Abstract
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.
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Change history
15 September 2022
In the original version of the chapter, the following belated corrections have been incorporated: The author name “Marcos Bautista L. Aznar” has been changed to “Marcos Bautista López Aznar” in the Frontmatter, Backmatter and in Chapter 17. The correction/erratum chapter and the book have been updated with the change.
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Liao, T., Haridevan, A., Liu, Y., Shan, J. (2022). Autonomous Vision-Based UAV Landing with Collision Avoidance Using Deep Learning. In: Arai, K. (eds) Intelligent Computing. SAI 2022. Lecture Notes in Networks and Systems, vol 507. Springer, Cham. https://doi.org/10.1007/978-3-031-10464-0_6
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DOI: https://doi.org/10.1007/978-3-031-10464-0_6
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