A robust butt welding seam finding technique for intelligent robotic welding system using active laser vision

Intelligent robotic welding requires automatic finding of the seam geometrical features in order for an efficient intelligent control. Performance of the system, therefore, heavily depends on the success of the seam finding stage. Among various seam finding techniques, active laser vision is the most effective approach. It typically requires high-quality lasers, camera and optical filters. The success of the algorithm is highly sensitive to the image processing and feature extraction algorithms. In this work, sequential image processing and feature extraction algorithms are proposed to effectively extract the seam geometrical properties from a low-quality laser image captured without the conventional narrow band filter. A novel method of laser segmentation and detection is proposed. The segmentation method involves averaging, colour processing and blob analysis. The detection method is based on a novel median filtering technique that involves enhancing of the image object based on its underlying structure and orientation in the image. The method when applied enhances the vertically oriented laser stripe in the image which improves the laser peak detection. The image processing steps are performed to make sure that the laser profile is accurately extracted within the region of interest (ROI). Feature extraction algorithm based on pixels’ intensity distribution and neighbourhood search is also proposed that can effectively extract the seam feature points. The proposed algorithms have been implemented and evaluated on various background complexities, seam sizes, material type and laser types before and during the welding operation.