The autonomous vision-based Unmanned Aerial Vehicles (UAVs) landing is an adaptive way to land in special environments such as the global positioning system denied. There is a risk of collision when multiple UAVs land simultaneously without communication on the same platform. This work accomplishes vision-based autonomous landing and uses a deep-learning-based method to realize collision avoidance during the landing process. Specifically, the landing UAVs are categorized into Level I and II. The YoloV4 deep learning method will be implemented by the Level II UAV to achieve object detection of Level I UAV. Once the Level I UAV’s landing has been detected by the onboard camera of Level II UAV, it will move and land on a relative landing zone beside the Level I UAV. The experiment results show the validity and practicality of our theory.
Autonomous Vision-Based UAV Landing with Collision Avoidance Using Deep Learning
Lect. Notes in Networks, Syst.
Science and Information Conference ; 2022 July 14, 2022 - July 15, 2022
07.07.2022
9 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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