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Future Internet
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Navigation, Deployment and Control of Intelligent Unmanned Vehicles
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Navigation, Deployment and Control of Intelligent Unmanned Vehicles

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Smart System Infrastructure and Applications".

Deadline for manuscript submissions: closed (28 February 2026) | Viewed by 4102

Special Issue Editors

Prof. Dr. Andrey V. Savkin

E-Mail Website
Guest Editor
School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia
Interests: robot navigation; deployment of drones; unmanned aerial vehicles; control of wireless communication networks; control of power systems; robust control and filtering; hybrid dynamical systems; control engineering; biomedical engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Hailong Huang

E-Mail Website
Guest Editor
Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Interests: multiagent systems; distributed control; shared control; wireless sensor networks; UAV-based applications: search and rescue, construction automation, surveillance, wireless communications, parcel delivery, etc.
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The use of intelligent unmanned vehicles such as unmanned ground vehicles (UGVs), unmanned aerial vehicles (UAVs), autonomous underwater vehicles (AUVs), and unmanned surface vehicles (USVs) is rapidly expanding due to recent technological advances. Applications of intelligent unmanned vehicles include wireless communication support, environmental monitoring, safety and rescue operations, policing, video surveillance, goods deliveries, smart agriculture, and precision mining. For all these applications, navigation, deployment, and control of intelligent unmanned vehicles are critical issues. This Special Issue focuses on new developments in the field of navigation, deployment, and control of intelligent unmanned vehicles for various applications.

Potential topics include, but are not limited to:

  • Advanced methods of UGV/UAV/AUV/USV navigation and control;
  • UAV-assisted wireless communication systems;
  • Internet of Things assisted by intelligent unmanned vehicles;
  • Internet of Vehicles;
  • Navigation of intelligent unmanned vehicles for surveillance, safety and rescue, sensor data collection, mobile edge computing, smart agriculture, policing, and security applications;
  • Navigation of teams of collaborating intelligent unmanned vehicles;
  • Sensor network/communication network-based navigation of intelligent unmanned vehicles;
  • Intelligent unmanned vehicles navigated or assisted by reconfigurable intelligent surfaces.

Prof. Dr. Andrey V. Savkin
Dr. Hailong Huang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent unmanned vehicles
  • autonomous navigation
  • unmanned aerial vehicles (UAVs)
  • autonomous underwater vehicles (AUVs)
  • unmanned ground vehicles (UGVs)
  • internet of things (IoT)
  • internet of vehicles (IoV)
  • reconfigurable intelligent surfaces (RISs)
  • path planning
  • UAV-supported wireless communications
  • sensor network based navigation
  • guidance and control
  • UAV/AUV/USV deployment

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Published Papers (3 papers)

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Research

26 pages, 1243 KB  
Article
Trajectory Planning for Autonomous Underwater Vehicles in Uneven Environments: A Survey of Coverage and Sensor Data Collection Methods
by Talal S. Almuzaini and Andrey V. Savkin
Future Internet 2026, 18(2), 79; https://doi.org/10.3390/fi18020079 - 2 Feb 2026
Viewed by 772
Abstract
Autonomous Underwater Vehicles (AUVs) play a central role in marine observation, inspection, and monitoring missions, where effective trajectory planning is essential for ensuring safe operation, reliable sensing, and efficient data transfer. In realistic underwater environments, uneven seafloor geometry, limited acoustic communication, navigation uncertainty, [...] Read more.
Autonomous Underwater Vehicles (AUVs) play a central role in marine observation, inspection, and monitoring missions, where effective trajectory planning is essential for ensuring safe operation, reliable sensing, and efficient data transfer. In realistic underwater environments, uneven seafloor geometry, limited acoustic communication, navigation uncertainty, and sensing visibility constraints significantly influence mission performance and challenge classical planar planning formulations. This survey reviews trajectory planning methods for AUVs operating in uneven environments, with a focus on two major classes of underwater sensing missions: underwater area coverage using onboard sensors and underwater sensor data collection within underwater acoustic sensor networks (UASNs) supporting the Internet of Underwater Things (IoUT). For area coverage, the survey examines the progression from classical planar coverage strategies to terrain-aware, occlusion-aware, multi-AUV, and online planning frameworks designed to address uneven terrain and sensing visibility. For underwater sensor data collection, it reviews mobile sink-based trajectory planning strategies, including energy-aware, channel-aware, and information-based formulations based on metrics such as Age of Information (AoI) and Value of Information (VoI), as well as cooperative architectures involving unmanned surface vehicles (USVs). By synthesizing these two bodies of literature, the survey clarifies current capabilities and limitations of trajectory planning methods for AUVs operating in uneven underwater environments. Full article
(This article belongs to the Special Issue Navigation, Deployment and Control of Intelligent Unmanned Vehicles)
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23 pages, 5004 KB  
Article
A Lightweight LSTM Model for Flight Trajectory Prediction in Autonomous UAVs
by Disen Jia, Jonathan Kua and Xiao Liu
Future Internet 2026, 18(1), 4; https://doi.org/10.3390/fi18010004 - 20 Dec 2025
Viewed by 1341
Abstract
Autonomous Unmanned Aerial Vehicles (UAVs) are widely used in smart agriculture, logistics, and warehouse management, where precise trajectory prediction is important for safety and efficiency. Traditional approaches require complex physical modeling including mass properties, moment of inertia measurements, and aerodynamic coefficient calculations, which [...] Read more.
Autonomous Unmanned Aerial Vehicles (UAVs) are widely used in smart agriculture, logistics, and warehouse management, where precise trajectory prediction is important for safety and efficiency. Traditional approaches require complex physical modeling including mass properties, moment of inertia measurements, and aerodynamic coefficient calculations, which creates significant barriers for custom-built UAVs. Existing trajectory prediction methods are primarily designed for motion forecasting from dense historical observations, which are unsuitable for scenarios lacking historical data (e.g., takeoff phases) or requiring trajectory generation from sparse waypoint specifications (4–6 constraint points). This distinction necessitates architectural designs optimized for spatial interpolation rather than temporal extrapolation. To address these limitations, we present a segmented LSTM framework for complete autonomous flight trajectory prediction. Given target waypoints, our architecture decomposes flight operations, predicts different maneuver types, and outputs the complete trajectory, demonstrating new possibilities for mission-level trajectory planning in autonomous UAV systems. The system consists of a global duration predictor (0.124 MB) and five segment-specific trajectory generators (∼1.17 MB each), with a total size of 5.98 MB and can be deployed in various edge devices. Validated on real Crazyflie 2.1 data, our framework demonstrates high accuracy and provides reliable arrival time predictions, with an Average Displacement Error ranging from 0.0252 m to 0.1136 m. This data-driven approach avoids complex parameter configuration requirements, supports lightweight deployment in edge computing environments, and provides an effective solution for multi-UAV coordination and mission planning applications. Full article
(This article belongs to the Special Issue Navigation, Deployment and Control of Intelligent Unmanned Vehicles)
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22 pages, 1557 KB  
Article
AI-Driven Damage Detection in Wind Turbines: Drone Imagery and Lightweight Deep Learning Approaches
by Ahmed Hamdi and Hassan N. Noura
Future Internet 2025, 17(11), 528; https://doi.org/10.3390/fi17110528 - 19 Nov 2025
Cited by 1 | Viewed by 1211
Abstract
Wind power plays an increasingly vital role in sustainable energy production, yet the harsh environments in which turbines operate often lead to mechanical or structural degradation. Detecting such faults early is essential to reducing maintenance expenses and extending operational lifetime. In this work, [...] Read more.
Wind power plays an increasingly vital role in sustainable energy production, yet the harsh environments in which turbines operate often lead to mechanical or structural degradation. Detecting such faults early is essential to reducing maintenance expenses and extending operational lifetime. In this work, we propose a deep learning-based image classification framework designed to assess turbine condition directly from drone-acquired imagery. Unlike object detection pipelines, which require locating specific damage regions, the proposed strategy focuses on recognizing global visual cues that indicate the overall turbine state. A comprehensive comparison is performed among several lightweight and transformer-based architectures, including MobileNetV3, ResNet, EfficientNet, ConvNeXt, ShuffleNet, ViT, DeiT, and DINOv2, to identify the most suitable model for real-time deployment. The MobileNetV3-Large network achieved the best trade-off between performance and efficiency, reaching 98.9% accuracy while maintaining a compact size of 5.4 million parameters. These results highlight the capability of compact CNNs to deliver accurate and efficient turbine monitoring, paving the way for autonomous, drone-based inspection solutions at the edge. Full article
(This article belongs to the Special Issue Navigation, Deployment and Control of Intelligent Unmanned Vehicles)
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