Modeling of robot inverse kinematics using two ANN paradigms

The performance of two artificial neural networks, trained to learn data obtained from the kinematics model of a robotic arm, was compared. The trained artificial neural network (ANN) simulators were implemented to position the robotic manipulator demonstrating the feasibility of using ANN technology in actual implementations. Graphs were plotted to show relevant errors for robotic workspace and conclusions derived with reference to ANN's level of accuracy