An approach to beacons detection for a mobile robot using a neural network model

Authors:
Abdelhalim Boutarfa;Nour-Eddine Bouguechal;Hubert Emptoz
Affiliations:
Advanced Electronics Laboratory, University of Batna, Batna, Algeria;Advanced Electronics Laboratory, University of Batna, Batna, Algeria;INSA Laboratoire de Vision et de Reconnaissance des Formes au LIRIS, Villeurbanne Cedex, France
Venue:
MOAS'07 Proceedings of the 18th conference on Proceedings of the 18th IASTED International Conference: modelling and simulation
Year:
2007

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Abstract

In this paper we propose a neuro-mimetic technique relating to the detection of beacons in mobile robotics. The objective is to bring a robot moving in an unspecified environment to acquire attributes for recognition. We develop a practical approach for the segmentation of images of objects of a scene and evaluate the performances in real time of them. The neuronal classifier used is a window of a network MLP (9-6-3-1) using the algorithm of retro-propagation of the gradient, where the distributed central pixel uses information in level of gray. The originality of the work lies in the use of the association of an enhanced neural network configuration and Standard Hough Transform. The results obtained with a momentum of 0.3 and one coefficient of training equal to 0.02 shows that our system is robust with an extremely appreciable computing time.