A Geometric Dexterous Motion Control of Redundant Robot Manipulators by using Nonlinear Optimization Method

A dexterou s motion control method of redundant robot manipulators based on nonlinear optimizatio n method is proposed t o satisfy multi-criteria such a s singularit y avoidance, minimizing energy consumption ,and avoiding physical limits of actuator, while performing a give n task. The method employs a neural optimization network with parallel processing capability as a nonlineae optimization method, where only a simple geometric analysis for resolved motion of each joint is required instead of computing of the Jacobian and its pseudo inverse matrix. For dexterous motion, a joint geometric manipulability measure t JGMM) i s proposed. JGMM evaluates a contribution of each joint differential motion in enlarging the length of shortest axis among principa laxes of the manipulability ellipsoid volume approximately obtained by a geometric analysis. Redundant robot manipulators is 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 squar e of each differential joint motio n is minimized where weightings are adjusted by JGMM. This paper was supported by Korea Research Fundation To show the validity of the proposed method, several numerical examples are illustrated.