A bio-inspired method for friction estimation

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. The estimation of the f...

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Detalles Bibliográficos
Autor principal:	Herrera, R.M.
Formato:	CONF
Materias:	Dexterous manipulation Neural networks Robotics Artificial intelligence Backpropagation Bionics Finite element method Friction Machine design Robots Tribology Artificial neural networks Bio-inspired Finite element analysis Friction coefficients Friction estimations Mechanoreceptors Simulated experiments
Acceso en línea:	http://hdl.handle.net/20.500.12110/paper_97807695_v_n_p385_Herrera
Aporte de:	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

id	todo:paper_97807695_v_n_p385_Herrera
record_format	dspace
spelling	todo:paper_97807695_v_n_p385_Herrera2023-10-03T16:42:49Z A bio-inspired method for friction estimation Herrera, R.M. Dexterous manipulation Neural networks Robotics Artificial intelligence Backpropagation Bionics Finite element method Friction Machine design Robotics Robots Tribology Artificial neural networks Bio-inspired Dexterous manipulation Finite element analysis Friction coefficients Friction estimations Mechanoreceptors Simulated experiments Neural networks 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. The estimation of the friction coefficient of the object's material is a crucial information in a human dexterous manipulation. Humans estimate the friction coefficient based on the responses of their tactile mechanoreceptors. In this paper, we propose a method to estimate the friction coefficient 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 of objects. Finite element analysis was used to model a finger 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 estimate the friction coefficient. © 2008 IEEE. Fil:Herrera, R.M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. CONF info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_97807695_v_n_p385_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 Artificial intelligence Backpropagation Bionics Finite element method Friction Machine design Robotics Robots Tribology Artificial neural networks Bio-inspired Dexterous manipulation Finite element analysis Friction coefficients Friction estimations Mechanoreceptors Simulated experiments Neural networks
spellingShingle	Dexterous manipulation Neural networks Robotics Artificial intelligence Backpropagation Bionics Finite element method Friction Machine design Robotics Robots Tribology Artificial neural networks Bio-inspired Dexterous manipulation Finite element analysis Friction coefficients Friction estimations Mechanoreceptors Simulated experiments Neural networks Herrera, R.M. A bio-inspired method for friction estimation
topic_facet	Dexterous manipulation Neural networks Robotics Artificial intelligence Backpropagation Bionics Finite element method Friction Machine design Robotics Robots Tribology Artificial neural networks Bio-inspired Dexterous manipulation Finite element analysis Friction coefficients Friction estimations Mechanoreceptors Simulated experiments Neural networks
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. The estimation of the friction coefficient of the object's material is a crucial information in a human dexterous manipulation. Humans estimate the friction coefficient based on the responses of their tactile mechanoreceptors. In this paper, we propose a method to estimate the friction coefficient 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 of objects. Finite element analysis was used to model a finger 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 estimate the friction coefficient. © 2008 IEEE.
format	CONF
author	Herrera, R.M.
author_facet	Herrera, R.M.
author_sort	Herrera, R.M.
title	A bio-inspired method for friction estimation
title_short	A bio-inspired method for friction estimation
title_full	A bio-inspired method for friction estimation
title_fullStr	A bio-inspired method for friction estimation
title_full_unstemmed	A bio-inspired method for friction estimation
title_sort	bio-inspired method for friction estimation
url	http://hdl.handle.net/20.500.12110/paper_97807695_v_n_p385_Herrera
work_keys_str_mv	AT herrerarm abioinspiredmethodforfrictionestimation AT herrerarm bioinspiredmethodforfrictionestimation
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A bio-inspired method for friction estimation

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