A Machine Learning Approach for Visual Recognition of Complex Parts in Robotic Manipulation

Abstract

This research presents a method for the visual recognition of parts using machine learning services to enable the manipulation of complex parts. Robotic manipulation of complex parts is a challenging application due to the uncertainty of the parts' positioning as well as the gripper's grasping instability. This instability is caused by the non-symmetrical and complex geometries that may result in a slightly variable orientation of the part after being grasped, which is outside the handling/assembly process tolerance. To compensate for this, a visual recognition approach is implemented via classifiers. Finally, a case study focusing on the manipulation of consumer goods is demonstrated and evaluated.