Optimization Method for Obstacle Crossing of Transmission Line X-ray Inspection Robot Based on Multiple Points of Interest

In transmission lines, only the position of obstacles can serve as constraints, resulting in low obstacle crossing efficiency. Therefore, a obstacle crossing optimization method for transmission line X-ray detection robots based on multiple points of interest is proposed. Based on the points of interest, the robot mechanism size is designed with line curvature, geometric dimensions and positions of obstacles as constraints, and the total time and energy consumption for obstacle crossing are calculated. Constructed an obstacle crossing optimization function with the goal of short exercise time and low energy consumption. Combine the improved A* algorithm with DWA, and combine deep learning and multi-sensor fusion technology to optimize obstacle crossing paths. By combining optimization functions to obtain the optimal obstacle crossing path, the effectiveness of the contour can be evaluated. The experimental results show that the optimized obstacle crossing route is more direct, the travel length is shortened, the efficiency is improved, and the acceleration of key joints is significantly reduced, ensuring that the robot can perform tasks stably.