Resolved motion control of redundant robot manipulators by neural optimization networks

Abstract

An effective resolved motion control method of redundant manipulators is proposed to minimize the energy consumption and to increase the dexterity while satisfying the physical actuator constraints. The method employs the neural optimization networks, where the computation of Jacobian matrix is not required. Specifically, end-effector movement resulting from each joint differential motion is first separated into orthogonal and tangential components with respect to a given desired trajectory. Then the resolved motion is obtained by neural optimization networks in such a way that: (1) the linear combination of the orthogonal components should be null; (2) the linear combination of the tangential components should be the differential length of the desired trajectory; (3) the differential joint motion limit is not violated, and (4) the weighted sum of the square of each differential joint motion is minimized. The weighting factors are controlled by a newly defined joint dexterity measure as the ratio of the tangential and orthogonal components. >