Advanced spraying task strategy for bicycle-frame based on geometrical data of workpiece

Abstract

Path planning and trajectory generation are the primary tasks for spray painting applications. However, for the most efficient spraying application, the detailed dimensional information of the workpiece along the generated path is essential. This paper introduces the shape signature approach for automatic identification and measurement of cross-sectional information of a bicycle frame model for advanced spraying task of 6-DOF robot. The path planning is generated from skeletal line of the point cloud model of the bicycle frame which is reconstructed by Kinect Fusion based algorithm. Cross-sectional shape and dimensions are autonomously identified along the generated spray-gun path and subsequently the corresponding spray task is applied in each segment. The spray-gun orientation and painting condition such as the spray speed, volume rate and internal and external pressures are automatically adjusted according to the cross-sectional information and nature of the path at the current pose of the end effector.