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General robot kinematics decomposition without intermediate markers

Autor
Ulbrich, S.; Ruiz De Angulo, V.; Asfour, T.; Torras, C.; Dillmann, R.
Tipus d'activitat
Article en revista
Revista
IEEE transactions on neural networks
Data de publicació
2012
Volum
23
Número
4
Pàgina inicial
620
Pàgina final
630
DOI
https://doi.org/10.1109/TNNLS.2012.2183886 Obrir en finestra nova
Repositori
http://hdl.handle.net/2117/17693 Obrir en finestra nova
URL
http://dx.doi.org/10.1109/TNNLS.2012.2183886 Obrir en finestra nova
Resum
The calibration of serial manipulators with high numbers of degrees of freedom by means of machine learning is a complex and time-consuming task. With the help of a simple strategy, this complexity can be drastically reduced and the speed of the learning procedure can be increased: When the robot is virtually divided into shorter kinematic chains, these subchains can be learned separately and, hence, much more efficiently than the complete kinematics. Such decompositions, however, require either...
Citació
Ulbrich, S. [et al.]. General robot kinematics decomposition without intermediate markers. "IEEE transactions on neural networks", 2012, vol. 23, núm. 4, p. 620-630.
Paraules clau
learning (artificial intelligence) robot kinematics robots PARAULES AUTOR: KB-maps
Grup de recerca
KRD - Cinemàtica i Disseny de Robots
ROBiri - Grup de Robòtica de l'IRI

Participants