Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/55811
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Title: 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
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
URI: http://hdl.handle.net/10261/55811
ISSN: 10834419
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Citation: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 42 (4) : 1215-1230 (2012)
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