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Proceedings Paper

Framework for load apportioning and interactive force control using a Hopfield neural network
Author(s): Bruce R. Copeland; Joseph N. Anderson
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Paper Abstract

This paper considers the control of two robotic manipulators jointly graspingwith no slippagea rigid object. Two conflicting control problems are addressed: Adaptively apportioning the object load between the manipulators and application of commanded interactive forces (or bias forces) to the object. By controlling the interactive forces damage to the manipulators and/or the object can be prevented. Additionally a desired tension torsion or cornpression can be applied to the object. Adaptive load apportioning (sharing) increases the carrying capacity of the system by using possible mechanical advantage. The Hopfleld net is used to minimize a quadratic energy function in the joint torques. The results are the optimal joint torques required to drive the load and supply the commanded bias force. Simulations are presented that show effective control of interactive forces while sharing the load in an optimal fashion.

Paper Details

Date Published: 1 February 1991
PDF: 12 pages
Proc. SPIE 1381, Intelligent Robots and Computer Vision IX: Algorithms and Techniques, (1 February 1991); doi: 10.1117/12.25148
Show Author Affiliations
Bruce R. Copeland, Tennessee Technological Univ. (United States)
Joseph N. Anderson, Tennessee Technological Univ. (United States)


Published in SPIE Proceedings Vol. 1381:
Intelligent Robots and Computer Vision IX: Algorithms and Techniques
David P. Casasent, Editor(s)

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