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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2008
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| Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_97807695_v_n_p385_Herrera http://hdl.handle.net/20.500.12110/paper_97807695_v_n_p385_Herrera |
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paper:paper_97807695_v_n_p385_Herrera2023-06-08T16:37:08Z A bio-inspired method for friction estimation Matuk Herrera, Rosana Isabel 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. 2008 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_97807695_v_n_p385_Herrera 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 Matuk Herrera, Rosana Isabel 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. |
| author |
Matuk Herrera, Rosana Isabel |
| author_facet |
Matuk Herrera, Rosana Isabel |
| author_sort |
Matuk Herrera, Rosana Isabel |
| 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 |
| publishDate |
2008 |
| url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_97807695_v_n_p385_Herrera http://hdl.handle.net/20.500.12110/paper_97807695_v_n_p385_Herrera |
| work_keys_str_mv |
AT matukherrerarosanaisabel abioinspiredmethodforfrictionestimation AT matukherrerarosanaisabel bioinspiredmethodforfrictionestimation |
| _version_ |
1768545211824209920 |