Performance and storage requirements of topology-conserving maps for robot manipulator control

A new programming paradigm for the control of a robot manipulator by learning the mapping between the Cartesian space and the joint space (inverse Kinematic) is discussed. It is based on a Neural Network model of optimal
A new programming paradigm for the control of a robot manipulator by learning the mapping between the Cartesian space and the joint space (inverse Kinematic) is discussed. It is based on a Neural Network model of optimal mapping between two high-dimensional spaces by Kohonen. This paper describes the approach and presents the optimal mapping, based on the principle of maximal information gain. It is shown that Kohonens mapping in the 2-dimensional case is optimal in this sense. Furthermore, the principal control error made by the learned mapping is evaluated for the example of the commonly used PUMA robot, the trade-off between storage resources and positional error is discussed and an optimal position encoding resolution is proposed.
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Metadaten
Author:Rüdiger W. Brause
URN:urn:nbn:de:hebis:30-79687
ISSN:1432-9611
Parent Title (German):Universität Frankfurt am Main. Fachbereich Informatik: Interner Bericht ; 89,5
Series (Serial Number):Interner Bericht / Fachbereich Informatik, Johann Wolfgang Goethe-Universität Frankfurt a.M. (89, 05)
Document Type:Working Paper
Language:English
Year of Completion:1989
Year of first Publication:1989
Publishing Institution:Univ.-Bibliothek Frankfurt am Main
Release Date:2010/09/15
SWD-Keyword:Roboter; Selbstorganisierende Karte; Speicherbedarf
HeBIS PPN:228359945
Institutes:Informatik
Dewey Decimal Classification:004 Datenverarbeitung; Informatik
Sammlungen:Universitätspublikationen
Licence (German):License Logo Veröffentlichungsvertrag für Publikationen

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