Automatic 3-D Optical Detection on Orientation of Randomly Oriented Industrial Parts for Rapid Robotic Manipulation

This paper proposes a novel method employing a developed 3-D optical imaging and processing algorithm for accurate classification of an object’s surface characteristics in robot pick and place manipulation. In the method, 3-D geometry of industrial parts can be rapidly acquired by the developed one-shot imaging optical probe based on Fourier Transform Profilometry (FTP) by using digital-fringe projection at a camera’s maximum sensing speed. Following this, the acquired range image can be effectively segmented into three surface types by classifying point clouds based on the statistical distribution of the normal surface vector of each detected 3-D point, and then the scene ground is reconstructed by applying least squares fitting and classification algorithms. Also, a recursive search process incorporating the region-growing algorithm for registering homogeneous surface regions has been developed. When the detected parts are randomly overlapped on a workbench, a group of defined 3-D surface features, such as surface areas, statistical values of the surface normal distribution and geometric distances of defined features, can be uniquely recognized for detection of the part’s orientation. Experimental testing was performed to validate the feasibility of the developed method for real robotic manipulation