Ingeniería en Sistemas, Electrónica e Industrial
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Item Sistema de control para la optimización de trayectorias en plataformas móviles mediante computadoras industriales (IPCS) y algoritmos de aprendizaje profundo(Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Telecomunicaciones, 2024-08) Altamirano Tixe, Diego Patricio; Manzano Villafuerte, Víctor SantiagoThe growth in global demand for industrial robots is revolutionizing the manufacturing industry, underscoring the need to develop efficient and safe autonomous navigation systems. Traditional methods of trajectory optimization have difficulty adapting to unforeseen changes in dynamic environments, these problems reduce operational efficiency in the industrial industry. This project seeks to optimize the trajectories of the omnidirectional mobile platform of the KUKA youBot robot using an industrial computer (IPC) and deep learning algorithms. These algorithms allow the robot to learn and generalize movement patterns by optimizing trajectories in real time. This system consists of two nodes, the KUKA youBot robot as the master node and the IPC as the publisher node. To configure communication between nodes, the TCP/IP protocol and ROS functionalities, such as rosmaster URIs and ROS_IP, are used. In the master node, it performs data sampling with a LIDAR sensor and executes the generated trajectories, while the publisher node selects the environment and performs the execution of the deep learning algorithms. In system testing, the DQN algorithm excelled in static scenarios with high peaks in Q-values. However, the Dueling DQN algorithm showed greater robustness and long-term stability, although it required more time and training episodes. In dynamic environments, Dueling DQN earned better rewards, excelling in situations with constant and variable changes. The efficient communication between the robot and the IPC reached an efficiency of 96.92% allowing accurate coordination in real time.Item Sistema electrónico de entrenamiento de salto de longitud mediante visión artificial aplicado a deportistas con discapacidad visual(Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Ingeniería en Electrónica y Comunicaciones, 2024-02) Arias Gualpa, Oscar Eduardo; Gordón Gallegos, Carlos DiegoThis research project describes the development of a training system for athletes with visual impairment supported by artificial vision. Initially, a study was carried out on the current state of the difficulties that visually impaired athletes are exposed to when doing any sport, and thanks to the advancement of artificial vision in the different application areas, an electronic system based on hardware and software that allows the long jump training process to be carried out, in such a way that the athlete can be monitored and controlled, avoiding complications and injuries during training. The main objective of the electronic training system is based on the processing of images, these images depend on deep learning (Deep Learning), which reaches different levels of hierarchical details, so that the information that flows upwards is united, creating abstractions. and higher and more complex representations. For example, with digital images, pixels are combined into edges, edges into outlines, outlines into shapes, and finally shapes into objects. Obtaining as a result the recognition of the long jump track and the athletes in it. The system consists of 3 stages, the first stage of image acquisition, consisting of the sony wcx550 webcam that allows obtaining images in real time. In the second stage, processing, stable results were obtained, thanks to the use of a Raspberry Pi B3+ single-board computer, which processes the information obtained in the image acquisition stage and helps to detect people, contour detection and data management for the alert management stage. The system sends detection alerts for presence on the runway, alerts for the jump performed and stores the number of tests performed. The system will provide security and confidence, at the time of training, both to the athlete with visual impairment, and to the coach.Item Sistema cuantificador de calidad de cultivo de manzana para monitoreo de la producción utilizando algoritmos de Aprendizaje Profundo con Visión Artificial y Segmentación de Instancias(Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Maestría en Electrónica y Automatización, 2022) Garcés Cadena, Andrés Alejandro; Prado Romo, Álvaro JavierNowadays, agriculture is an activity of marked influence in the economy world, therefore, in order to satisfy the progressive food needs, human beings have been introducing technological tools for the optimization of agricultural practices, this management is also known as Precision Agriculture (PA) Artificial Vision is a technology that has given greater support to Precision Agriculture (PA), granting a wide range of tools with the ability to reduce difficulties faced by the farmer during his hand labor. The aim of this project is to provide farmers a tool to improve the process for apple harvest management, by using Deep Learning (DL) algorithms and a Computer Vision system. The system development includes two study analyses: apple type detection and quality quantification for its inspection and validation using a non-invasive method. For apple type detection, SSD-MobileNet model was used and for apple quality segmentation, a fully convolutional network FCN-ResNet-18 was used. For both studies, networks were retrained with customized databases generated specifically for the development of this project. Lastly, evaluation parameters of the detection and segmentation systems are presented with metrics such as confusion matrices, and overlapping of objects on the IoU, respectively.