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Real-Time Neuromorphic Algorithms For Inverse Kinematics Of Redundant Manipulators

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.