Drones

UAV aerial cinematography has become increasingly important in film production, surveying, and smart-city applications due to its efficiency and creative potential. However, existing UAV filming workflows still rely heavily on manual operation and professional piloting skills, resulting in complex mission design, limited planning autonomy, and inconsistent visual quality. To address these challenges, this paper proposes a unified aesthetics-aware trajectory planning framework for multi-region-of-interest (multi-ROI) UAV aerial cinematography that automatically generates safe, efficient, and visually coherent flight paths from user-specified ROIs. The proposed framework consists of three main components. First, for each ROI, candidate viewpoints are sampled using a spiral trajectory, and a learning-based aesthetic evaluation network is applied to select visually optimal viewpoints for local trajectory generation. Second, transition trajectories between ROIs are generated using a Goal-biased Bidirectional Rapidly exploring Random Tree Star (Goal-biased BiRRT*) planner and evaluated through a multi-objective cost function to determine the most suitable transition paths. Third, the global connection of multiple ROIs is formulated as a Set Traveling Salesman Problem (STSP) to obtain an efficient visiting sequence. By integrating learning-based aesthetic evaluation with hierarchical trajectory planning and coordinated multi-ROI route organization, the proposed framework jointly considers flight feasibility, planning efficiency, visual composition quality, and trajectory continuity within a unified planning pipeline. Experimental results demonstrate that the proposed method generates more visually appealing and coherent aerial trajectories than traditional manual or rule-based approaches, while significantly reducing operational complexity. The proposed system provides an effective solution for autonomous UAV aerial cinematography with improved global consistency, aesthetic performance, and practical planning capability in complex environments. Full article

Vegetative restoration in degraded landscapes requires scalable deployment strategies capable of adapting to heterogeneous terrain conditions. Conventional aerial seeding methods typically operate in open-loop mode, distributing seeds uniformly without considering terrain suitability. This study presents a modular, vision-guided unmanned aerial vehicle (UAV) system for real-time adaptive seed deployment based on the closed-loop integration of onboard perception and actuation under embedded computational constraints. The proposed system combines RGB-based terrain classification, embedded processing, and altitude-adaptive seed dispensing within a unified perception–decision–actuation framework, enabling selective and context-aware seed deployment during flight. Terrain suitability is evaluated onboard using three convolutional neural network (CNN) models and a color-based baseline to distinguish sowable and non-sowable areas. A confidence-based decision strategy with temporal filtering improves reliability, while an altitude-adaptive control mechanism regulates seed distribution across varying flight heights. Field experiments conducted in semi-arid environments demonstrate classification accuracy above 85% with inference latency below 100 ms on a Jetson Nano platform. Additional offline evaluation under varying altitude, speed, illumination, and terrain conditions confirms the robustness of the perception module. The results demonstrate the feasibility of integrating real-time perception with adaptive actuation, enabling UAVs to transition from passive sensing platforms to active agents for environmental intervention. The proposed system provides a practical and scalable approach for precision vegetative restoration in heterogeneous environments. Full article

Deploying unmanned aerial vehicles (UAVs) cooperatively with legged robots for disaster response and inspection requires autonomous docking on miniature walking platforms. This study addresses the problem of landing a foldable quadrotor onto the back of a trotting wheel-legged robot ($300\times 180$ mm) and subsequently taking off while carrying it as a payload. Four tightly coupled challenges distinguish this task from conventional mobile-platform landing: (i) an extremely small landing surface, (ii) gait-induced periodic vibrations at $2.5$ Hz, (iii) continuous platform translation at $0.3$–$0.8$ m/s, and (iv) surface docking that requires simultaneous position and attitude matching rather than mere point tracking. The proposed framework comprises four components: (1) a novel single-servo crank-rocker folding mechanism that reduces the folded body footprint by 48.5% and the maximum linear dimension from 590 mm to 309 mm (↓47.6%) compared with the prior dual-servo design; (2) a staged Continuous Fast Nonsingular Terminal Sliding Mode (CFNTSM) controller combined with a Gait-Frequency-Aware Finite-time Extended Observer (GFA-FEO); (3) a Feature-wise Linear Modulation Soft Actor-Critic (FiLM-SAC) residual reinforcement-learning policy conditioned on physical states and mission phase, with an adaptive trust weight $\lambda \left(t\right)$; and (4) a payload-adaptive takeoff strategy with parameter hot-switching to handle the twofold mass increase. Extensive Monte Carlo simulations and ablation studies across three experiment groups demonstrate that the proposed hierarchical framework achieves sub-centimetre (<10 mm) position accuracy and <3° attitude matching on a walking platform. Quantitatively, the full method reduces docking RMSE by 42% relative to the model-based CFNTSM + GFA-FEO controller without residual RL (4.2 vs. 7.2 mm) and reduces post-lock takeoff RMSE by 63% through FEO hot-switching (16.2 vs. 44.2 mm). Full article

The Unmanned Aircraft System Traffic Management (UTM) system is designed to autonomously coordinate dense Unmanned Aerial Vehicles (UAVs) within shared airspace, ensuring both the efficiency and safety of aerial traffic. With the rapid proliferation of UAV applications, autonomous UTM systems have become increasingly essential, motivating various stakeholders to develop their distinct UTM solutions. However, due to the lack of common guidelines, these emerging solutions exhibit substantial incompatibilities, which hinder the transferability of existing techniques and the overall standardization of UTM. To address the fragmentation, this paper provides a systematic survey of existing UTM research and identifies commonalities across various UTM systems. Specifically, this paper summarizes core UTM service modules and groups them with similar objectives, thereby proposing a unified UTM framework with four layers: Fundamental Infrastructure, Pre-flight UTM, In-flight UTM, and UTM Application. Based on the framework, existing solutions for each module are reviewed in detail. Furthermore, this paper draws analogies between UTM systems and more mature transportation systems, like railways, to identify transferable solutions and derive UTM future trends. This survey aims to clarify the current state of UTM research and provide guidance for future studies in this field. Full article

Unmanned aerial vehicles (UAVs) serve as both communication relays and aerial couriers in modern urban logistics networks. Conventional trajectory optimization methods assume perfect localization and isotropic free-space tracking signal propagation, which limits their effectiveness in urban canyons. To address the positional uncertainty and signal blockage from buildings, we propose a digital twin-driven framework for continuous trajectory and resource optimization in UAV swarms. We model an urban environment containing random high-rise structures, applying a non-line-of-sight (NLoS) uncertainty to reflect realistic communication degradation. The digital twin (DT) architecture utilizes a dual-layer spatial representation that captures a dynamically decaying positional uncertainty radius of the recipient. We define a strict visual localization boundary that initiates deterministic target tracking with a state transition mechanism. To manage the complexity of swarm routing, we apply Density-Based Spatial Clustering of Applications with Noise (DBSCAN), assigning one UAV courier and one logistics transfer station to each cluster. The system executes a continuous re-optimization loop using an adaptive multi-objective Genetic Algorithm. This framework jointly minimizes cumulative outage probability and total flight time while enforcing a signal-to-noise ratio threshold and throughput constraints. This continuous adaptation mechanism mitigates NLoS blockage risks, supporting reliable communication and efficient delivery in Global Navigation Satellite System (GNSS)-degraded and obstacle-dense urban environments. Full article

The ability of uncrewed aerial vehicles (UAVs) to hover, recognize, and localize ground targets is crucial for efficient and accurate intelligent low-altitude operations, such as material delivery, emergency rescue, and firefighting. This paper presents a technical solution for low-altitude UAV target recognition and search localization. The core algorithm is a RepViT-enhanced detection model, which integrates the Re-Parameterization Vision Transformer (RepViT) lightweight neural network with an efficient object detection framework, further augmented by the Convolutional Block Attention Module (CBAM) to improve detection accuracy. The search localization strategy implements a tiered approach for exploring nearby areas from the current position, assigning targets to priority tiers and visiting them in order of priority. Experimental results demonstrate that the RepViT-enhanced model achieves a mean average precision (mAP) of 98.58% on a custom emergency rescue dataset, improving real-time detection speed by two frames per second (18.70 FPS vs. 16.70 FPS for the standard YOLOv4 baseline). Thus, the proposed method effectively enhances both detection accuracy and speed, enabling better target search and localization in complex environments. The search strategy was validated through simulations, confirming its feasibility. Full article

Accurate detection of drowning victims in open water remains a major challenge for search-and-rescue (SAR) operations due to low illumination, reflections, occlusions, and complex backgrounds that degrade human visual performance. This study proposes a multi-modal AI-assisted UAV system for real-time drowning detection using a multi-rotor platform (<15 kg) equipped with integrated visual, thermal, and distance sensing, along with geolocation capabilities. A deep learning-based detection model was trained on 7103 images collected from real human subjects simulating four drowning scenarios in riverine and coastal environments, with additional stabilization and preprocessing modules to improve data quality. The proposed system achieves 98% detection accuracy, with a mean Average Precision (mAP@0.5) of 0.991 and a peak F1-score of 0.97. Results demonstrate reliable detection performance under challenging conditions, including low light, reflective water surfaces, and complex backgrounds, and show improved identification of low-contrast targets such as dark-clothed victims. These findings indicate that the proposed system provides a robust and scalable solution for real-time aquatic SAR applications and enhances the effectiveness of UAV-assisted rescue operations. Full article

This study presents the aerodynamic design of the wing system for a fixed-wing vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV), developed to enhance energy efficiency and operational performance in agricultural applications. The design responds to the limitations of conventional multirotor drones, which are limited by low endurance and high energy consumption, and crop-dusting aircraft, which are unsuitable for irregular terrain such as that found in Chihuahua, Mexico. A comprehensive methodology was adopted, integrating the selection of airfoils optimized for low-Reynolds-number conditions, computational fluid dynamics (CFD) simulations, winglet incorporation, and experimental validation through wind tunnel testing. The SELIG 1223 airfoil was selected for its superior aerodynamic efficiency, demonstrating a potential reduction of up to 55% in power requirements compared to multirotor configurations. Despite some variability in experimental results, the proposed design demonstrated consistent feasibility and reliability. Future work will focus on field validation and geometric adaptation to diverse operational scenarios, reinforcing its applicability across heterogeneous agricultural landscapes. Full article

Unmanned aerial vehicles (UAVs) are broadly used for high-throughput plant phenotyping, yet their long-term use in public-sector research is increasingly challenged by regulatory restrictions and reliance on proprietary platforms. This study presented a regulation-compliant, modular multi-sensor unmanned aerial system (UAS) designed to deliver flexible, high-quality phenotyping data without dependence on restricted ecosystems. A dual-mount, open-architecture payload integrated RGB, multispectral, and thermal sensors, enabling simultaneous acquisition of structural, spectral, and thermal information within a unified workflow. Field validation in a lantana (Lantana camara) breeding trial demonstrated high-precision multi-sensor data fusion and reliable trait extraction. Spatial co-registration achieved centimeter-level accuracy, with alignment errors of 0.88 cm (multispectral) and 3.23 cm (thermal) relative to the RGB reference. UAV-derived canopy height closely matched ground measurements (R² up to 0.98; RMSE as low as 1.57 cm), while canopy coverage estimates showed consistency across sensing modalities (R² = 0.99; RMSE = 0.02 m²). Calibrated thermal orthomosaics provided robust canopy temperature estimation (RMSE = 3.13 °C), supporting a quantitative assessment of plant physiological status. Together, these results demonstrate that a regulation-compliant, open-architecture UAV platform can achieve high accuracy in multi-modal phenotyping while maintaining flexibility and cost efficiency. This work demonstrates a scalable and sustainable framework for UAV-based phenotyping, enabling researchers to adapt to evolving regulations while advancing data-driven crop improvement. Full article

This paper establishes a theoretical framework for treating communication quality as a navigable resource in long-endurance unmanned aerial vehicle (UAV) control under stochastic degradation. We prove that a hierarchical architecture integrating communication-aware model predictive control (MPC) achieves $\epsilon$-optimality with respect to the intractable stochastic dynamic programming formulation while maintaining exponential stability guarantees under switched system dynamics governed by continuous-time Markov chains. Three primary theoretical contributions were made: (1) A stochastic optimality theorem is given showing that sigmoid penalty function approximation yields bounded suboptimality of $\eta \le 0.12$ under mild ergodicity conditions; (2) a formal stability result for mode switching based on hysteresis was established using multiple Lyapunov functions, and it showed exponentially fast convergence with a decay rate of $\lambda \ge 0.23$; and (3) bifurcation analysis showed that there is a critical time threshold of 72 h at which thermal-induced gyro-drift in the GPS sensor causes a transition in navigation error dynamics from linear to catastrophic nonlinear growth. The validation through 2430 Monte Carlo missions over 54,686 flight hours resulted in an average increase in endurance by 243% (18.2 days versus 5.3 days), while keeping CEP at approximately 8.7 m and achieving 82% mission success under extreme communication degradation ($q_{comm}<0.3$). The statistical results confirm a very strong positive relationship between the Resilience Quotient (RQ) and the length of successful missions ($R^2=0.89$, $p<0.001$), supporting the theoretical model with empirical evidence. Full article

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. Full article

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