Research on UAV Patrol 3D Path Planning Method Based on Improved Deep Q-Network
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.
UAVpath planning3D environmentDQNattention enhancement model
万方数据
维普
