Trajectories generation for a robotic manipulator

The inverse kinematics determines the joint angles that result in the desired position of the manipulator's end-effector with regard to the reference coordinated system. The inverse kinematics solution is difficult a time that the mapping between the Cartesian space and the joint space is nonlinear and involves equations that can have multiple solutions. This work presents the application of a neural network with radial basis function (RBF) for the inverse kinematics solution of a robotic manipulator with three degrees of freedom. For a good learning generalization in the training phase of RBF network, some initial and final points had been initially generated inside of the manipulator's work volume. In order to prevent extreme angular oscillations, the angles for each one of the n generated points, they had been optimized by genetic algorithms. In accordance with the cartesian coordinate (x, y) supplied, the RBF neural network implemented in this work supplied angles that it had presented very next to the desired values.