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 620

Special Issue Editors

School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: satellite communications; artificial intelligence for networking; network slicing

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Guest Editor
Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China
Interests: semantic computing; future internet architecture; network virtualization; artificial intelligence for networking
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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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Published Papers (1 paper)

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Research

28 pages, 4494 KB  
Article
A Low-Cost, Energy-Aware Exploration Framework for Autonomous Ground Vehicles in Hazardous Environments
by Iosif Polenakis, Marios N. Anagnostou, Ioannis Vlachos and Markos Avlonitis
Electronics 2025, 14(18), 3665; https://doi.org/10.3390/electronics14183665 - 16 Sep 2025
Viewed by 194
Abstract
Autonomous ground vehicles (AGVs) are of major importance in exploration missions since they perform difficult tasks in changing or harmful environments. Mapping and exploration is crucial in hazardous areas, or areas inaccessible to humans, demanding autonomous navigation. This paper proposes a lightweight, low-cost [...] Read more.
Autonomous ground vehicles (AGVs) are of major importance in exploration missions since they perform difficult tasks in changing or harmful environments. Mapping and exploration is crucial in hazardous areas, or areas inaccessible to humans, demanding autonomous navigation. This paper proposes a lightweight, low-cost AGV platform, which will be used in resource-constrained situations and aimed at scenarios like exploration missions (e.g., cave interiors, biohazard environments, or fire-stricken buildings) where there are serious security threats to humans. The proposed system relies on simple ultrasonic sensors when navigating and applied traversal algorithms (e.g., BFS, DFS, or A*) during path planning. Since on-board microcomputers have limited memory, the traversal data and direction decisions are stored in a file located on an SD card, which supports long-term, energy-saving navigation and risk-free backtracking. A fish-eye camera set on a servo motor captures three photos ordered from left to right and stores them on the SD card for further off-line processing, integrating each frame into a low-frame-rate video. Moreover, when the battery level falls below 50%, the exploration path does not extend further and the AGV returns to the base station, thus combining a secure backtracking procedure with energy-efficient decisions. The resultant platform is low-cost, modular, and efficient at augmenting; thus it is suitable for exploring missions with applications in search and rescue, educational robotics, and real-time applications in low-infrastructure environments. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Unmanned Aerial Vehicles)
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