Real-time neuromorphic algorithms for inverse kinematics of redundant manipulatorsThe paper presents an efficient neuromorphic formulation to accurately solve the inverse kinematics problem for redundant manipulators. The 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 implementations, 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 the theoretical framework and illustrate its computational efficacy.
Document ID
19900024709
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Barhen, Jacob (Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Gulati, Sandeep (Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Zak, Michail (California Institute of Technology Jet Propulsion Laboratory, Pasadena, United States)