High precision positioning method via robot-driven three-dimensional measurement

In the process of intelligent manufacturing, all kinds of complex workpieces in aerospace, automobile and other fields need to be measured and identified with high precision, so that industrial robots can sort or assemble the workpieces. The structure of the workpieces is complex, the surface texture is weak, and they are scattered and stacked on the automatic production line, so there are some problems such as low accuracy of three-dimensional (3D) measurement and positioning and low efficiency. To solve these problems, a high precision positioning method based on robot-driven 3D measurement is proposed. Firstly, the 3D point cloud data of the complex workpieces is obtained from the structured-light 3D measurement device, and then the point cloud data is processed by the sampling consistent initial registration algorithm (SAC-IA) and the iterative nearest point algorithm (ICP). Through the rough estimation and accurate solution of the position and attitude of the workpiece, the 3D attitude of the workpieces in the coordinate system of the structured-light 3D measurement device is obtained. Finally, the spatial pose solution algorithm is used to calculate the 3D attitude of the workpieces in the robot coordinate system, and guide the robot to grasp automatically. The experiments show that the grasping position error is 0.34mm, and the grasping angle error is 0.36°. It can accurately measure and identify the point cloud target, calculate the 3D attitude of the complex workpieces, and accurately guide the robot to grab the workpiece automatically, which can be popularized and applied in the industry.