Smoothness optimization Based on measured 3D point cloud in robotic drilling

In large assembly, the smoothness is important for fluid dynamic configuration and installation stability and which is partly depended on the perpendicularity of rivet installation. Thus, the normal direction of a drilling surface is required to be measured online other than extracted from CAD model because of deflection between CAD model and real product for tool error, assembly deformation and other factors. Besides the normal direction, the local surface smoothness after rivet installation should also be taken into account significantly before drilling. To this end, a smoothness optimization method based on measured 3D point is proposed. First the structured-light-based 3D sensor is used for measuring the drilling surface profile; Second, the tensor voting framework is utilized to processing raw point cloud for denoising; Third, a quadratic surface is fitted on the TVF output unciformed points set and a novel smoothness metric of the surface with rivet installation is defined by continuity and consistency based on local fitted quadratic surface; Finally, the surface smoothness after rivet installation is optimized. The proposed methods are verified by experiments conducted on several simulation scenarios and the result shows effectiveness of the proposed method.