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Resumen-Se propone un sistema capaz de brindar un apoyo al paciente diabético dado el gran desconocimiento que la población tiene respecto a esta enfermedad. La base de conocimientos se ha tomado gracias a la asesoría de médicos y laboratorista clínicos. Esta primera versión del sistema inteligente utiliza como motor de inferencia lógica difusa dadas sus características de manejo de incertidumbre. Este proyecto permitirá llevar un registro preciso de los niveles de diferentes parámetros sanguíneos de un paciente así como generar representaciones gráficas y estadísticas de control de forma que permita apoyar en la prevención y toma de decisiones oportunas de la diabetes. Palabras clave-Base de conocimiento, parámetros sanguíneos, monitoreo.Abstract-We propose a system capable to help a patient with diabetes taking into account that in general the persons have little knowledge about this disease. This knowledge base was developed in cooperation with medic personnel. The system uses a fuzzy logic inference engine and, thus, is capable of managing uncertainty. This project allows keeping the records of values of various blood parameters, graphic representation of data and statistic information, and it is used in prevention and decision making for patients with diabetes.

Robots able to roll and jump are used to solve complex trajectories. These robots have a low level of autonomy, and currently, only teleoperation is available. When researching the literature about these robots, limitations were found, such as a high risk of damage by testing, lack of information, and nonexistent tools. Therefore, the present research is conducted to minimize the dangers in actual tests, increase the documentation through a platform repository, and solve the autonomous trajectory of a maze with obstacles. The methodology consisted of: replicating a scenario with the parrot robot in the gazebo simulator; then the computational resources, the mechanism, and the available commands of the robot were studied; subsequently, it was determined that the genetic micro-algorithm met the minimum requirements of the robot; in the last part, it was programmed in simulation and the solution was validated in the natural environment. The results were satisfactory and it was possible to create a parrot robot in a simulation environment analogous to the typical specifications. The genetic micro-algorithm required only 100 generations to converge; therefore, the demand for computational resources did not affect the execution of the essential tasks of the robot. Finally, the maze problem could be solved autonomously in a real environment from the simulations with an error of less than 10% and without damaging the robot.

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