Application of improved RANSAC-Based weld seam point cloud extraction in robotic welding

Objective To address the issues of severe point cloud noise interference and insufficient feature extraction accuracy in the automated welding of circular plug welds, this study aims to develop a high-precision robotic welding system guided by 3D point cloud data. A composite point cloud processing workflow was proposed by integrating normal filtering, Euclidean clustering, and an improved RANSAC algorithm. Methods This method was utilized to segment and fit high-noise point clouds for extracting the 3D geometric features of circular hole welds, guiding the robot to accomplish automated welding trajectory planning through a hand-eye vision system. Results Experimental results indicate that under complex working conditions with noise exceeding 20%, the center positioning error is ≤0.05 mm and the radius measurement error is ≤0.084 mm. Compared to the standard RANSAC algorithm, the positioning accuracy is improved by 84.6%, with the entire processing workflow completed within 545 ms. The system’s positioning repeatability reaches ±0.1 mm, increasing welding efficiency by approximately 30% compared to manual operations, and achieving uniform weld bead formation on both flat plates and circular ring workpieces. Conclusion The proposed system balances exceptional algorithmic robustness and industrial real-time performance, providing an efficient and reliable automated solution for the precise feature extraction and automated welding of circular hole welds in high-noise environments.