Task Unloading Strategy of Multi UAV for Transmission Line Inspection Based on Deep Reinforcement Learning

Abstract

Due to the limitation of the computing power and energy resources, an unmanned aerial vehicle (UAV) team usually offloads the inspection task to the cloud for processing when performing emergency fault inspection, which will lead to low efficiency of transmission line inspection. In order to solve the above problems, this paper proposes a task offloading strategy based on deep reinforcement learning (DRL), aiming for the application of a multi-UAV and single-edge server. First, a “device-edge-cloud” collaborative offloading architecture is constructed in the UAV edge environment. Secondly, the problem of offloading power line inspection tasks is classified as an optimization problem to obtain the minimum delay under the constraints of edge server computing and communication resources. Finally, the problem is constructed as a Markov decision, and a deep Q-network (DQN) is used to obtain the minimum delay of the system. In addition, an experience replay mechanism and a greedy algorithm are introduced in the learning process to improve the offloading accuracy. The experimental results show that the proposed offloading strategy in this paper saves 54%, 37% and 26% of the task completion time, respectively, compared with local offloading, cloud offloading and random offloading. It effectively reduces the UAV inspection delay and improves the transmission line inspection efficiency.