Unmanned aerial vehicle (UAV) flight assignment method for intensive equipment inspection in converter station

Jingxiang Li; Hao Lai; Yanhui Shi; Yang Yang; Huixiang Hu

doi:10.1117/12.3028962

22 May 2024 Unmanned aerial vehicle (UAV) flight assignment method for intensive equipment inspection in converter station

Jingxiang Li, Hao Lai, Yanhui Shi, Yang Yang, Huixiang Hu

Author Affiliations +

Proceedings Volume 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023); 131762Y (2024) https://doi.org/10.1117/12.3028962
Event: Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 2023, Hangzhou, China

Abstract

Conventional UAV flight assignment methods mainly use two-dimensional directed acyclic graph to generate flight task integration matrix, which is easily influenced by the time sequence of flight tasks, resulting in long task assignment time. Therefore, it is necessary to design a new UAV flight assignment method for dense equipment in converter stations. That is to say, the UAV flight task assignment model is constructed for the inspection of dense equipment in converter station, and the UAV flight task assignment algorithm is designed, thus realizing the UAV flight task assignment. The experimental results show that the designed UAV flight task assignment method has a good assignment effect, and the task assignment time under different task numbers is short, which is efficient and has certain application value, and has made certain contributions to improving the efficiency and ensuring the safety of UAV inspection.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jingxiang Li, Hao Lai, Yanhui Shi, Yang Yang, and Huixiang Hu "Unmanned aerial vehicle (UAV) flight assignment method for intensive equipment inspection in converter station", Proc. SPIE 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 131762Y (22 May 2024); https://doi.org/10.1117/12.3028962

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