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Volume 10
Issue 4
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Drones, Volume 10, Issue 4 (April 2026) – 84 articles

Cover Story (view full-size image): This paper presents a hybrid geometric computed torque control method for an aerial manipulation system composed of a quadrotor UAV and a 2-DOF planar robotic arm. The fully coupled 8-DOF Euler–Lagrange dynamic model captures all inertial, Coriolis, and gravitational coupling between the aerial platform and the manipulator. The architecture combines a geometric SE(3) controller with a model-based computed torque controller that explicitly compensates coupled dynamics. Three simulated experiments show significant improvements over decoupled control: a 46% reduction in end-effector error, 66% reduction in joint tracking error, and 82% reduction in quadrotor pitch RMS. Robustness to wind disturbances is also evaluated, confirming the coupled controller's advantage over the decoupled baseline. View this paper
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37 pages, 14444 KB  
Article
Unsteady Wake Dynamics and Rotor Interactions: A Canonical Study for Quadrotor UAV Aerodynamics Using LES
by Marcel Ilie
Drones 2026, 10(4), 311; https://doi.org/10.3390/drones10040311 - 21 Apr 2026
Viewed by 740
Abstract
Understanding the unsteady aerodynamic behavior of quadrotor unmanned aerial vehicle (UAV) is critical for improving flight stability, control, and performance, particularly in complex operational environments. In closely spaced multirotor configurations, coherent tip vortices shed from each blade convect downstream and form helical vortex [...] Read more.
Understanding the unsteady aerodynamic behavior of quadrotor unmanned aerial vehicle (UAV) is critical for improving flight stability, control, and performance, particularly in complex operational environments. In closely spaced multirotor configurations, coherent tip vortices shed from each blade convect downstream and form helical vortex streets that interact with subsequent blades and neighboring rotors. These interactions induce rapid fluctuations in local inflow velocity and effective angle of attack, resulting in transient lift variations, increased vibratory loads, and elevated acoustic emissions. This study presents a comprehensive computational investigation of quadrotor rotor interactions and wake dynamics using a large-eddy simulation (LES). Detailed analyses reveal that the formation and evolution of tip vortices and blade–vortex interaction phenomena significantly influence lift fluctuations and aerodynamic loading. The simulations capture transient wake structures and their effects on neighboring rotors, highlighting unsteady aerodynamic mechanisms that are not adequately predicted by conventional RANS or URANS approaches. Parametric studies examining vortex-street offset distance demonstrate the sensitivity of wake-induced instabilities to design and operational parameters. The results provide new physical insights into multirotor wake dynamics and establish the LES as a predictive framework for quantifying unsteady aerodynamic loading in quadrotor drones. The findings provide insights into the complex flow physics of multirotor systems, offering guidance for more accurate modeling, rotorcraft design optimization, and the development of control strategies that mitigate adverse unsteady aerodynamic effects. This study provides new insights into rotor–vortex-street interactions, with applications to multirotor UAVs, by isolating multi-vortex coupling effects and quantifying the influence of horizontal vortex spacing on unsteady aerodynamic loading, complementing existing high-fidelity LES research. Full article
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23 pages, 4407 KB  
Article
Measurement-Informed Latency Limits for Real-Time UAV Swarm Coordination
by Rodolfo Vera-Amaro, Alberto Luviano-Juárez, Mario E. Rivero-Ángeles, Diego Márquez-González and Danna P. Suárez-Ángeles
Drones 2026, 10(4), 310; https://doi.org/10.3390/drones10040310 - 21 Apr 2026
Viewed by 1007
Abstract
Communication latency is one of the main factors limiting the practical scalability of unmanned aerial vehicle (UAV) swarms operating with distributed formation control. In real-time UAV missions, such as coordinated swarm navigation, autonomous inspection, and aerial monitoring, delayed information exchange directly affects formation [...] Read more.
Communication latency is one of the main factors limiting the practical scalability of unmanned aerial vehicle (UAV) swarms operating with distributed formation control. In real-time UAV missions, such as coordinated swarm navigation, autonomous inspection, and aerial monitoring, delayed information exchange directly affects formation stability and operational safety. In practical aerial networks, inter-UAV communication latency is influenced by stochastic effects including jitter, burst delays, and multi-hop propagation, which are rarely captured by the simplified deterministic delay assumptions commonly adopted in analytical formation-control studies. This paper introduces a measurement-informed stochastic delay model and a communication–control delay-feasibility framework that jointly account for per-link latency behavior, multi-hop delay accumulation, and controller-level delay tolerance. The proposed framework is evaluated using an attractive–repulsive distance-based potential field (ARD–PF) formation controller, for which the maximum admissible end-to-end delay is quantified as a function of swarm size and inter-UAV separation. The delay model is calibrated and validated using more than 15,000 in-flight communication delay samples collected from a multi-UAV LoRa platform operating under realistic flight conditions. The results show that different mechanisms limit swarm operation under different operating scenarios. In some configurations, stochastic communication latency becomes the dominant constraint, whereas in others, formation geometry or network load determines the feasible operating region. Based on these elements, the proposed framework characterizes delay-feasible operating regions and predicts the maximum feasible swarm size under distributed formation control and realistic multi-hop communication latency. Full article
(This article belongs to the Special Issue Low-Latency Communication for Real-Time UAV Applications)
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37 pages, 4351 KB  
Article
Synthetic Learning and Control: MAPPO-Tuned MAADRC with Graph-Laplacian Enhancement for Resilient Multi-USV Formation in Dynamic Maritime Settings
by Xingda Li, Jianqiang Zhang, Yiping Liu, Pengfei Zhang and Jing Wang
Drones 2026, 10(4), 309; https://doi.org/10.3390/drones10040309 - 21 Apr 2026
Viewed by 562
Abstract
Formation control of unmanned surface vehicles (USVs) in complex marine environments is required to contend with strongly coupled, high-dimensional disturbances. A Multi-Agent Active Disturbance Rejection Control (MAADRC) framework is developed for this purpose. The design centers on a distributed extended state observer (DESO) [...] Read more.
Formation control of unmanned surface vehicles (USVs) in complex marine environments is required to contend with strongly coupled, high-dimensional disturbances. A Multi-Agent Active Disturbance Rejection Control (MAADRC) framework is developed for this purpose. The design centers on a distributed extended state observer (DESO) coupled with a dual-channel feedback structure—NEFL-GCO and LGL-FC—that collectively maintains formation geometry. Three main ideas underpin the approach. First, a bandwidth-efficient distributed observation scheme enables agents to share disturbance estimates while using substantially less communication bandwidth. Second, an adaptive consensus compensation mechanism accommodates parameter variations as formations evolve. Third, a formation-compatible obstacle avoidance algorithm enhances reliability in congested waters. To evaluate the control structure and optimize its parameters, a multi-agent reinforcement learning (MARL) method—specifically Multi-Agent Proximal Policy Optimization (MAPPO)—is employed. The MARL agent tunes two critical parameters: observer bandwidth and nonlinear feedback gain, thereby establishing a performance baseline. After ten million training steps, the MAPPO-optimized MAADRC achieves a tracking root-mean-square error (RMSE) of 1.18 m. This value lies within 3% of the manually tuned result of 1.21 m, indicating that the bandwidth parameterization is near-optimal. Extensive simulations incorporating realistic wind, wave and current disturbances demonstrate a dynamic obstacle avoidance success rate maintaining an expected level, alongside consistently low formation tracking errors. Collectively, these findings confirm the resilience and practical utility of the proposed framework in demanding maritime settings. Full article
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30 pages, 1739 KB  
Article
Predefined-Time Control for Automatic Carrier Landing Under Complex Wind Disturbances with Disturbance Observation and Prediction
by Zibo Wang, Qidan Zhu, Pujing Sun, Wenqiang Jiang and Lipeng Wang
Drones 2026, 10(4), 308; https://doi.org/10.3390/drones10040308 - 20 Apr 2026
Viewed by 587
Abstract
To improve performance for automatic carrier landing under complex wind disturbances, an active anti-disturbance control method integrating predefined-time control, disturbance observation, and online disturbance prediction is proposed. A nonlinear model carrier-based unmanned aerial vehicle (UAV) under a composite wind environment, including airwake, steady [...] Read more.
To improve performance for automatic carrier landing under complex wind disturbances, an active anti-disturbance control method integrating predefined-time control, disturbance observation, and online disturbance prediction is proposed. A nonlinear model carrier-based unmanned aerial vehicle (UAV) under a composite wind environment, including airwake, steady wind, and gusts, is modeled. A predefined-time sliding mode controller is then developed to ensure that the system errors converge within a user-specified time. To enhance active anti-disturbance performance, a predefined-time disturbance observer is designed for disturbance estimation, and an online prediction method based on recursive least squares with forgetting factor is introduced to predict disturbances and mitigate the lag caused by observation and UAV dynamics. Moreover, a predefined-time reference model is incorporated to avoid the exponential explosion problem. Simulation results demonstrate that, compared with the baselines, the proposed method reduces the maximum following error by 16.9–82.0% and the touchdown error by 53.4–84.1%. These results indicate that the proposed method can effectively enhance anti-disturbance performance and landing accuracy under complex wind environments. Full article
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27 pages, 10819 KB  
Article
A Task Allocation Cooperative Execution Method for Resource-Constrained UAVs in Complex Scenarios
by Liangbin Zhang, Weisheng Chen and Jing Chang
Drones 2026, 10(4), 307; https://doi.org/10.3390/drones10040307 - 20 Apr 2026
Viewed by 951
Abstract
Dynamic task allocation for UAV swarms in complex scenarios is often complicated by uncertain object discovery, potential UAV loss, as well as stringent battery and execution resource limitations. These resource constraints critically affect UAV survivability and mission success but are frequently neglected in [...] Read more.
Dynamic task allocation for UAV swarms in complex scenarios is often complicated by uncertain object discovery, potential UAV loss, as well as stringent battery and execution resource limitations. These resource constraints critically affect UAV survivability and mission success but are frequently neglected in existing studies. This paper develops an auction-based dynamic task allocation for resource-constrained UAV swarms conducting cooperative monitoring and interception missions in dynamic scenarios. Task priority is incorporated to prioritize high-urgency areas and identified objects, and a threshold-based cooperative engagement strategy is proposed to facilitate multi-UAV coordination for interception missions beyond individual UAV capabilities. Meanwhile, battery-aware resource allocation is adopted to improve utilization during cooperative operations. Simulation results across scenario scales and resource configurations demonstrate that the proposed method significantly improves UAV survivability while maintaining competitive mission completion rates, proving its effectiveness for resource-constrained UAV swarm operations. Full article
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20 pages, 8508 KB  
Article
SynthAirDrone: Synthetic Drone Detection Dataset for Airport-Runway Environments
by Jiuxia Guo, Jinxi Chen, Tianhang Zhang and Qi Feng
Drones 2026, 10(4), 306; https://doi.org/10.3390/drones10040306 - 20 Apr 2026
Viewed by 952
Abstract
Illegal drone intrusion near airport runways poses a critical threat to civil aviation safety, creating an urgent need for runway-side vision systems that can detect intruding UAVs early enough for safety warning and collision-risk mitigation. However, the development of such detectors is severely [...] Read more.
Illegal drone intrusion near airport runways poses a critical threat to civil aviation safety, creating an urgent need for runway-side vision systems that can detect intruding UAVs early enough for safety warning and collision-risk mitigation. However, the development of such detectors is severely hindered by the scarcity of annotated real-world data in this high-security scenario. To address this bottleneck, we present SynthAirDrone, the first high-fidelity synthetic dataset for UAV intrusion detection in airport runway environments, together with an intelligent data generation framework integrating scene-aware placement and multi-criteria quality assessment. The proposed method uses sky-region segmentation to guide physically plausible drone placement, and combines perspective-aware scaling, Poisson image editing, and a four-dimensional quality scoring system—covering sky overlap, lighting consistency, size plausibility, and edge continuity—to improve visual plausibility and semantic consistency. The resulting dataset comprises 6500 high-quality images, all annotated in YOLO-compatible format. Using the lightweight YOLOv11n model, we show that models trained solely on SynthAirDrone exhibit non-trivial cross-domain transfer to Anti-UAV, while mixed training with limited real data provides the strongest real-world performance under the present setting. Ablation studies further confirm that a quality threshold of τ=0.6 achieves the best trade-off between diversity and fidelity. Overall, this work delivers a reproducible and efficient synthetic data solution for UAV detector development in high-security, data-scarce airport-runway scenarios. Full article
(This article belongs to the Section Artificial Intelligence in Drones (AID))
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28 pages, 4245 KB  
Article
Knowledge-Driven Two-Stage Hybrid Algorithm for Collaborative Reconnaissance Routing Problem of Ground Vehicle and Drones Considering Multi-Type Targets
by Xiao Liu, Qizhang Luo, Tianjun Liao and Guohua Wu
Drones 2026, 10(4), 305; https://doi.org/10.3390/drones10040305 - 19 Apr 2026
Viewed by 461
Abstract
The collaboration of ground vehicles (GVs) and drones offers a powerful approach for enhancing drone capabilities. Current research focuses on drone-only or single-type target reconnaissance, failing to adequately account for practical scenarios. This paper introduces a GV–drone collaboration routing problem with multi-type target [...] Read more.
The collaboration of ground vehicles (GVs) and drones offers a powerful approach for enhancing drone capabilities. Current research focuses on drone-only or single-type target reconnaissance, failing to adequately account for practical scenarios. This paper introduces a GV–drone collaboration routing problem with multi-type target reconnaissance (GVD-MTR), which explicitly integrates GV–drone collaboration with simultaneous reconnoitering of point, line, and area targets. To address this problem, we propose a knowledge-driven two-stage hybrid algorithm (KDHA). In the first stage, K-means clustering combined with heuristic operators is applied to generate and refine routes for the GV. In the second stage, an improved Adaptive Large Neighborhood Search (IALNS) method is implemented to produce optimized drone routes. KDHA leverages hybrid search strategies, such as a population-based initialization strategy and a multi-level acceptance strategy, to efficiently generate high-quality solutions. Regarding recognizing the impacts of different target types on the total travel distance, we incorporate the related domain knowledge to design problem-specific search operators. Extensive simulation experiments demonstrate that KDHA consistently outperforms several state-of-the-art heuristics in terms of solution quality and runtime. Sensitivity analyses further confirm the robustness of the proposed approach across a range of parameter settings and problem instances. Full article
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36 pages, 887 KB  
Article
Optimized Synchronization Design for UAV Swarm Network Based on Sidelink
by Hang Zhang, Hua-Min Chen, Qi-Jun Wei, Zhu-Wei Wang and Yan-Hua Sun
Drones 2026, 10(4), 304; https://doi.org/10.3390/drones10040304 - 18 Apr 2026
Viewed by 666
Abstract
With the deployment and application of the Fifth-Generation (5G) mobile communication technologies and the ongoing research and development of the Sixth-Generation (6G) mobile communication technologies, the space–air–ground–sea integrated network has become the core development vision for future communications. As aerial nodes, Unmanned Aerial [...] Read more.
With the deployment and application of the Fifth-Generation (5G) mobile communication technologies and the ongoing research and development of the Sixth-Generation (6G) mobile communication technologies, the space–air–ground–sea integrated network has become the core development vision for future communications. As aerial nodes, Unmanned Aerial Vehicles (UAVs) can be applied in a wide range of scenarios, including emergency rescue, surveying and mapping, environmental monitoring, and communication coverage enhancement. In terms of communication coverage enhancement, the space–air–ground integrated network, with UAVs as a key component, can provide seamless communication coverage for the full-domain three-dimensional space such as remote areas, deserts, and oceans. Benefiting from advantages such as low cost and high flexibility, UAVs have become a critical research focus, and the one-hop Base Station (BS)–relay UAV–slave UAV architecture for communication coverage enhancement has emerged as an important development direction. However, the high mobility and wide coverage characteristics of UAVs also pose significant synchronization challenges. Aiming at the uplink synchronization problem on the sidelink between slave UAVs and the relay UAV, a two-step random-access scheme based on Asynchronous Non-Orthogonal Multiple Access (A-NOMA) is designed to mitigate the Doppler Frequency Offset (DFO), improve access efficiency, reduce resource consumption, and accommodate the asynchrony among different users. This scheme leverages the existing preamble sequences of the Physical Random Access Channel (PRACH) and realizes DFO estimation in combination with the pairing index. On this basis, a Successive Interference Cancellation (SIC) algorithm based on DFO and phase compensation is designed to complete the demodulation of user data. For the downlink synchronization problem on the sidelink between slave UAVs and the relay UAV, the frequency offset estimation performance is improved by redesigning the resource allocation scheme of the Sidelink Synchronization Signal Block (S-SSB). Meanwhile, considering the energy constraint of UAVs, a downsampling-based detection scheme is designed to reduce UAV power consumption, and a full-link algorithm is developed to support the practical implementation of the proposed scheme. Full article
(This article belongs to the Topic Next-Generation Wireless and Mobile Network Technologies: Architectures, Protocols, and Applications)
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33 pages, 8967 KB  
Article
Cross-Sectional Distribution Profile of Mineral Fertilizers Applied by Remotely Piloted Aircraft Under Different Operating Parameters
by Luis Felipe Oliveira Ribeiro, Edney Leandro da Vitória, Jacimar Vieira Zanelato, João Victor Oliveira Ribeiro, Maria Eduarda da Silva Barbosa, Francisco de Assis Ferreira, Paulo Augusto Costa and Francine Bonomo Crispim Silva
Drones 2026, 10(4), 303; https://doi.org/10.3390/drones10040303 - 18 Apr 2026
Viewed by 784
Abstract
In this study, we determined the distribution profile of different mineral fertilizers applied by a DJI Agras T50 remotely piloted aircraft (RPA) under different flight heights and speeds. The experiment was conducted in a randomized block design in a 3 × 3 × [...] Read more.
In this study, we determined the distribution profile of different mineral fertilizers applied by a DJI Agras T50 remotely piloted aircraft (RPA) under different flight heights and speeds. The experiment was conducted in a randomized block design in a 3 × 3 × 3 factorial scheme, involving three fertilizers (urea, potassium chloride, and single superphosphate), three flight heights (4, 6, and 8 m), and three flight speeds (16, 18, and 20 km h−1). The methodology included laboratory characterization of the physical properties of the fertilizers and the determination of the transverse distribution profile under field conditions. The data were processed using Adulanço software version 4.0 and subjected to statistical analyses (p-value < 0.05). The results indicated that flight height stood out as the main factor, increasing the total and effective swath widths; however, it reduced deposition per unit area and increased the relative error as height increased. The combination of 20 km h−1 with flight heights of 4 and 6 m maximized deposition within the effective swath and provided theoretical operational capacities greater than 8 ha h−1, regardless of the fertilizers. Correlation analysis indicated an operational trade-off, showing that fertilizers with different physical properties respond differently to flight height and flight speed. Full article
(This article belongs to the Special Issue Task-Oriented UAV Applications in Agro-Forestry and Livestock Systems)
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20 pages, 17293 KB  
Article
Acoustic Effects of Differential Rotor Speeds on Twin-Propeller UAV System
by Burak Buda Turhan, Djamel Rezgui and Mahdi Azarpeyvand
Drones 2026, 10(4), 302; https://doi.org/10.3390/drones10040302 - 18 Apr 2026
Cited by 4 | Viewed by 659
Abstract
This study investigates the aerodynamic, aeroacoustic, and psychoacoustic behaviour of a side-by-side twin-propeller Unmanned Aerial Vehicle (UAV) system operating under both static and forward-flight conditions, with particular focus on the effects of asynchronous rotational speeds. Experiments were conducted using two identical five-bladed constant [...] Read more.
This study investigates the aerodynamic, aeroacoustic, and psychoacoustic behaviour of a side-by-side twin-propeller Unmanned Aerial Vehicle (UAV) system operating under both static and forward-flight conditions, with particular focus on the effects of asynchronous rotational speeds. Experiments were conducted using two identical five-bladed constant pitch propellers with a diameter of 9 in (228.6 mm) and a pitch to diameter ratio of 1. Rotational speed differences between 0 and 300 rpm were examined in 50 rpm increments at inflow velocities of 0 m/s, 14 m/s and 24 m/s. The results show that variations in rotational speed have a significant influence on both acoustic levels and perceived annoyance. Asynchronous operation causes the dominant tonal peak at the blade passing frequency to split into two components, reducing tonal reinforcement. This produces noise level reductions of approximately 2 dB in static and high advance ratio conditions, increasing to about 5 dB reduction at low advance ratios. Psychoacoustic metrics show greater sensitivity to tonal structure than to overall sound pressure level, with annoyance reductions of about 5% in static conditions and up to 15% at low advance ratios. A modest aerodynamic penalty of about 5% at ΔN=50 rpm is observed, increasing with larger speed mismatches. Full article
(This article belongs to the Special Issue Advances in UAV Guidance, Navigation, and Control Through Acoustic Technologies)
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19 pages, 4121 KB  
Technical Note
drone2report: A Configuration-Driven Multi-Sensor Batch-Processing Engine for UAV-Based Plot Analysis in Precision Agriculture
by Nelson Nazzicari, Giulia Moscatelli, Agostino Fricano, Elisabetta Frascaroli, Roshan Paudel, Eder Groli, Paolo De Franceschi, Giorgia Carletti, Nicolò Franguelli and Filippo Biscarini
Drones 2026, 10(4), 301; https://doi.org/10.3390/drones10040301 - 18 Apr 2026
Viewed by 1116
Abstract
Unmanned aerial vehicles (UAVs) have become indispensable tools in precision agriculture and plant phenotyping, enabling the rapid, non-destructive assessment of crop traits across space and time. Equipped with RGB, multispectral, thermal, and other sensors, UAVs provide detailed information on canopy structure, physiology, and [...] Read more.
Unmanned aerial vehicles (UAVs) have become indispensable tools in precision agriculture and plant phenotyping, enabling the rapid, non-destructive assessment of crop traits across space and time. Equipped with RGB, multispectral, thermal, and other sensors, UAVs provide detailed information on canopy structure, physiology, and stress responses that can guide management decisions and accelerate breeding programs. Despite these advances, the downstream processing of UAV imagery remains technically demanding. Converting orthomosaics into standardized, biologically meaningful data often requires a combination of photogrammetry, geospatial analysis, and custom scripting, which can limit reproducibility and accessibility across research groups. We present drone2report, an open-source python-based software that processes orthomosaics from UAV flights to generate vegetation indices, summary statistics, derived subimages, and text (html) reports, supporting both research and applied crop breeding needs. Alongside the basic structure and functioning of drone2report, we also present five case studies that illustrate practical applications common in UAV-/drone-phenotyping of plants: (i) thresholding to remove background noise and highlight regions of interest; (ii) monitoring plant phenotypes over time; (iii) extracting information on plant height to detect events like lodging or the falling over of spikes; (iv) integrating multiple sensors (cameras) to construct and optimize new synthetic indices; (v) integrate a trained deep learning network to implement a classification task. These examples demonstrate the tool’s ability to automate analysis, integrate heterogeneous data and models, and support reproducible computation of agronomically relevant traits. drone2report streamlines orthorectified UAV-image processing for precision agriculture by linking orthomosaics to standardized, plot-level outputs. Its modular, configuration-driven design allows transparent workflows, easy customization, and integration of multiple sensors within a unified analytical framework. By facilitating reproducible, multi-modal image analysis, drone2report lowers technical barriers to UAV-based phenotyping and opens the way to robust, data-driven crop monitoring and breeding applications. Full article
(This article belongs to the Special Issue Advances in UAV-Based Remote Sensing for Climate-Smart Agriculture)
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17 pages, 2191 KB  
Article
A Study on Hydrogen-Based Hybrid Electric Propulsion Systems for Multirotors
by Iago Gomes, Frederico Afonso and Afzal Suleman
Drones 2026, 10(4), 300; https://doi.org/10.3390/drones10040300 - 18 Apr 2026
Viewed by 894
Abstract
The growing need for sustainable aviation propulsion has increased interest in hydrogen fuel cell systems as alternatives to combustion engines. This study presents the modeling, simulation, and optimization of a hybrid hydrogen–electric powertrain for the MIMIQ unmanned aerial vehicle (UAV). A 2 kW [...] Read more.
The growing need for sustainable aviation propulsion has increased interest in hydrogen fuel cell systems as alternatives to combustion engines. This study presents the modeling, simulation, and optimization of a hybrid hydrogen–electric powertrain for the MIMIQ unmanned aerial vehicle (UAV). A 2 kW proton exchange membrane fuel cell is integrated with a 12S lithium-polymer battery via a DC–DC converter, enabling parallel power sharing and in-flight battery recharging. A MATLAB-based dynamic model was developed using mission power profiles derived from flight data and refined using momentum theory. The developed model was benchmarked through a comparative simulation of a combustion-based hybrid-electric powertrain variant of the same platform, demonstrating consistency in electrical and energetic behavior. Multi-objective optimization using NSGA-II was performed to maximize hover endurance and to minimize energy consumption while maximizing payload over a full mission. Results from this computational framework show that endurance is primarily constrained by hydrogen availability rather than battery capacity, with the fuel cell operating near its optimal efficiency region. The findings indicate that hydrogen–electric architectures offer improved endurance, reduced emissions and better scalability compared to combustion-based systems, supporting their suitability for long-endurance UAV applications. Full article
(This article belongs to the Special Issue The Conceptual Design Methodology for UAV: New Research and New Development (2nd Edition))
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26 pages, 1487 KB  
Article
On the Performance of NOMA-Enhanced UAV-Relayed Smart Healthcare Systems Under Rician Fading
by Jing Ye, Bing Li, Ruixin Feng, Fanghui Huang, Junbin Lou, Tao Li, Dawei Wang and Yixin He
Drones 2026, 10(4), 299; https://doi.org/10.3390/drones10040299 - 17 Apr 2026
Viewed by 452
Abstract
This paper investigates the application of cooperative relaying systems with non-orthogonal multiple access (NOMA) in low-altitude intelligent networking-enabled medical Internet of Things (IoT) and analyzes their transmission performance. First, to enhance the communication quality of remote base stations, we deploy a relaying unmanned [...] Read more.
This paper investigates the application of cooperative relaying systems with non-orthogonal multiple access (NOMA) in low-altitude intelligent networking-enabled medical Internet of Things (IoT) and analyzes their transmission performance. First, to enhance the communication quality of remote base stations, we deploy a relaying unmanned aerial vehicle (UAV). A two-slot NOMA cooperative transmission mechanism is proposed accordingly. Next, for the NOMA-enhanced UAV-relayed smart healthcare system under Rician fading channels, an exact closed-form expression for the achievable rate is derived using the incomplete Gamma function. Then, to improve computational efficiency, a low-complexity approximation method based on Gauss–Chebyshev quadrature is designed, overcoming the high complexity of the exact expression. Finally, the simulation results validate a close match between the proposed approximation and the exact values (average approximation error below 6.17%), and demonstrate superior achievable rate performance compared to three state-of-the-art schemes. Full article
(This article belongs to the Section Drone Communications)
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28 pages, 381 KB  
Systematic Review
A Factors–Responses–Consequences Framework for Assessing Wildlife Impacts of Uncrewed Aerial Systems: A Systematic Review
by Ken Hellerud and Lizhen Huang
Drones 2026, 10(4), 298; https://doi.org/10.3390/drones10040298 - 17 Apr 2026
Viewed by 720
Abstract
Uncrewed aerial systems (UASs) have diverse applications in natural environments, yet their deployment can inadvertently disturb wildlife. This PRISMA-guided systematic review synthesised 39 studies (2015–2025) encompassing birds, mammals, and marine taxa to identify UAS operational drivers of disturbance. We applied a Factors–Responses–Consequences (F–R–C) [...] Read more.
Uncrewed aerial systems (UASs) have diverse applications in natural environments, yet their deployment can inadvertently disturb wildlife. This PRISMA-guided systematic review synthesised 39 studies (2015–2025) encompassing birds, mammals, and marine taxa to identify UAS operational drivers of disturbance. We applied a Factors–Responses–Consequences (F–R–C) framework linking UAS operational characteristics, observed wildlife responses, and ecological consequences. Three patterns emerged: (i) Factors: Contextual and operational conditions such as flight altitude, noise, and approach direction interact with species-specific sensitivities to shape disturbance potential. (ii) Responses: Physiological measures (e.g., elevated heart rates) often reveal hidden stress not evident from behaviour alone. (iii) Consequences: Short-term effects may accumulate into long-term impacts on health, reproduction, and habitat use. These findings highlight the need for species- and context-specific flight envelopes rather than solely uniform altitude limits. By structuring existing evidence within the F–R–C framework, this synthesis provides a transferable foundation for UAS mission planning, drone development, operational decision-making, ethical practice, and environmental impact assessment in conservation and wildlife-management contexts. As all screening and data extraction were conducted by a single reviewer, the findings should be interpreted with appropriate caution pending independent validation. Full article
(This article belongs to the Special Issue UAVs for Nature Conservation Tasks in Complex Environments)
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38 pages, 20887 KB  
Article
Cooperative Online 3D Path Planning for Fixed-Wing UAVs
by Yonggang Nie, Xinyue Zhang, Chaoyue Li and Dong Zhang
Drones 2026, 10(4), 297; https://doi.org/10.3390/drones10040297 - 17 Apr 2026
Viewed by 673
Abstract
Addressing high dynamics, stringent non-holonomic constraints, and limited onboard computation in cooperative online trajectory planning for multiple fixed-wing UAVs in complex 3D obstacle environments, this paper proposes a Cooperative-3D-Quick-Dubins-RRT*. First, an offline motion-primitive database is engineered to align with RRT* mechanics: an unconstrained [...] Read more.
Addressing high dynamics, stringent non-holonomic constraints, and limited onboard computation in cooperative online trajectory planning for multiple fixed-wing UAVs in complex 3D obstacle environments, this paper proposes a Cooperative-3D-Quick-Dubins-RRT*. First, an offline motion-primitive database is engineered to align with RRT* mechanics: an unconstrained expansion mode facilitates rapid space exploration, while a constrained rewiring mode ensures kinodynamic continuity. This architecture, synergized with four targeted acceleration strategies (dimensionality reduction, elliptical sampling, tree pruning, and pre-discretized collision checking), significantly accelerates convergence. Second, a Dubins-detour-based time-coordination mechanism is designed to map cooperative timing constraints into controllable path-length adjustments, and the feasible adjustment range is analyzed to ensure realizability. Finally, simulations and hardware-in-the-loop experiments across a variety of representative scenarios are conducted for validation. The results show that, compared with the classical Dubins-RRT*, the proposed method achieves clear advantages in planning time and path length, demonstrating its suitability for online cooperative obstacle-avoidance planning of multiple UAVs. Full article
(This article belongs to the Special Issue Intelligent Cooperative Technologies of UAV Swarm Systems)
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29 pages, 12195 KB  
Article
Unmanned Aerial System Localization Using Smartphones as a Dispersed Sensor Platform
by Fred Taylor III, John Ryan and Dennis Akos
Drones 2026, 10(4), 296; https://doi.org/10.3390/drones10040296 - 17 Apr 2026
Viewed by 542
Abstract
The continued advancement of small unmanned aircraft systems (UASs) has resulted in growing concerns regarding the potential threat that UASs present. To deal with harmful or disruptive drones, techniques that can be performed using affordable, widely distributed sensor platforms would provide an immense [...] Read more.
The continued advancement of small unmanned aircraft systems (UASs) has resulted in growing concerns regarding the potential threat that UASs present. To deal with harmful or disruptive drones, techniques that can be performed using affordable, widely distributed sensor platforms would provide an immense benefit. One such sensor platform is Android smartphones, which continue to see improved sensor quality and orientation estimation while being prevalent worldwide. In this work, the results of crowdsourced drone localization experiments using a custom-built Android smartphone app will be presented. Using GPS positions and angular measurements collected from human-operated smartphones, the ability to localize a static and dynamic target will be demonstrated, as the positions of these targets are estimated from the intersection of line-of-sight vectors. The results from these tests show that the position of these targets can be computed to below 10 m using correction techniques to alleviate measurement errors introduced by environmental or human factors. The results from these tests validate the potential of using readily available smartphones as sensor platforms as an alternative to specially designed localization technology. The inclusion of environmental and human errors can significantly influence the resulting solution, but steps can be taken to alleviate their impact. Full article
(This article belongs to the Section Drone Communications)
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21 pages, 6896 KB  
Article
Comparative Evaluation of Segmentation-Based and Pose-Assisted Head Temperature Estimation from UAS Thermal Imagery Under Controlled Conditions
by Owais Ahmed, Justin Guye, M. Hassan Tanveer and Adeel Khalid
Drones 2026, 10(4), 295; https://doi.org/10.3390/drones10040295 - 17 Apr 2026
Viewed by 428
Abstract
This paper presents a vision-based framework for detecting humans and estimating head surface temperature from aerial thermal imagery acquired by Unmanned Aerial Systems (UAS). A comparative evaluation of recent object detection architectures was conducted to identify the most stable and reliable model for [...] Read more.
This paper presents a vision-based framework for detecting humans and estimating head surface temperature from aerial thermal imagery acquired by Unmanned Aerial Systems (UAS). A comparative evaluation of recent object detection architectures was conducted to identify the most stable and reliable model for thermal human detection under varying flight altitudes. The selected framework integrates two head localization strategies, namely, segmentation-based mask slicing and pose-assisted keypoint localization, to extract head regions and compute per-pixel temperature values from radiometric metadata. The results show that cross-domain inference using pre-trained YOLOv11 models achieves reliable human detection across controlled outdoor environments. Between the two pipelines, the pose-assisted method produced temperature estimates closer to the expected human physiological range (36–38 °C), whereas the segmentation-based approach exhibited higher values attributable to mask boundary contamination and solar surface heating. In the absence of ground-truth validation from medical-grade sensors, these findings are characterized as relative comparisons rather than absolute accuracy claims. This study establishes a methodological foundation for future UAS-based thermal assessment systems and identifies critical calibration and validation requirements for field deployment. Full article
(This article belongs to the Special Issue Innovative Applications of UAVs in Search and Rescue: Improving Safety and Effectiveness)
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19 pages, 3333 KB  
Article
Energy-Harvesting-Assisted UAV Swarm Anti-Jamming Communication Based on Multi-Agent Reinforcement Learning
by Yongfang Li, Tianyu Zhao, Zhijuan Wu, Yan Lin and Yijin Zhang
Drones 2026, 10(4), 294; https://doi.org/10.3390/drones10040294 - 16 Apr 2026
Cited by 1 | Viewed by 742
Abstract
Considering that the unmanned aerial vehicles (UAVs) are susceptible to both co-channel interference and malicious jamming with limited onboard battery energy, this paper proposes an energy-harvesting-assisted anti-jamming communication framework for UAV swarm networks. Specifically, we first model the problem as a decentralized partially [...] Read more.
Considering that the unmanned aerial vehicles (UAVs) are susceptible to both co-channel interference and malicious jamming with limited onboard battery energy, this paper proposes an energy-harvesting-assisted anti-jamming communication framework for UAV swarm networks. Specifically, we first model the problem as a decentralized partially observable Markov decision process (Dec-POMDP), aiming to achieve a long-term trade-off between data transmission success rate and energy consumption. Then we propose a multi-agent independent advantage actor–critic (IA2C)-based energy-harvesting-assisted anti-jamming communication solution, which enables each cluster head (CH) to learn its transmit channel, power, and energy harvesting time policy independently. By constructing a time-space-based extended Dec-POMDP, the spatiotemporal correlations among neighboring nodes are learned by allowing adjacent agents to share discounted local observations. Extensive simulations show that, compared with the benchmark schemes, the proposed scheme improves the average cumulative reward and average cumulative success rate by 17.26% and 10.37%, respectively, while achieving a higher transmission success rate with lower energy consumption under different numbers of available channels. Full article
(This article belongs to the Special Issue Intelligent Spectrum Management in UAV Communication)
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18 pages, 5905 KB  
Article
A Method of Deep Mineralization Potential Exploration Based on UAVs and Its Application in an Abandoned Mine in the Democratic Republic of the Congo
by Xin Wu, Guoqiang Xue, Yufei Gao, Yanbo Wang, Yefei Li, Zhaoming Qian, Yusuo Zhao, Junjie Xue, Song Cui and Nannan Zhou
Drones 2026, 10(4), 293; https://doi.org/10.3390/drones10040293 - 16 Apr 2026
Viewed by 438
Abstract
In recent years, unmanned aerial vehicles (UAVs) have increasingly become carrying platforms for Earth observation systems equipped with optical, microwave, and other types of sensors, primarily enabling high-resolution observations of above-ground targets. With the development of geophysical methods, bulky instruments originally designed for [...] Read more.
In recent years, unmanned aerial vehicles (UAVs) have increasingly become carrying platforms for Earth observation systems equipped with optical, microwave, and other types of sensors, primarily enabling high-resolution observations of above-ground targets. With the development of geophysical methods, bulky instruments originally designed for deep subsurface detection have been progressively miniaturized and made more lightweight, allowing their integration with civilian UAVs and opening new technological avenues for subsurface investigation. We have developed a semi-airborne transient electromagnetic system based on a UAV that is capable of simultaneously obtaining underground resistivity and polarization rate parameters. A survey was conducted over the M’sesa mining area in the Democratic Republic of the Congo. This is a mine pit that has been abandoned for over 50 years and has been flooded to form a lake, making it difficult to detect its deep mineralization potential using traditional ground-based methods. The results clearly delineate the spatial distribution of the Shangoluwe–M’sesa compressional fault and reveal a deep low-resistivity and high-chargeability zone, which provides clues for the exploration of deep deposits. This study will be of significant importance for accelerating the promotion and application of UAV-based semi-airborne electromagnetic exploration technologies. Full article
(This article belongs to the Special Issue Advances in Civil Applications of Unmanned Aircraft Systems: Third Edition)
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25 pages, 1271 KB  
Review
Recent Advances for Generative AI-Enabled Unmanned Aerial Vehicle Systems and Applicable Technologies
by Hyunbum Kim
Drones 2026, 10(4), 292; https://doi.org/10.3390/drones10040292 - 16 Apr 2026
Cited by 1 | Viewed by 1844
Abstract
Unmanned Aerial Vehicles (UAVs) have been key platforms to perform sensing, analytics and automation across intelligent transportation, construction, smart agriculture, logistics and defense. Generative AI (GenAI) accelerates intelligence of UAVs by creating synthetic data, simulating environments and improving learning with restricted data conditions. [...] Read more.
Unmanned Aerial Vehicles (UAVs) have been key platforms to perform sensing, analytics and automation across intelligent transportation, construction, smart agriculture, logistics and defense. Generative AI (GenAI) accelerates intelligence of UAVs by creating synthetic data, simulating environments and improving learning with restricted data conditions. When integrated with digital twin and AI frameworks, GenAI enables advanced design, modeling, adaptation and making a decision. In this paper, we survey recent advances for generative AI-enabled UAVs systems and applicable scenarios. Then, we categorize four applicable research branches using generative AI-enabled UAVs for intelligent transportation systems, digital twin and smart infrastructure, smart agriculture, last-mile logistics and delivery. Full article
(This article belongs to the Section Artificial Intelligence in Drones (AID))
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19 pages, 1764 KB  
Review
Coastal Environmental Monitoring in Transition: A Citation Network Analysis of Methodological Influence and Persistence in Drone Research (2013–2024)
by Eduardo Augusto Werneck Ribeiro, Raul Borges Guimarães, Natália Lampert Bastista, Mauricio Rizzatti, Nicolas Firmiano Flores and Igor Engel Cansian
Drones 2026, 10(4), 291; https://doi.org/10.3390/drones10040291 - 16 Apr 2026
Viewed by 698
Abstract
Unmanned Aerial Vehicles (UAVs/drones) have emerged as transformative tools for coastal environmental monitoring, yet the field’s intellectual evolution and operational maturity remain incompletely characterized. This study employs citation network analysis via Litmaps to map the structure, consolidation, and knowledge diffusion patterns of coastal [...] Read more.
Unmanned Aerial Vehicles (UAVs/drones) have emerged as transformative tools for coastal environmental monitoring, yet the field’s intellectual evolution and operational maturity remain incompletely characterized. This study employs citation network analysis via Litmaps to map the structure, consolidation, and knowledge diffusion patterns of coastal drone research from 2013 to 2024. A corpus of 47 influential articles was identified through systematic citation connectivity criteria, revealing three distinct phases: Seminal (≤2016), Consolidation (2017–2022), and Innovation (≥2023). Results demonstrate that foundational RGB photogrammetry protocols established in 2013–2016 remain standard references in 2024, indicating methodological maturity rather than obsolescence. However, substantial geographic concentration exists (Mediterranean institutions dominate early development), with application imbalances: temporal monitoring (46.8%) dominates while policy-relevant erosion/risk assessment comprises only 8.5%. Despite documented technical adequacy (sub-centimeter accuracy, 70–80% cost reduction vs. alternatives), the transition to operational coastal programs faces institutional rather than technological barriers. The analysis concludes that realizing UAV operational potential requires coordinated institutional development across management agencies, research institutions, capacity-building programs, and equitable data governance frameworks. Full article
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34 pages, 3701 KB  
Article
Efficient Multi-Fidelity Surrogate Modeling for UAV Aerodynamic Analysis via Active Transfer Learning
by Dun Yang, Li Liu and Bojing Yao
Drones 2026, 10(4), 290; https://doi.org/10.3390/drones10040290 - 16 Apr 2026
Viewed by 1047
Abstract
During the design and optimization phase of unmanned aerial vehicles (UAVs), high-fidelity aerodynamic analysis methods often come with high computational costs, significantly restricting the efficiency of design exploration. To address this challenge, a multi-fidelity surrogate modeling method based on active transfer learning is [...] Read more.
During the design and optimization phase of unmanned aerial vehicles (UAVs), high-fidelity aerodynamic analysis methods often come with high computational costs, significantly restricting the efficiency of design exploration. To address this challenge, a multi-fidelity surrogate modeling method based on active transfer learning is proposed. The method leverages transfer learning to capture implicit correlations among multi-fidelity analysis models, while an active learning-based adaptive sampling strategy is introduced to reduce the computational cost during model construction. To further reduce the computational burden, a Gaussian process regression-assisted active learning criterion is formulated to efficiently select high-value samples and a model updating strategy is designed to ensure feature consistency, accelerate convergence, and enhance the robustness during the transfer process. Numerical benchmarks, NACA 0012 airfoil aerodynamic analysis and UAV with strut-braced wing aerodynamic analysis cases, are conducted to validate the proposed approach. The results demonstrate that the proposed method achieves a higher accuracy under small-sample conditions compared with traditional approaches. Full article
(This article belongs to the Section Drone Design and Development)
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29 pages, 20198 KB  
Article
A Generative Task Allocation Method for Heterogeneous UAV Swarms Empowered by Heterogeneous Toolchains
by Lei Ai, Bin Ma, Jianxing Zhang, Yao Ai, Ziqi Hao, Jianan Li, Zhuting Yu and Jiayu Cheng
Drones 2026, 10(4), 289; https://doi.org/10.3390/drones10040289 - 16 Apr 2026
Cited by 1 | Viewed by 1090
Abstract
Task allocation for heterogeneous unmanned aerial vehicle (UAV) swarms requires complex spatiotemporal coordination. While traditional algorithms struggle to interpret abstract semantic intents, general large language models (LLMs) often suffer from physical hallucinations and superficial tactical reasoning. To address these limitations, we propose a [...] Read more.
Task allocation for heterogeneous unmanned aerial vehicle (UAV) swarms requires complex spatiotemporal coordination. While traditional algorithms struggle to interpret abstract semantic intents, general large language models (LLMs) often suffer from physical hallucinations and superficial tactical reasoning. To address these limitations, we propose a generative task allocation paradigm augmented by a heterogeneous toolchain, shifting the approach from rigid numerical optimization toward tool-grounded semantic planning. To implement this and overcome domain data scarcity, we design a decoupled dual-model architecture. This architecture is optimized through an execution-manifold-anchored orthogonal evolution training method. By utilizing simulated self-play within a stable execution environment, this approach prevents gradient conflicts and autonomously generates abundant training data. Furthermore, to resolve the credit assignment problem in long-horizon scenarios, we develop a Recursive Causal Probe (RCP) algorithm. By tracing failures backward through the simulation, RCP synthesizes counterfactual preference data, effectively translating tactical mistakes into precise corrections for the planning model. Extensive simulations demonstrate that our method achieves an 82.34% mission success rate in complex scenarios, requiring significantly fewer interactive corrections than general LLMs, fully verifying its physical feasibility and practical robustness. Full article
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42 pages, 8620 KB  
Article
Multi-Strategy Improved Stellar Oscillation Optimizer for Heterogeneous UAV Task Allocation in Post-Disaster Rescue
by Min Ding, Jing Du, Yijing Wang and Yue Lu
Drones 2026, 10(4), 288; https://doi.org/10.3390/drones10040288 - 15 Apr 2026
Cited by 1 | Viewed by 601
Abstract
To address load–energy dynamic coupling in heterogeneous unmanned aerial vehicle (UAV) emergency rescue, this paper proposes an energy-coupled heterogeneous UAV task allocation (EC-HUTA) model that explicitly characterizes nonlinear interdependencies among payload, velocity, and power consumption, minimizing aggregate mission costs subject to physical and [...] Read more.
To address load–energy dynamic coupling in heterogeneous unmanned aerial vehicle (UAV) emergency rescue, this paper proposes an energy-coupled heterogeneous UAV task allocation (EC-HUTA) model that explicitly characterizes nonlinear interdependencies among payload, velocity, and power consumption, minimizing aggregate mission costs subject to physical and temporal constraints. To tackle the resulting high-dimensional, nonconvex problem, we introduce a multi-strategy improved stellar oscillation optimizer (MISOO), establishing a closed-loop synergistic system through three coupled stages: (i) evolutionary game-theoretic strategy competition via replicator dynamics for adaptive exploration–exploitation balance; (ii) intuitionistic fuzzy entropy (IFE)-driven dimension-wise parameter control, where IFE calibrates global exploration intensity while dimension-specific crossover probabilities accommodate heterogeneous convergence; and (iii) memory-driven differential escape mechanisms modulated by historical memory parameters to evade local optima. Cross-stage coupling through IFE ensures state information flows across the “strategy selection-refined search-dynamic escape” pipeline. Coupled with a dual-layer encoding scheme, this framework ensures efficient feasible search. Ablation studies validate each mechanism’s contribution. Evaluations on CEC2017 benchmarks demonstrate MISOO’s superior convergence against six metaheuristics. Large-scale earthquake rescue simulations confirm that EC-HUTA/MISOO strictly adheres to nonlinear energy constraints while enhancing task completion and temporal compliance. These results validate the framework’s efficacy for time-critical emergency resource allocation. Full article
(This article belongs to the Special Issue Mission Planning, Perception and Control for Drones in Wide-Area Operations)
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36 pages, 6120 KB  
Article
A Rapid Trajectory Planning Method for Heterogeneous Swarms via Fusion of Visual Navigation and Explainable Decision Trees
by Yang Gao, Hao Yin, Wenliang Wang, Bing Guo, Yue Wang, Guopeng Li, Lingyun Tian and Dongguang Li
Drones 2026, 10(4), 287; https://doi.org/10.3390/drones10040287 - 14 Apr 2026
Viewed by 508
Abstract
For complex tasks such as search and recovery in uncharted maritime areas, the use of heterogeneous unmanned swarms (UAVs and USVs) is highly promising, yet effective cross-domain cooperative trajectory planning remains a key challenge, often leading to mission delays. This paper proposes a [...] Read more.
For complex tasks such as search and recovery in uncharted maritime areas, the use of heterogeneous unmanned swarms (UAVs and USVs) is highly promising, yet effective cross-domain cooperative trajectory planning remains a key challenge, often leading to mission delays. This paper proposes a rapid Cooperative Cross-domain Path Planning framework (CCPP) and its associated algorithm for heterogeneous UAV–USV swarms. The framework first establishes a visual-fusion modeling pipeline, converting visual pose estimation, uncertainties, and semantic dynamic obstacles into a planning representation with robust safety margins and time-varying risk fields. A hybrid velocity-path co-optimization algorithm is then designed to simultaneously generate curvature-feasible trajectories and speed profiles under heterogeneous kinematics and explicit temporal constraints. In the end, an adaptive interpretable decision tree acts as a meta-strategy for online replanning and real-time adjustment of modes and weights. To address the critical issue of uneven arrival time distribution, this paper introduces, inspired by economic inequality analysis, a normalized Gini coefficient-based arrival time consistency index to quantify and optimize coordination timing. Comprehensive experiments validate the effectiveness of the proposed approach in enhancing cooperative efficiency and real-time adaptability. Full article
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26 pages, 5513 KB  
Article
Leader–Follower UAV Formation Control with Cost-Effective Coordination and Pre-Flight Simulation
by Ping-Tse Lin, Ruey-Beei Wu and Shi-Chung Chang
Drones 2026, 10(4), 286; https://doi.org/10.3390/drones10040286 - 14 Apr 2026
Viewed by 870
Abstract
This study presents a leader–follower flight control architecture for a small-scale UAV swarm, demonstrated using a three-UAV system built on heterogeneous autopilots, GPS positioning, Raspberry Pi 3B+ units, and Wi-Fi communication. The follower UAVs autonomously maintain predefined formations while tracking the leader’s trajectory. [...] Read more.
This study presents a leader–follower flight control architecture for a small-scale UAV swarm, demonstrated using a three-UAV system built on heterogeneous autopilots, GPS positioning, Raspberry Pi 3B+ units, and Wi-Fi communication. The follower UAVs autonomously maintain predefined formations while tracking the leader’s trajectory. During flight, each Raspberry Pi establishes inter-UAV communication via a Wi-Fi network using the UDP protocol, enabling real-time data exchange and attitude adjustments. An outer-loop proportional–integral control design implemented on the Raspberry Pi generates corrective commands to the corresponding autopilot to reduce the followers’ position errors. Under the tested conditions, the framework achieves formation tracking with horizontal and vertical errors of approximately 60 and 20 cm, respectively, providing initial experimental validation in a small-scale setting. In addition, a simulation environment based on pre-recorded UAV and environmental data with 3D visualization is developed to support behavior prediction, performance evaluation, and control tuning prior to real-world deployment, although its applicability beyond the tested scenarios remains to be established. Full article
(This article belongs to the Section Drone Communications)
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48 pages, 9242 KB  
Article
Spherical Coordinate System-Based Fusion Path Planning Algorithm for UAVs in Complex Emergency Rescue and Civil Environments
by Xingyi Pan, Xingyu He, Xiaoyue Ren and Duo Qi
Drones 2026, 10(4), 285; https://doi.org/10.3390/drones10040285 - 14 Apr 2026
Viewed by 518
Abstract
This study proposes a heterogeneous fusion path planning framework for unmanned aerial vehicles (UAVs) operating in complex emergency rescue and civil environments. Existing single-mechanism metaheuristics—including Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Genetic Algorithms (GAs)—suffer from fundamental limitations in three-dimensional kinematic [...] Read more.
This study proposes a heterogeneous fusion path planning framework for unmanned aerial vehicles (UAVs) operating in complex emergency rescue and civil environments. Existing single-mechanism metaheuristics—including Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Genetic Algorithms (GAs)—suffer from fundamental limitations in three-dimensional kinematic path planning: PSO converges rapidly but stagnates at local optima due to population variance collapse; ACO offers robust local exploitation but incurs prohibitive cold-start overhead; GAs maintain diversity at the cost of expensive crossover operations. To address these complementary deficiencies simultaneously, the proposed framework introduces a spherical coordinate representation that reduces computational complexity and naturally enforces UAV kinematic constraints, combined with adaptive weight factors and a serial PSO-ACO fusion strategy, and subsequently incorporates adaptive weight factors. A serial fusion strategy is then introduced, wherein the sub-optimal trajectory generated by the Spherical PSO phase is mapped into the ACO pheromone field via a Gaussian Kernel Density Mapping (GKDM) mechanism, enabling the ACO phase to perform fine-grained local exploitation within a kinematically feasible corridor. Various constraints along the flight path are formulated into distinct cost functions, which cover aircraft track length, pitch angle variation, altitude difference variation, obstacle avoidance, and smoothness; the core task of the algorithm is to find the flight path with the minimum total cost. The proposed algorithm is dedicated to UAV path planning in complex emergency rescue environments (disaster-stricken areas, hazardous zones) and is further applicable to civil low-altitude logistics delivery, industrial facility inspection, ecological environment monitoring and urban air mobility (UAM) scenarios with complex obstacle constraints. It can effectively improve the safety and efficiency of UAVs in reaching rescue points, delivering emergency supplies, conducting disaster surveys, and completing various civil low-altitude operation tasks. Full article
(This article belongs to the Section Innovative Urban Mobility)
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24 pages, 13462 KB  
Article
Low-Altitude Mission Test Design for UAV Swarms via Constrained Multi-Objective Optimization
by Yanfei Miao, Haixin Chen, Xu Zhang, Yuan Gao and Qing Cai
Drones 2026, 10(4), 284; https://doi.org/10.3390/drones10040284 - 14 Apr 2026
Viewed by 442
Abstract
This paper studies low-altitude mission test design for UAV swarm ground missions in complex urban environments. Traditional test design workflows depend heavily on expert-crafted rules and static settings, which limits adaptability under dynamic mission conditions. To address this issue, we propose an intelligent [...] Read more.
This paper studies low-altitude mission test design for UAV swarm ground missions in complex urban environments. Traditional test design workflows depend heavily on expert-crafted rules and static settings, which limits adaptability under dynamic mission conditions. To address this issue, we propose an intelligent framework that combines a Multi-Stage Constrained Multi-Objective Optimization algorithm with Proximal Policy Optimization-based adaptive hyperparameter tuning. The framework optimizes resource allocation by balancing mission effectiveness, mission risk, and mission cost under mission constraints. Simulation results show improved convergence behavior, solution quality, and robustness compared with baseline settings. Full article
(This article belongs to the Special Issue Electric-Powered and Hybrid-Powered Unmanned Aerial Vehicle Technology and Applications of Low-Altitude Aviation)
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25 pages, 2513 KB  
Article
YOLO-DAA: Directional Area Attention for Lightweight Tiny Object Detection in Maritime UAV Imagery
by Kuan-Chou Chen, Vinay Malligere Shivanna and Jiun-In Guo
Drones 2026, 10(4), 283; https://doi.org/10.3390/drones10040283 - 14 Apr 2026
Viewed by 853
Abstract
Tiny object detection in maritime Unmanned Aerial Vehicles (UAV) imagery remains challenging due to low-resolution targets, dynamic lighting, and vast water backgrounds that obscure fine spatial cues. This study introduces You Only Look Once – Directional Area Attention (YOLO-DAA), a lightweight yet direction-aware [...] Read more.
Tiny object detection in maritime Unmanned Aerial Vehicles (UAV) imagery remains challenging due to low-resolution targets, dynamic lighting, and vast water backgrounds that obscure fine spatial cues. This study introduces You Only Look Once – Directional Area Attention (YOLO-DAA), a lightweight yet direction-aware detection framework designed to enhance spatial reasoning and feature discrimination for maritime environments. The proposed model integrates two key components: the Spatial Reconstruction Unit (SRU), which dynamically filters redundant activations and reconstructs informative spatial features, and the Directional Area Attention (DAA), which introduces controllable row–column attention to model anisotropic dependencies. Together, they enable the network to capture orientation-sensitive structures such as elongated vessels and vertically aligned swimmers while maintaining real-time efficiency. Experimental results on Common Objects in Context (COCO) and SeaDronesSee datasets demonstrate that YOLO-DAA achieves significant improvements in both precision and recall, outperforming the YOLOv12-turbo baseline across multiple scales. In particular, the lightweight YOLO-DAA-n variant achieves a 12.5% AP95 gain on SeaDronesSee with minimal computational overhead. The findings confirm that directional attention and spatial reconstruction jointly enhance the representation of tiny maritime targets, offering an effective balance between accuracy and efficiency for real-world UAV deployments. Full article
(This article belongs to the Special Issue Advances in Deep Learning for Drones and Its Applications: 2nd Edition)
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29 pages, 10011 KB  
Article
Method for Controlling the Movement of an AUV Follower Based on Visual Information About the Position of the AUV Leader Using Reinforcement Learning Methods
by Evgenii Norenko, Vadim Kramar and Aleksey Kabanov
Drones 2026, 10(4), 282; https://doi.org/10.3390/drones10040282 - 14 Apr 2026
Viewed by 505
Abstract
This paper considers the problem of controlling the motion of an autonomous underwater vehicle (AUV) following a leader in a leader–follower scheme based on visual information about the leader’s position. It is assumed that the leader is equipped with a system of light [...] Read more.
This paper considers the problem of controlling the motion of an autonomous underwater vehicle (AUV) following a leader in a leader–follower scheme based on visual information about the leader’s position. It is assumed that the leader is equipped with a system of light markers with known geometry, and the follower determines its relative position based on data from an onboard camera without using a hydroacoustic communication channel or direct exchange of navigation information. To synthesize the control law, a reinforcement learning method based on the Proximal Policy Optimization algorithm is used. Policy learning is performed in a simulation environment, taking into account the dynamic model of the agent in the horizontal plane and observation noise. A structure of state space, actions, and reward function is proposed, aimed at minimizing the error in relative position and orientation. Additionally, Bayesian optimization of the weight coefficients of the reward function is performed. Bayesian optimization of the reward function weights reduces the RMS tracking error from 0.24 m to 0.09 m and demonstrates that heading regulation has a significantly stronger impact on stability than position penalties. The results of modeling, testing in the Webots environment, and experiments on MiddleAUV class devices confirm the feasibility and scalability of the approach. It is shown that a single trained policy ensures stable formation maintenance when the number of follower agents and initial conditions change without additional retraining. Full article
(This article belongs to the Special Issue Intelligent Cooperative Technologies of UAV Swarm Systems)
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