Search Results (10,666)

Global Navigation Satellite Systems (GNSS)-denied environments pose significant challenges for autonomous drone navigation, requiring robust visual localization systems capable of real-time performance. Existing approaches either sacrifice accuracy for speed or fail to adapt to varying flight altitudes and orientations, limiting their practical deployment. We present Real-Time Elevation and Orientation-Aware Localization Architecture (REOLA), a visual localization system that combines similarity-driven autonomous window sizing, element-wise correlation-based orientation detection, and reinforcement learning with human feedback (RLHF) enhancement for publicly available satellite imagery. On desktop hardware (i7-10700K + RTX 3070), the REOLA achieved approximately 59 FPS performance with sub-5-m accuracy across diverse flight conditions through intelligent similarity-based matching, combined with efficient MobileNet-V3 embeddings and FAISS similarity search. For embedded deployment on NVIDIA Jetson Orin Nano, the system achieved 22.5 FPS, meeting real-time requirements for autonomous drone localization. The system autonomously selects optimal window sizes corresponding to the current elevation and determines drone orientation through element-wise correlation scoring across discrete rotation angles. Enhanced through RLHF, the REOLA achieved a $97.1\%$ success rate (sub-5-m localization) while processing frames in 17 milliseconds on desktop hardware (44.4 ms on embedded hardware), providing a substantial margin over real-time requirements. The approach demonstrates particular superiority over traditional keypoint-based methods in challenging environments with repetitive patterns such as agricultural fields, rocky mountains, dense forests, and grasslands, where conventional keypoint detection struggles. We explicitly identify featureless sand dune deserts and open-sea or coastal water flights as out of scope, since the reference satellite imagery in those regimes does not contain stable landmarks. Full article

This paper proposes a spatial analog of the compass rose, interpreted as a discrete analog of cylindrical coordinates and considered as a basis for direction-based command filtering in Unmanned Aerial Vehicle (UAV) groups. The initial formulation is the problem of determining the direction to a radio signal source using data obtained by a group of four UAVs located at different altitudes. It is shown that, under conditions where the distance to the signal source significantly exceeds the characteristic size of the UAV spatial configuration, the direction to the source is determined much more reliably than the range to it. The results of Monte Carlo simulations confirm that the angular component of the solution remains meaningful under Time Difference of Arrival (TDoA) noise, whereas range reconstruction is substantially less stable. On this basis, a transition from a continuous description to a discrete sector representation of directions is proposed. The spatial compass rose is defined as a partition of the cylinder’s surface into a finite number of elements differing in azimuth and altitude. It is shown that this representation admits a natural algebraization: discrete directions can be one-to-one mapped to elements of finite fields and, therefore, interpreted in terms of multivalued logic. The obtained result creates the basis for simplifying computational procedures related to direction-sector classification and command processing in the on-board systems of UAV groups, provided that the method is interpreted as directional classification rather than complete three-dimensional localization. Full article

Long-horizon trajectory prediction for unmanned aerial vehicles (UAVs) operating in constrained environments remains challenging because of strongly nonlinear dynamics, hidden control effects, and evolving destination-oriented behavior. This challenge is particularly pronounced for highly maneuverable cross-domain unmanned aerial vehicles (CDUAVs), whose glide trajectories are strongly coupled with control and environmental constraints. To address this problem, this paper proposes an intent-aware CNN–Informer framework for accurate long-horizon trajectory prediction. First, a control-affine reformulation of the vehicle dynamics is used to construct physically interpretable DBL control parameters, which reduce the learning difficulty associated with hidden control effects. Second, three continuous intent features—tangential no-fly zone avoidance distance, heading error angle, and relative closing velocity—are introduced to encode destination tendency and avoidance requirements. These features are fused with historical trajectory states and fed into a hybrid CNN–Informer network, where the CNN extracts local maneuver patterns and the Informer captures long-range temporal dependencies. Experiments on a constrained trajectory dataset demonstrate that the proposed method achieves the best performance among all compared models, including SSD-LSTM, Transformer, iTransformer, DLinear, and Informer. Compared with Informer, the proposed approach reduces the average prediction error by 17.2% and significantly improves terminal and maximum prediction errors. These results indicate that the proposed framework provides an effective and physically interpretable solution for long-horizon UAV trajectory prediction in constrained flight scenarios, with potential extensions to behavior-aware forecasting and guidance support in autonomous aerial systems. Full article

Early wildfire monitoring in power transmission corridors requires reliable detection of weak fire and smoke cues under complex field conditions and strict edge-computing constraints. To address these issues, this paper proposes an edge-deployable RGB–thermal framework based on visible and thermal infrared (TIR) imaging for unmanned aerial vehicle (UAV)-based corridor monitoring, including a spatial detector, YOLO-MMSC, and a temporal-enhanced version, YOLO-MMSC-T. The study also establishes a self-collected corridor-oriented RGB–thermal (RGB–T) dataset to complement public wildfire data. Unlike existing RGB–thermal wildfire datasets that mainly focus on forest or wildland fire scenes, the proposed dataset is specifically organized for complex-background power transmission-corridor monitoring, including continuous UAV sequences, nighttime conditions, smoke/vegetation occlusion, long-range small targets, and hard-negative interference. To the best of our knowledge, this is the first self-collected RGB–thermal wildfire dataset designed for this specific application scenario. The framework integrates a mobile inverted bottleneck convolution (MBConv) lightweight backbone, a Shallow Detail Fusion Module (SDFM) for shallow cross-modal alignment and denoising, a Content-Guided Attention (CGA) module for adaptive fusion, and normalized Wasserstein distance (NWD)-based box regression for long-range small-target localization. Experiments on public and self-collected datasets show that YOLO-MMSC achieves 94.6% mAP@0.5, 95.0% precision, and 93.9% recall while running at 60 FPS on Jetson Orin NX. With temporal fine-tuning, YOLO-MMSC-T reaches a continuous detection rate (CDR) of 95.6% with a jitter index of $2.8\times {10}^{-3}$. Field experiments using a DJI Matrice 4T further indicate a practical operating altitude of 120–180 m. These results support lightweight RGB–thermal remote sensing for real-time wildfire monitoring in complex transmission-corridor environments. Full article

In the multi-class recognition task for unmanned aerial vehicle (UAV) radio frequency (RF) signals, noise interference and complex backgrounds can significantly degrade recognition performance. The stability and accuracy of existing recognition methods often fail to meet the requirements of practical applications. To enhance the stability and accuracy of UAV RF signal recognition, especially to mitigate performance degradation in complex backgrounds, a UAV RF signal classification method, MD-Net, is proposed that integrates Adaptive Time-Frequency Masking and a dual-network architecture. First, an Adaptive Time-Frequency Masking mechanism is constructed. By analyzing the energy distribution of RF signals in the time-frequency domain, the masking region is automatically determined, ensuring that the training data maintains a diverse distribution across different interference scenarios. This significantly improves the model’s anti-interference performance and discriminative stability in complex environments. Subsequently, a dual-branch recognition network architecture is designed, integrating a multi-layer perceptron (MLP) and a long short-term memory (LSTM) network. The MLP extracts static amplitude features from the signals, while the LSTM learns time-series features. These two feature types are then fused to achieve complementary characteristics, ultimately enabling accurate classification of UAV RF signals. Extensive comparative experiments conducted on the DroneRF dataset demonstrate that the MD-Net model achieves an average recognition accuracy of 85.58%, an improvement of 5.27 percentage points over the baseline model. The experimental results show that Adaptive Time-Frequency Masking can effectively enhance the model’s adaptability to real-world interference environments, while the dual-network fusion mechanism fully integrates static amplitude and time-series features, providing a feasible and highly reliable technical approach for UAV RF signal recognition. Full article

Current urban logistics models often struggle to reconcile diurnal traffic dynamics with rigid spatial–temporal regulations. This decoupling causes “cascading infeasibility,” where traffic delays trigger structural regulatory violations and UAV energy depletion. This study formulates a time-dependent vehicle–UAV joint routing problem that strictly couples time-varying speeds with vehicle-restricted zones and no-fly zones. The mixed-integer program minimizes a composite cost by integrating speed curves, geometric detour models, and coupled energy functions. To solve large-scale instances, we propose a hybrid metaheuristic solver (IHGA-VNS-SL) combining genetic algorithms, variable neighborhood search, simulated annealing, and self-learning. Tested on calibrated Wuhan instances, IHGA-VNS-SL quantitatively outperforms baseline heuristics (GA and ALNS). It achieves a tight 2.31% optimality gap against exact solvers (CPLEX) and up to a 20% cost reduction over ALNS, alongside near-zero tardiness. Results demonstrate that this strict coupling effectively mitigates synchronization failures, confirming the framework’s robustness for megacity distribution. Full article

Unmanned aerial vehicle (UAV)-assisted millimeter-wave (mmWave) and terahertz (THz) communications are promising enablers of ultra-reliable and low-latency communication in next-generation wireless networks. However, the initial access and beam alignment process remains challenging because highly directional beams must be rapidly aligned in a three-dimensional environment. In this paper, we investigate a risk-aware beam alignment framework for UAV-assisted mmWave/THz systems, where user equipment scans a 3D spherical region to detect UAV base stations. The objective is to jointly minimize the expected cell-search latency and its variance while satisfying detection-failure and link-quality constraints. To solve this non-convex optimization problem efficiently, we employ the Lévy Self-Renewable Flow Direction Algorithm (LSRFDA), which combines Lévy-flight exploration with self-renewal to improve convergence robustness. A unified propagation model is adopted to cover both mmWave and THz regimes by incorporating free-space spreading loss and frequency-dependent molecular absorption. Extensive Monte Carlo simulations compare the proposed approach with Particle Swarm Optimization, Random Search, Reinforcement Learning, and PPO-Lagrangian methods. The results show that LSRFDA achieves lower latency, lower latency variation, more reliable detection, and lower energy consumption across a wide range of UAV densities and coverage radii. These outcomes highlight the effectiveness of risk-aware geometric optimization for fast and dependable initial access in UAV-assisted 5G mmWave and 6G THz networks. Full article

To enhance path planning efficiency in unmanned aerial vehicle (UAV) search missions in complex environments, this paper proposes a coverage path planning (CPP) algorithm for a UAV that integrates the deep Q-network (DQN) with the A* algorithm (DQN-A*). In the proposed DQN-A* algorithm, a dual-driven reward mechanism is established, comprising a probability-weighted reward and a step-dependent reward, steering the UAV toward high-probability regions. Furthermore, to handle previously unknown obstacles in real time, the algorithm employs a multi-stage obstacle-identification strategy, enabling the UAV to improve coverage of traversable cells by dynamically adjusting its local path when newly detected obstacles are encountered. A theoretical analysis derives a principled recommended range for the UAV positional identifier based on statistical feature analysis; this range is then validated through extensive simulations. Additionally, Hamiltonian path pre-training is introduced to accelerate convergence. Comparative simulations demonstrate that the proposed DQN-A* algorithm achieves higher area-coverage and target-detection probabilities than benchmark algorithms in environments with unknown obstacles, offering valuable insights for positional encoding in deep reinforcement learning (DRL)-based robotic coverage problems. Full article

Cooperative deployment of mobile agents under geometric and line-of-sight constraints gives rise to high-dimensional constrained optimization problems whose underlying physical configuration often exhibits exploitable structure. This paper develops a symmetry-driven inverse design framework that leverages two structural features of the engagement geometry—the ${\mathbb{Z}}_2\times {\mathbb{Z}}_2$ mirror symmetries of the extended target silhouette and a closed-form forward–inverse correspondence between line-of-sight-aligned burst locations and physical agent parameters—to construct low-dimensional seeds for subsequent physical parameter optimization. The framework is developed and validated on a representative naval defense instance in which a fleet of unmanned aerial vehicles (UAVs) releases spherical obscuration payloads to interrupt the line of sight between incoming mobile threats and a cylindrical extended target. Instead of searching only over the four-dimensional UAV parameter space (heading angle, speed, drop time, fuse delay), the method first specifies a desired burst location in a two-dimensional inverse space and analytically back-calculates feasible agent parameters, which are then refined by multi-start Nelder–Mead optimization in the physical parameter space. A conservative three-dimensional cylindrical line-of-sight obscuration model is developed by constructing four extreme tangent sightlines from the missile to the cylindrical target and verifying whether the spherical smoke cloud simultaneously blocks all of them. A hierarchical multi-agent task allocation framework combines a performance matrix, assignment enumeration, and joint multi-start refinement. Numerical experiments on five progressively complex sub-problems demonstrate obscuration durations of $1.362$$\mathrm{s}$ (single fixed shot), $4.580$$\mathrm{s}$ (optimized shot), $7.324$$\mathrm{s}$ (three-shot relay), $11.140$$\mathrm{s}$ (three-UAV cooperation), and $20.652$$\mathrm{s}$ (full five-UAV three-missile assignment). Additional high-dimensional benchmarks, sensitivity tests, and error analyses clarify the reproducibility and limitations of the approach. Full article

Lodging is a major agricultural hazard that can substantially reduce crop yields. Timely and accurate monitoring of winter wheat lodging is important for assessing potential yield losses, guiding field management, and mitigating further lodging damage. Recent advances in unmanned aerial vehicle (UAV) remote sensing and artificial intelligence have provided new opportunities for lodging assessment. In this study, a novel monitoring framework was proposed by integrating deep features extracted from UAV multi-spectral images with machine learning algorithms. Sensitivity analysis was conducted to identify vegetation indices (VIs), which are highly correlated with lodging. These sensitive VIs were combined with original multi-spectral bands, and YOLOv8, YOLO12, SAM1, and SAM2 were used for feature extraction. The SHAP method was applied to analyze feature importance and model interpretability. The results indicated that VARI, EXG, and MCARI were the most effective VIs for lodging monitoring. Furthermore, three feature representations, including a spectral feature set, deep features, and fused features, were evaluated. The highest accuracy was achieved using YOLO12 deep features combined with a BP classifier, reaching an accuracy of 98.20%, a precision of 98.38%, a recall of 98.56%, and an F1-score of 98.56%. Overall, incorporating deep features significantly improved monitoring performance. The proposed framework provides an accurate and effective approach for crop lodging monitoring using UAV multi-spectral imagery. Full article

There is a substantial demand for lightweight, low-profile, and conformal antenna integration on the wing platforms of unmanned aerial vehicles (UAVs). This paper presents an S-band (2–4 GHz) flexible conformal metasurface array antenna based on a highly conductive graphene-assembled film (GAF). The main contributions of this work are twofold. First, flexible and highly conductive GAF is used as the conductor together with a flexible polyimide (PI) dielectric substrate to form a GAF-based wing-conformal antenna configuration with a low-profile, lightweight, and easily conformal performance. Second, a GAF conformal antenna element is developed by combining a dipole antenna with a directive and reflective frequency selective surface (FSS), achieving effective control of the beam and stable directional radiation at 2.4 GHz. Full-wave simulations using CST Studio Suite show that the directive FSS narrows the feed beam, whereas the reflective FSS redirects and narrows the H-plane radiation. The simulated results show that the integrated wing-conformal antenna operates over 2.19–2.65 GHz and achieves a gain of 4.65 dBi at 2.4 GHz. The measurement results indicate that the GAF conformal antenna and 1 × 4 GAF conformal array antenna shows measured reflection coefficients below $-10$ dB at 2.4 GHz and effective adjacent-element isolation. In addition, simulated results indicate that the GAF array antenna can perform beam scanning within the ±40° range, verifying the beam-control capability of this structure for UAV forward communication. Overall, this work highlights the feasibility of using GAF as a conductive material for both a high-efficiency radiator and an FSS beamforming structure, offering a practical material and design approach for lightweight, low-profile, and wing-conformal airborne array antennas. Full article

High-throughput and non-destructive phenotyping approaches are increasingly needed to support precision agriculture and plant breeding. This study evaluates the use of unmanned aerial vehicle (UAV) multispectral imagery combined with machine learning to estimate anthocyanin content in red lettuce genotypes under field conditions. High-resolution RGB and multispectral images were acquired using a low-cost UAV platform, and vegetation indices sensitive to pigment variation were extracted at the plot scale. Ridge regression, decision tree, and random forest models were trained using 80% of the dataset and validated with the remaining 20%. Random forest achieved the highest performance for anthocyanin estimation, with coefficients of determination reaching R² = 0.84 and lower prediction errors than linear approaches. Overall, the results demonstrate that UAV-based multispectral sensing integrated with machine learning provides a robust, scalable, and cost-effective solution for non-destructive pigment phenotyping, with direct applications in biofortification-oriented breeding and precision agriculture. Full article

Lift-plus-cruise electric vertical takeoff and landing (eVTOL) aircraft exhibit complex stability characteristics during transition flight, when rotor-borne and wing-borne regimes coexist. This work investigates the dynamic stability of a lift-plus-cruise eVTOL using a nonlinear six-degree-of-freedom model incorporating aerodynamic forces, tractor propulsion, and vertical lifter dynamics. Linearization about representative trimmed conditions enables longitudinal and lateral–directional modal analysis. The results identify a critical near-stall region where lift-curve slope reduction markedly decreases short-period damping. Residual lifter authority partially compensates for this degradation, improving stability in the transition regime. To ensure smooth control transfer, an airspeed-dependent blending strategy between hover and fixed-wing controllers is implemented. Comparative analyses show that a sigmoid blending law improves the minimum short-period damping ratio relative to a linear strategy while preserving similar overall damping variation. Closed-loop simulations of a complete mission profile demonstrate the effectiveness of the proposed approach and reveal an asymmetric dynamic response between hover-to-forward and forward-to-hover transitions. These findings provide a physically grounded explanation for stability degradation during transition and establish practical guidelines for control authority blending in lift-plus-cruise eVTOL aircraft. Full article

Cooperative localization for multi-Unmanned Aerial Vehicle (UAV) systems in GPS-degraded environments is often compromised by ideal-communication or uniform-quality assumptions. This paper proposes Quality-Aware Distributed State Estimation (QA-DSE), which combines three operational quality factors—freshness (Age of Information), accuracy (covariance trace), and link reliability (packet loss and channel noise)—into a single multiplicative score $q_{ij}$, modulated by a bounded history-consistency factor based on velocity-propagated self-trajectory continuity. A dual-constraint AND-gate on AoI and covariance trace excludes jointly degraded neighbors, while admitted neighbors are fused through a quality-squared information-matrix update under a stated bounded residual cross-correlation assumption, with an adaptive Covariance-Intersection fallback when the assumption is stressed. Under explicit observability, bounded-noise, bounded-quality, joint-connectivity, and bounded residual cross-correlation assumptions, we establish mean-square bounded error, exponential convergence at a rate inherited from the Kalman update operator, $O\left(n^3+nm\right)$ per-step complexity, Bounded-Input Bounded-Output (BIBO) stability, soft attenuation of single-axis faults (Theorem 4), and hard exclusion under joint AoI–covariance violation (Theorem 5). Under a Ultra-Wideband (UWB)-style cooperative-observation model, Monte Carlo experiments across five scenarios show 74.08–74.24% position- Root Mean Square Error (RMSE) reductions over Covariance Intersection, with the relative advantage held within 73.04–74.24% as the fleet scales from 3 to 50 UAVs; QA-DSE remains within 8.1% of an idealized no-cooperation single-vehicle Kalman filter, demonstrating graceful degradation rather than improvement above that floor. Per-step Central Processing Unit (CPU) time scales from 0.09 ms (5 UAVs) to 0.31 ms (50 UAVs); embedded validation is left to future work. Full article

Aiming at the problem that it is difficult for existing path planning methods to plan UAV paths in real time in complex atmospheric turbulence environments, this work proposes a dynamic path planning method for UAVs based on an improved artificial lemming algorithm. First, using temperature, pressure, and wind vectors from WRF/NWP forecast data, a dynamic turbulence-change environment model in the airspace is constructed. Then, a UAV dynamic path planning model is formulated by comprehensively considering the turbulence change rate and path safety evaluation factors. Next, to address premature convergence of existing algorithms under turbulence influence, a solving method for the UAV dynamic path planning model based on an improved artificial lemming algorithm is developed. Simulation results show that, under the proposed replanning mechanism, the improved algorithm reduces the final fitness by 36.19% and cumulative turbulence exposure by 16.28% on average compared with all competing methods. Full article