Article
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UAV takeoff in Windy Conditions using DQN
Version 1
: Received: 16 September 2023 / Approved: 18 September 2023 / Online: 21 September 2023 (09:43:56 CEST)
How to cite: Olaz, X.; Aláez, D.; De Porcellinis, P.; Prieto, M.; Astrain, J. J. UAV takeoff in Windy Conditions using DQN. Preprints 2023, 2023091469. https://doi.org/10.20944/preprints202309.1469.v1 Olaz, X.; Aláez, D.; De Porcellinis, P.; Prieto, M.; Astrain, J. J. UAV takeoff in Windy Conditions using DQN. Preprints 2023, 2023091469. https://doi.org/10.20944/preprints202309.1469.v1
Abstract
Drone navigation is critical, particularly during the initial and final phases, such as the initial ascension, where pilots may fail due to strong external disturbances that could lead to a crash. In this ongoing work, a drone has been successfully trained to perform an ascent of up to 6 meters with external disturbances simulating wind pushing it up to 24 mph, with the 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. The learning process is carried out in the Gazebo simulator, which emulates interferences, while ROS is used to communicate with the agent.
Keywords
machine learning; DQN; gazebo; navigation
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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