Kinematic inversion

We propose a new solution to the inverse kinematic problem of redundant robots subject to a set of criteria and constraints. First, a study of existing methods leads us to develop an on-line algorithm based on an adaptive neural network. This solution needs only a few iterations to converge, offers substantially better accuracy, verifies the repeatability propriety for the closed trajectory and avoids the computation of the inverse or pseudoinverse Jacobian matrix. Our approach guarantees a good minimization of any performance criterion subject to either equality or inequality constraints while achieving the end-effector task. Then, our method can solve the inverse kinematic problem of a redundant robot for it to follow a desired trajectory while avoiding moved or fixed obstacles.