Tesis Telecomunicaciones

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Subject: search.filters.subject.OPTIMIZACIÓN DE TRAYECTORIAS

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    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 Santiago
    The 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.
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    Optimización de trayectorias en plataformas robóticas móviles usando técnicas de inteligencia artificial
    (Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Telecomunicaciones, 2023-08) Soto Rodríguez, Andrés David; Manzano Villafuerte, Víctor Santiago
    As time progresses, the growth of the robotics industry is exponential and companies that seek to automate their processes do so to optimize resources, such as the time spent. The use of artificial intelligence is one of the current solutions for the optimization of various processes, by allowing learning in supervised environments, where robotic instrumentation tends to minimize the margin of error in the future thanks to implementations of AI algorithms. In the present project, a solution for the optimization of trajectories is exposed using as support an AI algorithm of reinforcement learning with neural networks implemented in the omnidirectional robotic platform of the KUKA youBot robot to move from one point to another avoiding obstacles presented in its path. The AI algorithm used for learning is Deep Q Network (DQN), this algorithm consists of deep neural networks to maximize some notion of rewards in a cumulative way. whereby means of a Hokuyo lidar motion sensor, placed in the front part of the robotic platform, they are acquired. You sample data from an environment, which is processed in the algorithm to be recognized as collisions or rewards. As the rewards learned by the algorithm are greater. the possibility of collision with an obstacle decreases, moving the robotic platform towards an obstacle-free zone. The programming language of this DON algorithm is based on Python 2, this language works together with ROS (robotic operating system) and allows to know, in an understandable way, how the execution of the movement is carried out through the publication and subscription to the topics corresponding to the robotic platform, thus facilitating the calibration of the parameters used in it.