Self-Organizing Neuromorphic Architecture for Manipulator Inverse Kinematics

Abstract

We describe an efficient neuromorphic formulation to accurately solve the inverse kinematics problem for redundant manipulators, thereby enabling development of enhanced anthropomorphic capability and dexterity. Our approach involves a dynamical learning procedure based on a novel formalism in neural network theory: the concept of “terminal” attractors, that are shown to correspond to solutions of the nonlinear neural dynamics with infinite local stability. Topographically mapped terminal attractors are then used to define a neural network whose synaptic elements can rapidly encapture the inverse kinematics transformations using a priori generated examples and, subsequently generalize to compute the joint-space coordinates required to achieve arbitrary end-effector configurations. Unlike prior neuromorphic implementations, this technique can also systematically exploit redundancy to optimize kinematic criteria, e.g. torque optimization, manipulability etc. and is scalable to configurations of practical interest. Simulations on 3-DOF and 7-DOF redundant manipulators, are used to validate our theoretical framework and illustrate its computational efficacy.