Development and Experimental Evaluation of a 3D Vision System for Grinding Robot

Abstract

If the grinding robot can automatically position and measure the machining target on the workpiece, it will significantly improve its machining efficiency and intelligence level. However, unfortunately, the current grinding robot cannot do this because of economic and precision reasons. This paper proposes a 3D vision system mounted on the robot’s fourth joint, which is used to detect the machining target of the grinding robot. Also, the hardware architecture and data processing method of the 3D vision system is described in detail. In the data processing process, we first use the voxel grid filter to preprocess the point cloud and obtain the feature descriptor. Then use fast library for approximate nearest neighbors (FLANN) to search out the difference point cloud from the precisely registered point cloud pair and use the point cloud segmentation method proposed in this paper to extract machining path points. Finally, the detection error compensation model is used to accurately calibrate the 3D vision system to transform the machining information into the grinding robot base frame. Experimental results show that the absolute average error of repeated measurements at different locations is 0.154 mm, and the absolute measurement error of the vision system caused by compound error is usually less than 0.25 mm. The proposed 3D vision system could easily integrate into an intelligent grinding system and may be suitable for industrial sites.

Keywords

• grinding robot
• 3D vision system
• machining target
• point cloud