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Proceedings Paper

Self-learning self-broadening knowledge base for calibration-free robot vision
Author(s): Minh-Chinh Nguyen; Volker Graefe
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Paper Abstract

A new concept of a self-learning, self-broadening knowledge base that may be used as the long-term memory for a completely calibration-free robot vision to manipulate objects is presented. With this concept the robot automatically acquires during its normal operation the necessary knowledge which can be saved afterwards in the knowledge base and allowing the robot to adapt itself to changing conditions. Thus, the robot presents self-learning characteristics. The robot control using the knowledge base is then based on the human way of solving problems, i.e. new, additional facts (in our case control words) are developed from available facts. Such a control enables improving skills of the robot. The concept has been successfully realized and tested in real-world experiments with an uncalibrated vision-guided manipulator involving the grasping of various objects with nearly any shape and in an arbitrary orientation in horizontal plane.

Paper Details

Date Published: 6 October 1998
PDF: 9 pages
Proc. SPIE 3522, Intelligent Robots and Computer Vision XVII: Algorithms, Techniques, and Active Vision, (6 October 1998); doi: 10.1117/12.325769
Show Author Affiliations
Minh-Chinh Nguyen, Univ. der Bundeswehr Munchen (Germany)
Volker Graefe, Univ. der Bundeswehr Munchen (Germany)

Published in SPIE Proceedings Vol. 3522:
Intelligent Robots and Computer Vision XVII: Algorithms, Techniques, and Active Vision
David P. Casasent, Editor(s)

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