Drones

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. Full article

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. Full article

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 $\gamma >1$ ). Additionally, by introducing a temporal anti-jitter mechanism (parameterized by a smoothing window $\Delta t$), 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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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 ($\overline{S}=0.160$) while Phi-3-Vision attains the best energy efficiency ($66.3$ Wh/1000 queries); energy consumption and task accuracy are statistically uncorrelated (Spearman $\rho =0.001$; $p=0.995$). 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 (${\chi }^2=40.43$; $p=3.5\times {10}^{-8}$) and navigation reasoning performance (${\chi }^2=13.35$; $p=0.010$). 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. Full article

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. Full article

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. Full article

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. Full article

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)@ --> Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article