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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
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.
References
Google colab. https://colab.research.google.com (accessed 9 August 2021)
Opencv: Camera calibration and 3d reconstruction. https://docs.opencv.org/3.4/d9/d0c/group_calib3d.html (accessed 9 August 2021)
Kharpal, A.: Alibaba tests drone deliveries after Amazon push (May 2015)
Bochkovskiy, A., Wang, C.-Y., Mark Liao, H.-Y.: Yolov4: optimal speed and accuracy of object detection (2020). ar**v preprint, ar**v:2004.10934
Fiala, M.: Artag, a fiducial marker system using digital techniques. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 2, pp. 590–596 (2005)
Mao, Q.-C., Sun, H.-M., Zuo, L.-Q., Jia, R.-S.: Finding every car: a traffic surveillance multi-scale vehicle object detection method. Appl. Intell. 50(10), 3125–3136 (2020). https://doi.org/10.1007/s10489-020-01704-5
Tripathi, R., Singla, V., Najibi, M., Singh, B., Sharma, A., Davis, L.: Asap-nms: accelerating non-maximum suppression using spatially aware priors (2020). ar**v preprint, ar**v:2007.09785
Wang, J., Olson, E.: Apriltag 2: efficient and robust fiducial detection. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, pp. 4193–4198 (2016)
Yang, L.C., Kuchar, J.K., Yang, L.C., Kucharf, J.K.: Prototype conflict alerting system for free flight. J. Guidance Control Dynam. 20, 768–773 (1997)
Zang, Z.: A flexible new technique for camera calibration determination of thermal properties of composting bulking materials. IEEE Trans. Pattern Anal. Mach. Intell. 22, 1330–1334 (2000)
Zimmer, M.: Surveillance, privacy and the ethics of vehicle safety communication technologies. Ethics Inf. Technol. 7(4), 201–210 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-031-10464-0_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-10463-3
Online ISBN: 978-3-031-10464-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)