Use of Artificial Neural Networks for the Development of an Inverse Kinematic Solution and Visual Identification of Singularity Zone(s)

Abstract

This paper presents a non-conventional technique for solving the inverse kinematics problem using artificial neural networks. A feed forward multi-layer perceptron with backpropagation neural network is selected for this research. An inverse kinematic solution for a PUMA 560 robot is developed by training the neural network with the robot's end-effector Cartesian co-ordinates and its corresponding joint configurations. Once the network is well trained (90th percentile) and confident predictions can be achieved, a test input set (singularity conditions) is introduced to the trained network to simulate results. This technique proves promising since it requires little computation time over other traditional methods.