Unified seam tracking algorithm via three-point weld representation for autonomous robotic welding

Autonomous robotic welding based on real-time seam tracking has shown excellent prospects in the field of intelligent manufacturing. In previous research, real-time seam tracking requires teaching trajectories, which has significant limitations. Moreover, as different types of welds have different shapes, their geometric distribution also exists in various forms such as straight lines, planar curves, and spatial curves, which brings significant challenges to the visual recognition of welds. The use of existing methods for tracking specific types of welds has limited applications. Currently, the existing methods are carried out for tracking specific types of welds, resulting in limited application scope. To handle various types of weld joints, a unified tracking paradigm named the three-point seam tracking algorithm (TSTA) is proposed in this study. First, a feature description method using three feature points is established to define different weld joints. The feature points are employed to reconstruct the position and attitude of welds, which allows the method to track welds of arbitrary geometries. Subsequently, a recognition method for the feature points combining morphology extraction and kernel correlation filter (KCF) tracker is designed. Combined with their respective advantages, the algorithm can identify the three feature points accurately and robustly in strong-noise environments. Extensive experiments including actual welding were conducted to verify the superiority of the proposed TSTA. Eleven representative types of weld seams were tested and all of them could be tracked in real time without any prior information. Compared with similar studies, the proposed recognition algorithm currently achieves the highest accuracy and best robustness. The TSTA provides a complete solution in autonomous robotic welding for unknown welds and shows excellent application prospects.