Point Cloud-Based Weld Seam Recognition and Localization for Robotic Welding

The identification and spatial positioning of welds are key links in welding process matching and automatic welding path planning. Therefore, achieving automatic recognition and spatial positioning of weld point cloud features under online scanning imaging conditions is of great significance for the promotion and application of teaching free automatic welding technology. This article is based on a point cloud neural network model and conducts in-depth research on the recognition and spatial positioning algorithm of weld point cloud features. Specifically, the PointNet++ network, which is a point cloud neural network model, is first used to perform feature recognition on the three-dimensional point cloud of the welded parts obtained by laser line scanning. PointNet++ can distinguish different types of welded joints based on point cloud features and further perform preliminary rough positioning of the spatial position of the weld seam. After retaining the coarse positioning point cloud containing weld seam features, different algorithms are used to accurately locate the spatial position of the weld seam based on different weld seam features. The experimental results show that based on the PointNet++model for rough positioning, the weld length error does not exceed 0.3 mm, and the recognition accuracy and efficiency are much higher than traditional algorithms. The research results of this article can provide important references for the further intelligent development of automatic welding robots.