In this paper, we present the development of a kinematic model, for controlling and simulating through computer, a Kawasaki RS 003N robotic arm, which is located at ESPOCH in the Mechanical Engineering Faculty. For this purpose, it has been developed a CAD modeling for the robotic arm using SolidWorks, later on this modeling was imported to Simulink. The 3D modeling of the robotic arm in Simulink has been defined with the aid of a tool called SimMechanics, then the controlling algorithm has been developed to insert trajectories to the manipulator, and finally it has been performed the simulation and control tests for Robotic arm movement using Matlab and Simulink. Within the results of the present research we can emphasize that Dnavith Hatemberg algorithm allows the correct movement of the robotic arm. The graphic interface of both the line diagram and the 3D Simulink Robot correctly shows the positioning and orientation of the robot according to the command emitted by the user in the control panels, being able to visualize the positions of each one of the robot´s links and the opening and closing of the robotic clamp.

This paper addresses the synchronization and operation of a 140kVA inverter system connected to the main grid as part of a decentralized microgeneration system. The considerations for the supply of electrical energy stored in battery banks, mostly of photovoltaic origin, involve a study of the details of a rigid nonlinear system, which parallels the generation and distribution standards typical of hydroelectric and thermoelectric plants. Considering aspects related to power electronics operation, this paper presents both the modeling and the controlling aspects necessary to synchronize and ensure a stable operation of the microgeneration systems when connected to the main grid. Statistical processing was developed to guarantee synchronization between the systems without presenting electric shocks by simulating the magnetic link in asynchronous generators to meet this aim. The proposed model simulates the increase in power by a phase shift by maintaining a constant frequency based on a Chirp wave generator. The proposed process considers a generation power baseband operation. A Mamdani-type fuzzy proportional-integral controller is used to determine the power setpoint, which sets the Chirp generator phase shift setpoint, which includes a Mamdani fuzzy proportional-type controller. Both controllers are connected in a cascade. The applied correlational technique to achieve the synthesis of the sinusoid and the synchronization presented optimal performance when using 17 samples per signal period. The design of the transformer primarily, guaranteed a phase shift of 4.3018, allowed for a THD below 2.75.

Este trabajo presenta el diseño e implementación de un sistema de estimulación eléctrica funcional (FES) y estimulación vibratoria con registro inercial en tiempo real, basado en ROS2.Se mencionan algunos estudios previos con sistemas similares, los cuales establecen los parámetros a considerar y la estrategia para el desarrollo del prototipo.Se explican las fases para determinar los umbrales de percepción y motor de cada sujeto, el sistema permite ejecutar cuatro protocolos experimentales (FES-posición, FES-velocidad, VIB-posición y VIB-velocidad). La estimulación se administra a través del dispositivo TremUNA o mediante microactuadores vibratorios sincronizados con la cinemática del antebrazo. La arquitectura presentada posibilita el control preciso de la estimulación, la aleatorización de los ensayos y la segmentación automática de eventos, almacenando todos los datos para un análisis intra- e inter-sujeto. Los resultados demuestran la viabilidad de la plataforma como herramienta de investigación para estudiar la influencia de aferencias eléctricas y vibratorias sobre la propiocepción.

The continual development of the automotive industry has evolved to astronomical levels, capable of promoting a new technological generation of several automated systems (both mechanical and electronic systems). As a result, a generation of autonomous vehicles – also known as intelligent vehicles – capable of avoiding human errors has emerged. In the present investigation, an Ackermann-type mobile prototype is used to validate autonomous navigation algorithms in different environmental conditions, which is developed by covering artificial vision stages such as the detection of the bicycle lane and traffic signals at scale. The control system consists of three parts – First is the detection algorithm through an artificial vision to collect information from the environment through a Pi camera that can later be processed through Python with its OpenCV tool. The second deals with the training of traffic signs (Stop and Speed Signage) through the Haar Cascade, as well as the detection of road lines through which various filters such as Canny, edge detection, and the transformation of Hough are executed on the Python platform with the OpenCV tool. The third part is the communication between the processing of the codes and their respective actuators (Motor, Servomotor) to finally collect the statistical data and validate the algorithms in the Ackerman-type mobile prototype. Keywords: Python, OpenCV, Canny Filter, Hough Transform, Haar Cascade. Resumen El desarrollo constante de la industria automotriz evoluciono a niveles astronómicos capaz de impulsar una nueva generación tecnológica de automatización de varios los sus sistemas ya sean: mecánicos o electrónicos y así forma la generación de vehículos autónomos o llamados también vehículos inteligentes, capaces de evitar errores humanos al conducir. En la presente investigación se implementa un prototipo móvil tipo Ackermann para validar algoritmos de navegación autónoma en diferentes condiciones de su entorno que se desarrolle cubriendo etapas de visión artificial como la detección del carril de una ciclovía y señales de tránsito a escala. El sistema de control consta de tres partes, la primera parte es el algoritmo de detección por medio de visión artificial para recolectar información del entorno a través de una cámara Pi y posteriormente ser procesada a través de Python con su herramienta OpenCV. La segunda parte se encarga del entrenamiento de las señales de tránsito (Pare y Señalética de Velocidad) por medio de Haar cascade, además la detección las líneas de la carretera las cuales a través. de varios filtros como Canny, detección de bordes y la transformada de Hough todos estos filtros se lo ejecutan en la plataforma de Python con la herramienta de OpenCV, y la tercera parte es la comunicación entre el procesamiento de los códigos a sus respectivos actuadores (Motor, Servomotor) para finalmente recolectar datos estadísticos y Validar los algoritmos en el prototipo móvil tipo Ackerman. Palabras Clave: Python, Open, Filtro Canny, Tranformada de Houg, Haar Cascade.