Search Results (292)

Small unmanned aerial vehicle (UAV) acoustic signatures are relevant to environmental noise assessment, passive monitoring, and preliminary detectability analysis. This study presents a physics-informed reduced-order framework that combines blade-passing frequency (BPF) harmonic synthesis, rotational-speed acoustic scaling, propagation, and signal-to-noise ratio (SNR) threshold crossing. The RPM–OASPL law was calibrated using ten digitized measurements for a four-rotor DJI Phantom II with Original 9450 propellers and validated, without refitting, against eleven Aftermarket 9443 measurements. The fitted exponent was m = 5.285, and the fitted reference level was Lref = 77.01 dB(A) at 5000 RPM and 1 m. A 100,000-realization Monte Carlo analysis that propagated ±100 RPM and ±0.5 dB digitization bounds, together with residual scatter, produced total 95% intervals of 5.01–5.55 for m and 76.58–77.43 dB(A) for Lref. Calibration yielded RMSE = 0.39 dB and R² = 0.9979; independent-configuration validation yielded MAE = 2.27 dB, RMSE = 2.38 dB, and R² = 0.9168. At 5000 RPM, nominal free-field threshold-crossing distances were 70.9, 22.4, and 7.1 m for quiet rural, semi-urban, and urban scenarios. Combined statistical 95% screening intervals were 41.3–121.7, 13.0–38.5, and 4.1–12.2 m, respectively. Sensitivity analyses show that β controls harmonic-specific range but not normalized OASPL, while ground interference, band-limited masking, and detector processing can materially change operational range. The framework is therefore a rapid, interpretable screening tool rather than a universal detector performance model. Full article

A multi-rotor UAV-based oscillating-tube broadcasting device incorporating replaceable material-specific screw metering units was developed for low-rate seed and granular-fertilizer application. The key structural parameters of the oscillating tube were screened through theoretical analysis and single-factor experiments using pre-germinated conventional Huanghuazhan rice. An L16(4³) orthogonal experiment was then conducted solely for exploratory screening of the main effects of flight height, forward speed, and broadcasting material on the single-pass broadcasting-uniformity coefficient of variation (${\mathrm{C}\mathrm{V}}_{\mathrm{b}}$) and effective spreading width (We). Forward speed significantly affected ${\mathrm{C}\mathrm{V}}_{\mathrm{b}}$, whereas all three factors significantly affected We, with flight height showing the strongest effect. Separate two-factor central composite design experiments were subsequently conducted for conventional Huanghuazhan rice, hybrid Yongyou 12 rice, Yangguang 131 rapeseed, and Stanley compound fertilizer. Material-specific quadratic models were established, yielding model-recommended flight height and forward speed combinations of 3.04 m and 2.35 m s⁻¹, 2.79 m and 1.70 m s⁻¹, 3.00 m and 1.80 m s⁻¹, and 3.20 m and 2.25 m s⁻¹, respectively. These combinations represent constrained model-based recommendations rather than global or independently verified optima. In a limited consecutive-pass field demonstration, the field-sample coefficient of variation (${\mathrm{C}\mathrm{V}}_{\mathrm{f}}$) ranged from 13.76% to 20.34%, and the mean absolute pointwise deviation from the target count ranged from 15.11% to 19.05%. The results demonstrate the preliminary mechanical and operational feasibility of the prototype under the tested materials, operating settings, site, and environmental conditions. Full article

Fixed-wing UAVs operating in long-range missions often fly through airspace subject to heterogeneous multi-source constraints that vertically compress the flyable space into a constraint corridor of continuously varying thickness. Conventional path planning methods incur high online computational costs in such scenarios. Deep reinforcement learning can generate reactive decisions from local observations, yet existing approaches predominantly target multirotor obstacle avoidance and rely on observations designed for discrete obstacles, lacking a unified representation for corridor constraints. Moreover, constraint conditions vary across mission scenarios, demanding cross-scenario policy generalization. This paper proposes the Egocentric Constraint Corridor (ECC), which fuses multi-source constraints into upper and lower boundary surfaces defining the corridor, then egocentrically encodes the surrounding corridor relative to the vehicle into a margin field serving as structured policy input. A deep reinforcement learning framework built on ECC is trained end-to-end, with its multi-branch network and composite reward function following from the structure of the corridor encoding. Experiments show that ECC-DRL achieves path efficiency approaching that of globally informed A*, and that it is the only one of the compared methods that computes its decisions online within the decision interval. Ablation studies confirm the margin field is necessary for reliable navigation, and the ECC encoding enables zero-shot transfer to scenarios with unseen terrains and radar deployments without retraining. Hardware-in-the-loop experiments on an embedded platform verify real-time closed-loop feasibility. Full article

With the increasing scale and density of low-altitude unmanned aerial vehicle (UAV) operations, safety separation between multirotor UAVs has become a critical parameter for low-altitude airspace management. Existing studies mainly consider aircraft geometry, navigation errors, trajectory deviations, and conventional collision risk models, while rotor-downwash-induced aerodynamic interference remains insufficiently addressed. This study proposes a safety separation assessment method for quadrotor UAVs by integrating computational fluid dynamics (CFD) with an improved Event collision model. A small-scale quadrotor UAV is analyzed, and its rotor downwash flow fields under vertical- and horizontal-motion conditions are simulated using the multiple reference frame method. Based on a 5 m/s crosswind-resistance capability threshold, aerodynamic-interference characteristic distances are extracted and used to construct a basic collision box. To better represent the actual aerodynamic hazard region, an I-shaped improved collision box is further developed and incorporated into the Event collision model. Under a target level of safety, the longitudinal, lateral, and vertical minimum safety separations are determined as 1.68 m, 1.72 m, and 1.08 m, respectively. The results show that the proposed CFD–Event coupled method can transform rotor downwash characteristics into collision risk parameters and provide a quantitative basis for safety separation assessment in dense low-altitude multirotor UAV operations. Full article

Small multirotor UAVs frequently operate in crosswind conditions, yet the aerodynamic interaction between windward and leeward propeller pairs remains incompletely understood. This study investigates the performance of a quadcopter propeller system under lateral crosswind using steady-state RANS simulations with the Transition SST turbulence model, validated against wind tunnel measurements (thrust and torque deviations within $5.4\%$). A parametric matrix of five rotational speeds (8000–12,000 RPM) and six freestream velocities (0–10 m/s) is systematically examined. While thrust and power coefficients of all propellers increase monotonically with freestream velocity, the figure of merit ($FM$) of leeward propellers exhibits a previously unreported non-monotonic response: it decreases from hover, reaches a minimum near $6 \mathrm{m}/\mathrm{s}$, and partially recovers at higher velocities. Windward propellers show no such degradation. Our velocity contour and streamline analyses reveal that this behavior originates from windward wake ingestion into the leeward inflow region, which peaks at intermediate freestream velocities and is progressively alleviated as the stronger crosswind convects the wake downstream. The non-monotonic $FM$ response is therefore a direct consequence of the competition between wake-induced inflow degradation and freestream-driven aerodynamic augmentation. Our findings provide a systematic aerodynamic dataset essential for crosswind attitude control and propulsion system design in multirotor UAVs. Full article

Small multi-rotor UAVs are difficult radar targets: their radar cross-section is low and aspect-dependent, and their slow body motion overlaps with birds and clutter in Doppler processing. We address W-band (94 GHz) detection with a physics-informed, non-coherent framework that exploits blade flexibility. A compact three-dimensional micro-Doppler model combines counter-rotating rotor kinematics, hub-dependent spatial phase, and a first-order out-of-plane bending term; the rigid rotational signature scales with $\cos \beta$ and the bending contribution with $\sin \beta$ in elevation $\beta$. Near zenith, where rigid micro-Doppler collapses toward DC, bending repopulates an observable low-velocity band. Detection uses cosine similarity between magnitude spectrograms, which is robust to the tested oscillator impairments. With a calibrated false-alarm rate ($P_{\mathrm{FA}}=0.01$) and unknown target rotor phase, the detector reaches $P_d\approx 0.9$ at SNR $\approx -14$ dB and stays stable for carrier-frequency offsets up to 500 $\mathrm{Hz}$ and phase random walk up to $0.2$ $\mathrm{rad}$/sample, where an uncompensated matched filter fails; it also retains detection against simulated bird-and-clutter micro-Doppler in the background-dominated regime where an energy detector collapses. A consistency check against measured single-blade no-IQ W-band records reproduces the one-sided time–frequency periodicity under a matched product-detector operator. The result is an interpretable, training-free baseline for phase-limited W-band UAV sensing. Full article

On 14 February 2025, the outer cladding of the Chornobyl New Safe Confinement (NSC) was damaged by an explosion caused by a one-way attack unmanned aerial vehicle (UAV), creating a hole of about 15 m in diameter and requiring about 300 penetrations to be made in the cladding during firefighting. This created an urgent need to assess radiation dose rates above damaged areas to support repair planning and worker radiation protection. This study presents a UAV-based survey of the equivalent gamma dose rate distribution above the damaged northern side of the NSC outer cladding. The survey used a bespoke system, integrating a multirotor UAV, an AccuRad Personal Radiation Detector (PRD), onboard data acquisition and transmission modules, and ground-based and server-side analytical components. Measurements were performed under real post-incident field conditions, including restricted flight zones, wind-induced turbulence, proximity to large metallic structures, and electronic warfare interference. The dataset was filtered for Global Positioning System (GPS) reliability, transformed into a metric coordinate system, and processed for spatial interpolation and mapping. The resulting distribution showed a spatially non-uniform radiation field: the main damage zone had relatively low equivalent gamma dose rates, whereas the highest values, up to 1092 μSv/h, were recorded over areas of the NSC closest to the Shelter Object. The study demonstrates UAV-based radiation mapping of a damaged large-scale confinement structure and provides data supporting Chornobyl Nuclear Power Plant repair planning. Full article

Vertical take-off and landing (VTOL) configurations incur a structural weight penalty that reduces payload fraction and endurance compared to conventional fixed-wing and multirotor aircraft of comparable gross weight. To extend the endurance of VTOL UAVs, this work presents the design, analysis and control of a novel unmanned tilt trirotor aircraft with tandem wings, named Gong-H, featuring VTOL capability and high aerodynamic efficiency. A prototype of this aircraft was built with the rotor system mounted between tandem wings with a high wing coverage rate, which can achieve a more compact structure than other VTOL aircraft. The control forces and torques are provided not only by the rotor system in VTOL flight mode and the two tandem wings in cruise mode, but also by both the rotor system and wings in transition mode. Additionally, Computational Fluid Dynamics (CFD) simulations are conducted to optimize the wing configuration to improve the efficiency of cruise mode. Moreover, an airspeed-scheduled hybrid control framework based on incremental nonlinear dynamic inversion (INDI) and PID is adopted for different flight modes to improve the robustness of control and the stability of flight mode switching. Hover experiments confirm improved power efficiency compared to tilt quadrotor configuration, which extends endurance time and increases range. Additionally, complete flight cycle field experiments were conducted to demonstrate the aerodynamic feasibility of the prototype, including VTOL flight, cruise flight, and transition flight modes. Control surface redundancy tests and comparative INDI-PID validation under asymmetric disturbances further verify the practical robustness of the control framework. This work provides a design concept of VTOL aircraft and a practical solution for VTOL applications. Full article

Large-scale UAV data collection creates a tension among wide-area coverage, operational efficiency, and delivery continuity. Data must be continuously delivered to a base-station coordinator, but real-time replanning becomes increasingly difficult as the number of sensors and UAVs grows. Standard vehicle-routing methods slow down once routes have to be regenerated often, while reinforcement learning struggles with fixed-wing UAVs that cannot hover or turn sharply. We address this with a two-layer framework. In the lower layer, multirotor UAVs visit sensor nodes and buffer the collected payload until it is retrieved by a fixed-wing UAV. Their routes come from clustering the nodes and solving a capacitated vehicle routing problem within each cluster, with the cost biased toward older data and a short cooldown against immediate revisits. In the upper layer, fixed-wing UAVs deliver the buffered payload to the base-station coordinator, guided by a Multi-Agent Proximal Policy Optimization (MAPPO) policy that receives a local buffer-summary map and selected high-priority cells from a compact global summary. A spacing reward encourages separation before agents enter close-proximity states, instead of only penalizing collisions afterward. Component-level experiments show that the lower-layer planner handles up to 600 active routing targets within 1.3 s on average and that the age/cooldown objective improves freshness and revisit behavior. In integrated simulations with 1000 nodes, 32 multirotor UAVs, and 2 fixed-wing UAVs, the learned fixed-wing policy maintains collection performance comparable to a strong exclusive greedy baseline while recording no collision or persistent-proximity termination events over the reported data-generation-rate sweep. These results support the proposed framework as a scalable coordination-layer design for dynamic sensor workloads, where adaptive multirotor routing and motion-constrained fixed-wing retrieval are evaluated together under a shared data-generation workload. Full article

UAVs operating in cluttered and dynamic environments face limitations when controlled using conventional Euclidean frameworks, which leads to degraded performance during aggressive maneuvers. This paper presents a geometry-aware predictive control framework for multirotor UAVs based on a two-manifold-inspired representation of actuator geometry. The proposed control strategy adopts an MPC-inspired prediction structure without solving a full online optimization problem. A comparative simulation study is conducted in MATLAB/Simulink under identical mission, obstacle, and noise conditions for both the conventional point-mass controller and the proposed two-manifold-based controller. Performance is evaluated in terms of trajectory tracking accuracy and control effort. Results indicate that the proposed framework achieves modest improvements in tracking accuracy and produces smoother control inputs compared to the point-mass model. It suggests that geometry-aware representations may enhance predictive control performance in obstacle-rich environments, although further validation under diverse scenarios is required. Full article

Multi-UAV cooperative persistent tracking in complex obstacle environments requires agents to approach dynamic targets while ensuring obstacle avoidance and flight safety; however, standard multi-agent reinforcement learning (MARL) methods typically rely on a single policy to implicitly handle both objectives, making it difficult to balance task performance and risk control. To address this issue, this paper proposes a Target-Aware Safety-Residual Pursuit Reinforcement Learning (TASRP) framework for constrained three-dimensional environments. A continuous-control 3D tracking environment is constructed in IsaacLab, where two multirotor UAVs cooperatively track a dynamic target under random, target-blocking, and gate-like obstacle layouts, boundary constraints, and inter-agent collision risks, with each UAV producing a four-dimensional action composed of normalized thrust and body-frame torques. TASRP adopts a dual-head residual policy in which a pursuit branch generates nominal actions, and a safety branch predicts corrective residuals, together with a risk-aware gating mechanism, a target-guided teacher for obstacle detouring, and a dual-critic safety-constrained optimization scheme. Under clean observations, TASRP achieves task success rates of 75–79%, obstacle crash rates of 13–15%, and boundary crash rates of 1–2% across three representative scenarios. Under noisy observations, TASRP achieves 72.1% task success, 20.3% obstacle crash, and 2.8% boundary crash, outperforming MAPPO (61.2%, 61.2%, 5.6%) and HAPPO (58.1%, 73.5%, 4.1%). These results indicate that explicitly decoupling target-oriented control and safety correction enables a more effective and robust performance–safety trade-off under both clean and moderately noisy observations. Full article

Multirotor unmanned aerial vehicles (UAVs) rely on electronic speed controllers (ESCs) that decode motor commands from pulse-width modulation (PWM) signals, making the flight-controller-to-ESC command path a physical-layer attack surface for intentional electromagnetic interference (IEMI). This paper presents a mechanism-based analysis of IEMI attacks that induce motor stoppage in UAV brushless DC motor controllers. We develop a timing-error model in which a sinusoidal disturbance on the PWM line shifts the detected edge instants and drives the decoded pulse width into stop-equivalent regimes, and we show that the disturbance reaching the ESC’s thresholding node is shaped by a frequency-selective cascade of the PWM cable’s coupling response and the ESC’s input-path transfer function. We experimentally characterize this model on five commercial ESCs through conducted and radiated injection. The measured thresholds differ by more than an order of magnitude across ESCs and are reordered between frequency bands and injection modes; comparing conducted and radiated results allows us to attribute these differences primarily to the cable coupling response and reveals cases where it either hides or amplifies an ESC’s susceptibility. The susceptible frequency also shifts with PWM cable length in qualitative agreement with transmission-line resonance, confirming that observed radiated susceptibility reflects the joint design of ESC and cable rather than a single intrinsic property. The cable lengths examined here (45–125 cm) are longer than those of compact multirotors and were chosen to place resonances within our antenna’s band; we discuss the implications of this choice and identify shorter, deployment-realistic cables as a priority for future work. Full article

In the paper, a systematic treatment of sensitivity analysis of multivariable cont rol systems within the framework of the characteristic transfer functions (CTFs) method is given. The CTFs method (also called characteristic gain loci method) allows one to associate with an N-dimensional multi-input multi-output (MIMO) system a set of N independent single-input single-output (SISO) characteristic systems and thereby to reduce the analysis and design of a MIMO system to the analysis and design of N SISO systems. The formulas determining the sensitivity functions of the CTFs and the sensitivity vectors of the canonical basis axes to small variations of parameters of general type MIMO systems are derived. The relations between the sensitivity functions of the open-loop and closed-loop MIMO systems are established. Two illustrative examples are considered. The first of them concerns the sensitivity of a two-dimensional non-robust system with a large degree of skewness of the canonical basis axes. In the second example, the sensitivity of the control system of a hexacopter (a multirotor UAV with six rotors) to small degradations in motors efficiency is analyzed. Full article

As unmanned aerial vehicle (UAV) systems evolve from automated execution toward autonomous decision-making, multirotor UAVs increasingly face complex dynamics, uncertain sensing conditions, and task-level autonomy demands. Reinforcement learning (RL) has emerged as a promising learning-based paradigm for addressing these challenges. Existing surveys on RL-based UAV control predominantly classify methods from an algorithmic or learning-paradigm perspective, while relatively little attention has been paid to the functional roles of RL policies within the control loop. This often leads to an unclear correspondence between algorithmic characteristics and the requirements of different control layers. To address this gap, this review proposes a biomimetic “spinal cord–cerebellum–cerebrum” framework, organizing existing RL studies into low-level dynamic stabilization, mid-level perception–action coordination, and high-level task planning and decision-making. The proposed hierarchy emphasizes the functional role and intervention depth of RL policies within the control architecture, further supporting a layer-wise analysis of sim-to-real challenges. This review aims to provide a structured understanding of the roles of reinforcement learning in hierarchical UAV control and to highlight future research directions toward robust real-world deployment. Full article

Existing fault-tolerant control methods for multicopter UAVs often exhibit degraded performance under actuator faults and modeling uncertainties; therefore, this paper presents a robust-adaptive control algorithm for multicopter UAVs operating under actuator fault conditions. A data-driven approach based on an Artificial Neural Network (ANN) is employed to estimate actuator fault using IMU measurements. The ANN is trained using data generated from a closed-loop system controlled by a robust-adaptive control, rather than an open-loop configuration, improving its ability to capture realistic fault dynamics. To mitigate limitations in training data coverage, an adaptive mechanism is incorporated to enhance robustness under varying operating conditions. In addition of inherent fault-tolerant control characteristics of the robust-adaptive control, the estimated fault signals are used for motor speed compensation to enhance the robustness of the algorithm. The inner-loop controller is designed based on a robust-adaptive algorithm, ensuring system stability and robustness against model uncertainties and fault estimation errors, even the actuator faults change both system dynamics and actuation. The outer-loop Proportional–Integral–Derivative (PID) controller is employed to achieve accurate trajectory tracking. For validation and benchmarking, a standalone robust-adaptive controller and a model-based recursive least squares (RLS) estimator are also implemented. Simulation results demonstrate that the proposed ANN-based approach provides accurate fault estimation and effective compensation, resulting in improved tracking performance under actuator fault conditions. Furthermore, the proposed framework contributes to the development of a fault-tolerant UAV systems by integrating robust-adaptive control, ANN-based fault estimation, and actuator compensation into a unified architecture, thereby enhancing reliability, robustness, and tracking performance in the presence of actuator faults and modeling uncertainties. Full article