An Intelligent Seam Detection Method for Welding Robots Based on Image Guided Point Cloud Registration

Abstract

Welding robots are often employed to improve the welding efficiency and quality of steel structures. In the past few years, weld seam detection technologies based on structured light have undergone huge advances. However, due to the limited measuring range of structured light sensors, high accuracy of workpiece placement is required by most popular approaches in order to avoid invalid sampling. This fails to meet the actual needs of manufacturing. To overcome this burden, a two-step seam detection method is proposed in this study. Firstly, a symbol-assisted 2D digital image contour detection method is employed to obtain the projection of the workpiece in the horizontal plane. Meanwhile, the actual point cloud and the model point cloud of the workpiece are generated based on projected image and CAD model, further calculating the transformation matrix between the two point clouds. Secondly, a linear structured light vision sensor is used to detect weld seams, whose sampling poses are adjusted by the obtained transformation matrix. The robustness and accuracy of the proposed method are demonstrated by simulation experiment results.