Normal Direction Measurement and Optimization With a Dense Three-Dimensional Point Cloud in Robotic Drilling

Abstract

In large-scale structure assembly, the normal direction of a drilled surface is required to be measured online instead of extracted directly from the computer-aided design model because of tool error, cutting force, and other factors. To this end, first, a high-resolution structured-light-based three-dimensional (3-D) measurement is adopted to improve measurement reliability; second, tensor voting is proposed to remove noise and fill in blanks in the measured 3-D point cloud to obtain a uniform point distribution for surface fitting; and third, the surface smoothness after rivet installation is defined and optimized. The proposed methods are verified by simulations and experiments. The results show that the proposed methods are effective.