Teaching computer programming to the visually impaired is a difficult task that has sparked a great deal of interest, in part due to its specific demands. Robotics has been one of the strategies adopted to help in this task. One system that uses robotics to teach programming for the visually impaired, called Donnie, has as its key part the need to detect Braille characters in a scaled-down environment. In this paper, we investigate the current state-of-the-art in Braille letter detection based on deep neural networks. For such, we provide a novel public dataset with 2818 labeled images of Braille characters, classified in the letters of the alphabet, and we present a comparison among some recent detection methods. As a result, the proposed Braille letters detection method could be used to assist in teaching programming for blind students using a scaled-down physical environment. The proposal of EVA (Ethylene Vinyl Acetate) pieces with pins to represent Braille letters in this environment is also a contribution.
The work aimed to use fiducial markers to locate a robot indoors using a simulation environment as a testing tool. An environment with scattered fiducial markers was developed for the robot to identify and be able to recalculate its position with the odometer sensors reference frame. It was observed that this method applied for indoor environments has a low computational cost, caused for the readability of the landmarks, and a high accuracy in robot localization compared to other methods.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.