Neural network control of a robotic manipulator arm for undersea applications

Abstract

A lightweight, direct-drive undersea testbed manipulator arm was configured for integration and subsequent evaluation of neural network technologies. The author reports them initial results of an artificial neural network model used to control this undersea manipulator. An iterative trajectory generator for the manipulator (constrained to planar motion) using a backpropagation network is described. It provided the intermittent desired joint angles given the relative position information about the arm and the target. This work built upon the extended work of D. Sobajic and L. Pao, (1988). The author discusses a preliminary neural network architecture which learns the internal and controller model for the undersea manipulator arm. This control structure was inspired by the work of D. Nguyen and B. Widrow, (1990). Although this work is still underway, preliminary tests are encouraging, and are aimed at satisfying the adaptive capability necessary for operating in an unstructured ocean environment.<>