Felipe Trujillo-Romero

En el presente artículo se muestra la metodología que se llevó a cabo para la realización de un prototipo de pinza antropomórfico, el cual debe cumplir con ciertas características como que sea capaz de sostener objetos pequeños con la ayuda de servomotores. El prototipo cuenta con cuatro dedos, lo que le permite adaptarse a diversas formas de objetos para un agarre y manipulación eficaces. Dicho diseño se realizó basándose en el estudio de pinzas robóticas ya existentes, de los cuales se recaudó las características más importantes para cumplir con nuestro objetivo, el diseño fue realizado en Inventor y posteriormente impreso en 3D para la obtención de un prototipo funcional. Se prevé su integración en materias de mecatrónica y mecánica, sirviendo como una herramienta práctica para demostrar y validar conceptos relacionados con el diseño de mecanismos, sistemas de control y manipulación de objetos en entornos educativos.

Optimizing the allocation of gasoline stations to refineries and depots enhances service coverage and response times, particularly during contingencies. Integrating biofuel production facilities supports sustainable energy initiatives and fuel supply. This study formulates an approach to optimize the allocation of gasoline stations to existing production and storage facilities while planning for bio-diesel plant expansion. It considers geographic distribution, balanced load allocation, minimal distances, and expansion costs. Using accurate data from Mexico, an extended k-means algorithm was developed to address the location/allocation problem involving gasoline stations, refineries, depots, and future biofuel facilities. Results demonstrate that additional biofuel facilities can alleviate service loads on existing infrastructure while enhancing sustainability and reducing environmental impact. These findings provide valuable insights for policymakers and urban planners aiming to improve fuel distribution networks and promote sustainable energy solutions.

El presente trabajo de investigación se orientó en proveer de un medio de interacción entre un ser humano y un sistema robótico, con el cual el usuario pueda dar instrucciones de movimiento. y manipulación al robot, pero, sobre todo, sin utilizar una herramienta electrónica de comunicación como el teclado. Se plantea el desarrollo de un sistema de reconocimiento multimodal que incluya las dos formas de comunicación más usadas por los humanos, señas y voz. Por una parte, la identificación de seis signos correspondientes al alfabeto del Lenguaje de Señas Mexicano (LSM), proporcionando la interacción con el robot por medio de señas. En tal caso, el reconocimiento no dependerá de mecanismos electrónicos para modelar los símbolos del alfabeto. Por otro lado, el reconocimiento de seis comandos vocales, también para el Español Mexicano, proporcionando la interacción mediante voz con el sistema robótico. En ambos casos, se interpretará un mensaje diseñado para controlar un sistema robótico de servicio.

In recent decades, the World Health Organization and the scientific community have provided convincing evidence of the harmful effects on health due to exposure to atmospheric particles with a diameter of fewer than 10 microns or PM10. For this reason, this organization has alerted governments to measure, control, and evaluate air quality in real-time and in the medium term. For this reason, this work predicts PM10 particles using time series and a multilayer perceptron system to estimate PM10 concentrations over time. Four-time series models were analyzed, and four different multilayer perceptron architectures were used to determine the best prediction and know the pollutant's in short-term and medium-term future behavior. The results show that the neural system provides a better forecast in the short term with an error of 7.68% compared to the best time series method, the TIE, whose error was 27.20%. While in the medium term, the error was 31.10% for the TIE and 9.16% for the neural network.

This paper outlines the development of a module capable of constructing a map-building algorithm using inertial odometry and visual features. It incorporates an object recognition module that leverages local features and unsupervised artificial neural networks to identify non-dynamic elements in a room and assign them positions. The map is modeled using a neural network, where each neuron corresponds to an absolute position in the room. Once the map is constructed, capturing just a couple of images of the environment is sufficient to estimate the robot's location. The experiments were conducted using both simulation and a real robot. The Webots environment with the virtual humanoid robot NAO was used for the simulations. Concurrently, results were obtained using a real NAO robot in a setting with various objects. The results demonstrate notable precision in localization within the two-dimensional maps, achieving an accuracy of ± (0.06, 0.1) m in simulations contrasted with the natural environment, where the best value achieved was ± (0.25, 0.16) m.

Despite autonomous navigation is one of the most proliferate applications of three-dimensional (3D) point clouds and imagery both techniques can potentially have many other applications. This work explores urban digitization tools applied to 3D geometry to perform urban tasks. We focus exclusively on compiling scientific research that merges mobile laser scanning (MLS) and imagery from vision systems. The major contribution of this review is to show the evolution of MLS combined with imagery in urban applications. We review systems used by public and private organizations to handle urban tasks such as historic preservation, roadside assistance, road infrastructure inventory, and public space study. The work pinpoints the potential and accuracy of data acquisition systems to handled both 3D point clouds and imagery data. We highlight potential future work regarding the detection of urban environment elements and to solve urban problems. This article concludes by discussing the major constraints and struggles of current systems that use MLS combined with imagery to perform urban tasks and to solve urban tasks.

In this work, the sensorless speed control of a brushless direct current motor utilizing a neural network is presented. This control is done using a two-layer neural network that uses the backpropagation algorithm for training. The values provided by a Proportional, Integral, and Derivative (PID) control to this type of motor are used to train the network. From this PID control, the velocity values and their corresponding signal control (u) are recovered for different values of load pairs. Five different values of load pairs were used to consider the entire working range of the motor to be controlled. After carrying out the training, it was observed that the proposed network could hold constant load pairs, as well as variables. Several tests were carried out at the simulation level, which showed that control based on neural networks is robust. Finally, it is worth mentioning that this control strategy can be realized without the need for a speed sensor.