Hierarchical Reinforcement Learning For Task Generation of Transmission Tower Inspection Using Point Clouds

Unmanned Aerial Vehicles (UAVs) are widely used for inspecting transmission towers. However, traditional waypoint planning relies heavily on manual experience. This leads to low efficiency, incomplete coverage, and a lack of standardization. Facing these problems, this paper proposes an intelligent generation method based on Hierarchical Reinforcement Learning (HRL). This method achieves end-to-end automation, converting raw point cloud data directly into an optimal set of waypoints. Preprocess and grid the point cloud data to build a model of the coverage area. Then design a hierarchical framework to break down the complex planning task. This framework divides the task into high-level waypoint selection and low-level pose optimization. Specifically, the high-level part uses a Deep Q-Network (DQN) to learn the best sequence of waypoints. The low-level part uses Q-learning tables to optimize the pitch and yaw angles for each point. Meanwhile, design a reward function to maximize coverage and minimize the number of waypoints. This guides the agent to independently learn a strategy that balances efficiency and accuracy. Experimental results show that the generated waypoints achieve balanced and comprehensive coverage. The method significantly improves the efficiency of transmission tower inspection and stability of training effect.