Drones doi: 10.3390/drones10050370

Authors: Nektarios Fotiou Konstantinos Katzis Stavros Katsaronas Hamed Ahmadi

The rapid integration of UAVs (Unmanned Aerial Platforms) introduces new operational capabilities but also raises critical challenges. This paper presents a quantitative risk assessment approach for evaluating the risks related to drone-assisted systems. The methodology combines established standards with the principles of the multi-criteria hierarchy concept. First, a qualitative analysis is performed to identify and register the required risk elements. Following this, a hierarchical model is developed to model the dependencies between systems’ components, environmental factors, structural limitations, and operational uncertainties. An AHP-based (Analytic Hierarchy Process) process is applied to enable elements quantification. To demonstrate the applicability and feasibility of the proposed methodology, two different drone-assisted systems are examined, showcasing their effectiveness in evaluating critical risk elements and computing cumulative risk contribution to quantify and prioritize potential risk events. The results indicate the significance of the methodology in ranking the verified risk elements and identifying those that made the greatest contribution to system failure. As revealed, power- and weather-related elements are among the most significant contributors to performance deterioration. In addition, operator-related factors significantly contribute to the system’s overall functional performance, especially when it is manually controlled. Finally, a comparative analysis underscores the sensitivity of risk ranking to variations in AHP scoring.

Drones doi: 10.3390/drones10050369

Authors: Van Dung Vu Xuan Sinh Mai Kieu Trang Le Minh Vu Tran Thanh Dong Nguyen

Reliable altitude and vertical speed estimation are fundamental for unmanned aerial vehicle (UAV) autonomous flight, especially during low-altitude operations such as takeoff and landing. Barometric altimeters are widely used due to their low cost, high availability, and good long-term stability, providing smooth altitude trends over a wide operating range. However, barometric measurements are indirectly inferred from static pressure and are therefore sensitive to local airflow disturbances. In particular, rotor downwash and ground effect-induced pressure perturbations near the surface can introduce significant biases and short-term fluctuations in barometric altitude, which propagate into erroneous vertical speed estimates during critical flight phases. Time-of-flight (TOF) altimeters, such as radar or laser sensors, provide direct above-ground-level (AGL) measurements and are largely insensitive to ground effect-related pressure disturbances. Within their limited operational range, TOF altimeters typically offer higher accuracy and lower short-term noise compared with barometric altitude. Nevertheless, TOF sensors are characterized by a restricted valid measurement range and frequently exhibit non-ideal behaviors in real-world UAV operations, including out-of-range outputs, frozen measurements, and in-range biased readings. These anomalies violate the nominal sensor assumptions used in conventional Kalman filter-based fusion and can significantly degrade estimation performance if not properly handled. This paper proposes a hybrid Kalman–rule-based altitude estimation framework that fuses barometric and TOF altitude measurements to exploit their complementary characteristics while mitigating their respective limitations. A vertical dynamic state-space model is formulated to jointly estimate altitude, vertical velocity, accelerometer bias, and ground height offset. A rule-based anomaly detection and classification module is developed to identify multiple TOF altimeter failure modes observed in operational UAV flights. The detected anomaly states are incorporated into the Kalman filter to adaptively weight, accept, or reject TOF measurements, thereby improving robustness against sensor non-idealities. The proposed approach is validated using 39 real UAV flight logs covering diverse flight regimes, including low-altitude maneuvers, cruise, and autonomous landing. Experimental results show that the proposed framework provides more stable and robust altitude and vertical speed estimation under practical sensor anomaly conditions compared with conventional barometer-only and standard Kalman fusion configurations. These results demonstrate the practical effectiveness of the proposed method for fault-aware altitude estimation in UAV autonomous flight.

Drones doi: 10.3390/drones10050368

Authors: Zhonghe He Xiyao Su Li Wang Kailong Li Min Li Xinxin Guo Ruosi Xu Zizheng Gan Shuang Li Kaixuan Zhai

Low-altitude UAV scheduling and planning has become a critical technological pillar in disaster response systems; however, systemic challenges in complex environments and under uncertain risk conditions remain insufficiently understood. Although substantial progress has been achieved in model formulation and algorithm design in recent years, scheduling and planning frameworks still lack a systematic representation of key risk factors, such as meteorological disturbances, terrain damage, and communication constraints, thereby undermining operational safety and decision reliability. This study conducts a systematic review of low-altitude UAV scheduling and planning research over the past decade, covering representative disaster scenarios including forest fires, large building fires, earthquakes, floods, major public health emergencies, and traffic accidents. By comparatively analyzing scheduling objectives and technical pathways across the pre-disaster, during-disaster, and post-disaster stages, this paper summarizes the dominant research paradigms and limitations of multi-UAV coordination, air–ground coordination, and risk reduction-oriented scheduling and planning. This review reveals that existing approaches generally lack explicit modeling of dynamic risks and uncertainties, highlighting an urgent need to incorporate risk-aware considerations and reliability analysis frameworks into scheduling and planning to enhance the overall robustness and decision credibility of UAV systems in disaster environments.

Drones doi: 10.3390/drones10050367

Authors: Jianan Chen Qing Liu Yong Wang Lihui Wang

Low-visibility environments induced by sea fog severely constrain the navigational efficiency and safety in narrow waterways, where traditional radar and Automatic Identification Systems (AIS) frequently encounter challenges such as perception blind spots and information lag. To address this critical issue, this study proposes a UAV-based perception and decision-making methodology for main navigational routes in fog, integrating physical priors with unmanned aerial vehicle (UAV) vision. Firstly, a joint physical dehazing and fog-domain adaptive detection network is constructed. This network addresses the overcomes the interference of non-uniform fog through feature-level enhancement, generating a spatio-temporally continuous visibility field and ship probability grids under a bird’s-eye view (BEV). Subsequently, a quantified “Sailability Score” model is established, providing a scientific basis for the dynamic diversion, speed limitation, and safe distance maintenance of main navigational routes. Simulation-based verifications using real-world fog navigation scenarios in the Qiongzhou Strait, coupled with a joint analysis of Vessel Traffic Service (VTS) and AIS data, suggest that at the critical visibility threshold (≤500 m), the proposed method improves the recall rate of long-distance small target detection by approximately 16.2% and reduces the visibility estimation error by 19.3%. Furthermore, the consistency between the proposed Sailability Score and the actual VTS navigation restriction windows reaches 82.1%, exhibiting a conservative preference for safety (i.e., risk preference ratio γ>1 ). Additionally, by introducing a temporal anti-jitter mechanism (parameterized by a smoothing window Δt), the proposed method extends the navigable time window of the main routes by approximately 12.4% while ensuring navigational safety. The simulation results indicate the framework’s potential perception capabilities and engineering applicability, providing reliable technical support for smart shipping and intelligent VTS systems.

Drones doi: 10.3390/drones10050366

Authors: Han Wang Xiaolong Liang Jiaqiang Zhang Yueqi Hou Aiwu Yang

Target tracking of unmanned aerial vehicle (UAV) swarms remains a significant challenge due to highly maneuverable target swarms and complex environments. To address these challenges, a hierarchical target tracking architecture is proposed, comprising a leader layer and a follower layer. This design reduces task complexity while improving formation adaptability and system scalability. In the leader layer, a distributed time-varying optimization model and a distributed protocol are developed to enable the UAV swarm to track highly maneuverable target swarms in real time. In the follower layer, a control protocol based on an affine transformation is employed to enable adaptive formation control under complex environmental constraints (e.g., threat avoidance). Moreover, the convergence performance of the proposed method is rigorously demonstrated through theoretical analysis. Finally, simulation results validate the convergence, feasibility, and scalability of the proposed method. Comparative simulations further demonstrate the superiority of the proposed method.

Drones doi: 10.3390/drones10050365

Authors: Muhammad Mbarak Mohd Hasanul Alam Mohammed Awad

The rapid proliferation of unmanned aerial vehicles (UAVs) in civilian sectors has generated diverse research spanning platform engineering, application deployment, and regulatory governance. This scoping review systematically maps the current knowledge landscape of civilian UAVs, their applications, and their regulatory frameworks, and aims to serve as initial practical guidance for researchers and practitioners initiating drone-based projects. Following PRISMA-ScR guidelines, a structured three-stream literature search was conducted using Google Scholar, yielding 109 sources published between 2015 and 2025. This review synthesises findings across three domains: (1) technical specifications, including UAV platform configurations, their common applications, their advantages and limitations, electromechanical systems, flight control architectures, and communication technologies, while also providing key guidance on how to choose the appropriate components for a given application; (2) civil applications across eight sectors—delivery logistics, infrastructure inspection, precision agriculture, environmental monitoring, emergency response, waste management, and commercial uses—to provide inspiration as well as to capture important details on drone projects; and (3) regulatory frameworks and ethical considerations governing UAV operations. Analysis reveals concentrated research attention on autonomy and AI-driven control systems and emerging focus on communication infrastructure. Geographic representation is dominated by US, European, and Chinese contexts, with limited coverage of developing regions. Key knowledge gaps include economic feasibility analyses, standardisation frameworks, developing-world deployment contexts, and environmental lifecycle assessments. Contradictions emerge between optimistic application scalability claims and fundamental constraints in energy storage, swarm communication reliability, and privacy–efficiency trade-offs. This review provides researchers and practitioners with a comprehensive map of current UAV knowledge, identifies critical research gaps, and establishes a foundation for future research in civilian drone technologies. This study aims to systematically consolidate and synthesise fragmented research on civilian UAV technologies, applications, and regulatory frameworks into a unified reference for research and practice.

Drones doi: 10.3390/drones10050364

Authors: Ruiqi Wang Yang Liu

Urban last-mile delivery is an increasingly important source of transport-related emissions, yet evidence on low-altitude logistics under real-order demand and urban spatial constraints remains limited. Taking Shanghai as a representative megacity, this study integrates 185,673 real parcel orders with 3D urban spatial data to develop a unified unmanned aerial vehicle (UAV)–courier carbon accounting framework. The framework combines 3D UAV route-planning algorithms, UAV energy-consumption models, electric courier-vehicle energy models, and grid emission factors to compare carbon emissions between UAV and conventional delivery modes. The results show that, under the modeled operating assumptions, UAV delivery tends to provide lower per-delivery carbon emissions under lightweight and high-speed operating conditions. Scenario analysis further suggests that UAV deployment in Shanghai could reduce carbon emissions by approximately 343,300 t CO2 annually by 2030. These findings provide quantitative support for urban low-altitude logistics planning, infrastructure deployment, and policy design for low-carbon last-mile delivery. The framework is transferable to other Chinese cities with similar urban conditions, but the numerical results require local recalibration of parcel demand, urban morphology, airspace constraints, and electricity-related carbon factors.

Drones doi: 10.3390/drones10050363

Authors: Zhuang Liu Dingmeng Chi Jianing Tang Yabin Gao

With the gradual development of science and technology, increasingly complex application environments impose higher requirements on the control performance of quadrotor unmanned aerial vehicles (UAVs). This requires UAVs to achieve high-performance tracking control under various challenging conditions, such as model uncertainties, external disturbances, actuator saturation, and actuator faults. Considering these issues, this paper proposes a novel fixed-time controller. First, to address the external disturbances and model uncertainties that UAVs may encounter during flight, a non-singular fixed-time terminal sliding mode control method is proposed, and a variable exponential fixed-time adaptive sliding mode disturbance observer is introduced to improve the estimation accuracy of the lumped disturbances. Secondly, considering the impact of actuator input saturation, an auxiliary system is constructed to mitigate the actuator saturation problem. Finally, a fixed-time fault-tolerant control scheme with actuator saturation and prescribed performance constraints is investigated for quadrotor UAVs. The convergence performance of the controller is rigorously established based on Lyapunov stability theory. Comparative simulation results are provided to demonstrate the effectiveness of the proposed control strategy.

Drones doi: 10.3390/drones10050362

Authors: Xiwen Zhang Yuan Wang Shanghong Zhao Hang Hu Jianjia Li

The utilization of unmanned aerial vehicle (UAV) relays has significantly improved the availability and reliability of free-space optical (FSO) communication links within airborne communication backhaul networks. This paper proposes an FSO/RF dual-hop backhaul network employing multiple UAV relays and investigates a joint optimization scheme for three-dimensional (3D) trajectories and resource allocation of multiple UAVs. In this scheme, network throughput is maximized by jointly optimizing three variables: the association between the UAVs and the ground stations (GSs), power allocation, and the UAVs’ trajectories. Moreover, to enhance the engineering applicability of this research, we systematically incorporate multi-dimensional practical constraints—including the motion of the AWACS, platform dynamics, information causality, co-channel interference, the influence of weather variations, and multi-UAV collision avoidance. Furthermore, to address this challenging mixed-integer non-convex optimization problem, an iterative algorithm is developed. This algorithm integrates the principles of block coordinate descent with successive convex approximation, thereby alternately optimizing the three variable blocks within each iterative cycle. Numerical simulations confirm that the proposed scheme achieves a substantial throughput improvement in the multi-UAV-assisted FSO/RF hybrid backhaul network in comparison with other benchmark schemes.

Drones doi: 10.3390/drones10050361

Authors: J. de Curtò Mauro Liz I. de Zarzà Carlos T. Calafate

Drone-based smart sensing increasingly relies on Vision–Language Models (VLMs) for real-time scene interpretation, obstacle detection, and autonomous navigation reasoning. Deploying such systems at scale demands not only high perceptual accuracy but also energy efficiency, a critical constraint on battery-powered Unmanned Aerial Vehicle (UAV) platforms, and linguistic flexibility for multinational operational contexts. We present a systematic benchmarking framework that jointly evaluates perception performance and inference energy for five open-source VLMs across thirteen languages spanning six language families, including three low-resource varieties (Arabic, Basque, and Luxembourgish). Using imagery sampled from the Berkeley DeepDrive 10K (BDD10K), each model is evaluated on four sensing tasks of increasing difficulty scored via a sentence-transformer backbone, with energy measured following the AI Energy Score methodology (Wh per 1000 queries) through continuous NVML-based GPU power sampling. Across 65 language–model observations, LLaVA-1.6 achieves the highest perception score (S¯=0.160) while Phi-3-Vision attains the best energy efficiency (66.3 Wh/1000 queries); energy consumption and task accuracy are statistically uncorrelated (Spearman ρ=0.001; p=0.995). A formal UAV inference energy model instantiated for four commercial platforms confirms LLaVA-1.6 as Pareto-optimal on heavy-lift platforms (DJI Matrice 300/350 RTK) and LLaVA-1.5 on the energy-constrained Matrice 30; compact UAVs such as the Mavic 3 Enterprise exceed the budget of all evaluated models at standard query rates. Friedman tests reveal significant cross-language variability in energy demands (χ2=40.43; p=3.5×10−8) and navigation reasoning performance (χ2=13.35; p=0.010). Critically, we document a double penalty for low-resource languages, which simultaneously incur higher inference energy costs and lower task accuracy, with direct implications for equitable multilingual UAV deployments.

Drones doi: 10.3390/drones10050360

Authors: Qing Liang Yujie Yang Shihao Liang Hui Li

Multi-UAV cooperative encirclement in pursuit–evasion scenarios requires effective coordination under dynamic inter-agent interactions, sparse task feedback, and obstacle-constrained motion. While MADDPG offers a practical CTDE framework for multi-agent continuous control, its direct application to cooperative encirclement still faces challenges in modeling time-varying teammate dependencies, selecting informative replay samples, and maintaining stable learning under delayed rewards. To address these challenges, we propose SA-DSM-MADDPG, an enhanced multi-agent deep deterministic policy gradient method that integrates the following: (i) a self-attention critic to model dynamic inter-agent relevance, (ii) a double-screened experience replay strategy combining prioritized sampling and relevance screening to improve replay quality, and (iii) curriculum learning with staged reward shaping to provide denser and more stable training signals. We evaluate the proposed method in 3v1 cooperative encirclement environments with static obstacles and varying initial conditions. Experimental results show that SA-DSM-MADDPG improves the success rate by approximately 22 percentage points over MADDPG and 35 percentage points over MAPPO, while also exhibiting faster convergence and better training stability.

Drones doi: 10.3390/drones10050359

Authors: Ryan P. Case Joseph P. Hupy

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Drones doi: 10.3390/drones10050358

Authors: Israel Kolaïgué Bayaola Jean Louis Ebongué Kedieng Fendji Blaise Omer Yenke Marcellin Atemkeng Christiana Ibidun Obagbuwa

The growing deployment of unmanned aerial vehicles (UAVs) in energy-constrained applications has highlighted the need for appropriate energy consumption models. However, selecting between physics-based (white-box) and data-driven (black-box) modeling paradigms remains a largely implicit process. Researchers often navigate undocumented trade-offs among required predictive accuracy, empirical data availability, and access to aerodynamic testing infrastructure without a formalized structure. This study proposes a two-stage decision-making framework to formalize UAV energy model selection. In the first stage, a qualitative decision tree is inductively derived from a corpus of 23 recent studies, explicitly mapping infrastructural and informational constraints to five distinct modeling regimes. In the second stage, the Analytic Hierarchy Process (AHP) is applied to quantitatively evaluate the feasible alternatives based on context-specific criteria: accuracy, interpretability, development cost, and customization adaptability. The structural logic of the framework is evaluated against an independent set of 24 holdout studies, demonstrating a high degree of consistency between the framework’s recommendations and the methodologies employed in the literature. Furthermore, the quantitative AHP scoring introduces “fallback flexibility,” enabling researchers to mathematically identify alternative modeling strategies when primary experimental conditions are compromised. Supported by an open-source Python graphical interface, this framework aims to reduce methodological ambiguity and support more structured, reproducible model selection in UAV energy research.

Drones doi: 10.3390/drones10050357

Authors: Tahar Bendouma Saida Sarra Boudouh Chaker Abdelaziz Kerrache Jorge Herrera-Tapia

Unmanned Aerial Vehicles (UAVs) have become a key technology in precision agriculture, enabling efficient monitoring, inspection, and targeted interventions. However, effective UAV path planning in such environments requires the generation of safe, energy-efficient, and smooth trajectories in complex three-dimensional spaces. This paper proposes an Adaptive Q-Learning Guided Gorilla Troops Optimizer (AQGTO) for 3D UAV path planning. The proposed method integrates a state-aware Q-learning mechanism into the Gorilla Troops Optimizer (GTO), enabling the optimizer to adaptively select exploration, exploitation, and diversification strategies according to the current optimization state. A multi-objective cost function is formulated to simultaneously minimize path length, an energy-related surrogate cost, obstacle proximity, path smoothness, and altitude variation. In addition, a feasibility repair mechanism is introduced to ensure collision-free trajectories in environments with cylindrical obstacles. The proposed approach is evaluated in three representative agricultural scenarios: row-crop fields, orchard environments, and hilly terrains. Experimental results show that AQGTO achieves competitive and improved performance compared with classical A*, Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and the original GTO in terms of trajectory cost, path efficiency, and stability. Furthermore, an ablation study confirms that the integration of Q-learning significantly enhances optimization performance. These results suggest that AQGTO provides an effective and robust solution for UAV path planning in complex agricultural environments.

Drones doi: 10.3390/drones10050356

Authors: Jiayong Li Yifan Xia

Multi-UAV coverage planning in complex mountainous environments is often constrained by idealized energy modeling, the “wood barrel effect” of traditional global energy minimization paradigms, and a lack of dynamic fault tolerance. To address these limitations, this study proposes a survivability-oriented Min–Max optimization architecture driven by the novel Lévy–Flight Terrain-Following Constrained Planning Optimization (LF-TF-CPO) algorithm. Coupling a high-fidelity 3D topographical matrix with a nonlinear aerodynamic energy model, the framework prioritizes individual UAV safety. Monte Carlo simulations demonstrate that LF-TF-CPO compresses the average maximum individual energy consumption to 665.64 kJ, preserving an adequate operational margin below the 950 kJ physical redline to absorb unmodeled aerodynamic perturbations while ensuring a 31.30 min mission duration. Ablation studies verify the Min–Max objective mitigates localized overloads with a marginal 0.4% energy trade-off. Furthermore, an emergency recovery protocol validates dynamic resilience across simultaneous and cascading failures by consistently stabilizing post-failure peak loads within safe margins. Notably, statistical evaluations establish a robust empirical sweet spot (<!-- MathType@Translator@5@5@MathML2 (no namespace).tdl@MathML 2.0 (no namespace)@ -->

Drones doi: 10.3390/drones10050355

Authors: Pinglu Wang Jiahao Li Jiahua Wei Lei Shi Bei Hou Fei Xie

Timely situational awareness is essential in disaster management but normal Unmanned Aerial Vehicle (UAV) flight cannot take place when the Global Positioning System (GPS) signals are blocked or jammed. This paper addresses the issue of swarm cohesion and localization in these hostile conditions. We present a Cooperative Swarm-Mesh Network (CSMN), a hybrid structure that can alternate between an implicit Silent Mode and an explicit Leader–Follower mode based on distributed Extended Kalman Filters (DEKFs) in the face of communication failures. The system takes advantage of convex polygon decomposition to optimize the coverage in the area. The use of simulation studies with NS-3 and ROS has shown that the proposed framework can retain sub-meter localization error (RMSE < 0.9 m) in GPS-denied environments and provide 92% coverage of the area, which is 35% higher than the coverage with other baseline approaches. Within the simulated conditions evaluated using Gazebo/NS-3, sensor drift and network vulnerability are effectively addressed by the CSMN framework. These simulation-based results offer a promising blueprint for autonomous disaster evaluation, pending hardware-in-the-loop and field validation. Validation is conducted across two qualitatively distinct simulated environments: dense urban rubble and a sparse open field. Performance advantages generalise beyond a single test configuration, with mean localization RMSE remaining below 0.85 m in both scenarios.

Drones doi: 10.3390/drones10050354

Authors: Ziming Liu Yixin Wang Jialiang Weng Gan Shi Hua Chen

Bio-inspired flapping-wing micro air vehicles (FWMAVs) are a research hotspot in micro air vehicles due to their high maneuverability and hovering capabilities. Accurate motion damping modeling is a prerequisite for their attitude disturbance rejection and control law design. Addressing the key issues in existing research—namely, the low computational efficiency of high-fidelity flexible-wing aerodynamic simulations and the inability of efficient rigid-wing assumptions to capture dynamic deformation of flexible wings—this paper investigates motion damping modeling for FWMAVs and its application to lateral flight stability analysis. First, an aerodynamic damping model under lateral motion parameters is established by approximating the flexible-wing surface using the spatial topology of the spar and veins. Second, numerical simulations of the flapping trajectory and motion damping are conducted. Subsequently, the validity and reliability of the model are verified through wind tunnel and turntable experiments. Finally, leveraging this model, lateral flight dynamics equations are derived to perform lateral stability analysis. The results effectively address the gap in assessing flapping-induced aerodynamic damping for flexible wings, providing an accurate analytical damping model, an efficient simulation framework, and an effective open-loop dynamics assessment method for the rapid design iteration and control algorithm development of FWMAVs.

Drones doi: 10.3390/drones10050353

Authors: Antonio Palacio-Hurtado Rocío Muñoz-Mansilla José Manuel Díaz Sebastián Dormido

Unmanned aerial vehicles (UAVs) have two predominant configurations: fixed-wing, efficient in cruise flight and with long endurance, but dependent on runways or large areas for operation; and multirotor, capable of vertical take-offs and landings and precise maneuvers, although limited by their shorter range and efficiency. Hybrid VTOL UAVs, and especially QuadPlane UAVs, offer an intermediate solution, combining the aerodynamic efficiency of the fixed-wing UAV with the maneuverability of the multirotor through simple and versatile architecture. This work develops a complete and unified dynamic model of the Skywalker VTOL UAV, derived from the Skywalker X8 with the addition of four vertical rotors. The resulting dynamic model is formulated using a system of nonlinear ODEs, which realistically and comprehensively describe the vehicle behaviour, considering that the inputs from the airplane and quadcopter sections can act simultaneously, thus improving its efficiency. Implementation in MATLAB-Simulink and validation through simulations under equilibrium conditions and with varying inputs confirm the expected behaviour of a QuadPlane. This model provides a strong foundation for developing advanced multivariable control, guidance, and navigation strategies for next-generation hybrid UAVs.

Drones doi: 10.3390/drones10050351

Authors: Nathan Johnson Sima Shafaei Andrew Karem Sayani Sarkar

Effective mission planning, path search, and path following are critical for unmanned aerial vehicles (UAVs) operating in complex, dynamic, and resource-constrained environments. Classical path planning approaches, including graph-based search, sampling-based methods, and trajectory optimization, provide structured solutions with performance guarantees but often exhibit limited adaptability to uncertainty, environmental disturbances, and evolving mission constraints. Reinforcement learning (RL) offers a complementary capability by enabling adaptive decision-making and online response to dynamic obstacles and partial observability. This paper examines UAV path planning and navigation within a Risk-Calibrated, Certifiably Safe, and Resource-Aware (RCSR) framework, with emphasis on its implications for mission planning, path search, and path following. Classical planning techniques are reviewed alongside recent advances in RL-based navigation for single-UAV and multi-UAV systems. Particular attention is given to safe reinforcement learning, constrained optimization, and runtime assurance mechanisms that address safety, regulatory compliance, and resource limitations in real-world deployments. Through a comparative analysis of classical, learning-based, and hybrid planning architectures, this work highlights key trade-offs among adaptability, safety, computational cost, and energy efficiency. The paper concludes by identifying hybrid learning–planning approaches as a practical direction for scalable, reliable, and deployable UAV mission planning systems.

Drones doi: 10.3390/drones10050352

Authors: Eleftherios Nikolaou Spyridon Kilimtzidis Panagiota Kelverkloglou Vaios Lappas Vassilis Kostopoulos

This study presents an aerostructural optimization framework for the preliminary design of a low-Reynolds-number composite UAV wing, aiming to simultaneously enhance aerodynamic efficiency and structural performance. While previous work has primarily addressed aerodynamic optimization in isolation, the present approach integrates Computational Fluid Dynamics (CFD) and Finite Element Method (FEM) analyses within a surrogate-based optimization (SBO) framework. The design space includes both aerodynamic parameters—aspect ratio, taper ratio, sweep angle, and twist—and structural variables related to the internal wing layout and component thicknesses. To reduce the computational cost associated with high-fidelity simulations, Kriging surrogate models are employed in conjunction with an Expected Improvement (EI) infill strategy, enabling efficient exploration of the coupled design space. The framework is evaluated through multiple independent optimization runs using different initial sampling strategies, demonstrating consistent convergence toward feasible high-performance designs. The surrogate models exhibit strong predictive capability, as confirmed by Root Mean Square Error (RMSE) and Leave-One-Out (LOO) cross-validation metrics. The results indicate that aerodynamic variables, particularly aspect ratio and twist, are the primary drivers of range performance. However, structural variables—most notably skin thickness—strongly influence constraint satisfaction, especially with respect to buckling and strength requirements, and therefore play a key role in defining the feasible design space. The optimal configuration achieves a maximum range of approximately 203 km while satisfying all strength, stiffness, and aerodynamic constraints. Overall, the proposed methodology provides an efficient and robust tool for the early-stage aerostructural design of low-Reynolds-number UAV wings.

Drones doi: 10.3390/drones10050350

Authors: Qing Cheng Yan Jiang Yuan Gao Yun Qiu Yutao Tang Xiaoguang Tu

In the task of unmanned aerial vehicle UAV-based small object detection within complex aerial scenes, objects are characterized by significant scale variation, extremely low pixel occupancy, and dense distribution. These factors severely limit the feature representation capability and fine-grained information modeling of detectors, leading to frequent false positives and missed detections. Multi-modal image fusion, which leverages complementary information from different sensing modalities, is widely regarded as an effective approach to enhance detection performance. To improve the accuracy and robustness of object detection in aerial scenes, this paper proposes YOLO-CH, a multi-modal fusion detection method based on a dual-stream YOLOv11 architecture. The method develops parallel dual-stream feature extraction branches to encode modality-specific features from visible and infrared images. A Cross-modal Feature Transformer (CFT) module is introduced within the backbone network by step, which employs a self-attention mechanism to model intra-modal and inter-modal global dependencies, achieving deep feature interaction and enhanced representation. Furthermore, to mitigate the issue where multi-scale and small object features are susceptible to background interference, we redesigned and optimized the structure of the neck to form a high-level semantic screening feature pyramid network (High-level Screening Feature Pyramid Network, HSFPN). This module utilizes high-level semantic information in a top-down manner to refine low-level detail representations, thereby improving small object discrimination. Experimental results on the VEDAI and AVMS datasets demonstrate that the proposed method outperforms baseline models in both detection accuracy and robustness, while maintaining strong adaptability and performance across different input scales.

Drones doi: 10.3390/drones10050349

Authors: Lujing Chao Caihui Wang Dongzhu Feng Pei Dai

Prediction of the reachable domain for high-threat unmanned aerial vehicles (UAVs) is critical for enabling cross-domain flight vehicles to perform proactive avoidance maneuvers. To address this challenge, this paper proposes a novel generic framework that integrates a Radau pseudospectral method (RPM) with a BP neural network, supported by information acquired from satellites. The framework begins by estimating a preliminary state vector of the non-cooperative target, including its coarse position and velocity, via a Newton iterative algorithm. To refine this initial estimate and enable continuous tracking, an Extended Kalman Filter (EKF) is fused with a flight vehicle dynamics model. Subsequently, the RPM is employed to solve the trajectory planning problem, generating a comprehensive database for offline training. This database is then used to train a multilayer feedforward neural network within an offline training and online application framework, which drastically reduces computational complexity and time. Finally, numerical simulations demonstrate the method’s high prediction accuracy and strong robustness against tracking uncertainties. Crucially, the neural network predicts the reachable domain in just 0.01 s, making it highly viable for real-time online applications.

Drones doi: 10.3390/drones10050348

Authors: Gabriel Petre Badea Daniel Eugeniu Crunteanu Mădălin Dombrovschi

This study presents a flight-envelope-based methodology for aerodynamic load assessment and composite material selection applied to a hybrid fixed-wing tri-rotor VTOL (Vertical Take-Off and Landing) unmanned aerial vehicle (UAV). A certification-oriented maneuver and gust envelope was established to define the critical load cases. Reynolds-averaged Navier–Stokes (RANS) simulations of the full aircraft at nominal cruise were performed to determine global aerodynamic coefficients and distributed pressure fields, including interference effects from the fuselage and externally mounted VTOL system. A complementary wing-only angle-of-attack study was used to characterize lift, drag, and chordwise pressure distributions over the relevant incidence range. Critical envelope points were mapped to equivalent aerodynamic states in terms of lift coefficient and angle of attack, enabling a quasi-steady correlation between certification loads and CFD (Computational Fluid Dynamics) results. In parallel, carbon fiber-reinforced polymer (CFRP) laminates were experimentally evaluated under tensile, open-hole tensile, and flexural loading. The results indicate that, within the two investigated laminate configurations, the [0°/90°] CFRP laminate provides the more suitable strength and stiffness for primary wing structures, while off-axis laminates are better suited for secondary regions. The proposed workflow links flight-envelope definition, aerodynamic analysis, and material selection, providing a basis for preliminary structural design.

Drones doi: 10.3390/drones10050347

Authors: Shuhui Zhang Deqi Chen Wenhui Zhang Shuaiwen Mao

To address the issue of traffic accidents caused by livestock crossing roads on grassland highways, this paper proposes an adaptive cruise control method for unmanned aerial vehicles (UAVs) based on an ensemble Q-network and a Soft Actor-Critic (SAC) with delayed policy updates, namely the ED-SAC algorithm. Building upon the standard SAC framework, this method introduces multiple independent Critic networks to form an ensemble Q-network, and employs a random subset minimization strategy during the calculation of target Q-values to mitigate policy bias resulting from overestimated values; simultaneously, a delayed policy update mechanism decouples the optimization processes of the Actor and Critic networks, thereby enhancing training stability and control robustness. Using the PyBullet simulation platform, this paper constructs a UAV inspection scenario on grassland roads and designs three typical test tasks: infinite loop, grid scan and spiral trajectories, to conduct comparative validation of the PPO, TD3, SAC and ED-SAC algorithms. Experimental results demonstrate that, under disturbance-free conditions, ED-SAC achieves the highest mission success rate and the lowest tracking error across all three trajectory scenarios, with an average tracking error as low as 0.27 m and a mission success rate as high as 98.7%. Under continuous random external disturbances, ED-SAC still maintains high trajectory tracking accuracy and attitude control stability, with a mission success rate reaching up to 96.2%. The results demonstrate that the proposed ED-SAC algorithm can effectively enhance the trajectory tracking accuracy, training stability and anti-disturbance capability of UAVs in complex grassland road inspection scenarios, providing a reliable intelligent control method for active grassland road inspection and traffic safety early warning.

Drones doi: 10.3390/drones10050346

Authors: Ju Xiao Hanchen Qiu Yukun Zhou Rui Wang Peng Liu

Reliable autonomous navigation for Tianwen-3-class Mars rotorcraft must satisfy both sampling-level accuracy and hard real-time execution under severe onboard computational constraints. To address this challenge, we develop MarsBird-VII, a mission-constrained stereo visual–inertial navigation system that combines a computation-aware vision front-end with a Parity-Window sliding-window optimization back-end. The front-end decouples high-rate tracking from feature replenishment to bound perception latency, while the back-end alternates updates over interleaved state subsets and preserves full-window coupling through unified marginalization. Unlike simply reducing the sliding-window size, the proposed strategy reduces the per-update optimization cost without shrinking the geometric observation horizon, thereby improving the accuracy–runtime trade-off for embedded avionics. Earth-analog flight experiments demonstrate strong navigation performance under mission-relevant conditions. In full-sequence evaluation, the proposed system achieves an SE(3)-aligned translation APE of 0.31 m RMSE/0.47 m Max and further reaches 0.06 m RMSE/0.15 m Max on a nominal stable segment. Runtime profiling over 5000+ update cycles shows that the Parity-Window back-end keeps the maximum optimization latency below 58.32 ms, satisfying the 66.7 ms hard real-time deadline while maintaining accuracy close to full-window optimization. These results show that the proposed system provides a practical balance of accuracy, robustness, and deterministic real-time performance for Tianwen-3-class Mars rotorcraft navigation.

Drones doi: 10.3390/drones10050345

Authors: Yupeng Di Chuntao Li Junfeng Chen Zikang Su Xiang Wu Xuebing Li Changhui Jiang

Autonomous flight of UAVs in unmapped environments remains a hot topic in research. While vision- and LiDAR-based autonomy has advanced rapidly for multirotor platforms, research on low-altitude autonomous flight for fixed-wing UAVs is still limited. To address obstacle avoidance in unmapped and complex 3D environments for fixed-wing UAVs, this paper proposes a neural network named FW-MonoAvoid-Net. This network is an end-to-end monocular-vision-based framework for fixed-wing UAVs to accomplish environment awareness and obstacle avoidance. Firstly, we develop a simulation framework to collect safe flight-trajectory data across diverse flight conditions, which is employed to train the proposed FW-MonoAvoid-Net. The proposed flight-learning framework adopts a lightweight visual perception network and a kinematics-aware state fusion module to predict the trajectory. At the output stage, an explicit physics-constrained decoder is embedded to strictly confine predicted trajectories within the feasible flight envelope of fixed-wing UAVs. Experiments are conducted in aerial (simulated spherical obstacles), canyon, urban (simulated high buildings), and New York Manhattan city scenarios. In the Manhattan city test, it achieves an 82.26% success rate at approximately 105 m flight altitude and 20 m/s cruise speed. Manhattan city is not included in the training datasets, FW-MonoAvoid-Net did not “see” similar scenarios in the training process. The testing results demonstrate its strong generalization and zero-shot deployment capability across unmapped environments within the simulation setting. FW-MonoAvoid-Net is a “smaller” model which has fewer than 3 million parameters. A Jetson Orin NX 16GB platform was employed to test the computation efficiency, and the trajectory-generation module reaches an average inference latency of 5.21 ms (about 190 Hz). These results show that, under limited computation and power consumption, the proposed method maintains both high success rates and excellent real-time performance. FW-MonoAvoid-Net is expected to help develop a fully autonomous fixed-wing flight in unmapped 3D spaces.

Drones doi: 10.3390/drones10050344

Authors: Selma Kallil Sofiane Tahraoui

Modern applications are increasingly built around networking, collaboration, and automation. Drones, or Unmanned Aerial Vehicles (UAVs), are a key part of this shift. Many complex missions require multiple UAVs to work together as a team, which means deciding how to group them efficiently is a real optimization challenge. This paper introduces EPICEAg (Enhanced Preference-Inspired Co-Evolutionary Algorithm with goal vectors), a new algorithm for forming optimal UAV teams, called coalitions. EPICEAg builds on an existing algorithm called PICEAg but adds three important improvements: it uses k-medoids clustering through the Partitioning Around Medoids (PAM) algorithm for more reliable team leader selection, and applies two advanced sorting methods—shift-based density estimation and epsilon-ranking—to manage the complexity of the search. The algorithm optimizes seven goals at once: how well tasks are completed, how efficiently resources are used, how reliable the team and its communications are, how trustworthy the individual drones are, and how much energy they have left. Tests across several mission scenarios show that EPICEAg consistently performs better than PICEAg, NSGA-II, and MOPSO.

Drones doi: 10.3390/drones10050343

Authors: Jiahao Yan Zheng Wang Shuoxin Liu Huizi Liu Chaojie Zhang Binhao Wang Fengrong Sun Zhuoqun Shen Qian Liu Jingjing Xu

In persistent patrol and online task discovery in environments with obstacles, unmanned aerial vehicle (UAV) swarms are constrained by limited battery capacity and frequent recharging disrupts patrol continuity. In comparison, unmanned ground vehicle (UGV) fleets have higher endurance and payload capacity and can serve as mobile charging platforms while executing ground-service tasks. In such collaborative scenarios, UAVs patrol along a coverage path and discover tasks online, whereas UGVs execute discovered ground tasks and provide mobile charging support. To cope with rendezvous uncertainty due to obstacle-induced detours and inefficient usage of UGV time during charging, this study proposes an energy-constrained UAV-UGV coordination framework based on adaptive anticipatory rendezvous and time-window scheduling. In particular, the adaptive anticipatory rendezvous module handles anticipatory rendezvous planning, while the time-window scheduling module models the post-rendezvous charging stage as a schedulable time window for opportunistic ground-task insertion. Simulations demonstrate that the proposed framework consistently reduces system energy consumption, completion time, and the number of emergency landings compared with three representative baselines. Moreover, a UAV-UGV prototype with AprilTag-based visual landing and post-landing mechanical correction is developed to validate the engineering feasibility of the key closed-loop process.

Drones doi: 10.3390/drones10050342

Authors: Yang Chen Jinglong Niu

Multi-unmanned aerial vehicle (UAV) platforms integrate radio-frequency (RF) sensing, datalinks, and onboard embedded compute; adversarial electronic warfare (EW) degrades these subsystems through jamming and forces decentralized control policies to act on fragmented observations—a setting aligned with intelligent electronic systems and autonomous robotics in contested spectrum. Cooperative swarms then face two compounding failure modes: loss of coherent situational awareness, and reward-driven passive survival that suppresses mission completion. Memory-based multi-agent reinforcement learning (MARL) partially addresses the first but tends to reinforce the second; dense intent shaping addresses the second but becomes unreliable when observations are incomplete. We propose CIDA (Cognitive–Intent Dual-Stream Architecture), a reinforcement learning framework that decouples belief reconstruction from tactical intent at the representation level while coupling them through a unified actor–critic update. The cognitive stream encodes a 64-step observation history with a pre-normalized Transformer to reconstruct threat belief; the intent stream supplies a hierarchical potential field (reconnaissance, threat-weighted engagement, and approach incentives). A steady-state training mechanism (dynamic reward scaling and adaptive gradient clipping) stabilizes Transformer-based on-policy learning under non-stationary multi-agent dynamics. In a complex terrain scenario with SAM, AAA, and jammer assets, CIDA reaches 96.15% task success versus 12.21% (memoryless PPO) and 25.28% (MAPPO+RNN), with ablations showing nonlinear coupling and emergent tactics such as jammer bypass and weak-sector traversal. Results are robust to a four-fold sweep of the intent-shaping weight (above 90% success).

Drones doi: 10.3390/drones10050341

Authors: Haoyu Zhao Guoqing Zhang Heye Xiao Jiqiang Li

This paper presents a robust adaptive fault-tolerant control (FTC) strategy for the path-following maneuvering of a high-speed unmanned aerial vehicle (UAV) formation system. The designation integrates an actuator gain-adaptive mechanism which is capable of compensating partial loss of effectiveness and bias faults, with a cascaded event-triggered mechanism (ETM) that regulates both control-command updates and adaptation loops. To handle strong coupling and modeling uncertainties in the UAV dynamics, unknown nonlinear terms are approximated using a fuzzy logic system (FLS), and dynamic surface control (DSC) is employed to avoid differential explosion. A boundary-regulated intermediate control term further enhances robustness against time-varying gains. The cascaded ETM reduces communication and computation by enforcing update thresholds on control inputs and parameter-update signals. Lyapunov analysis establishes semi-global uniform ultimate boundedness of all closed-loop signals. Comparative simulations indicate improved tracking accuracy and reduced channel load relative to representative baselines.

Drones doi: 10.3390/drones10050340

Authors: Tengfei Yang Ziwen Zhang Guoqiang Tang Yan Yang Qiang Zhao Hao Wang Minyi Xu Shuai Li

Conventional cooperative control methods for multi-AUV systems typically rely on quasi-steady hydrodynamic assumptions and do not explicitly account for time-varying uncertainties in ocean dynamics. In addition, controller parameters are often tuned empirically. As a result, under complex disturbed flow fields and communication constraints, AUV swarms are prone to group fragmentation and reduced polarization, which undermines stable cooperative navigation. To address these limitations, we propose a double-gyre-flow-optimized autonomous underwater vehicle swarm (DGF-OAS) model for coordinated operations in time-varying flow fields. The proposed model incorporates a heading-aware graph attention mechanism to adaptively adjust adjacency weights among agents with different roles. It further integrates the Lennard–Jones potential to preserve safe inter-vehicle spacing and embeds a periodically varying double-gyre flow field to characterize ocean disturbances. Bayesian optimization is then employed to automatically identify suitable weights for the alignment and attraction–repulsion terms, thereby improving swarm cohesion and environmental adaptability. Simulation results demonstrate that, under flow-field disturbances, DGF-OAS achieves group polarization of up to 96%, reduces the average task completion time by 15.84% compared with the baseline model, and attains a task completion rate of 97%, significantly outperforming the compared methods. These findings indicate that the proposed approach exhibits strong adaptability and stability in complex environments and offers an effective solution for AUV swarm control.

Drones doi: 10.3390/drones10050339

Authors: Yao Jiang Qiang Miao Dewei Wu Jing He Chenhao Zhao

Reliable navigation is critical for UAVs operating in GNSS-denied environments, where conventional Inertial Navigation System/Global Navigation Satellite System (INS/GNSS) integrated navigation struggles to meet the requirements of high-reliability and long-endurance missions. As a passive and autonomous approach, terrain-aided navigation (TAN) offers strong concealment and a high degree of autonomy. However, most existing TAN methods rely on handcrafted features, which limit their ability to fully exploit multi-level terrain information, while sensitivity to elevation noise and attitude variations further degrades matching accuracy and robustness. To address these issues, this paper proposes a GNSS-denied UAV terrain matching navigation method based on an autoencoder network with contrastive learning. A Global–Local Dual-branch Feature Extraction Network (GL-DualNet) is designed to combine the local detail extraction capability of CNNs with the global dependency modeling ability of the Swin Transformer, enabling effective multi-scale terrain representation. In addition, an Autoencoder Contrastive Learning Model (ACLM) is developed to jointly optimize reconstruction and contrastive objectives, enabling unsupervised learning of terrain features with improved discriminability and robustness against noise and rotational disturbances. Experiments on a public terrain dataset show that the proposed method outperforms conventional terrain matching approaches under different noise levels, rotational disturbances, and search ranges, demonstrating its effectiveness and robustness for UAV navigation in complex GNSS-denied environments.

Drones doi: 10.3390/drones10050338

Authors: Yu Deng Yucong Hu Yun Ye Pengpeng Xu

The densely distributed, scale-varying objects in unmanned aerial vehicle (UAV) images, together with their dynamic, diverse, and unconstrained backgrounds, make conventional detection methods prone to missed detections, false alarms, and localization biases. To improve UAV vision tasks, we propose AD-YOLO, a unified method tailored for small object detection in traffic-dense settings. First, a module combining an adaptive rotation convolution unit and grouped directional attention with mixed-kernel features is introduced to enhance the model’s orientation invariance and multi-scale discrimination. Then, a dual-path collaborative feature pyramid network is proposed to jointly refine the model’s semantic and spatial details via a multi-directional context aggregation path and a hierarchical semantic progressive fusion path. Last, a hierarchically dense reparameterized large-kernel module is designed to produce broader receptive fields with reduced computational complexity. Extensive experiments on the VisDrone2019 and UAVDT datasets demonstrate that AD-YOLO outperforms state-of-the-art methods in detection accuracy while maintaining favorable computational efficiency.

Drones doi: 10.3390/drones10050337

Authors: Jianying Pang Xuedong Liang Zhentang Liang

This study aims to improve the accuracy of cruise-phase power consumption prediction for multirotor unmanned aerial vehicles operating under varying wind conditions. Existing parametric energy models typically retain the wind velocity vector in the ground or inertial reference frame, and this representation does not distinguish between axial drag contributions along the fuselage and lateral attitude-correction contributions perpendicular to it. The proposed framework addresses this limitation through a physics-informed coordinate transformation that projects the measured wind vector into the body frame of the aircraft using quaternion-derived heading angles, yielding separate axial and lateral wind components. These components enter the power model as two additional predictors that augment the induced-power baseline, with the axial term following a cubic airspeed–power relationship consistent with parasitic drag formulations and the lateral term following a quadratic relationship consistent with attitude-correction mechanics. The framework is validated on a publicly available flight dataset, which comprises 188 flights of a DJI Matrice 100 quadcopter across payloads of 0 to 0.75 kg, ground speeds of 4 to 12 m/s, and altitudes of 25 to 100 m. Compared with the induced-power baseline, the proposed model reduces the root mean square error by 15.9% and the mean squared error by 29.7% during the cruise phase. The improvement is larger when wind speeds exceed 6 m/s, a regime in which the baseline residuals increase while the proposed model retains a comparatively stable error profile. Residual analysis indicates that baseline errors follow an approximately quadratic trend relative to the axial and lateral wind components, consistent with established parasitic-power and attitude-correction formulations. The closed-form structure of the proposed model is compatible with onboard execution on flight controllers, which suggests a feasible pathway toward its use as the power-prediction module within downstream range-estimation and energy-reserve sizing routines.

Drones doi: 10.3390/drones10050336

Authors: Jonathan Javier Loor-Duque Santiago Castro-Arias Juan Pablo Astudillo León Clayanela J. Zambrano-Caicedo Iván Galo Reyes-Chacón Paulina Vizcaíno Leticia Lemus Cárdenas Manuel Eugenio Morocho-Cayamcela

Flying Ad Hoc Networks (FANETs) composed of multiple unmanned aerial vehicles (UAVs) are a promising solution for emergency wireless communications when terrestrial infrastructure is unavailable. However, ensuring reliable Quality of Service (QoS) in these highly dynamic networks remains challenging due to topology changes, varying propagation conditions, and congestion. This work proposes a lightweight machine learning-based QoS optimization framework for multi-UAV emergency communications that combines realistic mobility modeling, empirical channel measurements, and adaptive traffic prioritization. UAV mobility patterns are generated with ArduSim, while LoS/NLoS propagation models are derived from real UAV flight experiments and integrated into ns-3. Multiple supervised machine learning algorithms—including Decision Trees, Random Forest, Support Vector Machines, k-NN, Gradient Boosting, and CatBoost—are trained using four input features derived from the network state: CBRsrc, QPsrc, CBRdst, and QPdst. Simulation results show that the proposed AI SMOTE EMERGENCY scheme, based on CatBoost, improves the Packet Delivery Ratio (PDR) by approximately 43% over the No-QoS baseline, achieving 89–93% delivery across all four application ports. Compared with EDCA, the proposed scheme maintains reliable delivery for all services, increases emergency throughput by 34–36%, and reduces end-to-end delay by about 70%. In addition, the higher delivery reliability translates into clear communication energy benefits, reducing energy waste across all evaluated topologies when compared with the No-QoS baseline. The inference time remains below 0.002 s, supporting real-time QoS adaptation in resource-constrained UAV networks.

Drones doi: 10.3390/drones10050335

Authors: Liru Qin Weijun Pan Qinyue He Ying Liu Yang Shi

The large-scale operation of multiple fixed-wing unmanned aerial vehicles (UAVs) in shared airspace requires efficient flight conflict detection and resolution to ensure aviation safety. However, existing research predominantly lacks collaborative optimization of multi-dimensional maneuver recommendations and struggles with dynamic priority allocation in complex multi-UAV scenarios, leaving a critical gap in the field. To bridge this gap, this paper proposes a Complex Network-Based Multi-UAV Conflict Resolution (NCR) method, which first constructs a three-dimensional (3D) flight conflict detection and resolution model for fixed-wing UAVs. The core innovation lies in mapping dynamic multi-UAV conflict scenarios into a flight conflict network, where UAVs serve as nodes and conflict urgencies act as edge weights. By calculating network and node robustness, the method accurately identifies key UAVs requiring immediate maneuver. Subsequently, taking the minimum variation in the velocity vector as the core objective, NCR iteratively searches for optimal resolution recommendations for these key UAVs using an improved fitness function until the conflict network collapses. Simulation and comparative experiments in 3D airspace, including evaluations against serial-based resolution, random-recommendation resolution, and a classical reactive baseline, demonstrate that NCR efficiently resolves multi-UAV conflicts with minimal trajectory deviations and fewer maneuvering UAVs. Furthermore, a macro-micro bi-level validation architecture based on a six-degree-of-freedom (6-DOF) aerodynamic platform is introduced to verify the physical executability of the proposed strategies. Results demonstrate that by incorporating a dynamic aerodynamic compensation margin, the inevitable trajectory tracking deviations caused by system inertia are enveloped within the safety threshold, ensuring absolute flight safety in engineering practice. Notably, as conflict complexity increases, NCR exhibits prominent advantages in reducing velocity variation costs, minimizing the number of maneuvering UAVs, and avoiding unnecessary trajectory deviations.

Drones doi: 10.3390/drones10050334

Authors: Yuchen Guo Junming Zhao Mingbo Wu Xiangguo Peng Yu Xia Yankai Yu

Drone-based Urban Air Mobility (UAM) shows immense potential in urban logistics and emergency response; however, evidence regarding its systemic sustainability remains fragmented. In a systematic review using the PRISMA methodology, this study analyzes 301 core articles to construct an evaluation framework spanning environmental, economic, social, and systemic effectiveness dimensions. Given technical similarities, electric Vertical Take-off and Landing (eVTOL) findings are integrated to anticipate operational challenges. Results highlight a clear consensus: drone delivery is time-efficient in high-sensitivity scenarios, though noise, equity, and safety remain critical bottlenecks. Meanwhile, deep controversies persist across some dimensions. Environmental benefits are highly context-dependent, contingent on operating models, battery life cycles, and clean energy proportions from a Life Cycle Assessment (LCA) perspective. Economically, a mismatch between high costs and low willingness to pay (WTP) necessitates optimized pricing strategies. Socially, public acceptance is sensitive to the balance between perceived benefits and risks. Furthermore, systemic effectiveness depends on the coupling between vertiports and ground infrastructure. Concluding that sustainable drone-based UAM is a multistakeholder systemic endeavor, we urge future research to prioritize LCA, pricing strategies, public acceptance surveys, and integrated air-ground coordination to resolve controversies and foster sustainable systems.

Drones doi: 10.3390/drones10050333

Authors: Hua Yang Yalan Shi Yanli Xu Naoki Wakamiya

To support intelligent maritime applications, space–air–ground–sea integrated networks (SAGSINs) have been introduced in maritime communications to provide wide coverage and reliable network services. In unmanned aerial vehicle (UAV)-assisted SAGSIN architectures, UAVs can flexibly extend coverage and provide on-demand communication and computing support. However, due to the high mobility of low Earth orbit (LEO) satellites and the limited endurance of UAVs, single-platform deployment strategies struggle to provide both flexibility and scalability in maritime communication networks. To mitigate the service instability caused by satellite orbital dynamics and limited UAV endurance, we propose a Hybrid Multi-Agent Proximal Policy Optimization (H-MAPPO)-based joint satellite–UAV deployment scheme for UAV-assisted SAGSIN systems. The proposed method optimizes joint UAV positioning and resource allocation to enhance communication coverage while reducing overall operational cost. By incorporating satellite orbital dynamics and UAV mobility into a multi-agent reinforcement learning (MARL) framework, adaptive resource scheduling can be achieved under time-varying maritime demands. Simulation results show that the proposed H-MAPPO algorithm achieves superior convergence performance, higher user coverage, and lower total system cost compared with learning-based, random, and heuristic methods while maintaining stable and robust performance under varying user densities and network scales.

Drones doi: 10.3390/drones10050332

Authors: Yong Li Lihui Wang Xueyong Xu Renzhi Huang Yuhang Xu

Safety-oriented UAV motion planning relies on distance-to-obstacle fields and their gradients, yet onboard mapping is typically limited to bounded local distance updates. Consequently, optimization may stall outside the updated band due to missing gradients, while enlarging the update range substantially increases computational cost. Our key insight is that motion-planning locality implies only a small subset of obstacles governs local trajectory refinement. We term this subset points of interest (POIs). Motivated by this observation, we develop a locality-aware sequential motion planning framework with a POI-driven feedback mechanism that continuously identifies and augments these trajectory-relevant obstacles during search and optimization. The mechanism tightly couples mapping, search, and optimization and enables safe trajectory refinement without requiring global distance updates. The framework adopts a heuristic mapping strategy that combines a long-term occupancy grid with bounded incremental distance updates and a POI-based short-term k-d tree, enabling efficient nearest-neighbor queries and gradient proxies beyond the update band. The search process generates a dynamically feasible initial trajectory in the long-term map while collecting POIs, which are then used to construct the short-term component. The trajectory is subsequently refined through iterative optimization loops, where newly exposed closest obstacles are incorporated into the POI set and the short-term map is updated until convergence. Safety is enforced through conservative collision checking against the inflated long-term occupancy map. Simulations in building and forest environments show that 99.7% of trials converge within two refinements in sparse scenes and none exceed four overall. Compared with FastPlanner and EgoPlanner, the proposed method achieves consistently larger obstacle clearances. Onboard experiments further validate its practicality under real sensing and computational constraints.

Drones doi: 10.3390/drones10050330

Authors: Yonggang Wang Genwei Wang Zehua Chen Jiang Wang Pu Huang

UAV path planning in complex 3D terrain faces the dual challenges of computational efficiency and reliable obstacle avoidance. To address these issues, this paper proposes a Keypoint–Sparse Cache (KSC) strategy and a hierarchical KSC-PPO (Proximal Policy Optimization) framework for mountainous environments with both static terrain and dynamic obstacles. The KSC strategy reduces search complexity through orthogonal slice-based sparse keypoint extraction and path caching reuse, thereby improving the efficiency of global path planning. On this basis, PPO-based local obstacle avoidance is activated only when safety thresholds are exceeded, while the remaining path is replanned globally after threat clearance, which confines avoidance computation to a local scope while preserving global path quality. Experiments in static mountainous environments show that KSC requires substantially less computation time than RRT* and Informed RRT* while maintaining competitive path efficiency, and it also outperforms four bio-inspired optimization algorithms across terrains of increasing complexity. Hybrid navigation validation experiments further show that KSC-PPO achieves high mission success, low collision rates, and low avoidance overhead in dynamic mountainous environments. Experiments demonstrate that KSC-PPO decomposes exponential global search space into controllable linear subproblems, significantly enhancing efficiency while ensuring path quality, providing an effective solution for UAV navigation in complex terrain.

Drones doi: 10.3390/drones10050331

Authors: Nicola Angelo Famiglietti Bruno Massa Gaetano Memmolo Giovanni Testa Antonino Memmolo Annamaria Vicari

Investigating slope deformation in densely vegetated or remote areas is a major challenge for slope stability assessment. This study introduces and validates an integrated UAV-borne low-frequency Ground Penetrating Radar (UAV-GPR) and LiDAR methodology to characterize an unstable slope in Melizzano, Southern Italy. Radar data were acquired along an east–west transect at ~1 m above ground level, while high-resolution LiDAR were used to generate a detailed Digital Terrain Model for topographic correction and geomorphological analysis. The processed radargram images subsurface features down to ~15 m, revealing a laterally continuous high-amplitude reflector at ~10 m, interpreted as a key main sliding surface. Chaotic reflections above this interface indicate heterogeneous deposits associated with gravitational deformation, while more homogeneous reflections below correspond to stable geological units. The geometry of the reflector suggests a compound landslide mechanism. Borehole data validate the geophysical interpretation, showing depth discrepancies lower than 2 m. The integration of UAV-GPR and LiDAR enables a reliable correlation between surface morphology and subsurface structures. This non-invasive, spatially continuous approach provides an effective framework for subsurface characterization and for improving the interpretation of landslide geometry and internal structure in challenging environments. This study demonstrates the capability of low-frequency UAV-borne GPR to detect deep-seated sliding surfaces (>10 m) in vegetated environments when integrated with high-resolution LiDAR topography.

Drones doi: 10.3390/drones10050329

Authors: Yuxuan Xu Bo Jiang Longyang Huang Ruokun Qu Zhiyuan Wang

Accurate and robust UAV navigation in complex urban environments remains challenging due to dense buildings, dynamic obstacles, and unreliable GPS signals. To address this issue, this paper proposes GLP-VO, a hybrid visual odometry framework that combines geometric structure features with point features. An adaptive weighting strategy is introduced to balance the contributions of different feature types according to matching quality and scene complexity, while geometric constraints are incorporated into the optimization process to improve pose estimation accuracy and stability. Experiments on the TUM RGB-D dataset and real UAV flight sequences verify the effectiveness of the proposed method. GLP-VO achieves the best ATE results in five of the ten evaluated TUM sequences, including 0.91 cm on f1_xyz and 0.62 cm on f3_str_tex_far, and remains competitive on challenging sequences such as f2_360_kidnap with an ATE of 2.26 cm. In the ablation study, the full model reduces ATE and RPE by up to 44.9% and 43.1%, respectively. Moreover, the proposed system runs at approximately 35 FPS on the desktop platform and 11 FPS on the onboard platform, demonstrating a favorable balance between accuracy, robustness, and real-time performance.

Drones doi: 10.3390/drones10050328

Authors: Xi Chen Xinru Shi Lei Dong Jiachen Liu Guoqiang Yuan

With the rapid development of AI technology in the low-altitude field, the predictive uncertainty of ML models, particularly object-detection models, has become a key bottleneck restricting their airworthiness and safe deployment. A typical manifestation of this is that when facing test samples in open-world scenarios, object-detection models exhibit overconfidence in erroneous predictions. To address this issue, this paper proposes an anomaly-scoring algorithm for out-of-distribution (OOD) evaluation based on augmented deep network features, named PCA-HBOS. By integrating the high-dimensional semantic features extracted by deep networks, the algorithm can score sample distributions, thereby enabling the identification of both in-distribution and out-of-distribution samples. Through comparisons with mainstream OOD algorithms, the superiority of the PCA-HBOS in low-altitude scenarios is validated. Experimental results on three multi-sensor

Drones doi: 10.3390/drones10050327

Authors: Yanqueleth Molina-Tenorio Alfonso Prieto-Guerrero Luis Alberto Vásquez-Toledo

The detection and identification of unmanned aerial vehicles (UAVs) using radio frequency (RF) signals becomes particularly challenging in congested spectral environments, where conventional approaches relying solely on spectral characteristics often prove limited. This work introduces a novel technique for both UAV detection and classification based on temporal representations derived directly from the envelope of received RF signals. The proposed system follows a two-stage architecture: first, binary detection of UAV presence in a given RF channel, and second, identification of the specific UAV model among several commercial platforms. For the first stage, two signal representation methodologies are employed—Gramian Angular Fields and Hilbert curves—both generated from short-time RF windows and subsequently used as inputs to convolutional neural networks. Experimental evaluation demonstrates that the detection stage achieves accuracy rates exceeding 94% for the non-UAV class and approaching 99% for the UAV class with both approaches. In the identification stage, the system attains an accuracy above 90% for most considered UAV models, reaching up to 100% for certain platforms. These results confirm the effectiveness of the envelope-based approach for analyzing UAV-related RF signals.

Drones doi: 10.3390/drones10050326

Authors: Lingchen You Haibin Duan Yongqiong Yuan

This study proposes a bio-inspired control framework for unmanned aerial vehicle (UAV) swarms, designed to emulate the collective motion phase transitions observed in the homing behavior of pigeon flocks. A second-order self-propelled particle model is established, integrating a self-propulsion term, an interaction potential term, and a key roosting force term inspired by the roosting behavior of pigeons. The framework enables the swarm to dynamically switch between a translational motion phase and a vortex motion phase based on the distance to a designated roost location. Based on the proposed swarm model, theoretical analysis proves the stability property of the specific two motion phases under specific conditions. Numerical simulations validate the stability of the two motion phases, demonstrating that UAV swarms can reliably maintain each phase and execute phase transitions triggered by the roosting force. The proposed framework is able to describe the phase transition behavior in the process of pigeons returning home.

Drones doi: 10.3390/drones10050325

Authors: Jinxu Cai Juanzhang Xie Lanxin Zhang Ziyi Wang Xueshun Li Yongjun Zhao

Autonomous trajectory planning for electric Vertical Takeoff and Landing (eVTOL) Unmanned Aerial Vehicles (UAVs) faces the dual challenges of low-altitude environmental interference and limited onboard energy, which affects the reliability and safety of unmanned missions. To address these challenges, this paper develops the EA-TD3 autonomous trajectory planning framework for eVTOL UAV systems. First, a stochastic urban wind field model is established to simulate low-altitude interference. Then, by integrating eVTOL UAV battery discharge data from Carnegie Mellon University (CMU), a mapping relationship between maneuvers and energy consumption is identified to construct a nonlinear energy consumption model. Finally, an energy boundary penalty function is introduced into the TD3 algorithm to ensure that trajectory planning remains within battery safety margins. Experiments based on the parameters of the EH216-S platform show that EA-TD3 achieves a near 100.00% success rate under ideal conditions and outperforms benchmark algorithms while reducing average energy consumption by 11.6%. Under an energy constraint of 120 J, its success rate remains at 87.80%, which exceeds the performance of the DDPG, SAC, and standard TD3 algorithms. This study optimizes the autonomous trajectory planning of eVTOL UAV platforms in urban air mobility (UAM) to improve the energy perception and power management of the autonomous system.

Drones doi: 10.3390/drones10050324

Authors: Jingwei Liu José Lemus-Romani Eduardo J. Rueda Esteban González-Rauter Marcelo Becerra-Rozas

The rapid deterioration of road infrastructure requires accurate and efficient methods for detecting pavement distresses. Unmanned Aerial Vehicles (UAVs) have emerged as a reliable alternative to conventional inspection techniques, enabling high-resolution data acquisition and improved operational safety. This study investigates the application of the Smart Oblique Capture (SOC) technique for pavement inspection through a systematic calibration of UAV flight parameters, including Ground Sample Distance (GSD), frontal and lateral overlap, camera tilt angle, and flight pattern. A structured experimental campaign was conducted, comprising 135 parameter combinations evaluated across three independent scenarios, resulting in a total of 405 UAV flights. The analysis focused on assessing the impact of these parameters on the visual quality of two-dimensional pavement reconstructions and processing efficiency. The results show that a configuration consisting of a 0.5 cm/pixel GSD, 70% frontal overlap, 80% lateral overlap, and a 70° camera tilt angle achieves the best balance between reconstruction quality and computational cost. Furthermore, the findings indicate that Smart Oblique Capture does not provide a statistically significant improvement in reconstruction quality for linear infrastructure compared to conventional oblique configurations, despite requiring a higher number of images and longer processing times. Overall, the results demonstrate that flight parameter calibration plays a more critical role than the adoption of advanced acquisition strategies such as Smart Oblique Capture. This study provides practical and reproducible guidelines for UAV-based pavement inspection, supporting efficient data acquisition while minimizing redundant information and unnecessary computational costs in infrastructure monitoring workflows.

Drones doi: 10.3390/drones10050323

Authors: Xiaoming Mai Weixu Lin Na Dong Shuai Liu

Quadrotor motion planning in cluttered environments presents significant challenges in achieving both computational efficiency and trajectory smoothness, particularly in low-altitude economy and intelligent energy system applications where autonomous aerial vehicles perform infrastructure inspection and power line monitoring. Many existing methods either rely on sampling-based algorithms that suffer from long computation times and suboptimal paths, or employ trajectory representations that produce high-order derivative discontinuities unsuitable for agile flight. In this work, we propose an efficient hierarchical motion planning framework that integrates a JPS–Bresenham-based path search with safe flight corridor construction and Bézier curve optimization. Our approach addresses trajectory generation through a two-stage process: a front-end path search that efficiently identifies collision-free paths with reduced waypoints, followed by a back-end optimization that leverages convex safe corridors with overlapping regions to expand the solution space. Through comprehensive benchmark experiments across six different map scenarios, we demonstrate that our method outperforms RRT* and PRM in both path quality and computational efficiency. Monte Carlo experiments across varying map sizes and obstacle densities confirm robustness and scalability advantages. Comparative studies with state-of-the-art planners demonstrate superior success rates and cost efficiency while maintaining strict kinodynamic feasibility. The Bézier-based optimization reduces snap integral by up to 55% compared to ordinary polynomial approaches, demonstrating its superiority for fast quadrotor trajectory planning in complex environments.

Drones doi: 10.3390/drones10050322

Authors: Xiya Dong Benhe Gao Runjia Liu

Flood disasters often disrupt road networks and severely reduce ground accessibility, hindering the timely delivery of emergency supplies. To address this challenge, this study investigates a collaborative routing problem involving multiple vehicles and multiple UAVs under road disruptions and formulates a mixed-integer linear programming model that jointly minimizes mission makespan and priority-weighted response time for critical nodes. The model explicitly captures road feasibility, vehicle speeds affected by flood depth, multi-point UAV sorties, payload-dependent energy consumption, and vehicle–UAV spatiotemporal synchronization. To balance solution quality and scalability, a dual-track solution framework is developed: exact optimization is used for small instances, while a adaptive large neighborhood search algorithm with embedded dynamic programming is designed for larger instances. A case study based on the 2024 Guangdong flood with 135 demand points shows that the heuristic can obtain high-quality solutions efficiently and outperforms time-limited MILP solutions on large instances. Comparative experiments further demonstrate that multi-point sorties, integrated coordination, and embedded sortie refinement are all crucial to performance improvement. Sensitivity analysis indicates that setting the trade-off coefficient α within 0.2–0.8 provides a robust balance between overall mission efficiency and timely response to critical nodes.

Drones doi: 10.3390/drones10050321

Authors: Alican Yilmaz Erkan Caner Ozkat Fatih Gul

Unmanned aerial vehicles (UAVs) operating in safety-critical missions require effective anomaly detection methods to identify propulsion-system faults before they cause catastrophic failures. However, current vibration-based diagnostic models typically rely on datasets representing only discrete, isolated fault states, and do not capture the continuous structural degradation that occurs during real flight operations. To address this gap, this study proposes a severity-ordered vibration data augmentation framework for anomaly detection in rotary-wing UAV propulsion systems. Controlled experiments were conducted under healthy, tape-induced imbalance, scratch, and cut propeller conditions using stepped throttle excitation from 10% to 100% in 10% increments, with 40 s per level. A severity-ordered arrangement strategy based on throttle level and a robust peak-to-peak severity metric generated approximately 7.5 h of augmented vibration data per axis, representing a continuous degradation trajectory. Three-axis continuous wavelet transform (CWT) scalograms of size 48×96×3 were used to train an unsupervised anomaly detection framework. Comparative experiments with Isolation Forest, One-Class SVM, and LSTM–AE demonstrated that the proposed Convolutional Neural Network (CNN)–Bidirectional Gated Recurrent Unit (BiGRU)–State-Space Model (SSM)–Autoencoder (AE) architecture achieved the best performance, reaching 0.9959 precision, 0.4428 recall, 0.6131 F1-score, and 0.9284 Area Under the Receiver Operating Characteristic Curve (AUROC). The ablation study further showed that incorporating temporal modeling and state-space dynamics improves detection robustness compared with CNN–AE and CNN–BiGRU–AE baselines. These results show that combining severity-ordered augmentation with deep temporal learning improves progressive propulsion anomaly detection in UAV vibration monitoring. This work introduces a methodology that connects rotor dynamics principles with deep learning, providing a continuous degradation manifold that improves early-stage detection and condition monitoring of UAV propulsion systems.

Drones doi: 10.3390/drones10050319

Authors: Dongna Qiao Hongxin Zhang

Unmanned Aerial Vehicle (UAV) tracking of ground moving targets holds significant applications in domains such as intelligent transportation, logistics distribution, and environmental monitoring, placing greater demands on efficient and stable path-planning methods for vehicular tracking. This study investigates a UAV path tracking approach based on a deep reinforcement learning algorithm, Proximal Policy Optimization (PPO). Starting from the kinematic characteristics of UAVs and ground vehicles, a 3D path planning model was constructed that considers spatial coordinates, velocity, and attitude constraints. A well-designed objective function—including tracking error minimization, energy optimization, and safety distance constraints—was incorporated. By designing the state space, action space, and reward function, the PPO algorithm is capable of adaptive learning in complex environments. Compared with traditional Artificial Potential Field (APF), Q-learning, and TD3 algorithms, PPO better balances exploration and exploitation and demonstrates stronger learning stability and global optimization capability in dynamic multi-obstacle scenarios. Simulation results show that PPO-based UAV path planning outperforms Q-learning and other comparative algorithms in terms of tracking accuracy, convergence speed, and robustness. In specific scenarios, Q-learning achieves a trajectory error of approximately 1 m, TD3 and APF exhibit errors around 0.3 m with noticeable oscillations, and PPO achieves an error of about 0.2 m. The UAV can follow the vehicle trajectory smoothly, with a more continuous path and rapidly converging, stable error curves, indicating the promising application potential of PPO in intelligent UAV control. The PPO-based UAV-tracking path planning method effectively enhances the UAV’s intelligent decision-making and path optimization capabilities, providing new technical approaches and a research foundation for intelligent UAV traffic and cooperative control systems.

Drones doi: 10.3390/drones10050320

Authors: Mingzhan Chen Yaqin Xie

To address challenges such as the sudden onset of urban fires, data synchronization delays in early warning systems, response lags, and insufficient routine monitoring, this paper proposes a Spatio-Temporal Collaborative Optimization for Joint Control and Scheduling (STCO-JCS) algorithm tailored for unmanned aerial vehicles (UAVs) and wireless sensor networks (WSNs). First, spatial autocorrelation analysis based on fire data classifies areas into ultra-high, high, medium, and low risk zones to assist in determining UAV access priorities. Second, we construct optimal inspection trajectories for the UAV by taking into account the inspection sequence and the city’s topography. By modeling the path deviations caused by wind interference and designing precision control algorithms, we improve the accuracy of the UAV’s flight path, ultimately achieving the goal of reducing UAV inspection time. Finally, by coordinating the spatiotemporal operations of drones and wireless sensor networks, we can achieve early detection and rapid response in high-risk fire zones, thereby reducing drone energy consumption while enhancing the efficiency of the UAV-WSN fire monitoring system. Simulation results demonstrate that under a 20-square-kilometer simulation area, STCO-JCS controls inspection paths within 14–17 km. In the multi-UAV scenario, the proposed method achieves approximately 3.17–9.66% improvement in energy efficiency, while in the single-UAV scenario, improvements of 10.83%, 50.54%, and 9.26% are observed in metrics. This provides effective decision support for the dynamic deployment of firefighting and rescue resources.

Drones doi: 10.3390/drones10050318

Authors: Yushu Yu Jiali Sun Ganghua Lai Xin Meng Jianrui Du Yingjun Fan Vincenzo Lippiello Yibo Zhang Tianhao Wang

Integrated aerial platforms (IAPs), composed of multiple unmanned aerial vehicles (UAVs), can perform tasks such as aerial grasping and cooperative manipulation. In this paper, we introduce and design an IAP with joint-driven active deformation capability. During deformation and tasks such as aerial grasping, configuration-dependent variations in inertia and the center of mass (CoM) challenge control stability. To address this issue, a geometric model reference adaptive control (MRAC) scheme is developed on SO(3) to ensure robust and decoupled control under these time-varying conditions. The almost global stability of the closed-loop system is rigorously established through Lyapunov-based analysis and verified in simulations. The advantages of the proposed controller are further validated through real-world deformation experiments on a self-developed prototype, which successfully performs aerial grasping and assembly tasks.

Drones doi: 10.3390/drones10050317

Authors: Yuan Ma Guifen Chen Yijun Wang Dakun Liu

To address the critical issues of geometric ill-conditioning and non-line-of-sight (NLOS) interference faced by broadcast radio positioning systems in long-distance transmission (≥200 km) and low-altitude flight scenarios (1000 m to 3000 m), this paper proposes a Differential and Robust Positioning method for Airborne Platforms (DPAP). Integrating radio differential positioning, the proposed method enhances the single-point positioning algorithm through a grid search and iteratively reweighted least squares to mitigate geometric ill-conditioning and numerical instability in low-altitude environments. Furthermore, a passive differential positioning approach is introduced to eliminate common errors using neighboring reference stations. Finally, a scenario-aware safe fusion strategy ensures that the fused solution is never inferior to the optimal sub-solution under any circumstances. Simulation results demonstrate that, under conditions involving six ground stations, user-to-station distances of no less than 200 km, and 15% of links experiencing NLOS propagation, the differential and robust positioning method achieves a positioning accuracy of 0.588 m RMS. This represents an improvement of approximately one order of magnitude compared to RSPP (12.304 m), and outperforms traditional Huber M-estimation (0.678 m) and elevation-weighted least squares methods (1.462 m). All results are based on Monte Carlo simulations; real-world validation with SDR hardware and flight tests is left for future work. This work provides a scalable, infrastructure-light backup for safe UAV operations in GNSS-hostile environments, directly supporting the emerging low-altitude economy.

Drones doi: 10.3390/drones10050316

Authors: Ruishuo Li Xiaowen Shan Hao Wang

Stability plays essential roles for Vertical Take-Off and Landing (VTOL) vehicles. This paper investigates the stability characteristics of a novel tail-sitter VTOL vehicle employing vector thrust control, specifically focusing on nonlinear modeling and parameter optimization. Firstly, the tail-sitter VTOL which employs vector thrust controlling principles, is designed, and manufactured using 3D printing and carbon-fiber reinforced techniques, with a customized flight controller implemented on the PX4 architecture. To address the nonlinear dynamic characteristics introduced by the vector thrust mechanism, a nonlinear dynamic model for cruise flight is established based on an offline database and validated against cruise flight test data. Flight tests show that the vector-thrust-based pitch control provides rapid response and accurate tracking during cruise flight. Furthermore, based on the validated model, a hybrid optimization strategy combining pattern search and sequential quadratic programming (SQP) is used to tune the cascaded control parameters. Simulation results demonstrate that the optimized controller reduces the rise time from 6.8 s to 0.2 s and the settling time from 10.1 s to 0.9 s under the tested cruise-condition step response, indicating a marked improvement in dynamic response performance. This study provides a practical framework for cruise-flight modeling, pitch-stability analysis, and control-parameter optimization of vector-thrust tail-sitter UAVs.

Drones doi: 10.3390/drones10050315

Authors: Bader Alwasel Ahmed Salim Pravija Raj Patinjare Veetil Ahmed M. Khedr Walid Osamy

In today’s densely populated and technology-driven smart cities, natural and human-made disasters increasingly threaten the resilience of communication infrastructures, creating critical challenges for maintaining reliable connectivity. The failure of conventional networks during crises significantly hampers emergency response, coordination, and information dissemination. To address these challenges, this paper presents Weighted Average Algorithm-based Clustering and Routing (WAA-CR), a novel, secure, and adaptive UAV-based framework for disaster response and recovery. WAA-CR integrates three key components: shelters or Ground Control Stations (GCSs) as communication anchors and support hubs, survivable clustering and routing using a WAA-based metaheuristic optimizer, and secure and trustworthy drone communication enabled by a lightweight trust evaluation mechanism, and authentication model. The framework formulates a multi-objective optimization model that simultaneously minimizes the number of active UAVs and routing cost, while maximizing trust, communication reliability, and coverage. Cluster head (CH) election and routing decisions are guided by a composite fitness function that considers residual energy, link stability, mobility, and dynamic trust scores. Additionally, an adaptive maintenance mechanism enables dynamic reconfiguration to handle CH failures, trust degradation, or mobility-driven topology changes. Extensive simulations conducted in MATLAB R2020ademonstrate that WAA-CR significantly outperforms existing baseline FANET protocols in terms of energy efficiency, cluster stability, trust accuracy, and end-to-end delivery performance. These results validate the proposed framework’s effectiveness in building resilient, scalable, and secure UAV-based communication networks for post-disaster environments.

Drones doi: 10.3390/drones10050314

Authors: Shuwen Zhao Heming Zhao Zhiling Peng Jun Wang Fei Xie Xiaoyu Guo

Due to their low noise and high efficiency, ducted fans are extensively used in unmanned aerial vehicles (UAVs). As the core lift and propulsion units, the aerodynamic performance of dual-ducted fans critically determines propulsion efficiency and flight stability. However, when operating near the ground, variations in ground clearance and the gap between ducts disrupt the isolated flow fields, introducing ground effect and aerodynamic coupling that pose significant stability risks. To address this, we developed a high-fidelity numerical model using the Unsteady Reynolds-Averaged Navier–Stokes approach with sliding mesh technology and the Shear-Stress Transport k-ω turbulence model. This study reveals the macroscopic aerodynamic characteristics of dual-ducted fans as functions of ground clearance and duct gap, and clarifies the underlying flow mechanisms. The research results indicate that the performance of a signle-ducted fan is highly sensitive to ground clearance: a critical threshold of thrust occurs when the ground clearance (h) at the duct outlet is 0.75 times the rotor disk diameter (D). Under ground-effect-free conditions, the dual duct gap dominates the aerodynamic interference pattern: the total thrust of the system reaches its maximum value when the minimum spacing between the outer edges of the two ducts is 6 times the rotor disk radius. The coupling effect of ground clearance and duct gap exhibits significant nonlinear characteristics: thrust first decreases and then increases with increasing ground clearance, and the sensitive range of gap variation is h/D=0.5–1.0. These findings are crucial for optimizing the layout of ducted UAVs and enhancing UAV flight control to ensure safe and efficient operation under near-ground conditions.

Drones doi: 10.3390/drones10050313

Authors: Maoming Zou Zhengyu Guo Jian Zhang Yu Han Caiyi Chen Huimin Chen Delin Luo

Autonomous task assignment in multi-unmanned aerial vehicle (UAV) systems operating in dynamic and safety-critical airspace environments is highly challenging due to complex spatial interactions and rapidly changing relative geometries. This paper proposes a hierarchical decision-making framework that bridges individual maneuvering behaviors with cooperative task allocation in multi-agent aerial systems. First, a high-fidelity single-agent maneuver model is learned using a physics-consistent simulation environment, where spatial advantage is evaluated based on relative distance and angular relationships within a kinematically feasible interaction zone (KIZ). Subsequently, a Geometry-Aware Graph Attention Network (GA-GAT) is developed to address scalable multi-agent assignment problems. Unlike conventional approaches that rely on flat feature representations, the proposed method explicitly incorporates kinematic feasibility constraints into the attention mechanism via a novel gating module, enabling efficient relational reasoning under dynamic conditions. The proposed framework is applicable to a range of civilian and safety-oriented scenarios, including UAV swarm coordination, emergency response monitoring, infrastructure inspection, and autonomous airspace management. Simulation results demonstrate that the GA-GAT-based approach significantly outperforms heuristic baselines in terms of coordination efficiency and overall system performance in complex multi-agent environments. This study highlights that decoupling maneuver-level control from high-level coordination provides a scalable and computationally efficient solution for real-time multi-UAV decision-making in safety-critical applications. The proposed framework is designed for general multi-agent coordination problems in civilian aerial applications.

Drones doi: 10.3390/drones10050312

Authors: Yang Yuan Li Yang Liu He

Unmanned Aerial Vehicle Ad Hoc Networks (UAVANETs) are characterized by highly dynamic topology changes and unstable link conditions, which necessitate deep collaboration between transport-layer congestion control and network-layer routing decisions to ensure service quality. However, the existing layered architecture of Software-Defined Networking (SDN) results in a significant separation between routing information and congestion control mechanisms, rendering traditional protocols ineffective in handling severe performance fluctuations caused by highly dynamic route switching. The significant disconnect between network-layer route planning and transport-layer congestion control strategies in Software-Defined Unmanned Aerial Vehicle Ad Hoc Networks (SD-UAVANETs) leads to degraded transmission performance of BBR (Bottleneck Bandwidth and Round-trip propagation time) under high-dynamic route switching scenarios. As such, this paper proposes an in-band network telemetry (INT)-based cross-layer optimization scheme for BBR, named SDN-BBR. Firstly, a lightweight real-time route switching detection mechanism based on INT is designed. Secondly, a QoS inequality model before and after path switching is established, deriving the critical bandwidth of the new path and integrating it into the BBR algorithm to accelerate convergence and avoid congestion. Finally, the BBR state machine is redesigned to achieve cross-layer information fusion and coordinated control, thereby optimizing transmission performance. Experimental results show that the proposed scheme reduces convergence time by 69.8% and increases throughput by 73.9% in low-bandwidth to high-bandwidth switching scenarios; decreases packet loss rate by 86.8% and reduces delay by 8.3% in high-bandwidth to low-bandwidth switching scenarios; and improves throughput by 12.3%, lowers packet loss rate by 21%, and reduces delay by 7.9% in multi-traffic flow concurrent scenarios. The scheme significantly enhances the transmission performance of BBR in highly dynamic routing environments of SD-UAVANET.

Drones doi: 10.3390/drones10040311

Authors: Marcel Ilie

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.

Drones doi: 10.3390/drones10040310

Authors: Rodolfo Vera-Amaro Alberto Luviano-Juárez Mario E. Rivero-Ángeles Diego Márquez-González Danna P. Suárez-Ángeles

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.

Drones doi: 10.3390/drones10040309

Authors: Xingda Li Jianqiang Zhang Yiping Liu Pengfei Zhang Jing Wang

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.

Drones doi: 10.3390/drones10040308

Authors: Zibo Wang Qidan Zhu Pujing Sun Wenqiang Jiang Lipeng Wang

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.

Drones doi: 10.3390/drones10040306

Authors: Jiuxia Guo Jinxi Chen Tianhang Zhang Qi Feng

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.

Drones doi: 10.3390/drones10040307

Authors: Liangbin Zhang Weisheng Chen Jing Chang

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.

Drones doi: 10.3390/drones10040305

Authors: Xiao Liu Qizhang Luo Tianjun Liao Guohua Wu

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.

Drones doi: 10.3390/drones10040304

Authors: Hang Zhang Hua-Min Chen Qi-Jun Wei Zhu-Wei Wang Yan-Hua Sun

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.

Drones doi: 10.3390/drones10040303

Authors: 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 Francine Bonomo Crispim Silva

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.

Drones doi: 10.3390/drones10040302

Authors: Burak Buda Turhan Djamel Rezgui Mahdi Azarpeyvand

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.

Drones doi: 10.3390/drones10040301

Authors: Nelson Nazzicari Giulia Moscatelli Agostino Fricano Elisabetta Frascaroli Roshan Paudel Eder Groli Paolo De Franceschi Giorgia Carletti Nicolò Franguelli Filippo Biscarini

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.

Drones doi: 10.3390/drones10040300

Authors: Iago Gomes Frederico Afonso Afzal Suleman

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.

Drones doi: 10.3390/drones10040299

Authors: Jing Ye Bing Li Ruixin Feng Fanghui Huang Junbin Lou Tao Li Dawei Wang Yixin He

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.

Drones doi: 10.3390/drones10040298

Authors: Ken Hellerud Lizhen Huang

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.

Drones doi: 10.3390/drones10040297

Authors: Yonggang Nie Xinyue Zhang Chaoyue Li Dong Zhang

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.

Drones doi: 10.3390/drones10040295

Authors: Owais Ahmed Justin Guye M. Hassan Tanveer Adeel Khalid

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.

Drones doi: 10.3390/drones10040296

Authors: Fred Taylor John Ryan Dennis Akos

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.

Drones doi: 10.3390/drones10040294

Authors: Yongfang Li Tianyu Zhao Zhijuan Wu Yan Lin Yijin Zhang

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.

Drones doi: 10.3390/drones10040293

Authors: Xin Wu Guoqiang Xue Yufei Gao Yanbo Wang Yefei Li Zhaoming Qian Yusuo Zhao Junjie Xue Song Cui Nannan Zhou

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.

Drones doi: 10.3390/drones10040292

Authors: Hyunbum Kim

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.

Drones doi: 10.3390/drones10040291

Authors: Eduardo Augusto Werneck Ribeiro Raul Borges Guimarães Natália Lampert Bastista Mauricio Rizzatti Nicolas Firmiano Flores Igor Engel Cansian

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.

Drones doi: 10.3390/drones10040290

Authors: Dun Yang Li Liu Bojing Yao

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.

Drones doi: 10.3390/drones10040289

Authors: Lei Ai Bin Ma Jianxing Zhang Yao Ai Ziqi Hao Jianan Li Zhuting Yu Jiayu Cheng

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.

Drones doi: 10.3390/drones10040288

Authors: Min Ding Jing Du Yijing Wang Yue Lu

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.

Drones doi: 10.3390/drones10040287

Authors: Yang Gao Hao Yin Wenliang Wang Bing Guo Yue Wang Guopeng Li Lingyun Tian Dongguang Li

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.

Drones doi: 10.3390/drones10040286

Authors: Ping-Tse Lin Ruey-Beei Wu Shi-Chung Chang

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.

Drones doi: 10.3390/drones10040285

Authors: Xingyi Pan Xingyu He Xiaoyue Ren Duo Qi

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.

Drones doi: 10.3390/drones10040284

Authors: Yanfei Miao Haixin Chen Xu Zhang Yuan Gao Qing Cai

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.

Drones doi: 10.3390/drones10040283

Authors: Kuan-Chou Chen Vinay Malligere Shivanna Jiun-In Guo

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.

Drones doi: 10.3390/drones10040281

Authors: Jory Alqahtani Ahmad Ihsan Ramdani Pavel Golikov Artem Timoshenko Grigoriy Yashin Ilya Mashkov Van Do Ezzedeen Alfataierge

Generally, land seismic data acquisition in arid areas is a labor-intensive, costly, and challenging process, often hindered by challenging terrain and safety risks. To overcome these limitations, we propose the integration of autonomous Unmanned Aerial Vehicles (UAVs) into land seismic data acquisition, enabling efficient data collection in difficult, inaccessible terrain. This is a cooperative mission workflow combining a Scouting UAV for high-resolution aerial scouting, followed by the swarm deployment of an Autonomous Seismic Acquisition Device (ASAD) for seismic data recording. The cooperative system allows for precise landing and subsequent deployment of seismic sensors in optimal locations. Previously, we demonstrated the applicability of passive seismic recorded with ASAD drones to near-surface characterization. This study covers the results of a field trial, where both the ASAD and Scouting UAV systems successfully acquired high-resolution seismic data with an active source, comparable to that of a conventional seismic data acquisition system. The results show that the ASAD seismic data exhibit a slightly higher noise level due to coupling variances and the fact that geophones were hardwired into 9-sensor arrays. However, due to its single-point sensing nature, it yields a superior frequency bandwidth, making it suitable for imaging shallow anomalies. The system underwent P-wave refraction tomography modeling and accurately detected a shallow subsurface cavity, showcasing its potential for near-surface characterization and shallow geohazard identification. This heterogeneous robotic system can support seismic data acquisition by enhancing safety, improving efficiency, and streamlining equipment mobilization, while minimizing environmental footprint.

Drones doi: 10.3390/drones10040282

Authors: Evgenii Norenko Vadim Kramar Aleksey Kabanov

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.

Drones doi: 10.3390/drones10040280

Authors: Sen Zhang Weilin Du Zheng Li Junmin Rao

The query propagation paradigm provides a unified theoretical framework for end-to-end multi-object tracking, yet it still faces challenges in complex scenarios involving multi-scale variations, dense interactions, and trajectory fragmentation, including insufficient query initialization quality, imprecise feature alignment, and difficult identity recovery. Building upon MOTRv2, this paper proposes three core improvements. First, we design a geometric prior injection strategy based on sine–cosine encoding, which explicitly encodes target location and scale information into detection queries, providing high-quality initialization for tracking queries. Second, we propose a width–height-modulated deformable attention mechanism that dynamically adjusts the sampling range of deformable convolution according to target size, enabling fine-grained feature matching for multi-scale targets. Third, we construct a motion-direction-consistency-based trajectory re-association module that leverages motion continuity to efficiently recover lost trajectories without introducing additional appearance models. Furthermore, we introduce a progressive joint training strategy that optimizes detection and tracking modules in stages, effectively mitigating gradient competition in multi-task learning. Extensive quantitative and qualitative experiments on the BEE24, UAVSwarm, and VTMOT infrared datasets validate the effectiveness of the proposed method. On the UAVSwarm dataset, our method achieves state-of-the-art performance with 52.4% HOTA, 72.1% MOTA, and only 51 identity switches. Ablation studies further reveal the synergistic enhancement mechanism among the proposed modules.

Drones doi: 10.3390/drones10040279

Authors: Li Cao Jianjiang Zhou Wei Wang

The rapid proliferation of low-altitude drones has led to increasingly congested and heterogeneous electromagnetic environments, posing significant challenges to fine-grained spectrum awareness and reliable drone management. Specific emitter identification (SEI), which exploits inherent hardware-dependent radio frequency fingerprints, provides an effective physical-layer solution for emitter-level discrimination. However, practical SEI systems often suffer from two critical issues: extremely limited labeled samples for newly emerging emitters and heterogeneous data distributions collected by geographically distributed receivers with mismatched label spaces. To address these challenges, this paper proposes a heterogeneous federated learning (HFL)-based framework for few-shot specific emitter identification (FS-SEI). The proposed framework decouples feature embedding learning from task-specific classification and enables collaborative representation learning across distributed receivers without sharing raw signal data. A metric learning-based training strategy is adopted, where only the feature embedding models are aggregated in the federated process, effectively alleviating the impact of label space mismatch by utilizing center loss and an improved triplet loss. Moreover, two federated optimization schemes, namely gradient averaging (GA) and model averaging (MA), are systematically investigated to analyze their effectiveness under fully heterogeneous settings. Extensive experiments conducted on a real-world dataset demonstrate that the proposed HFL framework significantly outperforms isolated local training. In particular, the GA-based scheme achieves a few-shot identification performance that closely approaches centralized learning while preserving data privacy and robustness against data heterogeneity. The results validate the effectiveness of the proposed approach for practical FS-SEI in low-altitude drone management scenarios.

Drones doi: 10.3390/drones10040278

Authors: Chong Yu Jiaqi Liu Peng Xie Wenjun Xie

To systematically review the research progress on unmanned aerial vehicle (UAV) formation control, this paper proposes a mission-driven full-lifecycle analysis architecture. The architecture summarizes the core scenarios and key technologies involved in the three main stages: formation assembly, formation maintenance, and formation reconfiguration. Moreover, a comprehensive evaluation framework is established that covers pre-event, in-event, and post-event phases from the perspectives of resilience, robustness, reliability, and vulnerability. The interrelationships among these four dimensions are explained in terms of time, function, and design. Finally, this paper identifies current research gaps and practical challenges in terms of algorithms, evaluation methodologies, and real-world deployment verification, and outlines future development directions.

Drones doi: 10.3390/drones10040277

Authors: Long He Liangquan Wang Shipeng Yang Jinwu Xiang Qinghua Zhu Dongxia Xu

High-speed capability is a defining feature of next-generation helicopters, enabling time-sensitive missions. This paper compares three high-speed configurations: tiltrotor, coaxial rigid rotor, and compound conventional rotor. Based on existing technology and operational needs, the study focuses on the aerodynamic layout of a compound conventional rotor high-speed unmanned helicopter. With key parameters, including a 300 kg takeoff weight and a maximum speed of 240 km/h, iterative optimization was conducted using theoretical analysis, numerical simulation, and flight dynamics evaluation. A feasible aerodynamic layout based on a “dual-side propulsion concept” was developed, followed by flight performance assessment and full-scale prototype flight tests. The results show: (1) the final layout comprises a two-blade hingeless rotor, three-blade pusher propellers, wings, skid landing gear, an H-tail, and a horizontal stabilizer; (2) flight performance meets all design targets, achieving maximum and cruise speeds of 260.48 km/h and 180 km/h at 1500 m altitude; and (3) full-scale prototype tests confirm the rationality of the aerodynamic layout and the reliability of the design process, achieving a high-speed flight of 242.6 km/h at an altitude of 1280 m. This work provides a valuable configuration reference for high-speed unmanned helicopter development.

Drones doi: 10.3390/drones10040276

Authors: Isabel Urbina-Barreto Evangelos Alevizos Telina Minolalaina Randrianary Manon Museux Ravo A. Mahandrisoa Randriamaroson Anne Chauvin Solofoarisoa Rakotoniaina Sébastien Jan Laurent Barillé Aline Tribollet

Madagascar’s expanding blue economy is largely underpinned by seaweed aquaculture, particularly Kappaphycus alvarezii (Cottonii), which offers an alternative to declining small-scale fisheries and strengthens the resilience of coastal socio-ecosystems. Ensuring the sustainability of this economic activity requires effective ecological monitoring of aquaculture sites and surrounding habitats. This study examines and compares the performance of two imaging configurations—an RGB composite derived from a subset of multispectral images capturing red (650 nm), green (560 nm), and blue (450 nm) bands; and a five-band multispectral (MS) image encompassing blue, green, red, red-edge (730 nm), and near-infrared (840 nm) bands—combined with a Random Forest (RF) classification model, for benthic habitat mapping in a seaweed cultivation context. High-resolution orthomosaics (2 cm/pixel) enabled the discrimination of Kappaphycus cultivation plots from three shallow-water habitats: (i) ‘benthic macrophytes’, which comprise: seagrass meadows and benthic macroalgal; (ii) ‘sandy bottom’ and (iii) ‘green algae’. The RF classification achieved an overall accuracy of 87% (Kappa = 0.82) across ~10 hectares. Producer’s accuracy exceeded 80% for Kappaphycus cultivation, green algae, and sandy bottom for both the RGB and MS datasets, indicating strong classification performance. However, early-stage seaweed was occasionally misclassified as benthic macrophytes, likely due to its low biomass and weak spectral signature. This UAS-based approach provided a robust and cost-effective framework for monitoring off-bottom seaweed farms and associated natural habitats. This approach supports sustainable aquaculture development and integrated coastal management in Madagascar and comparable tropical reef socio-ecosystems.

Drones doi: 10.3390/drones10040275

Authors: Mohammad Ahmed Alnakhli

Multi-UAV communication networks face significant challenges in achieving high energy efficiency and low communication latency under dynamic topology and interference conditions. This paper proposes a Dueling Deep Q-Network (DQN) framework for joint resource optimization in 6G-enabled multi-UAV systems. The proposed approach jointly optimizes transmit power allocation, inter-UAV link association, and adaptive graph density within a unified reinforcement learning framework. By employing a dueling value–advantage decomposition, the proposed model improves learning stability and convergence compared to conventional DQN and Double DQN (DDQN) schemes. Simulation results under varying network densities and UAV scales show that the proposed Dueling DQN achieves up to 15% higher energy efficiency and 12% lower end-to-end latency, while maintaining robust performance in dense connectivity scenarios. These results demonstrate the effectiveness and scalability of the proposed framework for energy- and latency-sensitive UAV communication applications.

Drones doi: 10.3390/drones10040274

Authors: Stamatina C. Barakou Costas S. Tzafestas Kimon P. Valavanis

This research presents a hybrid geometric computed torque control method for an aerial manipulation system composed of a quadrotor UAV and a 2-DOF planar manipulator. The fully coupled system’s dynamic model is derived following the Euler–Lagrange (E-L) formulation. The proposed control architecture leverages the geometric controller provided by the RotorS simulator as a high-level quadrotor trajectory tracking module. Tracking reference commands are generated using the geometric SE(3) position controller, which computes desired translational and angular accelerations from position/velocity and attitude/angular rate errors, respectively, serving as input to the low-level computed torque controller that explicitly accounts for the coupled 8-DoF aerial manipulator system dynamics. The desired generalized acceleration vector q¨des combines quadrotor translational and rotational acceleration commands with a PD-based joint acceleration command for the attached manipulator. The computed torque controller produces generalized forces for the coupled system, which are subsequently separated into quadrotor forces and moments and manipulator joint torques. The resulting quadrotor forces and moments are mapped to rotor speeds using the standard RotorS control allocation matrix, while the manipulator joints are controlled at the torque level via ROS built-in effort controllers. Extensive simulated experiments demonstrate the effectiveness of the coupled hybrid approach compared to decoupled control strategies, showing significant improvements in tracking accuracy and dynamic response.

Drones doi: 10.3390/drones10040273

Authors: Alizhan Tulembayev Alexandr Dolya Ainur Kuttybayeva Timur Jussupbekov Kalmukhamed Tazhen

Energy-efficient and resilient decentralized unmanned aerial vehicles (UAV) swarm operation in global navigation satellite system (GNSS) denied environments remains challenging because propulsion demand, communication load, and onboard inference are tightly coupled at the mission level. Although prior studies have examined some of these components separately, their joint evaluation within adaptive decentralized swarms remains limited under degraded navigation conditions. This study proposes an energy-aware adaptive communication-topology framework integrated with lightweight edge artificial intelligence (AI)-assisted navigation for decentralized UAV swarms operating without reliable GNSS support. The approach combines a unified mission-level energy-accounting structure for propulsion, communication, and onboard inference, a residual-energy-aware topology adaptation mechanism for preserving swarm connectivity, and a convolutional neural network-long short-term memory (CNN–LSTM) based edge-AI navigation module for improving localization robustness. The framework was evaluated in 1200 s Robot Operating System 2 (ROS2)–Gazebo–PX4 simulation scenarios against fixed topology and extended Kalman filter (EKF)-based baselines. Under the adopted simulation assumptions, the proposed configuration achieved a 22.7% reduction in total energy consumption, with the largest decrease observed in the communication-energy component, while preserving positive algebraic connectivity across all evaluated runs. The edge-AI module yielded a 4.8% root mean square error (RMSE) reduction relative to the EKF baseline, indicating a modest but meaningful improvement in localization performance. These results support the feasibility of integrated energy-aware swarm coordination in GNSS-denied environments; however, they should be interpreted as simulation-based evidence under the adopted modeling assumptions, and further high-fidelity propagation modeling, broader learning validation, and hardware-in-the-loop studies remain necessary.

Drones doi: 10.3390/drones10040272

Authors: Kyoung Joo Lee Sung-Man Kang Dong-Wook Park Ji-Hun Kim Jeong Min Woo

This study investigates the “soft-kill” mechanism of unmanned aerial vehicles (UAVs) under high-power electromagnetic pulse (EMP) exposure. Unlike previous research focused on hardware destruction, we identify flight control paralysis caused by Pulse Width Modulation (PWM) signal logic threshold violation as the primary failure mode. To resolve discrepancies between theory and experiment, a 1 × 1 m loop antenna model was implemented in CST Studio Suite. Results demonstrate that EMP coupling in drone arm wiring predominantly generates differential mode (DM) noise. This explains why conventional ferrite beads fail while full-body shielding remains effective. Our findings provide a theoretical basis for low-power anti-drone system optimization and hardened UAV design guides.

Drones doi: 10.3390/drones10040271

Authors: Adina Aniculaesei

The rapid growth of civilian unmanned aircraft systems (UAS) for various applications, such as logistics, inspection and surveillance has enabled increasingly complex UAS operations in shared airspace and in close proximity to third parties. European regulations for civilian UAS provide a comprehensive framework for operational approval, based on operational rules, risk-based approval processes, and airspace management concepts. While regulatory frameworks and current international standards provide detailed guidance for operational authorization for UAS, they do not prescribe how UAS should be developed and verified at a system and software level to support safety assurance in a structured and traceable manner. This paper addresses this gap by proposing a method for extracting system-level and software-level safety requirements from regulatory artifacts. The method interprets regulatory safety intent–expressed through operational constraints, mitigation measures, and robustness expectations–and translates it into development-relevant safety requirements under explicit operational assumptions. Building on these requirements, the paper introduces a software-centered system lifecycle for UAS development. The proposed lifecycle integrates regulatory safety intent, risk-proportionate assurance, and staged verification. Finally, through a cross-domain analysis, the paper positions the proposed approach relative to established practices from the automotive and the avionics domains, aiming to identify transferable and necessary adaptations for the development of unmanned aircraft systems.