Exploring motion acquisition of manipulators with multiple degrees-of-redundancy using soft computing techniques

A backpropagation neural network (NN) is presented for the inverse kinematic problem to obtain a position control system for manipulators with multiple degrees-of-redundancy, where information provided from a laser transducer at the end-effector is used for planning the trajectory. A fuzzy reasoning system is designed to generate an approximate joint angle vector, because the inverse kinematics in this problem has infinite number of solution vectors. This vector is fed into the NN as a hint input vector rather than as a training vector to limit and guide the searching space. Simulations are implemented on a four-link redundant planar manipulator to show that the present control system is capable of tracking the planned trajectory while avoiding the collision.