Partitioned neural network architecture for inverse kinematic calculation of a 6 DOF robot manipulator

Abstract

A parallel neural network architecture called a partitioned network is proposed for applications which demand high learning accuracy. The partitioned neural network is composed of a preprocessing layer and partition modules containing dedicated neurons. The learning equations used are those of the backpropagation algorithm. The network has been applied to learning of the inverse kinematic solution of a six-degree-of-freedom robot manipulator. After training, the partitioned network was able to predict robot joint angles not included in the training data set with average errors of 0.9 degrees , 3.6 degrees , 2.1 degrees , 6.9 degrees , 6.5 degrees , and 8.5 degrees for the first, second, third, fourth, fifth, and sixth joints, respectively.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>