Hierarchical Neurocontroller Architecture for Robotic Manipulation

Xavier J. R. Avula, Missouri University of Science and Technology
Luis C. Rabelo

This document has been relocated to http://scholarsmine.mst.edu/che_bioeng_facwork/351

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A hierarchical neurocontroller architecture consisting of two artificial neural network systems for the manipulation of a robotic arm is presented. The higher-level network system participates in the delineation of the robot arm workspace and coordinates transformation and the motion decision-making process. The lower-level network provides the correct sequence of control actions. A straightforward example illustrates the architecture''s capabilities, including speed, adaptability, and computational efficiency