Offloading strategy for UAV power inspection task based on deep reinforcement learning

Tong Jin; Gu Minghao; Shan Yun; Deng Fang-ming

doi:10.1117/12.2671522

28 April 2023 Offloading strategy for UAV power inspection task based on deep reinforcement learning

Tong Jin, Gu Minghao, Shan Yun, Deng Fang-ming

Proceedings Volume 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022); 126100V (2023) https://doi.org/10.1117/12.2671522
Event: Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 2022, Wuhan, China

Abstract

Due to the limitation of computer capacity and energy of equipment, unmanned equipment cannot perform intensive computer tasks well during emergency failure inspection. In order to solve the above problems, this paper proposes a task waste strategy based on Deep Reinforcement Learning (DRL), which is mainly applicable to several UAVs and individual ES scenarios. First of all, an end edge cloud cooperative unloading architecture is built in the edge environment of UAV, and the problem of unloading tasks is classified as an optimization problem to achieve the minimum delay under the limit of the computing and communication resources of the Edge Server (ES). Secondly, the problem is constructed as Markov decision, and Deep Q Network (DQN) is used to solve the optimization problem, and experience playback mechanism and greedy algorithm are introduced into the learning process. Experiments show that the mitigation strategy has lower latency and higher reliability.

Citation Download Citation

Tong Jin, Gu Minghao, Shan Yun, and Deng Fang-ming "Offloading strategy for UAV power inspection task based on deep reinforcement learning", Proc. SPIE 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 126100V (28 April 2023); https://doi.org/10.1117/12.2671522

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