Search Results (366)

At around 8:40 a.m. on 17 July 2024, the Wanshuitian landslide in the Three Gorges Reservoir Area (TGRA) experienced a deformation failure characterized by thrust load-caused deformations and high-speed sliding. Using geological surveys and unmanned aerial vehicle (UAV) photography, this study divided the Wanshuitian landslide area into five zones: sliding initiation (A1), secondary disintegration (A2), main accumulation (B1), right falling (B2), and left falling (B3) zones. Through monitoring data analysis and GeoStudio-based numerical simulations, this study revealed the mechanisms behind the landslide failure mode characterized by slope sliding approximately along the strike of the rock formation under the coupling effect of hydrological and geological conditions. The results indicate that factors inducing the landslide failure include the geomorphic feature of alternating grooves and ridges, the lithologic assemblage characterized by interbeds of soft and hard rocks, the slope structure with well-developed joints, and the sustained heavy rains in the preceding period. In the Wanshuitian landslide area, mudstone valleys are prone to accumulate rainwater, which can infiltrate directly into the weak interlayers of rock masses and soften the rock masses. Multi-peak rain events with a short time interval serve as a critical factor in groundwater recharge. Within 17 days preceding its failure, the Wanshuitian landslide experienced a superimposed process of heavy and secondary rain events with a short interval (four days). Rainwater from the first heavy rain event failed to completely discharge during the short interval, while the secondary rain event also caused rainwater accumulation. These led to a continuous rise in the groundwater table, a constant decrease in the shear strength of the slope, and ultimately the landslide instability. Since the landslide sliding in the dip direction of the rock formation was impeded, the main sliding direction of the landslide formed an angle of 88° with this direction. This led to a unique failure mode characterized by slope sliding approximately along the strike of the rock formation. Based on these findings, this study proposed characteristics for the early identification of the failure of similar landslides, aiming to provide a robust scientific basis for the monitoring, early warning, and prevention and control of the failure of similar landslides. Full article

Understanding the unsteady aerodynamic behavior of quadrotor unmanned aerial vehicle (UAV) is critical for improving flight stability, control, and performance, particularly in complex operational environments. In closely spaced multirotor configurations, coherent tip vortices shed from each blade convect downstream and form helical vortex streets that interact with subsequent blades and neighboring rotors. These interactions induce rapid fluctuations in local inflow velocity and effective angle of attack, resulting in transient lift variations, increased vibratory loads, and elevated acoustic emissions. This study presents a comprehensive computational investigation of quadrotor rotor interactions and wake dynamics using a large-eddy simulation (LES). Detailed analyses reveal that the formation and evolution of tip vortices and blade–vortex interaction phenomena significantly influence lift fluctuations and aerodynamic loading. The simulations capture transient wake structures and their effects on neighboring rotors, highlighting unsteady aerodynamic mechanisms that are not adequately predicted by conventional RANS or URANS approaches. Parametric studies examining vortex-street offset distance demonstrate the sensitivity of wake-induced instabilities to design and operational parameters. The results provide new physical insights into multirotor wake dynamics and establish the LES as a predictive framework for quantifying unsteady aerodynamic loading in quadrotor drones. The findings provide insights into the complex flow physics of multirotor systems, offering guidance for more accurate modeling, rotorcraft design optimization, and the development of control strategies that mitigate adverse unsteady aerodynamic effects. This study provides new insights into rotor–vortex-street interactions, with applications to multirotor UAVs, by isolating multi-vortex coupling effects and quantifying the influence of horizontal vortex spacing on unsteady aerodynamic loading, complementing existing high-fidelity LES research. Full article

Communication latency is one of the main factors limiting the practical scalability of unmanned aerial vehicle (UAV) swarms operating with distributed formation control. In real-time UAV missions, such as coordinated swarm navigation, autonomous inspection, and aerial monitoring, delayed information exchange directly affects formation stability and operational safety. In practical aerial networks, inter-UAV communication latency is influenced by stochastic effects including jitter, burst delays, and multi-hop propagation, which are rarely captured by the simplified deterministic delay assumptions commonly adopted in analytical formation-control studies. This paper introduces a measurement-informed stochastic delay model and a communication–control delay-feasibility framework that jointly account for per-link latency behavior, multi-hop delay accumulation, and controller-level delay tolerance. The proposed framework is evaluated using an attractive–repulsive distance-based potential field (ARD–PF) formation controller, for which the maximum admissible end-to-end delay is quantified as a function of swarm size and inter-UAV separation. The delay model is calibrated and validated using more than 15,000 in-flight communication delay samples collected from a multi-UAV LoRa platform operating under realistic flight conditions. The results show that different mechanisms limit swarm operation under different operating scenarios. In some configurations, stochastic communication latency becomes the dominant constraint, whereas in others, formation geometry or network load determines the feasible operating region. Based on these elements, the proposed framework characterizes delay-feasible operating regions and predicts the maximum feasible swarm size under distributed formation control and realistic multi-hop communication latency. Full article

This study presents experimental modal analysis of an ultra-lightweight composite structure representative of UAV application and to evaluate the suitability of different testing approaches for reliable identification of its dynamics characteristics. The investigated structure is a winglet made of carbon fiber reinforced polymer (CFRP) with a lightweight foam core. The experiment was based on impact hammer excitation combined with triaxial accelerometer measurements. Modal tests were performed under three different boundary conditions: free–free suspension using elastic cords, free–free approximation using compliant foam support, and fixed conditions reflecting the operational mounting of the winglet. The results confirm that boundary conditions constitute the dominant factor governing the dynamic response. Transition from free–free to fixed support shifted the dominant bending modal frequency from 331.5 Hz (single-sided response) and 329.9 Hz (double-sided response) 421.2 Hz in the fixed configuration, demonstrating a frequency increase of nearly 27%. Reciprocity and double-sided measurements revealed measurable frequency deviations (e.g., 116.3 Hz to 117.6 Hz) attributed to accelerometer mass loading and geometric misalignment. The 1 g triaxial accelerometer mass was shown to be non-negligible relative to the modal mass of the structure, producing observable shifts in higher-order modes. Full article

This paper develops a unified finite-bit distributed optimization framework for UAV swarms operating over bandwidth-limited communication graphs. We consider strongly convex and smooth global objectives decomposed over local UAV cost functions and study three communication-efficient algorithmic regimes. First, we design a quantized distributed gradient-tracking descent scheme with fixed finite-bit communication and show that, under bounded quantization errors, the method converges R-linearly to a quantization-dependent neighborhood of the global optimizer. Second, we introduce an adaptive quantization strategy that dynamically adjusts the number of transmitted bits according to the current convergence stage. By forcing the quantization distortion to decay proportionally to the optimization error, the proposed adaptive scheme recovers exact linear convergence to the optimal solution while substantially reducing the cumulative communication load. Third, we develop a fully distributed 1-bit communication mode in which UAVs exchange only sign information and use coordinate-wise majority voting to aggregate both descent and consensus directions. The robust linear-contraction property is proved to a small neighborhood under a sign-Polyak–Lojasiewicz condition and a probabilistic majority-correctness assumption. Full article

To address load–energy dynamic coupling in heterogeneous unmanned aerial vehicle (UAV) emergency rescue, this paper proposes an energy-coupled heterogeneous UAV task allocation (EC-HUTA) model that explicitly characterizes nonlinear interdependencies among payload, velocity, and power consumption, minimizing aggregate mission costs subject to physical and temporal constraints. To tackle the resulting high-dimensional, nonconvex problem, we introduce a multi-strategy improved stellar oscillation optimizer (MISOO), establishing a closed-loop synergistic system through three coupled stages: (i) evolutionary game-theoretic strategy competition via replicator dynamics for adaptive exploration–exploitation balance; (ii) intuitionistic fuzzy entropy (IFE)-driven dimension-wise parameter control, where IFE calibrates global exploration intensity while dimension-specific crossover probabilities accommodate heterogeneous convergence; and (iii) memory-driven differential escape mechanisms modulated by historical memory parameters to evade local optima. Cross-stage coupling through IFE ensures state information flows across the “strategy selection-refined search-dynamic escape” pipeline. Coupled with a dual-layer encoding scheme, this framework ensures efficient feasible search. Ablation studies validate each mechanism’s contribution. Evaluations on CEC2017 benchmarks demonstrate MISOO’s superior convergence against six metaheuristics. Large-scale earthquake rescue simulations confirm that EC-HUTA/MISOO strictly adheres to nonlinear energy constraints while enhancing task completion and temporal compliance. These results validate the framework’s efficacy for time-critical emergency resource allocation. Full article

The paper focuses on the practical benefits of using artificial neural networks (ANNs) in the control of unmanned aircraft vehicles (UAVs) and for the purposes of identification and surveillance. The presented methodology for modeling flight dynamics uses ANNs. Modeling of the object dynamics was based on experimental results obtained during flight tests. The aerodynamic g-loads were derived as a function of the flow parameters. The aim of ANN is to select weights of the neural network in such a way that it simultaneously generates all the necessary parameters to implement into the model with a high fidelity. Full article

Rapid and balanced situational awareness of fire fronts is critical for effective initial response to forest fires, yet suboptimal task planning for Unmanned Aerial Vehicle (UAV) swarms can delay intelligence delivery. This paper presents a cloud–edge collaborative approach that integrates edge-driven rapid task partitioning with cloud-based global workload balancing, explicitly addressing the NP-hard multiple traveling salesman problem underlying multi-UAV reconnaissance. At the edge, a fire-spread-informed line clustering algorithm quickly assigns monitoring points to UAVs, exploiting low-latency processing for initial sectorization. The cloud then refines this allocation through a novel cooperative–competitive task transfer mechanism that minimizes the makespan. Extensive simulations and a real-world case study based on the 2020 Liangshan wildfire show that the proposed method reduces makespan by up to 24.5% compared to conventional centralized and distributed baselines, while remaining robust under severe communication constraints. Full article

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. Full article

In the state estimation problem for nonlinear systems, the Unscented Kalman Filter (UKF) has gained attention as an algorithm capable of accurate state estimation based on high-fidelity discretization for strongly nonlinear systems. Furthermore, for applying the UKF to continuous-time state–space models, a method employing the Runge–Kutta method in the time-update equation for sigma points has already been proposed to achieve high-precision state estimation. While this method uses high-order numerical approximations, the associated decrease in computational efficiency due to processing time becomes problematic. It is thus unsuitable for the state estimation of relatively fast-moving objects, such as autonomous vehicles and drones, which require high sampling frequencies. In this study, to reduce computational load while achieving relatively high estimation accuracy, we newly apply the Adams–Bashforth method to the UKF algorithm. The effectiveness of the proposed method is demonstrated by first explaining a low-dimensional model’s state estimation problem, followed by a comparison of estimation accuracy and computation time in state estimation simulations for the UAV model of an Osprey-type drone. Full article

Heavy-load unmanned aerial vehicles (UAVs) are increasingly being applied in logistics, infrastructure installation, and emergency response missions, where complex payload dynamics and unstructured environments pose significant challenges to safe and efficient operation. Conventional manual teleoperation interfaces, such as dual-joystick control, impose a high cognitive workload and provide limited support for expressing high-level operator intent, while fully autonomous solutions remain difficult to deploy reliably under real-world uncertainty. To address these limitations, this paper proposes the Multimodal Fusion Cooperation Network (MFCN), an end-to-end shared autonomy framework that integrates speech commands, visual gestures, and haptic cues through cross-modal feature fusion to infer operator intent in real time. The fused intent representation is translated into dynamically feasible control commands by a cooperative control policy with embedded physics-aware constraints to suppress payload oscillations and ensure flight stability. Extensive semi-physical simulations and real-world experiments demonstrate that the MFCN significantly improves the task success rate, positioning accuracy, and payload stability while reducing the task completion time and operator cognitive workload compared with manual, unimodal, and heuristic multimodal baselines. Full article

Global biomass estimates suggest that plants hold 81% of the Earth’s 550 GT C, yet carbon stocks in non-forested and dryland ecosystems remain the largest source of uncertainty in the global carbon budget. Small uncrewed aerial vehicle (UAV) platforms are increasingly used to estimate aboveground biomass at landscape scales. We conducted a systematic review of the remote sensing literature to determine: (1) which plant traits and related remote sensing indicators were used to develop aboveground biomass models; (2) statistical approaches; and (3) the key sources of uncertainty among these methods and models. We found that tundra, dryland, and savanna ecosystems were most underrepresented in the remote sensing literature. Within our systematic review process, we found no consistent UAV sensor combination, platform, or workflow that improved the accuracy and reduced the uncertainty in aboveground biomass estimates. Machine learning and regression models resulted in similar uncertainty levels in shrubland and savanna ecosystems. Expanding allometric equation development in shrublands and savanna ecosystems could reduce uncertainty and improve aboveground biomass estimation. Improved reporting on UAV logistics and workflows would further strengthen comparability. Standardized and validated UAV methods for estimating biomass, carbon stocks, and fuel loads will be essential for producing consistent datasets and enabling robust future meta-analyses. Full article

Maintaining high power delivery under uncertain landing positions is a key challenge for wireless charging of unmanned aerial vehicles (UAVs). This paper presents a data-driven power optimization method based on the Soft Actor-Critic algorithm for multi-transmitter single-receiver wireless power transfer (MTSR-WPT) systems. To support effective learning without explicit online parameter identification, a physics-informed dual-current state representation is constructed from measurable current responses, combining a zero-phase current with the current response under the applied phase command. The agent is trained using a reward defined directly from normalized load power, and the transmitter voltage phases serve as the control actions. In simulations of a five-transmitter system, the learned policy achieves about 97% of the theoretical maximum power in the training region and about 96% in the expanded evaluation region. Additional robustness studies show strong performance under moderate measurement noise and substantial recovery under model mismatch after short fine-tuning. Experimental validation on a physical prototype confirms the effectiveness of the method, yielding an average power improvement of 188% from a zero-phase baseline and reaching 87% of the maximum power measured on the hardware platform. These results support the proposed method as a practical data-driven alternative to model-dependent MTSR-WPT power optimization for UAV wireless charging. Full article

Unmanned Aerial Vehicle (UAV) computing clusters face severe operational constraints due to limited computing capabilities and battery capacities, which complicate the simultaneous optimization of low offloading latency, long task endurance, and high cluster efficiency. To address these challenges, this paper proposes a Multi-Objective Reinforcement Learning framework based on Latency and Power Balance (MORL-LAPB). Instead of broad situational awareness descriptions, our framework directly combines a reward-shaping reinforcement learning algorithm with an evolutionary mechanism to construct a closed-loop optimization paradigm. Crucially, in this context, ’balancing’ extends beyond traditional computational workload distribution; it represents a joint optimization that balances task allocation to ensure short service delays while simultaneously equating the energy depletion rates across UAV nodes to maximize overall cluster efficiency and operational duration. By efficiently identifying Pareto optimal trade-offs, MORL-LAPB dynamically regulates UAV energy allocation and computational resource scheduling. Experimental results demonstrate that, compared to RSO, NSO, and DRLSO baselines, the proposed MORL-LAPB significantly reduces offloading latency, extends effective task execution duration, and improves cluster energy efficiency. The framework offers flexible adaptability and long-term sustainability for diverse operational scenarios under strict multi-objective constraints. Full article

The increasing adoption of high-aspect-ratio wings to improve aerodynamic efficiency introduces significant structural flexibility, necessitating the integration of aeroelastic considerations into the earliest design stages. While critical, existing frameworks often lack the multi-fidelity modeling capabilities and automated workflows required to bridge conceptual design and high-fidelity verification. This paper presents the Flexible Aero-Structural Toolbox (FAST), a modular framework supporting both beam and shell structural modeling and integrated with MSC NASTRAN for industry-standard aeroelastic simulation. The toolbox’s capabilities are demonstrated through modal, flutter, and static aeroelastic analyses across three distinct configurations: the P-FLEX UAV, the Ventus sailplane, and an A320-like transport aircraft, including its hydrogen-powered derivative. Results show that FAST accurately captures the aeroelastic characteristics of high-aspect-ratio wings and effectively predicts loads for large-scale flexible airframes. Notably, analysis of the hydrogen configuration reveals a significant 25% increase in wing bending moments for the “dry” wing condition compared to standard kerosene configurations. Furthermore, the tool’s ability to model unconventional mass distributions, such as cryogenic fuel tanks, highlights its adaptability for disruptive aircraft technologies. The study concludes that FAST provides a versatile, physics-based decision-making environment that significantly improves efficiency in the aeroelastic analysis process without compromising simulation fidelity. Full article