Artículo

Permanent URI for this collectionhttps://hdl.handle.net/11285/345284

Artículo científico o editorial en una publicación periódica académica sujeto a revisión de pares. Cumple con los índices internacionales o bases de datos de amplia cobertura, como el listado del Current Contents, ISI WEB of Knowledge (http://isiknowledge.com/) e índice de revistas mexicanas de CONACYT (www.conacyt.mx/dac/revistas). Éstos indizan y resumen los artículos de revistas seleccionadas, en todas las áreas del saber.

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    Editorial: New challenges and trends in rehabilitation devices based on AI and optimization
    (Frontiers, 2023-09-08) Ponce Cruz, Pedro; Alfaro Ponce, Mariel; López Caudana, Edgar Omar; McDaniel, Troy; Montesinos Silva, Luis Arturo; López Gutiérrez, Jesús Ricardo; Lugo González, Esther; https://ror.org/03ayjn504; https://ror.org/03efmqc40; https://ror.org/059ex5q34; https://ror.org/003eaqr04
    This editorial paper presents a comprehensive overview of recent advancements and breakthroughs in artificial intelligence (AI) and neural networks, with a particular focus on their applications in various fields such as human activity recognition, medical image steganography, lower limb prosthetics, assistive robots for Autism Spectrum Disorder (ASD), and wearable assistive devices for visually impaired individuals.
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    Implementation of NAO robot maze navigation based on computer vision and collaborative learning
    (Frontiers, 2022-04-04) Magallán Ramírez, Daniela; Martínez Aguilar, Jorge David; Rodríguez Tirado, Areli; Balderas Silva, David Christopher; López Caudana, Edgar Omar; Moreno García, Carlos Francisco; BALDERAS SILVA, DAVID CHRISTOPHER; 222222; https://ror.org/03ayjn504; https://ror.org/04f0qj703
    Maze navigation using one or more robots has become a recurring challenge in scientific literature and real life practice, with fleets having to find faster and better ways to navigate environments such as a travel hub, airports, or for evacuation of disaster zones. Many methodologies have been explored to solve this issue, including the implementation of a variety of sensors and other signal receiving systems. Most interestingly, camera-based techniques have become more popular in this kind of scenarios, given their robustness and scalability. In this paper, we implement an end-to-end strategy to address this scenario, allowing a robot to solve a maze in an autonomous way, by using computer vision and path planning. In addition, this robot shares the generated knowledge to another by means of communication protocols, having to adapt its mechanical characteristics to be capable of solving the same challenge. The paper presents experimental validation of the four components of this solution, namely camera calibration, maze mapping, path planning and robot communication. Finally, we showcase some initial experimentation in a pair of robots with different mechanical characteristics. Further implementations of this work include communicating the robots for other tasks, such as teaching assistance, remote classes, and other innovations in higher education.
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