Vision-guided path planning and joint configuration optimization for robot grinding of spatial surface weld beads via point cloud

The aerospace industry faces critical demands for automated and intelligent grinding of welds on curved surfaces. The combination of 3D vision technology and robot grinding system offers a feasible and promising solution. Local grinding path estimation remains a significant challenge due to the absence of a robust and fast method. To address this challenge, this article proposes a vision-guided method for robot grinding of spatial curved weld beads. Initially, a robust local point cloud descriptor is defined to identify and segment weld beads, generating Regions of Interest (ROI). Subsequently, Intrinsic Shape Signatures (ISS) key point detection is employed to extract points representing the trend of ROI, followed by Non-Uniform Rational B-Spline (NURBS) curve fitting for grinding path planning. Finally, an optimization objective function based on robot manipulability and pose difference is developed to enhance stability of machining. Curved weld grinding experiments on rocket skin are conducted and results demonstrate the highly accurate and robust, ensuring a removal error within 0.2 mm. This method is suitable for semi-precision machining or as a pre-stage in the high-precision machining of large workpieces.