Smart System Model to Disinfect SARS-CoV 2 in Physical Environments
Keywords:
Smart system, disinfecting robot, artificial neural networks, SARS-COV 2.Abstract
The present research project is the development of a de-disinfectant robot model (intelligent system), capable of moving and recognizing the physical environment to disinfect it from SARS-CoV2, by means of Ultraviolet C and Ozone light, having double disinfection. To this end, certain systematized actions have been carried out, such as achieving the training of artificial neural networks (ANN), multilayer perceptron using Matlab software, later programming logic in Arduino IDE, and then the implantation of the code in the Arduino microcontroller. The simulations of the disinfecting robot processes have allowed observing the behavior of its movement, location, obstacle detection, activation of the disinfectant module, timing of stages and disinfection of the environment. All this has been carried out and achieved within the Concurrent Design methodological framework that contains five stages: conceptual design, kinematic analysis, dynamic analysis, mechanical design and simulation. As a concrete result, there is the design of the disinfecting robot with a height of 1.35 meters, and that in its structure it has sensors, actuators and peripherals. In addition, estimates of time, distance, travel, motor rotation and displacement of the ANN disinfectant robot have been obtained using mathematical models, such as odontometric equations, state space system and fundamentally neural networks.
References
Arellano, L. (2015). Design and implementation of a mobile robot with trajectory control using odomometric principles. (Tesis de Grado). Universidad Nacional Mayor de San Marcos, Lima.
Contreras, W., Maldonado, J., & León, R. (2019). Application of feed-forward backpropagation neural network for the diagnosis of mechanical failures in engines provoked ignition. Science and Technology Magazine, 1(21), 32-40.
Dormido, S., Sánchez , J., & Kofman, E. (2008). Muestreo, Control y Comunicación basados en Eventos. Revista Iberoamericana de América e Informática Industrial, 5(1), 5-26.
Flores, J. (28 de Abril de 2020). Tecnología ultravioleta para combatir la propagación del coronavirus. Recuperado el 21 de Junio de 2020, de https://www.nationalgeographic.com.es/ciencia/tecnologia-ultravioleta-para- combatir-propagacion-coronavirus_15474
Hospital, E. (Abril de 2020). Cabina de desinfección automatizada para combatir el coronavirus en empresas. Recuperado el 19 de Mayo de 2020, de https://www.elhospital.com/temas/Cabina-de-desinfeccion-automatizada-para- combatir-el-coronavirus-en-empresas+134038
Hossian , A., Cejas , L., Echeverría , R., Olivera , V., & Alveal , M. (2015). Development of Experiments of a Navigator Robot with Neural Networks in a Structured Environment: Behavior Programming with Backpropagation Algorithm. Latin American Journal of Software Engineering, 3(1), 41-46.
Jingkui , S., Wenxin , H., & Liangliang, Y. (2010). A New Sensor-Follow Control Strategy for Tracking Control Mobile Robot. IEEE Computer Society, 1(1), 163-167.
Kwon, J.-W., & Chwa, D. (2012). Hierarchical Formation Control Based on a Vector Field Method for Wheeled Mobile Robots. IEEE Transactions on Robotics, 28(6), 1335-1345.
León, R., Maldonado, J., & Contreras, R. (2020). Prediction of CO and HC emissions in Otto motors through neural networks. Science and Technology Magazine, 1(23), 30-39.
Li, X., Sun, D., Lu, G., Krabicka, J., & Yan, Y. (2012). Prediction of NOx emissions throughflame radical imaging and neural network based soft computing. IEEE International Conference on Imaging Systems and Techniques Proceedings, Manchester, 502-505.
Li, Y., Wang, Z., & Zhu, L. (2010). Adaptive neural network PID sliding mode dynamic control of nonholonomic mobile robot. IEEE, 61, 735-757.
Navarro, D., Benet, G., Ríos , L., & Bueno, M. (2007). Improvements In The Odometric Location Of A Differential Robot By Correcting Systematic Errors. Scientia et Technica, 5(37), 37-42.
OMS. (12 de Octubre de 2020). Preguntas y respuestas sobre la enfermedad por coronavirus (COVID-19). Recuperado el 12 de Noviembre de 2020, de https://www.who.int/es/emergencies/diseases/novel-coronavirus- 2019/question-and-answers-hub/q-a-detail/coronavirus-disease-covid-19
Rezaee, H., & Abdollahi, F. (2014). A Decentralized Cooperative Control Scheme With Obstacle Avoidance for a Team of Mobile Robots. IEEE Transactions on Industrial Electronics, 61(1), 347-354.
Sabater, J., & Martínez, J. (2012). Teaching guide for the design of service robots.
ESpaña: Aidico.
Tardón, L. (11 de Marzo de 2020). El coronavirus puede sobrevivir varios días y transmitirse por las heces. Recuperado el 15 de Junio de 2020, de https://www.elmundo.es/ciencia-y- salud/salud/2020/03/10/5e677bfdfdddff2b9e8b4577.html
Urquijo, O., Izaguirre, E., & Hernández, L. (2017). Path control in 2GDL parallel robot Cartesian space using vector kinematic model. Revista de Ingenieria Electrónica, Automática y comunicaciones, 38(2), 72-82.
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