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Proceedings Paper

Real-Time Neuromorphic Algorithms For Inverse Kinematics Of Redundant Manipulators
Author(s): Jacob Barhen; Sandeep Gulati; Michail Zak
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Paper Abstract

We present an efficient neuromorphic formulation to accurately solve the inverse kinematics problem for redundant manipulators. Our approach involves a dynamical learning procedure based on a novel formalism in neural network theory: the concept of "terminal" attractors. Topographically mapped terminal attractors are used to define a neural network whose synaptic elements can rapidly encapture the inverse kinematics transformations, and, subsequently generalize to compute joint-space coordinates required to achieve arbitrary end-effector configurations. Unlike prior neuromorphic im-plementations, this technique can also systematically exploit redundancy to optimize kinematic criteria, e.g. torque optimization. Simulations on 3-DOF and 7-DOF redundant manipulators, are used to validate our theoretical framework and illustrate its computational efficacy.

Paper Details

Date Published: 27 March 1989
PDF: 11 pages
Proc. SPIE 1002, Intelligent Robots and Computer Vision VII, (27 March 1989); doi: 10.1117/12.960331
Show Author Affiliations
Jacob Barhen, California Institute of Technology (United States)
Sandeep Gulati, Louisiana State University (United States)
Michail Zak, California Institute of Technology (United States)


Published in SPIE Proceedings Vol. 1002:
Intelligent Robots and Computer Vision VII
David P. Casasent, Editor(s)

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