XXIII Congreso Internacional sobre Diversidad en Organizaciones, Comunidades y Naciones

Resumen

Drone navigation is critical, particularly during the initial phases such as the initial ascension, where pilots may fail due to strong external interferences that could potentially lead to a crash. In this ongoing work, a drone has been success fully trained to perform an ascent of up to 6 meters at speeds with external disturbances pushing it up to 24 mph, with the Deep Q-Network (DQN) algorithm managing external forces affecting the system. It has been demonstrated that the system can control its height, position, and stability in all three axes (roll, pitch, and yaw) throughout the process. We review the learning process carried out in the Gazebo simulator, which emulates interferences, and Robot Operating System (ROS) used to communicate with the agent.