UruBots Autonomous Cars Challenge Pro Team Description Paper for FIRA 2025

This paper describes the development of an autonomous car by the UruBots team for the 2025 FIRA Autonomous Cars Challenge (Pro). The project involves constructing a compact electric vehicle, approximately the size of an RC car, capable of autonomous navigation through different tracks. The design incorporates mechanical and electronic components and machine learning algorithms that enable the vehicle to make real-time navigation decisions based on visual input from a camera. We use deep learning models to process camera images and control vehicle movements. Using a dataset of over ten thousand images, we trained a Convolutional Neural Network (CNN) to drive the vehicle effectively, through two outputs, steering and throttle. The car completed the track in under 30 seconds, achieving a pace of approximately 0.4 meters per second while avoiding obstacles.

Implementación de Navegación en Plataforma Robótica Móvil Basada en ROS y Gazebo

This research focused on utilizing ROS2 and Gazebo for simulating the TurtleBot3 robot, with the aim of exploring autonomous navigation capabilities. While the study did not achieve full autonomous navigation, it successfully established the connection between ROS2 and Gazebo and enabled manual simulation of the robot's movements. The primary objective was to understand how these tools can be integrated to support autonomous functions, providing valuable insights into the development process. The results of this work lay the groundwork for future research into autonomous robotics. The topic is particularly engaging for both teenagers and adults interested in discovering how robots function independently and the underlying technology involved. This research highlights the potential for further advancements in autonomous systems and serves as a stepping stone for more in-depth studies in the field.

De la Extensión a la Investigación: Como La Robótica Estimula el Interés Académico en Estudiantes de Grado

This research examines the impact of robotics groups in higher education, focusing on how these activities influence the development of transversal skills and academic motivation. While robotics goes beyond just technical knowledge, participation in these groups has been observed to significantly improve skills such as teamwork, creativity, and problem-solving. The study, conducted with the UruBots group, shows that students involved in robotics not only reinforce their theoretical knowledge but also increase their interest in research and academic commitment. These results highlight the potential of educational robotics to transform the learning experience by promoting active and collaborative learning. This work lays the groundwork for future research on how robotics can continue to enhance higher education and motivate students in their academic and professional careers