Automatic Alignment Control Algorithm Based on Deep Reinforcement Learning for Robotic Instrument Inspection

Inspection robots are increasingly deployed in substations for automated instrument monitoring. However, due to navigation errors and mechanical wear, robots fail to capture inspection images with the required quality and precision. To address this issue, we propose a reinforcement learning-based alignment control method. The proposed approach combines visual perception with an attention mechanism to accurately extract contour features of target instruments. The state representation is formed using the extracted features, raw images, and robot pose information. The alignment task is formulated as a Markov Decision Process (MDP), and a Dueling Deep Q-Network (Dueling DQN) is employed to learn a control policy that guides the robot to adjust its pose toward optimal alignment in real time. Experimental results demonstrate that the proposed algorithm significantly improves alignment accuracy and efficiency compared to conventional algorithm, and provides a promising solution for intelligent inspection in substations.