Anti-collision system for blind people using evolutionary neural networks

Abstract

Introduction: This paper shows the design and implementation of an anti-collision system for blind people using evolutionary artificial neural networks (EANNs).
Objective: Present the implementation of evolutionary neural networks in a guide system for the blind in the detection of static and moving obstacles.
Method: Methodology is based on the creation of artificial neural networks from the cooperative co-evolutionary genetic algorithm (CCGA), which is responsible for structuring, modifying and training neural networks. It uses the network definition matrix (NDM). NDM is made of a chromosome "chromosome is genetic algorithm part" is taken as the basis. Once is done the NDM, it creates an artificial neural network to be trained.
Results: Program carried out several neural networks, and generated 10 chromosomes in each execution. Artificial neural networks were trained with the CCGA and it applies the cooperation, they obtained the best anti-collision neural networks considering a definite time, anti-collision neural networks worked effectively for the detection of physical obstacles and with movement.
Conclusions: In the anti-collision system for the blind, we observed the effectiveness of neural networks in giving an answer, detecting both static and moving objects, providing security to the blind, avoiding collisions with objects.