorithm for Power Inspection[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2025 , 45(5) : 751 -760 . DOI: 10.15892/j.cnki.djzdxb.2025.05.019

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