A bio-inspired method for incipient slip detection

Few years old children lift and manipulate unfamiliar objects more dexterously than today's robots. Therefore, it has arisen an interest at the artificial intelligence community to look for inspiration on neurophysiological studies to design better models for the robots. In a human dexterous ma...

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Detalles Bibliográficos
Autor principal:	Matuk Herrera, Rosana Isabel
Publicado:	2007
Materias:	Dexterous manipulation Neural networks Robotics Computer simulation Finite element method Mathematical models Neurophysiology Slip detection
Acceso en línea:	https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03029743_v4830LNAI_n_p347_Herrera http://hdl.handle.net/20.500.12110/paper_03029743_v4830LNAI_n_p347_Herrera
Aporte de:	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

id	paper:paper_03029743_v4830LNAI_n_p347_Herrera
record_format	dspace
spelling	paper:paper_03029743_v4830LNAI_n_p347_Herrera2023-06-08T15:28:27Z A bio-inspired method for incipient slip detection Matuk Herrera, Rosana Isabel Dexterous manipulation Neural networks Robotics Computer simulation Finite element method Mathematical models Neural networks Neurophysiology Dexterous manipulation Slip detection Robotics Few years old children lift and manipulate unfamiliar objects more dexterously than today's robots. Therefore, it has arisen an interest at the artificial intelligence community to look for inspiration on neurophysiological studies to design better models for the robots. In a human dexterous manipulation a crucial event is the detection of incipient slips. Humans detect the incipient slips based on the responses of their tactile mechanoreceptors. In this paper, we propose a method to detect the incipient slips using artificial neural networks that receive as input simulated human afferent responses. This method is strongly inspired on neurophysiological studies of the afferent responses during the human dexterous manipulation. Finite element analysis was used to model two fingers and an object, and simulated experiments using the proposed method were done. To the best of our knowledge, this is the first time that simulated human afferent signals are combined with finite element analysis and artificial neural networks, to detect the incipient slips. © Springer-Verlag Berlin Heidelberg 2007. Fil:Herrera, R.M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2007 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03029743_v4830LNAI_n_p347_Herrera http://hdl.handle.net/20.500.12110/paper_03029743_v4830LNAI_n_p347_Herrera
institution	Universidad de Buenos Aires
institution_str	I-28
repository_str	R-134
collection	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic	Dexterous manipulation Neural networks Robotics Computer simulation Finite element method Mathematical models Neural networks Neurophysiology Dexterous manipulation Slip detection Robotics
spellingShingle	Dexterous manipulation Neural networks Robotics Computer simulation Finite element method Mathematical models Neural networks Neurophysiology Dexterous manipulation Slip detection Robotics Matuk Herrera, Rosana Isabel A bio-inspired method for incipient slip detection
topic_facet	Dexterous manipulation Neural networks Robotics Computer simulation Finite element method Mathematical models Neural networks Neurophysiology Dexterous manipulation Slip detection Robotics
description	Few years old children lift and manipulate unfamiliar objects more dexterously than today's robots. Therefore, it has arisen an interest at the artificial intelligence community to look for inspiration on neurophysiological studies to design better models for the robots. In a human dexterous manipulation a crucial event is the detection of incipient slips. Humans detect the incipient slips based on the responses of their tactile mechanoreceptors. In this paper, we propose a method to detect the incipient slips using artificial neural networks that receive as input simulated human afferent responses. This method is strongly inspired on neurophysiological studies of the afferent responses during the human dexterous manipulation. Finite element analysis was used to model two fingers and an object, and simulated experiments using the proposed method were done. To the best of our knowledge, this is the first time that simulated human afferent signals are combined with finite element analysis and artificial neural networks, to detect the incipient slips. © Springer-Verlag Berlin Heidelberg 2007.
author	Matuk Herrera, Rosana Isabel
author_facet	Matuk Herrera, Rosana Isabel
author_sort	Matuk Herrera, Rosana Isabel
title	A bio-inspired method for incipient slip detection
title_short	A bio-inspired method for incipient slip detection
title_full	A bio-inspired method for incipient slip detection
title_fullStr	A bio-inspired method for incipient slip detection
title_full_unstemmed	A bio-inspired method for incipient slip detection
title_sort	bio-inspired method for incipient slip detection
publishDate	2007
url	https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03029743_v4830LNAI_n_p347_Herrera http://hdl.handle.net/20.500.12110/paper_03029743_v4830LNAI_n_p347_Herrera
work_keys_str_mv	AT matukherrerarosanaisabel abioinspiredmethodforincipientslipdetection AT matukherrerarosanaisabel bioinspiredmethodforincipientslipdetection
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A bio-inspired method for incipient slip detection

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