SYNERGISTIC NEURAL MODELS OF A ROBOT SENSOR FOR PART ORIENTATION DETECTION

This paper describes the use of neural networks to compute the orienta tion of a part from the output signals of an inertial sensor which is a device for determining the location of parts by measuring their iner tial parameters. The paper investigates an approach for increasing the accuracy of the computed orientation. This involves employing a group of neural networks and combining their outputs. The paper presents th e results obtained for several neural network combinations. These show that the accuracy achieved in a combined system is higher than that o f its individual components provided the number of components is not t oo large.