Navigation system using landmark images for combat UCAVs
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https://doi.org/10.54939/1859-1043.j.mst.80.2022.178-182Keywords:
UCAV; Computer vision; Landmark-based navigation.Abstract
In recent years, the appearance of unmanned aerial vehicles (UCAVs) on the battlefield has really changed the balance of forces in modern warfare. Since the Nagorno-Karabakh conflict in September 2020, combat UCAVs (UCAVs) with simple and flexible designs have attacked and destroyed a series of Armenian military targets in a short time bringing advantages for a total victory of Azerbaijan. However, UCAV has the weakness of relying on the Global Navigation Satellite System (GNSS) and radio control signals from the ground for operation in the air. Under electronic suppression, UCAV will be completely disabled. In this paper, we propose a system model that applies computer vision technology combined with AI to guide UCAV to fly along a machine-learned route, independent of radio and satellite navigation signals. Preliminary simulation results show that the system can accurately recognize landmarks accurately in real time.
References
[1]. Ilić, Damir & Tomasevic, Vladimir, “The impact of the Nagorno-Karabakh conflict in 2020 on the perception of combat drones”, Serbian Journal of Engineering Management. 6. 9-21. 10.5937/SJEM2101009I, (2021).
[2]. Marcus Roth, “AI in Military Drones and UCAVs – Current Applications”, (2019), https://emerj.com/ai-sector-overviews/ai-drones-and-UCAVs-in-the-military-current-applications.
[3]. B. Yun, K. Peng, and B. M. Chen, “Enhancement of GPS signals for automatic control of a UCAV helicopter system,” in Proc. IEEE International Conference on Control and Automation ICCA, pp. 1185–1189, (2007).
[4]. S. Park, D. H. Won, M. S. Kang, T. J. Kim, H. G. Lee, and S. J. Kwon, “RIC (robust internal-loop compensator) based flight control of a quad-rotor type UCAV” in Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3542–3547, (2005).
[5]. T. Hamel, R. Mahony, and A. Chriette, “Visual servo trajectory tracking for a four rotor vtol aerial vehicle,” in Proc. IEEE International Conference on Robotics and Automation ICRA ’02, vol. 3, pp. 2781–2786, (2002).
[6]. T. Templeton, D. H. Shim, C. Geyer, and S. S. Sastry, “Autonomous vision-based landing and terrain mapping using an mpc-controlled unmanned rotorcraft,” in Proc. IEEE International Conference on Robotics and Automation, pp. 1349–1356, (2007).
[7]. Blösch, M.; Weiss, S.; Scaramuzza, D.; Siegwart, R., “Vision Based MAV Navigation in Unknown and Unstructured Environments,” In Proceedings of the 2010 IEEE International Conference on Robotics and Automation, Anchorage, AK, USA, pp. 21–28, (2010).
[8]. Zhou, J.; Tian, Y.; Yuan, C.; Yin, K.; Yang, G.; Wen, M., “Improved UCAV Opium Poppy Detection Using an Updated YOLOv3 Model,” Sensors, 19(22):4851, (2019).
[9]. Martinez-Alpiste, I.; Casaseca-de-la-Higuera, P.; Alcaraz-Calero, J.; Grecos, C.; Wang, Q. “Benchmarking Machine-Learning-Based Object Detection on a UCAV and Mobile Platform,” In Proceedings of the 2019 IEEE Wireless Communications and Networking Conference (WCNC), Marrakesh, Morocco, (2019).
[10]. Zhang, T.; Hu, X.; Xiao, J.; Zhang, G. A “Machine Learning Method for Vision-Based Unmanned Aerial Vehicle Systems to Understand Unknown Environments,” Sensors, 20(11):3245. https://doi.org/10.3390/s20113245, (2020).
[11]. A. He, C. Luo, X. Tian, and W. Zeng. “A twofold siamese network for real-time object tracking,” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4834–4843, (2018).
[12]. B. Li, J. Yan, W. Wu, Z. Zhu, and X. Hu. “High performance visual tracking with siamese region proposal network,” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8971–8980, (2018).
