Peg-in-Hole Using 3D Workpiece Reconstruction and CNN-based Hole Detection
Michelangelo Nigro, Monica Sileo, Francesco Pierri, Katia Genovese, Domenico D. Bloisi, Fabrizio Caccavale
Abstract
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.
BibTeX
@inproceedings{iros2020_peginholeusing3d,
title = {Peg-in-Hole Using 3D Workpiece Reconstruction and CNN-based Hole Detection},
author = {Michelangelo Nigro and Monica Sileo and Francesco Pierri and Katia Genovese and Domenico D. Bloisi and Fabrizio Caccavale},
booktitle = {IROS 2020},
year = {2020}
}