FRB-BLINGUI: um modelo baseado em regras Fuzzy para previsão de riscos de colisão com obstáculos em apoio a pessoas com deficiência visual.

Abstract

It is possible to observe in the literature that several approaches are developed for the safe navigation and guidance of people with visual impairments, aiming to avoid collisions with obstacles. These approaches often use ultrasonic or infrared sensors, mobile applications with the aid of cameras for computer vision, or wearable devices. However, uncertainties and inaccuracies arising from the dynamics of the environment are often neglected, making navigation and guidance for people with visual impairments unsafe and subject to various types of collisions with different types of obstacles. Faced with this situation, there is a need to develop approaches that consider the degree of collision risk presented by each obstacle, considering the dynamics in the movement between the individual and the obstacle, as well as their location at high or low points that are difficult to perceive. In this context, Fuzzy Set Theory (FST) presents itself as an essential tool to deal with imprecision and uncertainties existing in the environment, since the use of systems based on FST has the advantage of intuitive interpretation of user behavior. Therefore, this thesis presents an innovative model for detecting obstacles and predicting collision risks in dynamic scenarios, called FRB-BlinGui (Blind navigation and guidance model fuzzy rules based). The proposed model was tested on a wearable device to detect obstacles and prevent collisions in real time, offering gradual warning about risks. The results show its effectiveness in providing alert, promising to improve the safety and mobility of people with visual impairments.