Target Classification Method of Tactile Perception Data with Deep Learning

In order to improve the accuracy of manipulator operation, it is necessary to install a tactile sensor on the manipulator to obtain tactile information and accurately classify a target. However, with the increase in the uncertainty and complexity of tactile sensing data characteristics, and the cont...

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Autores principales:	Xingxing Zhang, Shaobo Li, Jing Yang, Qiang Bai, Yang Wang, Mingming Shen, Ruiqiang Pu, Qisong Song
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	tactile sensor tactile perception data ResNet target classification Science Q Astrophysics QB460-466 Physics QC1-999
Acceso en línea:	https://doaj.org/article/06a0ee8ea5774c27bccb44ace8641f1a
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id	oai:doaj.org-article:06a0ee8ea5774c27bccb44ace8641f1a
record_format	dspace
spelling	oai:doaj.org-article:06a0ee8ea5774c27bccb44ace8641f1a2021-11-25T17:30:43ZTarget Classification Method of Tactile Perception Data with Deep Learning10.3390/e231115371099-4300https://doaj.org/article/06a0ee8ea5774c27bccb44ace8641f1a2021-11-01T00:00:00Zhttps://www.mdpi.com/1099-4300/23/11/1537https://doaj.org/toc/1099-4300In order to improve the accuracy of manipulator operation, it is necessary to install a tactile sensor on the manipulator to obtain tactile information and accurately classify a target. However, with the increase in the uncertainty and complexity of tactile sensing data characteristics, and the continuous development of tactile sensors, typical machine-learning algorithms often cannot solve the problem of target classification of pure tactile data. Here, we propose a new model by combining a convolutional neural network and a residual network, named ResNet10-v1. We optimized the convolutional kernel, hyperparameters, and loss function of the model, and further improved the accuracy of target classification through the K-means clustering method. We verified the feasibility and effectiveness of the proposed method through a large number of experiments. We expect to further improve the generalization ability of this method and provide an important reference for the research in the field of tactile perception classification.Xingxing ZhangShaobo LiJing YangQiang BaiYang WangMingming ShenRuiqiang PuQisong SongMDPI AGarticletactile sensortactile perception dataResNettarget classificationScienceQAstrophysicsQB460-466PhysicsQC1-999ENEntropy, Vol 23, Iss 1537, p 1537 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	tactile sensor tactile perception data ResNet target classification Science Q Astrophysics QB460-466 Physics QC1-999
spellingShingle	tactile sensor tactile perception data ResNet target classification Science Q Astrophysics QB460-466 Physics QC1-999 Xingxing Zhang Shaobo Li Jing Yang Qiang Bai Yang Wang Mingming Shen Ruiqiang Pu Qisong Song Target Classification Method of Tactile Perception Data with Deep Learning
description	In order to improve the accuracy of manipulator operation, it is necessary to install a tactile sensor on the manipulator to obtain tactile information and accurately classify a target. However, with the increase in the uncertainty and complexity of tactile sensing data characteristics, and the continuous development of tactile sensors, typical machine-learning algorithms often cannot solve the problem of target classification of pure tactile data. Here, we propose a new model by combining a convolutional neural network and a residual network, named ResNet10-v1. We optimized the convolutional kernel, hyperparameters, and loss function of the model, and further improved the accuracy of target classification through the K-means clustering method. We verified the feasibility and effectiveness of the proposed method through a large number of experiments. We expect to further improve the generalization ability of this method and provide an important reference for the research in the field of tactile perception classification.
format	article
author	Xingxing Zhang Shaobo Li Jing Yang Qiang Bai Yang Wang Mingming Shen Ruiqiang Pu Qisong Song
author_facet	Xingxing Zhang Shaobo Li Jing Yang Qiang Bai Yang Wang Mingming Shen Ruiqiang Pu Qisong Song
author_sort	Xingxing Zhang
title	Target Classification Method of Tactile Perception Data with Deep Learning
title_short	Target Classification Method of Tactile Perception Data with Deep Learning
title_full	Target Classification Method of Tactile Perception Data with Deep Learning
title_fullStr	Target Classification Method of Tactile Perception Data with Deep Learning
title_full_unstemmed	Target Classification Method of Tactile Perception Data with Deep Learning
title_sort	target classification method of tactile perception data with deep learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/06a0ee8ea5774c27bccb44ace8641f1a
work_keys_str_mv	AT xingxingzhang targetclassificationmethodoftactileperceptiondatawithdeeplearning AT shaoboli targetclassificationmethodoftactileperceptiondatawithdeeplearning AT jingyang targetclassificationmethodoftactileperceptiondatawithdeeplearning AT qiangbai targetclassificationmethodoftactileperceptiondatawithdeeplearning AT yangwang targetclassificationmethodoftactileperceptiondatawithdeeplearning AT mingmingshen targetclassificationmethodoftactileperceptiondatawithdeeplearning AT ruiqiangpu targetclassificationmethodoftactileperceptiondatawithdeeplearning AT qisongsong targetclassificationmethodoftactileperceptiondatawithdeeplearning
_version_	1718412266211115008

Target Classification Method of Tactile Perception Data with Deep Learning

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