Dynamics Model Abstraction Scheme Using Radial Basis Functions
This paper presents a control model for object manipulation. Properties of objects and environmental conditions influence the motor control and learning. System dynamics depend on an unobserved external context, for example, work load of a robot manipulator. The dynamics of a robot arm change as it...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2012-01-01
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| Series: | Journal of Control Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2012/761019 |
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| _version_ | 1832548637698162688 |
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| author | Silvia Tolu Mauricio Vanegas Rodrigo Agís Richard Carrillo Antonio Cañas |
| author_facet | Silvia Tolu Mauricio Vanegas Rodrigo Agís Richard Carrillo Antonio Cañas |
| author_sort | Silvia Tolu |
| collection | DOAJ |
| description | This paper presents a control model for object manipulation. Properties of objects and environmental conditions influence the motor control and learning. System dynamics depend on an unobserved external context, for example, work load of a robot manipulator. The dynamics of a robot arm change as it manipulates objects with different physical properties, for example, the mass, shape, or mass distribution. We address active sensing strategies to acquire object dynamical models with a radial basis function neural network (RBF). Experiments are done using a real robot’s arm, and trajectory data are gathered during various trials manipulating different objects. Biped robots do not have high force joint servos and the control system hardly compensates all the inertia variation of the adjacent joints and disturbance torque on dynamic gait control. In order to achieve smoother control and lead to more reliable sensorimotor complexes, we evaluate and compare a sparse velocity-driven versus a dense position-driven control scheme. |
| format | Article |
| id | doaj-art-cdc709fd043343d0a6d81541eebb2202 |
| institution | Kabale University |
| issn | 1687-5249 1687-5257 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Control Science and Engineering |
| spelling | doaj-art-cdc709fd043343d0a6d81541eebb22022025-02-03T06:13:28ZengWileyJournal of Control Science and Engineering1687-52491687-52572012-01-01201210.1155/2012/761019761019Dynamics Model Abstraction Scheme Using Radial Basis FunctionsSilvia Tolu0Mauricio Vanegas1Rodrigo Agís2Richard Carrillo3Antonio Cañas4Department of Computer Architecture and Technology, CITIC ETSI Informática y de Telecomunicación, University of Granada, SpainPSPC Group, Department of Biophysical and Electronic Engineering (DIBE), University of Genoa, ItalyDepartment of Computer Architecture and Technology, CITIC ETSI Informática y de Telecomunicación, University of Granada, SpainDepartment of Computer Architecture and Technology, CITIC ETSI Informática y de Telecomunicación, University of Granada, SpainDepartment of Computer Architecture and Technology, CITIC ETSI Informática y de Telecomunicación, University of Granada, SpainThis paper presents a control model for object manipulation. Properties of objects and environmental conditions influence the motor control and learning. System dynamics depend on an unobserved external context, for example, work load of a robot manipulator. The dynamics of a robot arm change as it manipulates objects with different physical properties, for example, the mass, shape, or mass distribution. We address active sensing strategies to acquire object dynamical models with a radial basis function neural network (RBF). Experiments are done using a real robot’s arm, and trajectory data are gathered during various trials manipulating different objects. Biped robots do not have high force joint servos and the control system hardly compensates all the inertia variation of the adjacent joints and disturbance torque on dynamic gait control. In order to achieve smoother control and lead to more reliable sensorimotor complexes, we evaluate and compare a sparse velocity-driven versus a dense position-driven control scheme.http://dx.doi.org/10.1155/2012/761019 |
| spellingShingle | Silvia Tolu Mauricio Vanegas Rodrigo Agís Richard Carrillo Antonio Cañas Dynamics Model Abstraction Scheme Using Radial Basis Functions Journal of Control Science and Engineering |
| title | Dynamics Model Abstraction Scheme Using Radial Basis Functions |
| title_full | Dynamics Model Abstraction Scheme Using Radial Basis Functions |
| title_fullStr | Dynamics Model Abstraction Scheme Using Radial Basis Functions |
| title_full_unstemmed | Dynamics Model Abstraction Scheme Using Radial Basis Functions |
| title_short | Dynamics Model Abstraction Scheme Using Radial Basis Functions |
| title_sort | dynamics model abstraction scheme using radial basis functions |
| url | http://dx.doi.org/10.1155/2012/761019 |
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