Multi-type patrol task assignment method for UAV based on deep reinforcement learning

Zhiming Lin; Huiyang Gui; Yuyang Guan; Renjie Xie; Kun Wang

doi:10.1117/12.3013410

29 November 2023 Multi-type patrol task assignment method for UAV based on deep reinforcement learning

Zhiming Lin, Huiyang Gui, Yuyang Guan, Renjie Xie, Kun Wang

Proceedings Volume 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023); 129371J (2023) https://doi.org/10.1117/12.3013410
Event: International Conference on Internet of Things and Machine Learning (IoTML 2023), 2023, Singapore, Singapore

Abstract

Conventional UAV multi-type patrol task allocation method mainly uses MEC(mobile edge computing) technology to unload UAV data, which is easily affected by the change of dynamic unloading mechanism, resulting in a high delay in patrol task allocation. Therefore, it is necessary to design a new UAV multi-type patrol task allocation method based on deep reinforcement learning. That is, the deep reinforcement learning technology is used to construct the multi-type inspection task allocation model of UAV, and the multi-type inspection task allocation algorithm of UAV is designed, thus realizing the multi-type inspection task allocation of UAV. The experimental results show that the design of UAV deep reinforcement learning multi-type inspection task allocation method has good distribution effect, reliability and certain application value, and has made certain contributions to improving inspection reliability and reducing comprehensive inspection cost.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhiming Lin, Huiyang Gui, Yuyang Guan, Renjie Xie, and Kun Wang "Multi-type patrol task assignment method for UAV based on deep reinforcement learning", Proc. SPIE 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023), 129371J (29 November 2023); https://doi.org/10.1117/12.3013410

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