Three methods of training multi-layer perceptrons to model a robot sensor

Abstract

Summary This paper discusses three methods of training multi-layer perceptrons (MLPs) to model a six-degrees-of- freedom inertial sensor. Such a sensor is designed for use with a robot to determine the location of objects it has to pick up. The sensor operates by measuring parameters related to the inertia of an object and computing its location from those parameters. MLP models are employed for part of the computation. They are trained to output the orientation of the object in response to an input pattern that includes the period of natural vibration of the sensor on which the object rests. After reviewing the working principle of the sensor, the paper describes the three MLP training methods (backpropagation, optimisation using the Levenberg-Marquardt algorithm, evolution based on the genetic algorithm) and presents the experimental results obtained.