Research on trajectory recognition and control technology of structured light vision-assisted welding

Three trajectory recognition and control techniques for laser multi-point positioning, pre-welding trajectory fitting, and seam real-time tracking for structured light vision-assisted welding are investigated. A CNN model, adaptive feature extraction algorithm, a priori model, and coordinate matrix conversion are proposed as the core of the welding trajectory recognition process for these three. Welding trajectory control models are proposed for each of the above three, which including a taught trajectory correction model, a pre-welding trajectory fitting model, and a real-time deflection correction model for seam tracking. The experiments prove that laser multi-point positioning, pre-welding trajectory fitting model can efficiently identify the weld trajectory curve before welding, and the welding trajectory basically coincides with the centerline of the seam; when welding in the real-time seam tracking model, the real-time deviation is mainly controlled within ± 0.2 mm, with an average deviation of 0.1160 mm. The results show that the welding trajectory identification process and trajectory control model mentioned in this paper are sufficient to ensure stable operation of structured light vision-assisted welding.