Kinematic Bézier maps
The kinematics of a robot with many degrees of freedom is a very complex function. Learning this function for a large workspace with a good precision requires a huge number of training samples, i.e., robot movements. In this paper, we introduce the Kinematic Bézier Map (KB-Map), a parameterizable m...
| Autores: | , , , , |
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| Formato: | artículo |
| Fecha de publicación: | 2012 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/17357 |
| Acesso em linha: | https://hdl.handle.net/2117/17357 https://dx.doi.org/10.1109/TSMCB.2012.2188507 |
| Access Level: | acceso abierto |
| Palavra-chave: | Artificial intelligence learning (artificial intelligence) robot kinematics robots PARAULES AUTOR: learning robot kinematics humanoid robots Intel·ligència artificial Classificació INSPEC::Cybernetics::Artificial intelligence Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
| Resumo: | The kinematics of a robot with many degrees of freedom is a very complex function. Learning this function for a large workspace with a good precision requires a huge number of training samples, i.e., robot movements. In this paper, we introduce the Kinematic Bézier Map (KB-Map), a parameterizable model without the generality of other systems but whose structure readily incorporates some of the geometric constraints of a kinematic function. In this way, the number of training samples required is drastically reduced. Moreover, the simplicity of the model reduces learning to solving a linear least squares problem. Systematic experiments have been carried out showing the excellent interpolation and extrapolation capabilities of KB-Maps and their relatively low sensitivity to noise. |
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