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Enhancing Perception with Tactile Object Recognition in Adaptive Grippers for Human–Robot Interaction
The use of tactile perception can help first response robotic teams in disaster scenarios, where visibility conditions are often reduced due to the presence of dust, mud, or smoke, distinguishing human limbs from other objects with similar shapes. Here, the integration of the tactile sensor in adapt...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5876667/ https://www.ncbi.nlm.nih.gov/pubmed/29495409 http://dx.doi.org/10.3390/s18030692 |
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author | Gandarias, Juan M. Gómez-de-Gabriel, Jesús M. García-Cerezo, Alfonso J. |
author_facet | Gandarias, Juan M. Gómez-de-Gabriel, Jesús M. García-Cerezo, Alfonso J. |
author_sort | Gandarias, Juan M. |
collection | PubMed |
description | The use of tactile perception can help first response robotic teams in disaster scenarios, where visibility conditions are often reduced due to the presence of dust, mud, or smoke, distinguishing human limbs from other objects with similar shapes. Here, the integration of the tactile sensor in adaptive grippers is evaluated, measuring the performance of an object recognition task based on deep convolutional neural networks (DCNNs) using a flexible sensor mounted in adaptive grippers. A total of 15 classes with 50 tactile images each were trained, including human body parts and common environment objects, in semi-rigid and flexible adaptive grippers based on the fin ray effect. The classifier was compared against the rigid configuration and a support vector machine classifier (SVM). Finally, a two-level output network has been proposed to provide both object-type recognition and human/non-human classification. Sensors in adaptive grippers have a higher number of non-null tactels (up to 37% more), with a lower mean of pressure values (up to 72% less) than when using a rigid sensor, with a softer grip, which is needed in physical human–robot interaction (pHRI). A semi-rigid implementation with 95.13% object recognition rate was chosen, even though the human/non-human classification had better results (98.78%) with a rigid sensor. |
format | Online Article Text |
id | pubmed-5876667 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-58766672018-04-09 Enhancing Perception with Tactile Object Recognition in Adaptive Grippers for Human–Robot Interaction Gandarias, Juan M. Gómez-de-Gabriel, Jesús M. García-Cerezo, Alfonso J. Sensors (Basel) Article The use of tactile perception can help first response robotic teams in disaster scenarios, where visibility conditions are often reduced due to the presence of dust, mud, or smoke, distinguishing human limbs from other objects with similar shapes. Here, the integration of the tactile sensor in adaptive grippers is evaluated, measuring the performance of an object recognition task based on deep convolutional neural networks (DCNNs) using a flexible sensor mounted in adaptive grippers. A total of 15 classes with 50 tactile images each were trained, including human body parts and common environment objects, in semi-rigid and flexible adaptive grippers based on the fin ray effect. The classifier was compared against the rigid configuration and a support vector machine classifier (SVM). Finally, a two-level output network has been proposed to provide both object-type recognition and human/non-human classification. Sensors in adaptive grippers have a higher number of non-null tactels (up to 37% more), with a lower mean of pressure values (up to 72% less) than when using a rigid sensor, with a softer grip, which is needed in physical human–robot interaction (pHRI). A semi-rigid implementation with 95.13% object recognition rate was chosen, even though the human/non-human classification had better results (98.78%) with a rigid sensor. MDPI 2018-02-26 /pmc/articles/PMC5876667/ /pubmed/29495409 http://dx.doi.org/10.3390/s18030692 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Gandarias, Juan M. Gómez-de-Gabriel, Jesús M. García-Cerezo, Alfonso J. Enhancing Perception with Tactile Object Recognition in Adaptive Grippers for Human–Robot Interaction |
title | Enhancing Perception with Tactile Object Recognition in Adaptive Grippers for Human–Robot Interaction |
title_full | Enhancing Perception with Tactile Object Recognition in Adaptive Grippers for Human–Robot Interaction |
title_fullStr | Enhancing Perception with Tactile Object Recognition in Adaptive Grippers for Human–Robot Interaction |
title_full_unstemmed | Enhancing Perception with Tactile Object Recognition in Adaptive Grippers for Human–Robot Interaction |
title_short | Enhancing Perception with Tactile Object Recognition in Adaptive Grippers for Human–Robot Interaction |
title_sort | enhancing perception with tactile object recognition in adaptive grippers for human–robot interaction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5876667/ https://www.ncbi.nlm.nih.gov/pubmed/29495409 http://dx.doi.org/10.3390/s18030692 |
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