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Integrating Unmanned Aerial Vehicles in Airspace: A Systematic Review

Yıl 2024, , 89 - 115, 28.02.2024
https://doi.org/10.51785/jar.1393271

Öz

In this article, a comprehensive review of the integration of Unmanned Aerial Vehicles (UAVs) into shared airspace is presented. By applying a systematic review methodology, the study clarifies the main challenges, problems, and possible fixes related to safety, coordination, and regulatory frameworks. The results demonstrate the critical role that several elements play in supporting the safety of UAV integration. These elements include multi-layered airspace models, careful path planning, secure communication networks, Conflict Detection and Resolution (CDR) strategies, and strong regulations. The paper explores the potential of Human-in-the-loop Reinforcement Learning (HRL) and Reinforcement Learning (RL) algorithms to train UAVs for maneuvering through complex terrain and adapting to changing circumstances. The study's conclusions highlight the importance of ongoing research projects, stakeholder cooperation and continuous support for technology developments-all of which are necessary to ensure the safe and orderly integration of UAVs into airspace.

Kaynakça

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İnsansız Hava Araçlarının Hava Sahasına Entegrasyonu: Sistematik Bir İnceleme

Yıl 2024, , 89 - 115, 28.02.2024
https://doi.org/10.51785/jar.1393271

Öz

Bu makalede, İnsansız Hava Araçlarının (İHA) ortak hava sahasına entegrasyonu kapsamlı bir şekilde incelenmektedir. Sistematik inceleme metodolojisi kullanılarak çalışmada yasal düzenlemeler, uçuş emniyeti ve koordinasyon ile ilgili temel zorlukları, sorunları ve olası çözümleri ortaya koymaktadır. Bulgular çok katmanlı hava sahası modelleri, dikkatli rota planlama, güvenli iletişim ağları, çatışma tespiti ve çözümü stratejileri ile yapısal olarak güçlendirilmiş düzenlemeler dahil olmak üzere çeşitli unsurların İHA entegrasyonunda kritik bir rol oynadığını göstermektedir. Ayrıca İHA'ların karmaşık hava sahalarında ve değişken koşullara uyum sağlamalarını desteklemek adına önerilen çözümleri inceleyerek Reinforcement Learning (RL) ve Human-in-the-loop Reinforcement Learning (HRL) algoritmalarının potansiyeli ortaya konmuştur. Çalışmanın sonuçları, İHA'ların hava sahasına emniyetli ve düzenli bir şekilde entegre edilmesi için araştırma projelerinin sürekli olarak yürütülmesinin, paydaş işbirliğinin ve teknoloji geliştirmelerine kararlı desteğin önemini vurgulamaktadır.

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  • Sun, Y., Li, L., Zhou, C., Yang, S., Shi, D., & An, H. (2022). Design and Implementation of a collaborative air-ground unmanned system path planning framework. In China Intelligent Robotics Annual Conference (pp. 83-96). Singapore: Springer Nature Singapore. https://link.springer.com/chapter/10.1007/978-981-99-0301-6_7
  • Tang, G., Du, P., Lei, H., Ansari, I. S., & Fu, Y. (2021). Trajectory design and communication resources allocation for wireless powered secure UAV communication systems. IEEE Systems Journal, 16(4), 6300-6308. DOI: 10.1109/JSYST.2021.3132010
  • Taylor, M. E. (2023). Reinforcement Learning Requires Human-in-the-Loop Framing and Approaches. In HHAI (pp. 351-360). https://alaworkshop2023.github.io/papers/ ala2023_paper_29.pdf
  • Tovarnov, M. S., & Bykov, N. V. (2022). Reinforcement learning reward function in unmanned aerial vehicle control tasks. In Journal of Physics: Conference Series (Vol. 2308, No. 1, p. 012004). IOP Publishing. DOI 10.1088/1742-6596/2308/1/012004
  • Volkert, A., Hackbarth, H., Lieb, T. J., & Kern, S. (2019). Flight tests of ranges and latencies of a threefold redundant C2 multi-link solution for small drones in VLL airspace. In 2019 Integrated Communications, Navigation and Surveillance Conference (ICNS) (pp. 1-14). IEEE. DOI: 10.1109/ICNSURV.2019.8735265
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  • Wang, W., Wei, X., Jia, Y., & Chen, M. (2023). UAV relay network deployment through the area with barriers. Ad Hoc Networks, 103222. https://doi.org/10.1016/ j.adhoc.2023.103222
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  • Zhang, D., Li, X., Ren, G., Yao, J., Chen, K., & Li, X. (2023a). Three-dimensional path planning of UAVs in a complex dynamic environment based on environment exploration twin delayed deep deterministic policy gradient. Symmetry, 15(7), 1371. https://doi.org/10.3390/sym15071371
  • Zhang, D., Xuan, Z., Zhang, Y., Yao, J., Li, X., & Li, X. (2023b). Path planning of unmanned aerial vehicle in complex environments based on state-detection twin delayed deep deterministic policy gradient. Machines, 11(1), 108. https://doi.org/10.3390/ machines11010108
  • Zhang, S., Li, Y., Ye, F., Geng, X., Zhou, Z., & Shi, T. (2023). A Hybrid Human-in-the-Loop Deep Reinforcement Learning Method for UAV motion planning for long trajectories with unpredictable obstacles. Drones, 7(5), 311. https://doi.org/10.3390/ drones7050311
Toplam 104 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Hava-Uzay Ulaşımı
Bölüm Derleme Makale
Yazarlar

Arif Tuncal 0000-0003-4343-6261

Ufuk Erol 0000-0001-5711-2423

Yayımlanma Tarihi 28 Şubat 2024
Gönderilme Tarihi 20 Kasım 2023
Kabul Tarihi 13 Şubat 2024
Yayımlandığı Sayı Yıl 2024

Kaynak Göster

APA Tuncal, A., & Erol, U. (2024). Integrating Unmanned Aerial Vehicles in Airspace: A Systematic Review. Journal of Aviation Research, 6(1), 89-115. https://doi.org/10.51785/jar.1393271

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