A geometric dexterous motion control of redundant robot manipulators by using nonlinear optimization method

Abstract

Summary form only given. A dexterous motion control method of redundant robot manipulators based on a nonlinear optimization method is proposed to satisfy multi-criteria such as singularity avoidance, minimizing energy consumption,and avoiding physical limits of actuators, while performing a given task. The method employs a neural optimization network with parallel processing capability as a nonlinear optimization method, where only a simple geometric analysis for resolved motion of each joint is required instead of computing the Jacobian and its pseudo inverse matrix. For dexterous motion, a joint geometric manipulability measure (JGMM) is proposed. The JGMM evaluates a contribution of each joint differential motion in enlarging the length of the shortest axis among principal axes of the manipulability ellipsoid volume approximately obtained by a geometric analysis. Redundant robot manipulators are then controlled by neural optimization networks in such a way that (1) linear combination of the resolved motion by each joint differential motion should be equal to the desired velocity, (2) physical limits of joints are not violated, and (3) weighted sum of the square of each differential joint motion is minimized where weightings are adjusted by the JGMM.