To address the problems of traditional UAV path planning algorithms,such as high algorithm dimensionality,difficult modelling and low efficiency,this paper proposes a UAV 3D path planning algorithm based on an improved deep Q-network(DQN).In this algorithm,DQN is constructed based on convolutional neural networks,an attention enhancement model is designed to improve the extraction of key terrain information by the network,and a reward function is designed to achieve comprehensive optimisation of flight distance and energy consumption.To address the problems of traditional deep reinforcement algorithms such as network convergence difficulties,a combined exploration strategy is designed in this paper.This paper compares the algorithm with the A* algorithm,and verifies that the algorithm can achieve UAV path planning with a trade-off between distance and energy consumption from both qualitative and quantitative perspectives,and significantly improves the planning efficiency.
关键词
无人机/路径规划/三维环境/深度Q网络/注意力增强模型
Key words
UAV/path planning/3D environment/DQN/attention enhancement model
维普
万方数据