Research on 3D curved weld seam trajectory position and orientation detection method

The "teaching-reproduction" mode and off-line programming mode are widely used in traditional welding robots. For curved welding workpieces, the operators need to visually inspect the position and orientation of the weld seam during processing to select the teaching points or the starting point for programming. The welding accuracy depends on the operators’ experience. Re-teaching or re-programming is required after adjusting the welding position or orientation, resulting in poor welding accuracy and efficiency. To enhance the adaptability of the robot to various welding conditions, we propose a 3D curved weld seam trajectory position and orientation detection method. A local welding workpiece point cloud is obtained by laser vision sensor scanning. The point cloud is smoothed by using the moving least squares (MLS) method. The surface variation of each point is calculated, and the surface variation threshold is set to extract the weld seam feature points. The weld seam orientation is calculated by solving the basic vectors of the spatial curve according to the parametric equation of the weld seam trajectory. The rotation angles of the robot are calculated by the transformation matrix between the weld seam local coordinate system and the robot base coordinate system. According to the experiments, the proposed method can adapt to various orientations of the curved welding workpiece and improve the robot's adaptability to orientations, therefore, the accuracy and efficiency of welding will be enhanced.