Deep reinforcement learning-based path planning for net sealing robot

For the path planning problem of overhead transmission net sealing robot in the process of sealing network, a visual perception and decision method based on deep reinforcement learning is proposed. By combining the perceptual capability of convolutional neural networks with the decision-making capability of reinforcement learning, the method achieves direct output control from the visual perception input of the environment to the action through end-to-end learning, forming a closed loop between the system environment perception and decision control directly, and obtaining the optimal decision strategy by maximizing the cumulative reward return of the robot's interaction with the dynamical environment. Simulation experimental results prove that the method can meet the requirements of multi-task intelligent perception and decision making, and better solve the problems of traditional algorithms such as easily falling into local optimum, oscillating in narrow passages and unreachable targets near obstacles, which greatly improve the real-time and adaptability of trajectory tracking and dynamic obstacle avoidance of the net sealing robot and ensure the safe operation of the net sealing robot in transmission line sealing operations.