An Efficient and Robust Complex Weld Seam Feature Point Extraction Method for Seam Tracking and Posture Adjustment

Abstract

To realize high-quality robotic welding, an efficient and robust complex weld seam feature point extraction method based on a deep neural network (Shuffle-YOLO) is proposed for seam tracking and posture adjustment. The Shuffle-YOLO model can accurately extract the feature points of butt joints, lap joints, and irregular joints, and the model can also work well despite strong arc radiation and spatters. Based on the nearest neighbor algorithm and cubic B-spline curve-fitting algorithm, the position and posture models of the complex spatially curved weld seams are established. The robot welding posture adjustment and high-precision seam tracking of complex spatially curved weld seams are realized. Experiments show that the method proposed in this article can extract weld seam feature points quickly and robustly, which enables welding robots to accurately track the weld seams and adjust the welding torch postures simultaneously.