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Fabric Classification Using a Finger-Shaped Tactile Sensor via Robotic Sliding

Tactile sensing endows the robots to perceive certain physical properties of the object in contact. Robots with tactile perception can classify textures by touching. Interestingly, textures of fine micro-geometry beyond the nominal resolution of the tactile sensors can also be identified through exp...

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Autores principales: Wang, Si-ao, Albini, Alessandro, Maiolino, Perla, Mastrogiovanni, Fulvio, Cannata, Giorgio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8904726/
https://www.ncbi.nlm.nih.gov/pubmed/35280844
http://dx.doi.org/10.3389/fnbot.2022.808222
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author Wang, Si-ao
Albini, Alessandro
Maiolino, Perla
Mastrogiovanni, Fulvio
Cannata, Giorgio
author_facet Wang, Si-ao
Albini, Alessandro
Maiolino, Perla
Mastrogiovanni, Fulvio
Cannata, Giorgio
author_sort Wang, Si-ao
collection PubMed
description Tactile sensing endows the robots to perceive certain physical properties of the object in contact. Robots with tactile perception can classify textures by touching. Interestingly, textures of fine micro-geometry beyond the nominal resolution of the tactile sensors can also be identified through exploratory robotic movements like sliding. To study the problem of fine texture classification, we design a robotic sliding experiment using a finger-shaped multi-channel capacitive tactile sensor. A feature extraction process is presented to encode the acquired tactile signals (in the form of time series) into a low dimensional (≤7D) feature vector. The feature vector captures the frequency signature of a fabric texture such that fabrics can be classified directly. The experiment includes multiple combinations of sliding parameters, i.e., speed and pressure, to investigate the correlation between sliding parameters and the generated feature space. Results show that changing the contact pressure can greatly affect the significance of the extracted feature vectors. Instead, variation of sliding speed shows no apparent effects. In summary, this paper presents a study of texture classification on fabrics by training a simple k-NN classifier, using only one modality and one type of exploratory motion (sliding). The classification accuracy can reach up to 96%. The analysis of the feature space also implies a potential parametric representation of textures for tactile perception, which could be used for the adaption of motion to reach better classification performance.
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spelling pubmed-89047262022-03-10 Fabric Classification Using a Finger-Shaped Tactile Sensor via Robotic Sliding Wang, Si-ao Albini, Alessandro Maiolino, Perla Mastrogiovanni, Fulvio Cannata, Giorgio Front Neurorobot Neuroscience Tactile sensing endows the robots to perceive certain physical properties of the object in contact. Robots with tactile perception can classify textures by touching. Interestingly, textures of fine micro-geometry beyond the nominal resolution of the tactile sensors can also be identified through exploratory robotic movements like sliding. To study the problem of fine texture classification, we design a robotic sliding experiment using a finger-shaped multi-channel capacitive tactile sensor. A feature extraction process is presented to encode the acquired tactile signals (in the form of time series) into a low dimensional (≤7D) feature vector. The feature vector captures the frequency signature of a fabric texture such that fabrics can be classified directly. The experiment includes multiple combinations of sliding parameters, i.e., speed and pressure, to investigate the correlation between sliding parameters and the generated feature space. Results show that changing the contact pressure can greatly affect the significance of the extracted feature vectors. Instead, variation of sliding speed shows no apparent effects. In summary, this paper presents a study of texture classification on fabrics by training a simple k-NN classifier, using only one modality and one type of exploratory motion (sliding). The classification accuracy can reach up to 96%. The analysis of the feature space also implies a potential parametric representation of textures for tactile perception, which could be used for the adaption of motion to reach better classification performance. Frontiers Media S.A. 2022-02-23 /pmc/articles/PMC8904726/ /pubmed/35280844 http://dx.doi.org/10.3389/fnbot.2022.808222 Text en Copyright © 2022 Wang, Albini, Maiolino, Mastrogiovanni and Cannata. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Wang, Si-ao
Albini, Alessandro
Maiolino, Perla
Mastrogiovanni, Fulvio
Cannata, Giorgio
Fabric Classification Using a Finger-Shaped Tactile Sensor via Robotic Sliding
title Fabric Classification Using a Finger-Shaped Tactile Sensor via Robotic Sliding
title_full Fabric Classification Using a Finger-Shaped Tactile Sensor via Robotic Sliding
title_fullStr Fabric Classification Using a Finger-Shaped Tactile Sensor via Robotic Sliding
title_full_unstemmed Fabric Classification Using a Finger-Shaped Tactile Sensor via Robotic Sliding
title_short Fabric Classification Using a Finger-Shaped Tactile Sensor via Robotic Sliding
title_sort fabric classification using a finger-shaped tactile sensor via robotic sliding
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8904726/
https://www.ncbi.nlm.nih.gov/pubmed/35280844
http://dx.doi.org/10.3389/fnbot.2022.808222
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