loading
Papers Papers/2020

Research.Publish.Connect.

Paper

Authors: Bruno Santo ; Liliana Antão and Gil Gonçalves

Affiliation: SYSTEC, Research Center for Systems and Technologies, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal

Keyword(s): Robotics, Grasping, Object Pose Estimation, Object Recognition.

Abstract: With the emergence of Industry 4.0 and its highly re-configurable manufacturing context, the typical fixed-position grasping systems are no longer usable. This reality underlined the necessity for fully automatic and adaptable robotic grasping systems. With that in mind, the primary purpose of this paper is to join Machine Learning models for detection and pose estimation into an automatic system to be used in a grasping environment. The developed system uses Mask-RCNN and Densefusion models for the recognition and pose estimation of objects, respectively. The grasping is executed, taking into consideration both the pose and the object’s ID, as well as allowing for user and application adaptability through an initial configuration. The system was tested both on a validation dataset and in a real-world environment. The main results show that the system has more difficulty with complex objects; however, it shows promising results for simpler objects, even with training on a reduced dat aset. It is also able to generalize to objects slightly different than the ones seen in training. There is an 80% success rate in the best cases for simple grasping attempts. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 34.207.247.69

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Santo, B.; Antão, L. and Gonçalves, G. (2021). Automatic 3D Object Recognition and Localization for Robotic Grasping. In Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - ICINCO, ISBN 978-989-758-522-7; ISSN 2184-2809, pages 416-425. DOI: 10.5220/0010552704160425

@conference{icinco21,
author={Bruno Santo. and Liliana Antão. and Gil Gon\c{C}alves.},
title={Automatic 3D Object Recognition and Localization for Robotic Grasping},
booktitle={Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - ICINCO,},
year={2021},
pages={416-425},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010552704160425},
isbn={978-989-758-522-7},
issn={2184-2809},
}

TY - CONF

JO - Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - ICINCO,
TI - Automatic 3D Object Recognition and Localization for Robotic Grasping
SN - 978-989-758-522-7
IS - 2184-2809
AU - Santo, B.
AU - Antão, L.
AU - Gonçalves, G.
PY - 2021
SP - 416
EP - 425
DO - 10.5220/0010552704160425

0123movie.net