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Learning inverse kinematics: reduced sampling through decomposition into virtual robots

Autor
Ruiz De Angulo, V.; Torras, C.
Tipus d'activitat
Article en revista
Revista
IEEE transactions on systems man and cybernetics Part B-Cybernetics
Data de publicació
2008-01
Volum
38
Número
6
Pàgina inicial
1571
Pàgina final
1577
DOI
https://doi.org/10.1109/TSMCB.2008.928232 Obrir en finestra nova
Repositori
http://hdl.handle.net/2117/2701 Obrir en finestra nova
URL
http://ieeexplore.ieee.org.recursos.biblioteca.upc.edu/document/4643433/ Obrir en finestra nova
Resum
We propose a technique to speed up the learning of the inverse kinematics of a robot manipulator by decomposing it into two or more virtual robot arms. Unlike previous decomposition approaches, this one does not place any requirement on the robot architecture and, thus, it is completely general. Parametrized Self-Organizing Maps (PSOM) are particularly adequate for this type of learning, and permit comparing results obtained directly and through the decomposition. Experimentation shows that time...
Citació
Ruiz de Angulo, Vicente; Torras, Carme. "Learning inverse kinematics: Reduced sampling through decomposition into virtual robots". IEEE transactions on systems, man and cybernetics: Part B, 2008, vol. 38, núm. 6, p. 1571-1577.
Paraules clau
Function approximation, learning inverse kinematics, parametrized self-organizing maps (PSOMs), robot kinematics
Grup de recerca
ROBiri - Grup de Robòtica de l'IRI

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