Drones

21 pages, 3017 KB

Open AccessArticle

Cooperative Encirclement and Obstacle Avoidance of Fixed-Wing UAVs via MADDPG with Curriculum Learning

by Xinrui Zhao, Jianwen Tan, Wenyue Meng, Ziping Yu, Yongzhao Yan and Zijian Zhang

Drones 2025, 9(10), 727; https://doi.org/10.3390/drones9100727 - 21 Oct 2025

Cited by 1 | Viewed by 1187

Multi-UAV cooperative encirclement tasks have attracted considerable attention in areas such as military defense and target interception. Fixed-wing UAVs face substantial challenges due to intrinsic dynamic limits, including their minimum velocity and turning radius, particularly when engaging evasive target and navigating in obstacle [...] Read more.

Multi-UAV cooperative encirclement tasks have attracted considerable attention in areas such as military defense and target interception. Fixed-wing UAVs face substantial challenges due to intrinsic dynamic limits, including their minimum velocity and turning radius, particularly when engaging evasive target and navigating in obstacle environments. This paper presents a hybrid deep reinforcement learning approach, in which a cooperative task environment is developed for fixed-wing UAVs that jointly integrates encirclement and obstacle avoidance. A composite MADDPG framework enhanced with curriculum learning is designed, employing progressive task staging and reward optimization to accelerate convergence and improve policy stability. Simulation results demonstrate that the proposed method achieves single-step encirclement success rates exceeding 80% in complex environments, while maintaining 10-step success rates around 70%, thereby strengthening both encirclement capability and obstacle avoidance safety in fixed-wing UAV swarm. This study provides new insights into the intelligent cooperative control of fixed-wing UAVs in high-risk missions. Full article

(This article belongs to the Special Issue Distributed Control, Optimization, and Game of UAV Swarm Systems (2nd Edition))

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20 pages, 4116 KB

Open AccessArticle

Temperature Field Distribution Testing and Improvement of Near Space Environment Simulation Test System for Unmanned Aerial Vehicles

by Jinghui Gao, Tianjin Cheng, Qing Hao, Chen Li, Chunlian Duan, Xiang Ma, Yanchu Yang, Hui Feng and Yongxiang Li

Drones 2025, 9(10), 726; https://doi.org/10.3390/drones9100726 - 21 Oct 2025

Viewed by 639

Abstract

Temperature distribution inside the vacuum chamber of the TRX 2000(A) near space environment simulation test system (NSESTS) was investigated through both experimentation and computational fluid dynamics simulation. Comparison between the experimental result and the simulation result showed that these two results were very [...] Read more.

Temperature distribution inside the vacuum chamber of the TRX 2000(A) near space environment simulation test system (NSESTS) was investigated through both experimentation and computational fluid dynamics simulation. Comparison between the experimental result and the simulation result showed that these two results were very close to each other, validating the feasibility of using the simulation method to study the temperature distribution inside the NSESTS. Then, the effect of wind, either downwind or upwind, on temperature uniformity inside the NSESTS was investigated through the simulation method. The simulation result showed that the non-uniformity coefficient will be reduced from 0.2757 to 0.2012 (by 27.1%) in the case of downwind and to 0.2055 (by 25.5%) in the case of upwind. Then, the simulation result was validated by experiment. The result of this research indicates that the temperature uniformity can be greatly improved through installment of additional fans inside the NSESTS. Full article

(This article belongs to the Special Issue Design and Flight Control of Low-Speed Near-Space Unmanned Systems)

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34 pages, 5164 KB

Open AccessArticle

Neuroadaptive Fixed-Time Bipartite Containment Tracking of Networked UAVs Under Switching Topologies

by Yulin Kang, Mengji Shi, Yuan Yao, Rui Zhou and Kaiyu Qin

Drones 2025, 9(10), 725; https://doi.org/10.3390/drones9100725 - 20 Oct 2025

Viewed by 676

Abstract

Fixed-time coordination is critical for networked unmanned aerial vehicle (UAV) systems to accomplish time-sensitive missions such as rapid target encirclement, cooperative search, and emergency response. However, dynamic topology variations, caused by mission reassignment, obstacle avoidance, or communication disruptions, along with model uncertainties and [...] Read more.

Fixed-time coordination is critical for networked unmanned aerial vehicle (UAV) systems to accomplish time-sensitive missions such as rapid target encirclement, cooperative search, and emergency response. However, dynamic topology variations, caused by mission reassignment, obstacle avoidance, or communication disruptions, along with model uncertainties and external disturbances, present significant challenges to robust and timely coordination. To address these issues, this paper investigates the fixed-time bipartite containment tracking control problem of uncertain multi-UAV systems under switching communication topologies. A neuroadaptive robust containment tracking controller is developed to guarantee that all follower UAVs converge within a fixed time to the region spanned by multiple dynamic leaders, regardless of initial conditions. To handle unknown nonlinear dynamics, a neuroadaptive estimator is constructed using online parameter adaptation. A topology-dependent multiple Lyapunov function framework is employed to rigorously establish fixed-time convergence under switching topologies. Moreover, an explicit upper bound on the convergence time is analytically derived as a function of system parameters and dwell time constraints. Comparative analysis demonstrates that the proposed method reduces conservativeness in convergence time estimation and enhances robustness against frequent topology changes. Simulation results are provided to validate the effectiveness and advantages of the proposed control scheme. Full article

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19 pages, 7586 KB

Open AccessArticle

Collision-Free Formation-Containment Control Based on Adaptive Sliding Mode Strategy for a Quadrotor Fleet Under Disturbances

by Carlos Katt and Herman Castañeda

Drones 2025, 9(10), 724; https://doi.org/10.3390/drones9100724 - 18 Oct 2025

Viewed by 616

Abstract

This manuscript presents a robust formation and collision-free containment control system designed for a quadrotor fleet operating under turbulent wind conditions. Emphasizing collision avoidance, we introduce a two-layer strategy in which a virtual leader defines a trajectory, and leaders and followers maintain their [...] Read more.

This manuscript presents a robust formation and collision-free containment control system designed for a quadrotor fleet operating under turbulent wind conditions. Emphasizing collision avoidance, we introduce a two-layer strategy in which a virtual leader defines a trajectory, and leaders and followers maintain their positions while avoiding collisions among them. A graph convention is used to illustrate the roles of leaders and followers, as well as their interactions. Inter-agent collision avoidance is proposed by expanding the desired distance relative to all neighboring agents, thereby guaranteeing the convergence stage. Moreover, the approach employs a class of adaptive sliding mode strategies to ensure finite-time convergence, as well as non-overestimation of the control gain in the presence of uncertainties and perturbations. A stability analysis demonstrates the practical finite-time stability of the system using the Lyapunov methodology. Results from the simulation underscore the effectiveness of our proposal in adhering to the desired time-varying trajectories and ensuring sensor-less inter-agent collision avoidance for the followers, even in the presence of turbulent wind conditions. Full article

(This article belongs to the Special Issue Swarm Intelligence-Inspired Planning and Control for Drones)

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21 pages, 2522 KB

Open AccessArticle

A Reinforcement Learning-Based Adaptive Grey Wolf Optimizer for Simultaneous Arrival in Manned/Unmanned Aerial Vehicle Dynamic Cooperative Trajectory Planning

by Wei Jia, Lei Lv, Ruizhi Duan, Tianye Sun and Wei Sun

Drones 2025, 9(10), 723; https://doi.org/10.3390/drones9100723 - 17 Oct 2025

Cited by 2 | Viewed by 1278

Abstract

Addressing the challenge of high-precision time-coordinated path planning for manned and unmanned aerial vehicle (UAV) clusters operating in complex dynamic environments during missions like high-level autonomous coordination, this paper proposes a reinforcement learning-based Adaptive Grey Wolf Optimizer (RL-GWO) method. We formulate a comprehensive [...] Read more.

Addressing the challenge of high-precision time-coordinated path planning for manned and unmanned aerial vehicle (UAV) clusters operating in complex dynamic environments during missions like high-level autonomous coordination, this paper proposes a reinforcement learning-based Adaptive Grey Wolf Optimizer (RL-GWO) method. We formulate a comprehensive multi-objective cost function integrating total flight distance, mission time, time synchronization error, and collision penalties. To solve this model, we design multiple improved GWO strategies and employ a Q-Learning framework for adaptive strategy selection. The RL-GWO algorithm is embedded within a dual-layer “global planning + dynamic replanning” framework. Simulation results demonstrate excellent convergence and robustness, achieving second-level time synchronization accuracy while satisfying complex constraints. In dynamic scenarios, the method rapidly generates safe evasion paths while maintaining cluster coordination, validating its practical value for heterogeneous UAV operations. Full article

(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs: 3rd Edition)

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20 pages, 667 KB

Open AccessArticle

Target Assignment for UAV Swarms: A Novel Genetic Algorithm with Hybrid Constraint-Handling Techniques

by Tingting Li, Zhixiao Sun, Jianliang Ai and Biao Wang

Drones 2025, 9(10), 722; https://doi.org/10.3390/drones9100722 - 17 Oct 2025

Viewed by 651

Abstract

Target assignment for UAV swarms represents a pivotal and formidable endeavor, characterized by intricate combinatorial optimization and complex constraints. This study introduces an innovative and sophisticated constraint-handling method that adeptly tackles the inherent challenges of this problem. By meticulously partitioning the intricate constraints [...] Read more.

Target assignment for UAV swarms represents a pivotal and formidable endeavor, characterized by intricate combinatorial optimization and complex constraints. This study introduces an innovative and sophisticated constraint-handling method that adeptly tackles the inherent challenges of this problem. By meticulously partitioning the intricate constraints into distinct soft constraints and hard constraints, we then employ an adaptive penalty function to gracefully manage the former, while leveraging a dedicated repair heuristic to effectively address the latter. This strategy effectively surmounts the daunting hurdle of managing a substantial number of infeasible individuals that resist conventional repair techniques. Through a comprehensive series of comparative experiments, we meticulously evaluate the validity and efficacy of our proposed method, juxtaposing it against conventional penalty-based and repair-based approaches. The results unequivocally underscore the superior performance of our approach, yielding extraordinary allocation outcomes by deftly managing both soft and hard constraints. Furthermore, the versatility and applicability of our methodology transcend the confines of the target assignment problem, offering promising avenues for resolving allocation problems in diverse domains such as transportation systems, distributed computing, and construction management. Full article

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

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20 pages, 960 KB

Open AccessArticle

A Machine Learning-Based Hybrid Approach for Safeguarding VLC-Enabled Drone Systems

by Ge Shi, Hongyang Zhou, Huixin Wu, Fupeng Wei and Wei Cheng

Drones 2025, 9(10), 721; https://doi.org/10.3390/drones9100721 - 16 Oct 2025

Viewed by 569

Abstract

This paper explores the physical layer security performance of collaborative drone fleets enabled by visible light communication (VLC) in a multi-eavesdropper scenario, where multiple drones leverage VLC to serve terrestrial users. To strengthen system security, we formulate a sum worst-case secrecy rate maximization [...] Read more.

This paper explores the physical layer security performance of collaborative drone fleets enabled by visible light communication (VLC) in a multi-eavesdropper scenario, where multiple drones leverage VLC to serve terrestrial users. To strengthen system security, we formulate a sum worst-case secrecy rate maximization problem. To address the non-convex optimization challenge of this problem, we develop two innovative Q-learning-based position decision algorithms (Q-PDA and Q-PDA-lite) with a dynamic reward mechanism, allowing drones to adaptively optimize their positions. Additionally, we propose an enhanced Tabu Search-based grouping algorithm (TS-GA) to establish the suboptimal user equipment (UE)–drone association by balancing candidate solution exploration and tabu constraint exploitation. Simulation results demonstrate that the proposed Q-PDA and Q-PDA-lite achieve worst-case secrecy rates significantly exceeding those of Random-PDA and K-means-PDA. While Q-PDA-lite exhibits 2% lower performance than Q-PDA, it offers reduced complexity. Additionally, the proposed TS-GA achieves a worst-case secrecy rate that substantially outperforms random grouping, UE-channel-gain-based grouping, and channel-gain-based grouping. Collectively, the hybrid approach integrating Q-PDA and TS-GA achieves 10% near-global optimality with guaranteed convergence, while preserving computational efficiency. Furthermore, this hybrid approach outperforms other combinations in terms of security metrics. Full article

(This article belongs to the Topic Advanced Technologies and Applications for Unmanned Systems)

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39 pages, 9661 KB

Open AccessArticle

Flight-Parameter-Based Motion Vector Prediction for Drone Video Compression

by Altuğ Şimşek, Ahmet Öncü and Günhan Dündar

Drones 2025, 9(10), 720; https://doi.org/10.3390/drones9100720 - 16 Oct 2025

Viewed by 827

Abstract

Block-based hybrid video coders typically use inter-prediction and bidirectionally coded (B) frames to improve compression efficiency. For this purpose, they employ look-ahead buffers, perform out-of-sequence frame coding, and implement similarity search-based general-purpose algorithms for motion estimation. While effective, these methods increase computational complexity [...] Read more.

Block-based hybrid video coders typically use inter-prediction and bidirectionally coded (B) frames to improve compression efficiency. For this purpose, they employ look-ahead buffers, perform out-of-sequence frame coding, and implement similarity search-based general-purpose algorithms for motion estimation. While effective, these methods increase computational complexity and may not suit delay-sensitive practical applications such as real-time drone video transmission. If future motion can be predicted from external metadata, encoding can be optimized with lower complexity. In this study, a mathematical model for predicting motion vectors in drone video using only flight parameters is proposed. A remote-controlled drone with a fixed downward-facing camera recorded 4K video at 50 fps during autonomous flights over a marked terrain. Four flight parameters were varied independently, altitude, horizontal speed, vertical speed, and rotational rate. OpenCV was used to detect ground markers and compute motion vectors for temporal distances of 5 and 25 frames. Polynomial surface fitting was applied to derive motion models for translational, rotational, and elevational motion, which were later combined. The model was validated using complex motion scenarios (e.g., circular, ramp, helix), yielding worst-case prediction errors of approximately −1 ± 3 and −6 ± 14 pixels at 5 and 25 frames, respectively. The results suggest that flight-aware modeling enables accurate and low-complexity motion vector prediction for drone video coding. Full article

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23 pages, 12369 KB

Open AccessArticle

Dual-Objective Model Predictive Control for Longitudinal Tracking and Connectivity-Aware Trajectory Optimization of Fixed-Wing UAVs

by Abdurrahman Talha Yildiz and Kemal Keskin

Drones 2025, 9(10), 719; https://doi.org/10.3390/drones9100719 - 16 Oct 2025

Viewed by 1562

Abstract

This paper presents a dual-objective Model Predictive Control (MPC) framework for fixed-wing unmanned aerial vehicles (UAVs). The framework was designed with two goals in mind: improving longitudinal motion control and optimizing the flight trajectory when connectivity and no-fly zone constraints are present. A [...] Read more.

This paper presents a dual-objective Model Predictive Control (MPC) framework for fixed-wing unmanned aerial vehicles (UAVs). The framework was designed with two goals in mind: improving longitudinal motion control and optimizing the flight trajectory when connectivity and no-fly zone constraints are present. A multi-input–multi-output model derived from NASA’s Generic Transport Model (T-2) was used and linearized for controller design. We compared the MPC controller with a Linear Quadratic Regulator (LQR) in MATLAB simulations. The results showed that MPC reached the reference values faster, with less overshoot and phase error, particularly under sinusoidal reference inputs. These differences became even more evident when the UAV had to fly in windy conditions. Trajectory optimization was carried out using the CasADi framework, which allowed us to evaluate paths that balance two competing requirements: reaching the target quickly and maintaining cellular connectivity. We observed that changing the weights of the cost function had a strong influence on the trade-off between direct flight and reliable communication, especially when multiple base stations and no-fly zones were included. Although the study was limited to simulations at constant altitude, the results suggest that MPC can serve as a practical tool for UAV missions that demand both accurate flight control and robust connectivity. Future work will extend the framework to more complete models and experimental validation. Full article

(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs: 3rd Edition)

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16 pages, 1850 KB

Open AccessArticle

Rapid Optimal Matching Design of Heterogeneous Propeller Propulsion Systems for High-Altitude Unmanned Airships

by Miao Zhang, Xiangyu Wang, Zhiwei Zhang, Bo Wang, Junjie Cheng and Jian Zhang

Drones 2025, 9(10), 718; https://doi.org/10.3390/drones9100718 - 16 Oct 2025

Viewed by 712

Abstract

In order to enhance the wind-resistance capability and achieve a lightweight design of high-altitude unmanned airships, this study proposes a rapid optimization method for a heterogeneous propeller propulsion system. This system integrates contra-rotating and ducted propellers to exploit their respective aerodynamic advantages. First, [...] Read more.

In order to enhance the wind-resistance capability and achieve a lightweight design of high-altitude unmanned airships, this study proposes a rapid optimization method for a heterogeneous propeller propulsion system. This system integrates contra-rotating and ducted propellers to exploit their respective aerodynamic advantages. First, surrogate models of the contra-rotating propeller, contra-rotating motor, ducted propeller, and ducted motor were constructed using an optimal Latin hypercube sampling method based on the max–min criterion. Then, within the optimization framework, propeller–motor matching principles and energy balance constraints were incorporated to minimize the total weight of the propulsion and energy systems. A case study on a conventional high-altitude unmanned airship demonstrates that, under the same wind-resistance capability, the adoption of the heterogeneous propeller electric propulsion system reduces the total propulsion-and-energy system weight by 24.94%. This method integrates the advantages of contra-rotating and ducted propellers, thereby overcoming the limitations of conventional propulsion architectures. It provides a new approach for designing lightweight, efficient, and long-endurance propulsion systems for near-space high-altitude platforms. Full article

(This article belongs to the Special Issue Design and Flight Control of Low-Speed Near-Space Unmanned Systems)

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25 pages, 2877 KB

Open AccessArticle

Integration of Field Data and UAV Imagery for Coffee Yield Modeling Using Machine Learning

by Sthéfany Airane dos Santos Silva, Gabriel Araújo e Silva Ferraz, Vanessa Castro Figueiredo, Margarete Marin Lordelo Volpato, Danton Diego Ferreira, Marley Lamounier Machado, Fernando Elias de Melo Borges and Leonardo Conti

Drones 2025, 9(10), 717; https://doi.org/10.3390/drones9100717 - 16 Oct 2025

Cited by 2 | Viewed by 1250

Abstract

The integration of machine learning (ML) techniques with unmanned aerial vehicle (UAV) imagery holds strong potential for improving yield prediction in agriculture. However, few studies have combined biophysical field variables with UAV-derived spectral data, particularly under conditions of limited sample size. This study [...] Read more.

The integration of machine learning (ML) techniques with unmanned aerial vehicle (UAV) imagery holds strong potential for improving yield prediction in agriculture. However, few studies have combined biophysical field variables with UAV-derived spectral data, particularly under conditions of limited sample size. This study evaluated the performance of different ML algorithms in predicting Arabica coffee (Coffea arabica) yield using field-based biophysical measurements and spectral variables extracted from multispectral UAV imagery. The research was conducted over two crop seasons (2020/2021 and 2021/2022) in a 1.2-hectare experimental plot in southeastern Brazil. Three modeling scenarios were tested with Random Forest, Gradient Boosting, K-Nearest Neighbors, Multilayer Perceptron, and Decision Tree algorithms, using Leave-One-Out cross-validation. Results varied considerably across seasons and scenarios. KNN performed best with raw data, while Gradient Boosting was more stable after variable selection and synthetic data augmentation with SMOTE. Nevertheless, limitations such as small sample size, seasonal variability, and overfitting, particularly with synthetic data, affected overall performance. Despite these challenges, this study demonstrates that integrating UAV-derived spectral data with ML can support yield estimation, especially when variable selection and phenological context are carefully addressed. Full article

(This article belongs to the Special Issue Innovative Approaches to Biodiversity and Ecology Monitoring: Artificial Intelligence (AI) and Drone Technology)

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24 pages, 13118 KB

Open AccessArticle

A Workflow for Urban Heritage Digitization: From UAV Photogrammetry to Immersive VR Interaction with Multi-Layer Evaluation

by Chengyun Zhang, Guiye Lin, Yuyang Peng and Yingwen Yu

Drones 2025, 9(10), 716; https://doi.org/10.3390/drones9100716 - 16 Oct 2025

Cited by 2 | Viewed by 1782

Abstract

Urban heritage documentation often separates 3D data acquisition from immersive interaction, limiting both accuracy and user impact. This study develops and validates an end-to-end workflow that integrates UAV photogrammetry with terrestrial LiDAR and deploys the fused model in a VR environment. Applied to [...] Read more.

Urban heritage documentation often separates 3D data acquisition from immersive interaction, limiting both accuracy and user impact. This study develops and validates an end-to-end workflow that integrates UAV photogrammetry with terrestrial LiDAR and deploys the fused model in a VR environment. Applied to Piazza Vittorio Emanuele II in Rovigo, Italy, the approach achieves centimetre-level registration, completes roofs and upper façades that ground scanning alone cannot capture, and produces stable, high-fidelity assets suitable for real-time interaction. Effectiveness is assessed through a three-layer evaluation framework encompassing vision, behavior, and cognition. Eye-tracking heatmaps and scanpaths show that attention shifts from dispersed viewing to concentrated focus on landmarks and panels. Locomotion traces reveal a transition from diffuse roaming to edge-anchored strategies, with stronger reliance on low-visibility zones for spatial judgment. Post-VR interviews confirm improved spatial comprehension, stronger recognition of cultural values, and enhanced conservation intentions. The results demonstrate that UAV-enabled completeness directly influences how users perceive, navigate, and interpret heritage spaces in VR. The workflow is cost-effective, replicable, and transferable, offering a practical model for under-resourced heritage sites. More broadly, it provides a methodological template for linking drone-based data acquisition to measurable cognitive and cultural outcomes in immersive heritage applications. Full article

(This article belongs to the Special Issue Implementation of UAV Systems for Cultural Heritage)

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38 pages, 72154 KB

Open AccessArticle

Dynamic Self-Triggered Fuzzy Formation Control for UAV Swarm with Prescribed-Time Convergence

by Jianhua Lu, Zehao Yuan and Ning Wang

Drones 2025, 9(10), 715; https://doi.org/10.3390/drones9100715 - 15 Oct 2025

Cited by 1 | Viewed by 1039

Abstract

This study focuses on the cooperative formation control problem of six-degree-of-freedom (6-DOF) fixed-wing unmanned aerial vehicles (UAVs) under constraints of limited communication resources and strict time requirements. The core innovation of the proposed framework lies in the deep integration of a dynamic self-triggered [...] Read more.

This study focuses on the cooperative formation control problem of six-degree-of-freedom (6-DOF) fixed-wing unmanned aerial vehicles (UAVs) under constraints of limited communication resources and strict time requirements. The core innovation of the proposed framework lies in the deep integration of a dynamic self-triggered communication mechanism (DSTCM) with a prescribed-time control strategy. Furthermore, a fuzzy control strategy is designed to effectively suppress system disturbances, enhancing the robustness of the formation. The designed DSTCM not only retains the adaptive triggering threshold characteristic of dynamic event-triggered communication, significantly reducing communication frequency, but also completely eliminates the need for continuous state monitoring required by traditional event-triggered mechanisms. As a result, both communication and onboard computational resources are effectively conserved. In parallel, a novel time-varying unilateral constrained performance function is introduced to construct a prescribed-time controller, which guarantees that the formation tracking error converges to a predefined residual set within a user-specified time. The convergence process is independent of initial conditions and strictly adheres to full-state constraints. A rigorous Lyapunov-based stability analysis demonstrates that all signals in the closed-loop UAV velocity and attitude system are semi-globally uniformly ultimately bounded (SGUUB). Furthermore, the proposed DSTCM ensures the existence of a strictly positive lower bound on the inter-event triggering intervals of the UAVs, thereby avoiding the occurrence of Zeno behavior. Numerical simulation results are provided to verify the effectiveness and superiority of the proposed control scheme. Full article

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

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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

Viewed by 1139

Abstract

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

Viewed by 382

Abstract

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

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18 pages, 9141 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

Cited by 2 | Viewed by 1208

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

Cited by 1 | Viewed by 1022

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, 1196 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

Viewed by 699

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

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23 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

Cited by 2 | Viewed by 1930

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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30 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

Viewed by 1453

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

Cited by 2 | Viewed by 2832

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 AccessEditor’s ChoiceArticle

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

Viewed by 2908

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

Viewed by 656

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

Cited by 2 | Viewed by 2128

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

Cited by 2 | Viewed by 1060

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

Cited by 1 | Viewed by 2341

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

Cited by 1 | Viewed by 924

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

Cited by 2 | Viewed by 2999

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

Cited by 2 | Viewed by 806

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

Viewed by 1659

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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