Real-Time Neuromorphic Algorithms For Inverse Kinematics Of Redundant Manipulators

Jacob Barhen; Sandeep Gulati; Michail Zak

doi:10.1117/12.960331

27 March 1989 Real-Time Neuromorphic Algorithms For Inverse Kinematics Of Redundant Manipulators

Jacob Barhen, Sandeep Gulati, Michail Zak

Proceedings Volume 1002, Intelligent Robots and Computer Vision VII; (1989) https://doi.org/10.1117/12.960331
Event: 1988 Cambridge Symposium on Advances in Intelligent Robotics Systems, 1988, Boston, MA, United States

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.

Citation Download Citation

Jacob Barhen, Sandeep Gulati, and Michail Zak "Real-Time Neuromorphic Algorithms For Inverse Kinematics Of Redundant Manipulators", Proc. SPIE 1002, Intelligent Robots and Computer Vision VII, (27 March 1989); https://doi.org/10.1117/12.960331

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