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Sensors 2018, 18(3), 683;

A Neural Network Approach for Building An Obstacle Detection Model by Fusion of Proximity Sensors Data

Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2147, Valparaíso 2362804, Chile
Departamento de Informática y Automática, Universidad Nacional de Educación a Distancia, Juan del Rosal 16, 28040 Madrid, Spain
Radar Research and Innovations, 11702 W 132nd Terrace, Overland Park, KS 66213, USA
Author to whom correspondence should be addressed.
Received: 20 December 2017 / Revised: 19 February 2018 / Accepted: 21 February 2018 / Published: 25 February 2018
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Spain 2017)
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Proximity sensors are broadly used in mobile robots for obstacle detection. The traditional calibration process of this kind of sensor could be a time-consuming task because it is usually done by identification in a manual and repetitive way. The resulting obstacles detection models are usually nonlinear functions that can be different for each proximity sensor attached to the robot. In addition, the model is highly dependent on the type of sensor (e.g., ultrasonic or infrared), on changes in light intensity, and on the properties of the obstacle such as shape, colour, and surface texture, among others. That is why in some situations it could be useful to gather all the measurements provided by different kinds of sensor in order to build a unique model that estimates the distances to the obstacles around the robot. This paper presents a novel approach to get an obstacles detection model based on the fusion of sensors data and automatic calibration by using artificial neural networks. View Full-Text
Keywords: proximity sensors; automatic calibration; neural networks proximity sensors; automatic calibration; neural networks

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Farias, G.; Fabregas, E.; Peralta, E.; Vargas, H.; Hermosilla, G.; Garcia, G.; Dormido, S. A Neural Network Approach for Building An Obstacle Detection Model by Fusion of Proximity Sensors Data. Sensors 2018, 18, 683.

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