An Identification and Localization Method for 3D Workpiece Welds Based on the DBSCAN Point Cloud Clustering Algorithm

With the development of robotic welding automation, there is a strong interest in welding seam identification and localization methods with high accuracy, real-time performance, and robustness. This paper proposed a 3D workpiece weld identification and localization method based on DBSCAN (density-based spatial clustering of applications with noise) to realize stable feature extraction for multiple joint types. Firstly, this method employs combinatorial filtering to effectively eliminate non-target point clouds, including outliers and installation platform point clouds, which can minimize the computational load. Secondly, DBSCAN is used to classify workpiece point clouds into different clusters, which can be used for point cloud segmentation of flat workpieces and curved workpieces. Thirdly, the edge detection and feature extraction methods are used to obtain joint gap and weld feature points while combining the information of point clouds for different types of welds. Finally, based on the identification and localization of the welds, welding path planning and attitude planning are implemented. Experimentation results indicated that the proposed method exhibits robustness across various types of welded joints, including butt joints with straight seams, butt joints with curved seams, butt joints with curved workpieces, and lap joints. Meanwhile, the average error of joint gap detection was 0.11 mm and the processing time of a 90 mm straight-seam butt joint is 701.12 ms.