Simulation and Animation of a 2 Degree of Freedom Planar Robot Arm Based on Neural Networks

The purpose of this paper is to deal with the solution of the inverse kinematics problem in Robotic arms. A two links, two degree of freedom (doft) planar robot arm (manipulator) is simulated using a multilayer static neural network (MSNN) and animated. For the neural learning scheme is used an iterative technique (Levenberg-Marquardt algorithm) that can be thought of as a combination of steepest descent and the Gauss-Newton method. When we changed the error goal, we observed an oscillation on the end-effector of the manipulator due to increase of the error. Simulation and animation results for a two dof manipulator provide evidence that this approach is indeed successful with respect to an ANFIS structure, in which the main characteristic are the qualitative values versus the quantitative values of the static structures.