Application of Artificial Intelligence in Unmanned Aerial Vehicles
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".
Deadline for manuscript submissions: 15 November 2025 | Viewed by 762
Special Issue Editors
Interests: satellite communications; artificial intelligence for networking; network slicing
Interests: semantic computing; future internet architecture; network virtualization; artificial intelligence for networking
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Unmanned aerial vehicles (UAVs) have become a transformative tool in diverse domains, including telecommunications, surveillance, disaster management, agriculture, logistics, etc. With the rapid evolution of low-altitude uses and 6G-enabled autonomous operations, UAVs now demand unprecedented levels of adaptability, efficiency, and intelligence. Excitingly, the integration of artificial intelligence (AI) into UAV systems has granted them unprecedented capabilities, enabling autonomous decision-making, adaptive navigation, real-time data processing, and intelligent coordination. However, challenges such as dynamic environmental conditions, energy constraints, limited computational resources, and security vulnerabilities demand innovative AI-driven solutions that optimize UAV performance, reliability, and scalability.
This Special Issue focuses on cutting-edge advancements in AI technologies tailored for UAV systems, with an emphasis on their design, deployment, and optimization. We aim to explore how AI methodologies, including machine learning, deep learning, reinforcement learning, federated learning, and swarm intelligence, can address critical challenges in UAV autonomy, resource management, communication, safety, etc. Contributions to this Special Issue should highlight novel algorithms, frameworks, and applications that bridge theoretical AI innovations with practical UAV implementations, creating sustainable, efficient, and secure UAV ecosystems.
Topics of interest include, but are not limited to, the following:
- AI-empowered UAV autonomous navigation and path planning;
- AI-empowered UAV communication and network management;
- AI-empowered integrated sensing and communication for UAVs;
- AI-empowered UAV millimeter-wave localization and DoA estimation;
- AI-empowered UAV intelligent beam scheduling and resource allocation;
- AI-empowered 6G-enabled edge intelligence for UAVs;
- Federated learning for privacy-preserving UAV data sharing;
- AI-empowered UAV intelligent swarm coordination and collaborative mission execution;
- Reinforcement learning-based dynamic decision-making in UAV operations;
- AI-empowered UAV integration with IoT, 5G/6G, and space–air–ground networks;
- Sustainable AI solutions for green UAV operations;
- Machine/deep learning for real-time UAV sensor data processing;
- Energy-efficient AI algorithms for UAV battery and resource optimization;
- Benchmarking, simulation platforms, and real-world UAV deployment case studies.
Dr. Ning Chen
Dr. Peiying Zhang
Guest Editors
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Keywords
- unmanned aerial vehicles (UAVs)
- artificial intelligence (AI)
- AI-empowered optimization
- autonomous navigation
- swarm intelligence
- edge intelligence
- ISAC
- real-time data processing
- UAV security
- sustainable UAV systems
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