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Open Access item Kinematic Bézier maps

Authors:Ulbrich, Stefan
Ruiz de Angulo, Vicente
Torras, Carme
Asfour, Tamim
Dillman, Rudiger
Keywords:Robot kinematics, Learning (artificial intelligence), Humanoid robots
Issue Date:Aug-2012
Publisher:Institute of Electrical and Electronics Engineers
Citation:IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 42 (4) : 1215-1230 (2012)
Abstract: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 work, we introduce the Kinematic Bézier Map (KB-Map), a parametrizable 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.
Description:16 páginas, 18 figuras.
Publisher version (URL):http://dx.doi.org/10.1109/TSMCB.2012.2188507
Appears in Collections:(IRII) Artículos

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