A neural network approach for building an obstacle detection model by fusion of proximity sensors data

GONZALO ALBERTO FARIAS CASTRO, Ernesto Fabregas, Emmanuel Peralta, Héctor Vargas, GABRIEL ENRIQUE HERMOSILLA VIGNEAU, Gonzalo Garcia, Sebastián Dormido

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

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.

Original languageEnglish
Article number683
JournalSensors (Switzerland)
Volume18
Issue number3
DOIs
StatePublished - 1 Mar 2018

Keywords

  • Automatic calibration
  • Neural networks
  • Proximity sensors

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