AN IMPROVED APPROACH TO BEACONS DETECTION
Main Article Content
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 0.03 and one coefficient of training equal to 0.002 shows that our system is robust with an extremely appreciable computing time.