A novel feature-guided trajectory generation method based on point cloud for robotic grinding of freeform welds

Robotic grinding of welds on freeform surfaces poses an increasing challenge to automatic generation of grinding trajectory while conventional teaching-playback mode and off-line programming method are ineffective. This paper proposes a novel feature-guided trajectory generation method based on point cloud data to perform an efficient grinding process for welds on a freeform surface. The 3D contour of the workpiece was measured by a laser profile scanner. Parent curve of each scanning line was fitted by means of moving average filter, and then, the weld feature points were reliably extracted out of the scattered point cloud through two stages of feature recognition. To achieve the movement guidance of the manipulator, B-spline fitting method was conducted to generate a smooth 3D curve which was discretized into actual tool contact points by an optimized interpolation algorithm and computed the tool postures by cross multiply algorithm. By using robotic force control, the desired force was planned for every tool contact point in order to compensate the error of the processing path. Verification shows that the maximum root mean square root error of recognition of the proposed algorithm is less than 0.7 mm and the computational time is saved by 65.12% in comparison with the reverse engineering method.