Kinematic Bézier maps

Cita com:
hdl:2117/17357
Document typeArticle
Defense date2012
Rights accessOpen Access
Except where otherwise noted, content on this work
is licensed under a Creative Commons license
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Attribution-NonCommercial-NoDerivs 3.0 Spain
ProjectINTELLACT - Intelligent observation and execution of Actions and manipulations (EC-FP7-269959)
GRASP - Emergence of Cognitive Grasping through Emulation, Introspection, and Surprise (EC-FP7-215821)
GRASP - Emergence of Cognitive Grasping through Emulation, Introspection, and Surprise (EC-FP7-215821)
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 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.
CitationUlbrich, S. [et al.]. Kinematic Bézier maps. "IEEE transactions on systems man and cybernetics Part B-Cybernetics", 2012, vol. 42, núm. 4, p. 1215-1230.
ISSN1083-4419
Publisher versionhttp://dx.doi.org/10.1109/TSMCB.2012.2188507
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