Hybrid neural-based guiding system for mobile robots

Abstract

A hybrid system is a dynamical system with both discrete and continuous state changes such as those that combine neural networks and fuzzy logic. In this paper, we propose a method for voice and image recognition by implementing optimized neural networks and fuzzy logic to guide a distributed robot. Generally, word recognition systems are divided into three stages: segmentation, feature extraction and classification. We use a computer vision method for feature extraction, which is known as the Mel Frequency Cepstral Coefficients (MFCC). Genetic Algorithms (GA) are used for the optimization process in order to improve image recognition. The robot's world is a white square area measuring 2 square meters, the robot receives a voice request for a geometric solid and it must search between the different solids to find the one asked for. After this it must direct itself to the solid using a fuzzy guiding system.