An adaptive feature extraction algorithm for multiple typical seam tracking based on vision sensor in robotic arc welding

Intelligent robotic welding is an indispensable part of modern welding manufacturing, and vision-based seam tracking is one of the key technologies to realize intelligent welding. However, the adaptability and robustness of most image processing algorithms are deficient during welding practice. To address this problem, an adaptive feature extraction algorithm based on laser vision sensor is proposed. According to laser stripe images, typical welding seams are classified into continuous and discontinuous welding seams. A Faster R-CNN model is trained to identify welding seam type and locate laser stripe ROI automatically. Before welding, initial welding point is determined through point cloud processing to realize welding guidance. During seam tracking process, the seam edges are achieved by a two-step extraction algorithm, and the laser stripe is detected by Steger algorithm. Based on the characteristics of two kinds of welding seams, the corresponding seam center extraction algorithms are designed. And a prior model is proposed to ensure the stability of the algorithms. Test results prove that the algorithm has good adaptability for multiple typical welding seams and can maintain satisfying robustness and precision even under complex working conditions. (C) 2019 Elsevier B.V. All rights reserved.