Automatic Extraction and Tracking of Robot Weld Seam Paths Based on Line Structured Light

Robot plane weld seam tracking boasts stable quality and high efficiency. Nonetheless, extracting 3-D features of the seams proves challenging due to factors such as the high reflectivity of welding materials and uneven illumination. To tackle these challenges, this article proposes a method for feature extraction and tracking of weld seams utilizing deep learning and structured light 3-D scanning imaging. First, a precise segmentation model for laser stripes based on deep learning is established to address interference caused by workpiece reflection. Deep learning models effectively mitigate reflection interference and enhance segmentation of laser stripe distribution characteristics. Second, the gray center of gravity algorithm is utilized to roughly extract the center of the laser stripe, followed by smoothing the centerline of the stripe to attain a smoother center. Last, the least squares fitting method is utilized to generate the welding path, with coordinate transformation applied to the robot system for weld tracking. Experimental results demonstrate the effectiveness of the proposed method in overcoming reflection interference, with a root mean square error of the welding fitting path less than 0.17 mm and an average tracking error less than 0.37 mm.