İnsansız Hava Araçlarının Hava Sahasına Entegrasyonu: Sistematik Bir İnceleme

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.

Anahtar Kelimeler

• İnsansız Hava Aracı
• İnsansız Hava Aracı Sistemleri Trafik Yönetimi
• Reinforcement Learning
• Otonom Araçlar
• Yapay Zeka

Integrating Unmanned Aerial Vehicles in Airspace: A Systematic Review

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.

Anahtar Kelimeler

• Reinforcement Learning
• Unmanned Aerial Vehicle
• Unmanned Traffic Management
• Autonomous Vehicles
• Artificial Intelligence

Kaynakça

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