Research and application of obstacle avoidance method for inspection robot based on road scene understanding

In order to improve the navigation obstacle avoidance ability to the inspection robot, deep learning technology is applied to scene recognition in this paper, and an autonomous obstacle avoidance method of inspection robot based on road scene understanding is proposed. Firstly, a high-precision road scene understanding network is constructed by combining multi-layer convolution with LeakyReLU activation function, and in the way of residual structure and sampling structure on pyramid; Secondly, an adaptive control module is designed to compare the deep feature information of the two images before and after, and automatically control the feature calculation of the subsequent network layer according to the feature difference, so as to avoid repeated extraction of similar features and ensure the network efficiency; Finally, the scene understanding results are transformed into target information in different areas in front of the inspection robot, and the feasible road area in front of the robot and the location of obstacles are analyzed to guide the inspection robot to realize navigation and obstacle avoidance. Experiments show that the proposed method effectively balances the recognition accuracy and calculation efficiency of the scene understanding network. At the same time, on the actual substation inspection robot platform, the method also shows strong adaptability, and can accurately and efficiently provide scene information for the robot and assist the robot to realize real-time autonomous obstacle avoidance.