Hierarchical Reinforcement Learning for Viewpoint Planning with Scalable Precision in UAV Inspection

Viewpoint planning is crucial to ensure both inspection efficiency and observation precision in UAV inspection tasks. To address the issues of excessive waypoints and inadequate observation precision in traditional methods, this paper proposes a hierarchical reinforcement learning-based viewpoint planning method. The proposed method decomposes the viewpoint planning task into a high-level waypoint planning strategy and a low-level pose and zoom planning strategy. Additionally, a reward function is designed to enhance inspection precision, enabling collaborative optimization of waypoint positions, viewpoint poses, and focal lengths. Experimental results show that, compared with the classic coverage path planning method and non-hierarchical reinforcement learning approaches, the proposed method reduces the number of waypoints by at least 70% across multiple inspection objects. Furthermore, experiments with viewpoint planning at different precision levels demonstrate that the proposed method achieves scalable precision during inspection, with the observation resolution improving to 1.51 pixels/mm. Finally, a qualitative comparison is made between the proposed method in this paper and other representative methods in viewpoint planning. These results effectively demonstrate the validity and superiority of the proposed method in improving both inspection task efficiency and observation precision.