Penalty approach for a constrained optimization to solve on-line the inverse kinematic problem of redundant manipulators

In this paper, a penalty approach which deals with a constrained optimization to solve the inverse kinematic of redundant robot manipulators is considered. An optimization procedure using neural networks is formulated, it produces on-line position and velocity trajectories in joint space from position and orientation trajectories in Cartesian space. This new method offers substantially better accuracy and guarantees a good minimization of a performance function subject to joint limitations while achieving the end-effector task