Drones

29 pages, 7541 KB

Open AccessArticle

An Underwater Salvage Robot for Retrieving Foreign Objects in Nuclear Reactor Pools

by Ming Zhong, Zihan Gao, Zhengxiong Mao, Ruifei Lyu and Yaxin Liu

Drones 2025, 9(10), 714; https://doi.org/10.3390/drones9100714 - 15 Oct 2025

In this paper, an underwater salvage robot is developed to retrieve foreign objects scattered in nuclear reactor pools. The robot mainly consists of an ROV platform and a 3-DOF Delta robotic arm. Utilizing fused IMU and LED beacon visual data for localization, it [...] Read more.

In this paper, an underwater salvage robot is developed to retrieve foreign objects scattered in nuclear reactor pools. The robot mainly consists of an ROV platform and a 3-DOF Delta robotic arm. Utilizing fused IMU and LED beacon visual data for localization, it achieves pool traversal via six dynamically controlled thrusters. An improved YOLOv8s algorithm is employed to identify foreign objects in underwater environments. During traversal, the robot identifies and retrieves foreign objects along the way. The prototype of the robot was subjected to a series of experiments in an indoor pool. Results show that the improved YOLOv8 algorithm achieves 92.2% mAP, surpassing the original YOLOv8s and Faster-RCNN by 3.7 and 3.3 percentage points, respectively. The robot achieved a foreign-object identification rate of 95.42% and a retrieval success rate of 90.64% under dynamic traversal conditions, indicating that it meets the operational requirements and has significant engineering application value. Full article

(This article belongs to the Topic Applications and Development of Underwater Robotics and Underwater Vision Technology, 2nd Edition)

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3 pages, 755 KB

Open AccessCorrection

Correction: Liu et al. UAV Path Planning in Threat Environment: A*-APF Algorithm for Spatio-Temporal Grid Optimization. Drones 2025, 9, 661

by Longhao Liu, Le Ru, Wenfei Wang, Hailong Xi, Rui Zhu, Shiliang Li and Zhenghao Zhang

Drones 2025, 9(10), 713; https://doi.org/10.3390/drones9100713 - 15 Oct 2025

Abstract

Error in Figure/Table [...] Full article

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18 pages, 9205 KB

Open AccessArticle

Investigation of Aerodynamic Interference Between Vertically Aligned Quadcopters at Varying Rotor Speeds and Separations

by Khan Muhammad Arslan, Liangyu Zhao and Kuiju Xue

Drones 2025, 9(10), 712; https://doi.org/10.3390/drones9100712 - 15 Oct 2025

Abstract

With the rapid proliferation of drone applications, multi-UAV formation flights are becoming increasingly prevalent. While most existing studies focus on the aerodynamics of a single drone, aerodynamic interactions within UAV formations—particularly in close-proximity hovering configurations—remain inadequately understood. This study employs computational fluid dynamics [...] Read more.

With the rapid proliferation of drone applications, multi-UAV formation flights are becoming increasingly prevalent. While most existing studies focus on the aerodynamics of a single drone, aerodynamic interactions within UAV formations—particularly in close-proximity hovering configurations—remain inadequately understood. This study employs computational fluid dynamics simulations to investigate the aerodynamic interactions between two hovering quadcopters at vertical distances of 1 m and 0.5 m, operating under different RPMs. The results indicate that, when the two quadrotors are spaced 1 m apart, increasing RPM enhances the downward airflow from the upper quadcopter, which benefits the lower quadcopter. When the vertical spacing is reduced to 0.5 m, the aerodynamic interaction between the UAVs becomes more pronounced. This configuration can be advantageous if the drones remain perfectly aligned at lower RPMs. However, at higher RPMs, especially above 5000, the intensified vortices disturb the lower UAV, causing destabilization. Additionally, the reduced spacing amplifies the downwash effect, increasing the risk of collisions and loss of control. This work highlights the importance of managing the spacing and RPMs of drone pairs to optimize performance and ensure stability in multiple drone formations. Full article

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28 pages, 3488 KB

Open AccessArticle

A Cooperative Longitudinal-Lateral Platoon Control Framework with Dynamic Lane Management for Unmanned Ground Vehicles Based on A Dual-Stage Multi-Objective MPC Approach

by Shunchao Wang, Zhigang Wu and Yonghui Su

Drones 2025, 9(10), 711; https://doi.org/10.3390/drones9100711 - 14 Oct 2025

Abstract

Cooperative longitudinal–lateral trajectory optimization is essential for unmanned ground vehicle (UGV) platoons to improve safety, capacity, and efficiency. However, existing approaches often face unstable formation under low penetration rates and rely on fragmented control strategies. This study develops a cooperative longitudinal–lateral trajectory tracking [...] Read more.

Cooperative longitudinal–lateral trajectory optimization is essential for unmanned ground vehicle (UGV) platoons to improve safety, capacity, and efficiency. However, existing approaches often face unstable formation under low penetration rates and rely on fragmented control strategies. This study develops a cooperative longitudinal–lateral trajectory tracking framework tailored for UGV platooning, embedded in a hierarchical control architecture. Dual-stage multi-objective Model Predictive Control (MPC) is proposed, decomposing trajectory planning into pursuit and platooning phases. Each stage employs adaptive weighting to balance platoon efficiency and traffic performance across varying operating conditions. Furthermore, a traffic-aware organizational module is designed to enable the dynamic opening of UGV-dedicated lanes, ensuring that platoon formation remains compatible with overall traffic flow. Simulation results demonstrate that the adaptive weighting strategy reduces the platoon formation time by 41.6% with only a 1.29% reduction in the average traffic speed. In addition, the dynamic lane management mechanism yields longer and more stable UGV platoons under different penetration levels, particularly in high-flow environments. The proposed cooperative framework provides a scalable solution for advancing UGV platoon control and demonstrates the potential of unmanned systems in future intelligent transportation applications. Full article

(This article belongs to the Section Innovative Urban Mobility)

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19 pages, 1197 KB

Open AccessArticle

Fixed-Time Formation Control for MAV/UAVs with Switching Threshold Event-Triggered Strategy

by Xueyan Han, Maolong Lv, Di Shen, Yuyuan Shi, Boyang Zhang and Peng Yu

Drones 2025, 9(10), 710; https://doi.org/10.3390/drones9100710 - 14 Oct 2025

Abstract

The cooperative flight of manned and unmanned aerial vehicles (MAV/UAVs) has recently become a focus in the research of civilian and humanitarian fields, in which formation control is crucial. This paper takes the improvement of convergence performance and resource conservation as the entry [...] Read more.

The cooperative flight of manned and unmanned aerial vehicles (MAV/UAVs) has recently become a focus in the research of civilian and humanitarian fields, in which formation control is crucial. This paper takes the improvement of convergence performance and resource conservation as the entry point to study control problems of cooperative formation configuration of MAV/UAVs. Following the backstepping recursive design procedures, an event-triggered fixed-time formation control strategy for MAV/UAVs operating under modeling uncertainties and external disturbances is presented. Moreover, a novel switching threshold event-triggered mechanism is introduced, which dynamically adjusts control signal updates based on system states. Compared with periodic sampling control (Controller 1), fixed threshold strategies (Controller 2) and relative threshold strategies (Controller 3), this mechanism enhances resource efficiency and prevents Zeno behavior. On the basis of Lyapunov stability theory, the closed-loop system is shown to be stable in the sense of the fixed-time concept. Numerical simulations are carried out in Simulink to validate the effectiveness of the theoretical findings. The results show that compared with the three comparison methods, the proposed control method saves 86%, 34%, and 43% of control transmission burden respectively, which significantly reduces the number of triggered events. Full article

24 pages, 11567 KB

Open AccessArticle

Georeferenced UAV Localization in Mountainous Terrain Under GNSS-Denied Conditions

by Inseop Lee, Chang-Ky Sung, Hyungsub Lee, Seongho Nam, Juhyun Oh, Keunuk Lee and Chansik Park

Drones 2025, 9(10), 709; https://doi.org/10.3390/drones9100709 - 14 Oct 2025

Abstract

In Global Navigation Satellite System (GNSS)-denied environments, unmanned aerial vehicles (UAVs) relying on Vision-Based Navigation (VBN) in high-altitude, mountainous terrain face severe challenges due to geometric distortions in aerial imagery. This paper proposes a georeferenced localization framework that integrates orthorectified aerial imagery with [...] Read more.

In Global Navigation Satellite System (GNSS)-denied environments, unmanned aerial vehicles (UAVs) relying on Vision-Based Navigation (VBN) in high-altitude, mountainous terrain face severe challenges due to geometric distortions in aerial imagery. This paper proposes a georeferenced localization framework that integrates orthorectified aerial imagery with Scene Matching (SM) to achieve robust positioning. The method employs a camera projection model combined with Digital Elevation Model (DEM) to orthorectify UAV images, thereby mitigating distortions from central projection and terrain relief. Pre-processing steps enhance consistency with reference orthophoto maps, after which template matching is performed using normalized cross-correlation (NCC). Sensor fusion is achieved through extended Kalman filters (EKFs) incorporating Inertial Navigation System (INS), GNSS (when available), barometric altimeter, and SM outputs. The framework was validated through flight tests with an aircraft over 45 km trajectories at altitudes of 2.5 km and 3.5 km in mountainous terrain. The results demonstrate that orthorectification improves image similarity and significantly reduces localization error, yielding lower 2D RMSE compared to conventional rectification. The proposed approach enhances VBN by mitigating terrain-induced distortions, providing a practical solution for UAV localization in GNSS-denied scenarios. Full article

(This article belongs to the Special Issue Autonomous Drone Navigation in GPS-Denied Environments)

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31 pages, 46947 KB

Open AccessArticle

Safety-Aware Pre-Flight Trajectory Planning for Urban UAVs with Contingency Plans for Mechanical and GPS Failure Scenarios

by Amin Almozel, Ania Adil and Eric Feron

Drones 2025, 9(10), 708; https://doi.org/10.3390/drones9100708 - 14 Oct 2025

Abstract

Urban drone operations are exposed to unpredictable risks, including engine failure and deliberate signal interference. A recent and ongoing disruption in Jeddah, Saudi Arabia, has seen widespread GPS spoofing that misleads devices by hundreds of kilometers, illustrating how fragile unmanned aerial vehicle (UAV) [...] Read more.

Urban drone operations are exposed to unpredictable risks, including engine failure and deliberate signal interference. A recent and ongoing disruption in Jeddah, Saudi Arabia, has seen widespread GPS spoofing that misleads devices by hundreds of kilometers, illustrating how fragile unmanned aerial vehicle (UAV) operations can become when over-reliant on GNSS-based navigation. Such disruptions highlight the urgent need for contingency planning in drone traffic management systems. This study introduces a safety-aware pre-flight path planning framework that proactively integrates emergency landing and GPS fallback options into UAV trajectory pre-flight planning. The planner considers proximity to predesignated emergency landing zones, communication coverage, and airspace restrictions, enabling UAVs to safely complete their operations. The approach is evaluated across realistic mission profiles such as delivery, inspection, and surveillance. Results show that the planner successfully maintains mission feasibility while embedding emergency readiness throughout each flight. This work contributes toward safer, failure-resilient drone integration in urban airspace, ensuring that contingency plans are proactively incorporated into path planning before the failure even occurs. Full article

(This article belongs to the Section Innovative Urban Mobility)

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26 pages, 10166 KB

Open AccessFeature PaperArticle

ADG-YOLO: A Lightweight and Efficient Framework for Real-Time UAV Target Detection and Ranging

by Hongyu Wang, Zheng Dang, Mingzhu Cui, Hanqi Shi, Yifeng Qu, Hongyuan Ye, Jingtao Zhao and Duosheng Wu

Drones 2025, 9(10), 707; https://doi.org/10.3390/drones9100707 - 13 Oct 2025

Abstract

The rapid evolution of UAV technology has increased the demand for lightweight airborne perception systems. This study introduces ADG-YOLO, an optimized model for real-time target detection and ranging on UAV platforms. Building on YOLOv11n, we integrate C3Ghost modules for efficient feature fusion and [...] Read more.

The rapid evolution of UAV technology has increased the demand for lightweight airborne perception systems. This study introduces ADG-YOLO, an optimized model for real-time target detection and ranging on UAV platforms. Building on YOLOv11n, we integrate C3Ghost modules for efficient feature fusion and ADown layers for detail-preserving downsampling, reducing the model’s parameters to 1.77 M and computation to 5.7 GFLOPs. The Extended Kalman Filter (EKF) tracking improves positional stability in dynamic environments. Monocular ranging is achieved using similarity triangle theory with known target widths. Evaluations on a custom dataset, consisting of 5343 images from three drone types in complex environments, show that ADG-YOLO achieves 98.4% mAP_0.5 and 85.2% mAP_0.5:0.95 at 27 FPS when deployed on Lubancat4 edge devices. Distance measurement tests indicate an average error of 4.18% in the 0.5–5 m range for the DJI NEO model, and an average error of 2.40% in the 2–50 m range for the DJI 3TD model. These results suggest that the proposed model provides a practical trade-off between detection accuracy and computational efficiency for resource-constrained UAV applications. Full article

(This article belongs to the Topic Advances in Integrative AI, Machine Learning, and Big Data for Transformative Applications)

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21 pages, 8957 KB

Open AccessArticle

Autonomous Navigation of Unmanned Ground Vehicles Based on Micro-Shell Resonator Gyroscope Rotary INS Aided by LDV

by Hangbin Cao, Yuxuan Wu, Longkang Chang, Yunlong Kong, Hongfu Sun, Wenqi Wu, Jiangkun Sun, Yongmeng Zhang, Xiang Xi and Tongqiao Miao

Drones 2025, 9(10), 706; https://doi.org/10.3390/drones9100706 - 13 Oct 2025

Abstract

Micro-Shell Resonator Gyroscopes have obvious SWaP (Size, Weight and Power) advantages and applicable accuracy for the autonomous navigation of Unmanned Ground Vehicles (UGVs), especially under GNSS-denied environments. When the Micro-Shell Resonator Gyroscope Rotary Inertial Navigation System (MSRG–RINS) operates in the whole-angle mode, its [...] Read more.

Micro-Shell Resonator Gyroscopes have obvious SWaP (Size, Weight and Power) advantages and applicable accuracy for the autonomous navigation of Unmanned Ground Vehicles (UGVs), especially under GNSS-denied environments. When the Micro-Shell Resonator Gyroscope Rotary Inertial Navigation System (MSRG–RINS) operates in the whole-angle mode, its bias varies as an even-harmonic function of the pattern angle, which leads to difficulty in estimating and compensating the bias based on the MSRG in the process of attitude measurement. In this paper, an attitude measurement method based on virtual rotation self-calibration and rotary modulation is proposed for the MSRG–RINS to address this problem. The method utilizes the characteristics of the two operating modes of the MSRG, the force-rebalanced mode and whole-angle mode, to perform virtual rotation self-calibration, thereby eliminating the characteristic bias of the MSRG. In addition, the reciprocating rotary modulation method is used to suppress the residual bias of the MSRG. Furthermore, the magnetometer-aided initial alignment of the MSRG–RINS is carried out and the state-transformation extended Kalman filter is adopted to solve the large misalignment-angle problem under magnetometer assistance so as to enhance the rapidity and accuracy of initial attitude acquisition. Results from real-world experiments substantiated that the proposed method can effectively suppress the influence of MSRG’s bias on attitude measurement, thereby achieving high-precision autonomous navigation in GNSS-denied environments. In the 1 h, 3.7 km, long-range in-vehicle autonomous navigation experiments, the MSRG–RINS, integrated with a Laser Doppler Velocimetry (LDV), attained a heading accuracy of 0.35° (RMS), a horizontal positioning error of 4.9 m (RMS), and a distance-traveled accuracy of 0.24% D. Full article

(This article belongs to the Topic Navigation and Positioning System: Opportunities and Obstacles)

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27 pages, 2667 KB

Open AccessArticle

Design of a Reinforcement Learning-Based Speed Compensator for Unmanned Aerial Vehicle in Complex Environments

by Guanyu Chen, Pengyu Feng and Xinhua Wang

Drones 2025, 9(10), 705; https://doi.org/10.3390/drones9100705 - 13 Oct 2025

Abstract

Due to the complexity of the marine environment and the uncertainty of ship movements, altitude control of UAV is particularly important when approaching and landing on the deck of a ship. This paper focuses on unmanned helicopters as its research subject. Conventional altitude [...] Read more.

Due to the complexity of the marine environment and the uncertainty of ship movements, altitude control of UAV is particularly important when approaching and landing on the deck of a ship. This paper focuses on unmanned helicopters as its research subject. Conventional altitude control systems may have difficulty in ensuring fast and stable landings under certain extreme conditions. Therefore, designing a new UAV altitude control method that can adapt to complex sea conditions has become a current problem to be solved. Designing a reinforcement learning based rotational speed compensator for UAV as a redundant controller to optimise UAV altitude control performance for the above problem. The compensator is capable of adjusting the UAV’s rotational speed in real time to compensate for altitude deviations due to external environmental disturbances and the UAV’s own dynamic characteristics. By introducing reinforcement learning algorithms, especially the DDPG algorithm, this compensator is able to learn the optimal RPM adjustment strategy in a continuous trial-and-error process, which improves the UAV’s rapidity and stability during the landing process. Full article

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33 pages, 9086 KB

Open AccessArticle

UAV Accident Forensics via HFACS-LLM Reasoning: Low-Altitude Safety Insights

by Yuqi Yan, Boyang Li and Gabriel Lodewijks

Drones 2025, 9(10), 704; https://doi.org/10.3390/drones9100704 - 13 Oct 2025

Abstract

UAV accident investigation is essential for safeguarding the fast-growing low-altitude airspace. While near-daily incidents are reported, they were rarely analyzed in depth as current inquiries remain expert-dependent and time-consuming. Because most jurisdictions mandate formal reporting only for serious injury or substantial property damage, [...] Read more.

UAV accident investigation is essential for safeguarding the fast-growing low-altitude airspace. While near-daily incidents are reported, they were rarely analyzed in depth as current inquiries remain expert-dependent and time-consuming. Because most jurisdictions mandate formal reporting only for serious injury or substantial property damage, a large proportion of minor occurrences receive no systematic investigation, resulting in persistent data gaps and hindering proactive risk management. This study explores the potential of using large language models (LLMs) to expedite UAV accident investigations by extracting human-factor insights from unstructured narrative incident reports. Despite their promise, the off-the-shelf LLMs still struggle with domain-specific reasoning in the UAV context. To address this, we developed a human factors analysis and classification system (HFACS)-guided analytical framework, which blends structured prompting with lightweight post-processing. This framework systematically guides the model through a two-stage procedure to infer operators’ unsafe acts, their latent preconditions, and the associated organizational influences and regulatory risk factors. A HFACS-labelled UAV accident corpus comprising 200 abnormal event reports with 3600 coded instances has been compiled to support evaluation. Across seven LLMs and 18 HFACS categories, macro-F1 ranged 0.58–0.76; our best configuration achieved macro-F1 0.76 (precision 0.71, recall 0.82), with representative category accuracies > 93%. Comparative assessments indicate that the prompted LLM can match, and in certain tasks surpass, human experts. The findings highlight the promise of automated human factor analysis for conducting rapid and systematic UAV accident investigations. Full article

(This article belongs to the Special Issue Next-Gen Drone Safety: Intelligent Systems, LLM Analytics and Operational Risk)

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22 pages, 9295 KB

Open AccessArticle

FedGTD-UAVs: Federated Transfer Learning with SPD-GCNet for Occlusion-Robust Ground Small-Target Detection in UAV Swarms

by Liang Zhao, Xin Jia and Yuting Cheng

Drones 2025, 9(10), 703; https://doi.org/10.3390/drones9100703 - 12 Oct 2025

Abstract

Swarm-based UAV cooperative ground target detection faces critical challenges including sensitivity to small targets, susceptibility to occlusion, and data heterogeneity across distributed platforms. To address these issues, we propose FedGTD-UAVs—a privacy-preserving federated transfer learning (FTL) framework optimized for real-time swarm perception tasks. Our [...] Read more.

Swarm-based UAV cooperative ground target detection faces critical challenges including sensitivity to small targets, susceptibility to occlusion, and data heterogeneity across distributed platforms. To address these issues, we propose FedGTD-UAVs—a privacy-preserving federated transfer learning (FTL) framework optimized for real-time swarm perception tasks. Our solution integrates three key innovations: (1) an FTL paradigm employing centralized pre-training on public datasets followed by federated fine-tuning of sparse parameter subsets—under severe non-Independent and Identically Distributed (non-IID) data distributions, this paradigm ensures data privacy while maintaining over 98% performance; (2) an Space-to-Depth Convolution (SPD-Conv) backbone that replaces lossy downsampling with lossless space-to-depth operations, preserving fine-grained spatial features critical for small targets; (3) a lightweight Global Context Network (GCNet) module leverages contextual reasoning to effectively capture long-range dependencies, thereby enhancing robustness against occluded objects while maintaining real-time inference at 217 FPS. Extensive validation on VisDrone2019 and CARPK benchmarks demonstrates state-of-the-art performance: 44.2% mAP@0.5 (surpassing YOLOv8s by 12.1%) with 3.2× superior accuracy-efficiency trade-off. Compared to traditional centralized learning methods that rely on global data sharing and pose privacy risks, as well as the significant performance degradation of standard federated learning under non-IID data, this framework successfully resolves the core conflict between data privacy protection and detection performance maintenance, providing a secure and efficient solution for real-world deployment in complex dynamic environments. Full article

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19 pages, 16829 KB

Open AccessArticle

An Intelligent Passive System for UAV Detection and Identification in Complex Electromagnetic Environments via Deep Learning

by Guyue Zhu, Cesar Briso, Yuanjian Liu, Zhipeng Lin, Kai Mao, Shuangde Li, Yunhong He and Qiuming Zhu

Drones 2025, 9(10), 702; https://doi.org/10.3390/drones9100702 - 12 Oct 2025

Abstract

With the rapid proliferation of unmanned aerial vehicles (UAVs) and the associated rise in security concerns, there is a growing demand for robust detection and identification systems capable of operating reliably in complex electromagnetic environments. To address this challenge, this paper proposes a [...] Read more.

With the rapid proliferation of unmanned aerial vehicles (UAVs) and the associated rise in security concerns, there is a growing demand for robust detection and identification systems capable of operating reliably in complex electromagnetic environments. To address this challenge, this paper proposes a deep learning-based passive UAV detection and identification system leveraging radio frequency (RF) spectrograms. The system employs a high-resolution RF front-end comprising a multi-beam directional antenna and a wideband spectrum analyzer to scan the target airspace and capture UAV signals with enhanced spatial and spectral granularity. A YOLO-based detection module is then used to extract frequency hopping signal (FHS) regions from the spectrogram, which are subsequently classified by a convolutional neural network (CNN) to identify specific UAV models. Extensive measurements are carried out in both line-of-sight (LoS) and non-line-of-sight (NLoS) urban environments. The proposed system achieves over 96% accuracy in both detection and identification under LoS conditions. In NLoS conditions, it improves the identification accuracy by more than 15% compared with conventional full-spectrum CNN-based methods. These results validate the system’s robustness, real-time responsiveness, and strong practical applicability. Full article

(This article belongs to the Special Issue Advances in Detection, Security, and Communication for UAV: 2nd Edition)

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21 pages, 1647 KB

Open AccessArticle

UAV-Centric Privacy-Preserving Computation Offloading in Multi-UAV Mobile Edge Computing

by Chao Gao, Dawei Wei, Keying Li and Wenjin Liu

Drones 2025, 9(10), 701; https://doi.org/10.3390/drones9100701 - 12 Oct 2025

Abstract

Unmanned aerial vehicles (UAVs) offer high mobility, cost-effectiveness and flexible deployment, but their limited computing and battery resources constrain their development. Mobile edge computing (MEC) can alleviate these constraints by computation offloading. Although reinforcement learning (RL) has recently been applied to optimize offloading [...] Read more.

Unmanned aerial vehicles (UAVs) offer high mobility, cost-effectiveness and flexible deployment, but their limited computing and battery resources constrain their development. Mobile edge computing (MEC) can alleviate these constraints by computation offloading. Although reinforcement learning (RL) has recently been applied to optimize offloading strategies, using raw UAV data poses a risk of privacy leakage. To address this issue, we design a privacy-preserving RL-based offloading approach that applies local differential privacy (LDP) to perturb decision trajectories. We theoretically derive the

O (\sqrt{M} / ϵ)

regret bound and achieve

(ϵ, δ)

-LDP for the perturbation mechanism. Finally, we evaluate the efficiency of the proposed approach through experiments. Full article

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32 pages, 1036 KB

Open AccessReview

A Survey on UxV Swarms and the Role of Artificial Intelligence as a Technological Enabler

by Alexandros Dimos, Dimitrios N. Skoutas, Nikolaos Nomikos and Charalabos Skianis

Drones 2025, 9(10), 700; https://doi.org/10.3390/drones9100700 - 12 Oct 2025

Abstract

In recent years, there has been an ever increasing interest in UxVs and the technology surrounding them. A more recent area of interest within the UxV ecosystem is the development of UxV swarms. In these systems, multiple UxVs synchronize, continuously exchange information, and [...] Read more.

In recent years, there has been an ever increasing interest in UxVs and the technology surrounding them. A more recent area of interest within the UxV ecosystem is the development of UxV swarms. In these systems, multiple UxVs synchronize, continuously exchange information, and operate as a cohesive unit. This evolution requires a higher level of autonomy, enhanced coordination, and more efficient communication channels. In this survey, we present relevant research on swarms of UxVs, always considering artificial intelligence (AI) as the key technological enabler for the swarm operations. We view the swarm from three distinct perspectives; these are intelligence-wise, communication-wise, and security-wise. Our main goal is to explore in which ways and to what extent AI has been integrated in these aspects. We aim to identify which of these aspects are the most researched and which need deeper investigation, the types of AI that are mainly used, and which types of vehicles are preferred. We then discuss the results of our work and present current limitations as well as areas of future research in the realm of UxVs, AI, swarm intelligence, communications, and security. Full article

(This article belongs to the Section Artificial Intelligence in Drones (AID))

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21 pages, 4761 KB

Open AccessArticle

Active Fault-Tolerant Cooperative Control for Multi-QUAVs Using Relative Measurement Information

by Yujiang Zhong, Xi Chen, Ping Li, Pinfan Hou, Zhen Wang and Kunlin Nie

Drones 2025, 9(10), 699; https://doi.org/10.3390/drones9100699 - 11 Oct 2025

Abstract

This paper investigates actuator fault-tolerant cooperative control of multiple quadrotor unmanned aerial vehicles (multi-QUAVs) under restricted communication conditions, where only relative output measurements are available. By appropriately transforming and scaling the control inputs and outputs of the multi-QUAVs, an observable subsystem is constructed. [...] Read more.

This paper investigates actuator fault-tolerant cooperative control of multiple quadrotor unmanned aerial vehicles (multi-QUAVs) under restricted communication conditions, where only relative output measurements are available. By appropriately transforming and scaling the control inputs and outputs of the multi-QUAVs, an observable subsystem is constructed. A decoupled fault estimation observer is then designed for this subsystem to estimate actuator faults and the leader’s input signal. Based on the fault estimation information and relative measurement information among QUAVs, a node-based active fault-tolerant cooperative control law is developed. This approach enables multi-QUAVs to achieve consensus-based formation solely relying on relative output information, even in the presence of actuator faults. Finally, the effectiveness of the proposed active fault-tolerant cooperative control method is verified by simulation. Full article

(This article belongs to the Section Artificial Intelligence in Drones (AID))

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28 pages, 6310 KB

Open AccessArticle

UAV Equipped with SDR-Based Doppler Localization Sensor for Positioning Tactical Radios

by Kacper Bednarz, Jarosław Wojtuń, Rafał Szczepanik and Jan M. Kelner

Drones 2025, 9(10), 698; https://doi.org/10.3390/drones9100698 - 11 Oct 2025

Abstract

The accurate localization of radio frequency (RF) emitters plays a critical role in spectrum monitoring, public safety, and defense applications, particularly in environments where global navigation satellite systems are limited. This study investigates the feasibility of a single unmanned aerial vehicle (UAV) equipped [...] Read more.

The accurate localization of radio frequency (RF) emitters plays a critical role in spectrum monitoring, public safety, and defense applications, particularly in environments where global navigation satellite systems are limited. This study investigates the feasibility of a single unmanned aerial vehicle (UAV) equipped with a Doppler-based software-defined radio sensor to localize modern RF sources without the need for external infrastructure or multiple UAVs. A custom-designed localization system was developed and tested using the L3Harris AN/PRC-152A tactical radio, which represents a class of real-world, dual-use emitters with lower frequency stability than laboratory signal generators. The approach was validated through both emulation studies and extensive field experiments under realistic conditions. The results show that the proposed system can localize RF emitters with an average error below 50 m in 80% of cases even when the transmitter is more than 600 m away. Performance was evaluated across different carrier frequencies and acquisition times, demonstrating the influence of signal parameters on localization accuracy. These findings confirm the practical applicability of Doppler-based single-UAV localization methods and provide a foundation for further development of lightweight, autonomous RF emitter tracking systems for critical infrastructure protection, spectrum analysis, and tactical operations. Full article

(This article belongs to the Special Issue Advances in IoT and Wireless Networks of UAVs: State of the Art, Achievements and Perspectives)

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24 pages, 7481 KB

Open AccessArticle

Loop Shaping-Based Attitude Controller Design and Flight Validation for a Fixed-Wing UAV

by Nai-Wen Zhang and Chao-Chung Peng

Drones 2025, 9(10), 697; https://doi.org/10.3390/drones9100697 - 11 Oct 2025

Abstract

This study presents a loop-shaping methodology for the attitude control of a fixed-wing unmanned aerial vehicle (UAV). The proposed controller design focuses on achieving desired frequency–domain characteristics—such as specified phase and gain margins—to ensure stability and robustness. Unlike many existing approaches that rely [...] Read more.

This study presents a loop-shaping methodology for the attitude control of a fixed-wing unmanned aerial vehicle (UAV). The proposed controller design focuses on achieving desired frequency–domain characteristics—such as specified phase and gain margins—to ensure stability and robustness. Unlike many existing approaches that rely on oversimplified plant models or involve mathematically intensive robust-control formulations, this work develops controllers directly from a high-fidelity six-degree-of-freedom UAV model that captures realistic aerodynamic and actuator dynamics. The loop-shaping procedure translates multi-objective requirements into a transparent, step-by-step workflow by progressively shaping the plant’s open-loop frequency response to match a target transfer function. This provides an intuitive, visual design process that reduces reliance on empirical PID tuning and makes the method accessible for both hobby-scale UAV applications and commercial platforms. The proposed loop-shaping procedure is demonstrated on the pitch inner rate loop of a fixed-wing UAV, with controllers discretized and validated in nonlinear simulations as well as real flight tests. Experimental results show that the method achieves the intended bandwidth and stability margins on the desired design target closely. Full article

(This article belongs to the Section Drone Design and Development)

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19 pages, 6468 KB

Open AccessArticle

Assessment of the Permanent Gully Morphology Measurement by Unmanned Aerial Vehicle Photogrammetry with Different Flight Schemes in Dry–Hot Valley of Southwest China

by Ji Yang, Yifan Dong, Jiangcheng Huang, Xiaoli Wen, Guanghai Wang and Xin Zhao

Drones 2025, 9(10), 696; https://doi.org/10.3390/drones9100696 - 10 Oct 2025

Abstract

Unmanned Aerial Vehicle (UAV) photogrammetry technique offers significant potential for generating highly detailed digital surface models (DSM) of gullies. However, different flight schemes can considerably influence measurement accuracy. The objectives were (i) to evaluate the influences of flight altitude, photo overlap, Ground Control [...] Read more.

Unmanned Aerial Vehicle (UAV) photogrammetry technique offers significant potential for generating highly detailed digital surface models (DSM) of gullies. However, different flight schemes can considerably influence measurement accuracy. The objectives were (i) to evaluate the influences of flight altitude, photo overlap, Ground Control Points (GCPs), and other environmental factors on the accuracy of the UAV-derived DSMs and (ii) to analyze the main factors affecting the accuracy of UAV gully monitoring and explore flight schemes that balance accuracy and efficiency. The results indicated that DSM accuracy improved markedly as the number of GCPs increased from 0 to 3, with consideration given to both horizontal and vertical distribution. However, further increases in the number of GCPs did not lead to significant improvements. The accuracy of DSMs increased with a decrease in the flight altitude, but was not substantially affected by photo overlap when it exceeded 50%/40% The accuracy of DSM was significantly reduced by shadows, and flight altitude rather than slope gradient was identified as the key factor leading to high-error checkpoints (error > 0.1 m). The proportion of point clouds penetrating tree canopies decreased when the flight altitude was 150 m or higher, which could help reduce the influence of vegetation on the accuracy of DSMs. In general, with a reasonable spatial distribution of GCPs, flight altitude is the primary factor affecting monitoring accuracy. However, when balancing accuracy and efficiency, the optimal flight scheme was determined to be a flight altitude of 70 m, photo overlap of 80%/70%, and nine GCPs. Full article

(This article belongs to the Section Drones in Ecology)

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