UAV coverage path planning based on deep reinforcement learning
To improve the performance of overlay path planning tasks,a multi-scale map UAV coverage path planning method based on deep reinforcement learning is proposed.Firstly,the map is centralized and mapped with different sizes.Secondly,the Luong attention mechanism is added to extract features of more interest on the map.Finally,the reward function with different weights is designed.The experiments show that the improved UAV coverage path planning method can improve the coverage and successful landing rate of the UAV to the target area.
万方数据
维普
