Vision-guided robot application for metal surface edge grinding

Abstract

The combination of machine vision and grinding robots can be visualized as a collaboration between human eyes and limbs to achieve a deep integration between external perception and execution actions. This combination will give the grinding robot more operability and flexibility, which will enable it to better realize the purpose of replacing humans with machines. In response to the demand for flexible grinding of titanium surface edges proposed by a titanium manufacturer, this paper conducts an in-depth study on the prototype system of vision-guided grinding robots and related applications. Firstly, this study analyzes the shortcomings of the existing robotic regrinding process and achieves the improvement of the regrinding process by introducing machine vision technology. Subsequently, this study further utilizes machine vision and image processing algorithms to achieve high-quality recognition and high-precision positioning of metal surface edges. Then, the D–H parameter model of the regrinding robot is established, and the planning and simulation of the regrinding trajectory is carried out using the position information of the identified regrinding edges. Finally, the simulation-validated grinding trajectory is introduced into the grinding robot, and the effectiveness of the proposed scheme is verified by actual grinding experiments.