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...
Guardado en:
Autor principal: | |
---|---|
Formato: | SER |
Materias: | |
Acceso en línea: | http://hdl.handle.net/20.500.12110/paper_03029743_v4830LNAI_n_p347_Herrera |
Aporte de: |
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 |