Peg-in-hole using 3D workpiece reconstruction and CNN-based hole detection

This paper presents a method to cope with autonomous assembly tasks in the presence of uncertainties. To this aim, a Peg-in-Hole operation is considered, where the target workpiece position is unknown and the peg-hole clearance is small. Deep learning based hole detection and 3D surface reconstruction techniques are combined for accurate workpiece localization. In detail, the hole is detected by using a convolutional neural network (CNN), while the target workpiece surface is reconstructed via 3D-Digital Image Correlation (3D-DIC). Peg insertion is performed via admittance control that confers the suitable compliance to the peg. Experiments on a collaborative manipulator confirm that the proposed approach can be promising for achieving a better degree of autonomy for a class of robotic tasks in partially structured environments.