Paint surface estimation and trajectory planning for automated painting systems

Abstract

This paper investigates the problem of paint surface estimation and trajectory planning for the automated painting process using a six-degree-of-freedom (6DOF) robot. We first present the kinematic model of the 6DOF articulated spraying robot and calculate the coordinate transformation of the robot's end effector relative to the base position. Then we design the size of the robot's joints to ensure sufficient spraying working space for the outer coverings of automobiles. Next, we stitch the acquired workpiece point cloud data (PCD) and perform noise reduction. The iterative closest point (ICP) algorithm integrates the workpiece PCD obtained from different locations into a unified coordinate system. Based on the features of the workpiece surface, we compute the normal vector for the point cloud. Then, the original point cloud data is segmented into several pieces by the different components of the normal vector on the coordinate axis. By slicing the segmented PCD evenly, we approximate the spraying paths of each surface of the car cover. Finally, we validate the effectiveness of our proposed algorithm through simulation.