Knowledge representation and knowledge module structure for uncalibrated vision-guided robots

Minh-Chinh Nguyen; Doan-Trong Bui

doi:10.1117/12.403776

11 October 2000 Knowledge representation and knowledge module structure for uncalibrated vision-guided robots

Minh-Chinh Nguyen, Doan-Trong Bui

Proceedings Volume 4197, Intelligent Robots and Computer Vision XIX: Algorithms, Techniques, and Active Vision; (2000) https://doi.org/10.1117/12.403776
Event: Intelligent Systems and Smart Manufacturing, 2000, Boston, MA, United States

Abstract

A new concept for knowledge representation and structure of the knowledge module for vision-guided robots is introduced. It allows the robot to acquire, accumulate and adapt automatically whatever knowledge it may need and to gain experience in the course of its normal operation, i.e., learning by doing, thus, to improve its skills and operating speed over time. The knowledge module is structured into a set of a fairly independent submodules each performing a limited task, and sub-knowledge bases each contains limited knowledge. Such a structure allows to use the acquired knowledge flexibly and efficiently. It makes also easily to extend the knowledge base when the robot's number of degrees of freedom that must be controlled increases. The concept was realized and evaluated in real-world experiments on an uncalibrated vision-guided 5-DOF manipulator to grasp a variety of differently shaped objects.

Citation Download Citation

Minh-Chinh Nguyen and Doan-Trong Bui "Knowledge representation and knowledge module structure for uncalibrated vision-guided robots", Proc. SPIE 4197, Intelligent Robots and Computer Vision XIX: Algorithms, Techniques, and Active Vision, (11 October 2000); https://doi.org/10.1117/12.403776

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