Intelligent Navigation Method for Substation Inspection Robot Based on 3D Point Cloud

Abstract

During the inspection process of substations, robots may encounter dynamic obstacles such as personnel, tools, or other equipment. Due to the influence of space environment, there are problems such as signal limitation, multipath effects, and interference, which affect the positioning accuracy of robots. To this end, a smart navigation method for substation inspection robots based on 3D point clouds has been developed. Using histograms and multi-scale retinas, the degraded laser point cloud image of the substation is enhanced. By iterating the nearest point algorithm, the point cloud set is updated, and the point cloud is accurately registered. The Kruskal algorithm is selected to reconstruct the multi frame point cloud data map. The feature extraction operator is used to extract the position information of key points in the image, and a local map optimization model is constructed. By adjusting the direction angle of the robot's operating point, Realize precise intelligent navigation of robot calibration paths. The research results indicate that the proposed method significantly improves the visual effect of the enhanced processed substation point cloud image, with more complete details. In multiple obstacle environments, the intelligent navigation effect of the robot path is better, and it can accurately avoid obstacles.