Sensor fusion for distance estimation under disturbance with reflective optical sensors using Multi Layer Perceptron (MLP)

Abstract

There are many methods to perform distance measurement, among them the reflexive optical sensors, which are low cost but present some issues such as nonlinear response, limited operating ranges and the measurement shows sensitivity to infrared or visible radiation. For this reason, this work presents a sensory fusion model combining three reflective optical distance sensors of different ranges, a color sensor (VIS), an ultraviolet radiation sensor (UV) and an infrared sensor (IR) to estimate the distance using a Multi Layer Perceptron (MLP).  The purpose of combining different distance sensors is to have a higher overall range and achieve redundancy in some regions, and the objective of the UV-VIS-NIR sensors is to compensate for the radiation at which the distance sensors are exposed to adjust the measurement. With the attained information, the influence of each type of radiation on the distance measurement was evaluated, the net was trained switching his architecture between four and sixteen neurons per layer, and between three and five hidden layers. Finally the training and selection of several MLP architectures for sensory fusion with an error lower than 1 % was presented.