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...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autor principal: Herrera, R.M.
Formato: SER
Materias:
Dexterous manipulation
Neural networks
Robotics
Computer simulation
Finite element method
Mathematical models
Neurophysiology
Slip detection
Acceso en línea: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 todo:paper_03029743_v4830LNAI_n_p347_Herrera
record_format dspace
spelling todo:paper_03029743_v4830LNAI_n_p347_Herrera2023-10-03T15:19:01Z A bio-inspired method for incipient slip detection Herrera, R.M. 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. SER info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar 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
Herrera, R.M.
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.
format SER
author Herrera, R.M.
author_facet Herrera, R.M.
author_sort Herrera, R.M.
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
url http://hdl.handle.net/20.500.12110/paper_03029743_v4830LNAI_n_p347_Herrera
work_keys_str_mv AT herrerarm abioinspiredmethodforincipientslipdetection
AT herrerarm bioinspiredmethodforincipientslipdetection
_version_ 1782029940154171392

Ejemplares similares