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Visual control system for grip of glasses oriented to assistance robotics

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 10 No. 6 (2020)

DOI
10.11591/ijece.v10i6.pp6330-6339
ISSN
2722-2578, 2088-8708
Published
2020-12-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Assistance robotics is presented as a means of improving the quality of life of people with disabilities, an application case is presented in assisted feeding. This paper presents the development of a system based on artificial intelligence techniques, for the grip of a glass, so that it does not slip during its manipulation by means of a robotic arm, as the liquid level varies. A faster R-CNN is used for the detection of the glass and the arm's gripper, and from the data obtained by the network, the mass of the beverage is estimated, and a delta of distance between the gripper and the liquid. These estimated values are used as inputs for a fuzzy system which has as output the torque that the motor that drives the gripper must exert. It was possible to obtain a 97.3% accuracy in the detection of the elements of interest in the environment with the faster R-CNN, and a 76% performance in the grips of the glass through the fuzzy algorithm.

Collaboration network of the authors

18 authors · 32 co-authorship links · 9 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universidad Militar Nueva Granada
  • Universidad de los Llanos
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Javier Eduardo Martínez BaqueroUniversidad de los Llanos11
Javier Orlando Pinzon-ArenasUniversidad Militar Nueva Granada7
César G. PachónUniversidad Militar Nueva Granada5
Jose Luis RamirezUniversidad Militar Nueva Granada3
Luis Alfredo Rodríguez UmañaUniversidad de los Llanos2
Anny Astrid Espitia CubillosUniversidad Militar Nueva Granada2
Carlos Javier Enciso AragónUniversidad Militar Nueva Granada1
Paula UsecheAffiliation unknown1
Cristian AlmanzaUniversidad de los Llanos1
Jairo David Cuero OrtegaUniversidad de los Llanos1
Andrés Mauricio Díaz MelgarejoUniversidad Militar Nueva Granada1
Juan Sebastian Sanabria JimenezUniversidad Militar Nueva Granada1
Jorge HerreraUniversidad Militar Nueva Granada1
Paula Catalina Useche-MurilloUniversidad Militar Nueva Granada1
Juan C. Guachetá-AlbaUniversidad Militar Nueva Granada1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 1 Nov 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Visual control system for grip of glasses oriented to assistance robotics</dc:title><dc:creator>Jimenez-Moreno, Robinson</dc:creator><dc:creator>Rubiano, Astrid</dc:creator><dc:creator>Ramirez, Jose L.</dc:creator><dc:description>&lt;jats:p&gt;Assistance robotics is presented as a means of improving the quality of life of people with disabilities, an application case is presented in assisted feeding. This paper presents the development of a system based on artificial intelligence techniques, for the grip of a glass, so that it does not slip during its manipulation by means of a robotic arm, as the liquid level varies. A faster R-CNN is used for the detection of the glass and the arm's gripper, and from the data obtained by the network, the mass of the beverage is estimated, and a delta of distance between the gripper and the liquid. These estimated values are used as inputs for a fuzzy system which has as output the torque that the motor that drives the gripper must exert. It was possible to obtain a 97.3% accuracy in the detection of the elements of interest in the environment with the faster R-CNN, and a 76% performance in the grips of the glass through the fuzzy algorithm.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2020-12-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v10i6.pp6330-6339</dc:identifier><dc:identifier>10.11591/ijece.v10i6.pp6330-6339</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 10 No 6 (2020); 6330</dc:source><dc:source>2722-2578</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/21943/14421</dc:relation><dc:rights>http://creativecommons.org/licenses/by-sa/4.0</dc:rights></oai_dc:dc>