Search Results (1,906)

This paper presents an adaptive event-triggered nonsingular fast terminal sliding-mode control (AETSMC) framework for quadrotor unmanned aerial vehicles subject to aerodynamic disturbances, actuator loss of effectiveness (LOE) of up to 60%, and intermittent denial-of-service (DoS) attacks. First, a nonsingular fast terminal sliding-mode (NFTSM) surface is constructed using fractional powers of the tracking error rather than fractional-order derivatives. This design ensures finite-time convergence while avoiding the singularity associated with conventional terminal sliding-mode schemes. Second, a smooth positive-semidefinite barrier function (Smooth-PSBF) is incorporated into the adaptive gain law. The resulting law provides only the compensation required to maintain the prescribed bound, thereby limiting gain overestimation and chattering. Third, a dual-mode event-triggering mechanism combines an exponentially decaying threshold with a zero-order hold. A positive lower bound on the inter-event interval is derived from the closed-loop dynamics, which excludes Zeno behaviour. Simulations under matched conditions show that the proposed method reduces the pitch-channel root-mean-square error by 79.4% and the integral squared error by 95.8% relative to the first reproduced baseline. In a separate 15-s communication experiment sampled at 1 kHz, the controller generated 128 transmissions instead of 15,000 periodic updates, corresponding to a 99.15% reduction. These results indicate that the proposed framework can improve fault-tolerant tracking while reducing communication demand under intermittent DoS attacks. Full article

Recreational coastal waters are increasingly affected by urbanization, maritime activities, and tourism, creating a need for predictive tools that support proactive water quality management. This study proposes an explainable machine learning framework for forecasting near-future changes in the Tourism-Oriented Seawater Quality Index (SeaWQI-T) using high-frequency seawater monitoring data collected in the Gulf of Vlorë, Albania. A summer monitoring campaign (June–August 2025) produced 102,988 physicochemical observations from six monitoring stations using an unmanned surface vehicle equipped with a Horiba U53 multiparameter sonde. Following quality control and temporal aggregation, the data were used to formulate a multiclass forecasting problem (decrease, stable, or increase), and Logistic Regression, Random Forest, and Extreme Gradient Boosting (XGBoost) models were evaluated across multiple forecasting horizons. XGBoost achieved the best validation performance, while Random Forest demonstrated superior generalization on the independent test dataset and provided the most stable explainability results. SHapley Additive exPlanations (SHAP) identified SeaWQI-T dynamics, turbidity, dissolved oxygen, and oxidation–reduction potential as the most influential predictors. The proposed framework demonstrates that integrating explainable machine learning with autonomous high-frequency monitoring can provide accurate, interpretable forecasts to support intelligent coastal recreation management and sustainable tourism planning. Full article

Despite the rapid proliferation of unmanned aerial vehicles (UAVs) across industrial, agricultural, and scientific domains, their deployment remains constrained by limited endurance, aerodynamic inefficiency, and acoustic emissions, all mediated by a shared onboard energy budget. Existing biomimetic UAV reviews have generally treated aerodynamics, structures, sensing, control, and energy systems as parallel topics rather than as interacting components of a unified aerial architecture. Drawing primarily on literature published between 2015 and June 2026 and identified through searches of Web of Science, Scopus, and Google Scholar, this review addresses this gap by examining bio-inspired technologies across six principal domains: aeroacoustic and passive flow control, aerodynamic efficiency, multifunctional structural composites, neuromorphic sensing and control, ionic energy storage, and energy harvesting. Its principal contribution is a cross-domain synergy analysis identifying five performance couplings and one structural enabling architecture through which these domains interact physically and functionally. Representative examples include serration-based propeller geometries that can simultaneously reduce noise and power demand; morphing wing surfaces that serve as both aerodynamic structures and triboelectric harvesting substrates; and neuromorphic spiking neural networks that have been reported, in specific event-vision inference benchmarks, to reduce inference energy by three to four orders of magnitude relative to embedded graphics processing unit (GPU)-based implementations. Mechanical harvesting outputs nonetheless remain orders of magnitude below propulsion requirements and are thus positioned as supplementary. Four systemic barriers (unquantified mass–energy balance, undocumented durability, aeroelastic co-design gaps, and heterogeneous metrics) are evaluated, and the resulting synthesis indicates that advancing bio-inspired UAVs requires a transition from structural imitation to functional, system-level biomimetics. Full article

Atmospheric icing is one of the most critical meteorological hazards for unmanned aerial vehicles (UAV), whose operation under adverse conditions—high latitudes, elevated altitudes, long-endurance missions without pilot intervention—particularly exposes them to ice accumulation on aerodynamic surfaces and propellers. Unlike manned aviation, where this phenomenon has been extensively studied and regulated, a significant knowledge gap exists in the UAV domain that limits the development of effective protection systems adapted to energy constraints. This article provides an integrative review—conducted with a systematic search strategy following PRISMA reporting guidelines—of atmospheric ice formation mechanisms, their specific effects on UAV propellers, and the two most promising mitigation approaches: electrothermal modelling for the optimisation of electric heating systems and the development of functional surface materials including superhydrophobic coatings (SHC); composites with conductive nanofillers (graphene, carbon nanotubes); and piezoelectric actuators. The analysis demonstrates that hybrid systems combining passive and active strategies managed by intelligent control represent the most viable solution for extending UAV operational envelopes under known icing conditions, with a projected reduction in anti-icing system energy consumption of at least 40% relative to conventional continuous heating. This estimate is based on the most conservative published evidence: pulsed electrothermal de-icing achieves 40–60% savings versus continuous anti-icingSHC-assisted hybrid heating reduces IPS power by more than 80% on static aerofoils; and rotary-wing pulsed systems reduce mean consumption by 60–75% relative to continuous operation. Key research gaps are identified, and a prioritised future research agenda is proposed to support the development of certifiable anti-icing systems for rotary-wing UAV platforms. Full article

This study proposes a safety-enhanced path planning system that integrates a Control Barrier Function (CBF)-based Safety Shield with Deep Reinforcement Learning (DRL). This framework addresses the critical limitations of conventional DRL-based Unmanned Surface Vehicle (USV) navigation models, which can output hazardous control commands in edge cases and violate the International Regulations for Preventing Collisions at Sea (COLREGs). The proposed system continuously operates during navigation via an Encounter Classifier that identifies multi-vessel situations (such as Head-on, Crossing, and Overtaking) in real time. The nominal control inputs generated by the DRL policy are verified and safely filtered through a Control Barrier Function-Quadratic Programming (CBF-QP) optimization layer immediately prior to execution, incorporating ship safety radii and asymmetric COLREGs constraints. Furthermore, we introduce a ‘Shielded Training’ mechanism that penalizes the agent based on the magnitude of the shield’s interventions during the training loop. This effectively diminishes the policy’s over-reliance on the safety filter and guides the network toward discovering robust, inherently safe trajectories. Extensive simulations conducted under diverse single- and multi-vessel encounter scenarios quantitatively demonstrate that the proposed method substantially reduces collision and COLREGs violation rates compared to baseline DRL-only or reward-shaping methods, while maintaining excellent computational scalability and real-time responsiveness. Consequently, by unifying the adaptive environmental exploration of reinforcement learning with model-based runtime safety constraints derived from control theory, this study provides a practical runtime assurance framework for future marine deployment. Full article

Environmental monitoring of freshwater ecosystems faces significant challenges in detecting illicit wastewater discharges, which often remain unrecognized due to their intermittent nature and limited spatial footprint. This study presents a novel integrated strategy combining satellite remote sensing, Unmanned Aerial Vehicle (UAV)-based proximal sensing, and in situ measurements to enhance pollution detection in vulnerable aquatic environments. The methodology was applied to Lake Avernus (Italy), a volcanic lake historically affected by eutrophication and toxic cyanobacterial blooms. Landsat 8–9 thermal analysis revealed no detectable anomalies, reflecting the limitations of its coarse spatial resolution. Sentinel-2 multispectral imagery was then analyzed through spectral indices, band ratios, and reflectance signatures, revealing localized variations in surface reflectance and spatial heterogeneity in water optical properties. These satellite-derived anomalies guided targeted high-resolution UAV surveys. UAV-based thermal imaging revealed an elevated-temperature zone along the adjacent shoreline. In situ field screening flagged a candidate chemical anomaly at this location. The hierarchical framework demonstrates that satellite screening effectively identifies areas of concern, while UAV thermal imaging enables high-resolution localization of features invisible to satellite sensors, and in situ measurements provide essential ground-truth validation. This replicable, low-cost methodology offers a powerful tool for early warning, surveillance, and sustainable management of sensitive freshwater ecosystems. Full article

Surface icing poses a significant risk to unmanned aerial vehicles (UAVs) and compact aerospace platforms, where limited onboard power and space require efficient anti-/de-icing surfaces. In this study, micro/nanostructures were fabricated on TC4 titanium alloy (Ti–6Al–4V) surfaces by femtosecond laser processing at different scanning speeds. The effects of scanning speed on surface morphology, wettability, static freezing, dynamic droplet behavior, and electrothermal de-icing performance were systematically investigated. Increasing the scanning speed induced nonlinear changes in microstructure height and surface roughness, while variations in ablation intensity caused nonuniform material redistribution. The surface processed at 250 mm/s showed the best anti-icing performance, with a water contact angle of 157.5 ± 0.5° and a maximum freezing delay 21.5 times longer than untreated TC4. During electrothermal de-icing, melting initiated at discrete ice–substrate contact points, forming coalesced meltwater films, while interfacial stress concentration promoted crack propagation and rapid ice detachment. Compared with untreated surfaces, ice detachment time (250 mm/s) achieved complete ice detachment at approximately 152 s, whereas ice on the untreated surface remained adhered after 270 s of continuous heating, representing a de-icing time reduction of at least 44%. These results demonstrate that combining laser-fabricated microstructures with electrothermal heating effectively reduces real ice–substrate contact, providing an enhanced anti-/de-icing strategy for lightweight, long-endurance UAV applications under identical electrical input. Full article

A post-flight analysis of unmanned aerial vehicle (UAV) imagery has the potential to result in a delay in the inspection of source water. This delay can occur when visible debris or changes in the water surface necessitate a prompt response. The present study does not evaluate in-flight operation; rather, it presents a bench-level feasibility assessment of two deployment tasks—broad two-class screening and close-range debris classification—using lightweight YOLO detectors on an edge-AI camera in a host-fed configuration that approximates the timing constraints of a future UAV workflow. The YOLOv8, YOLO11, and YOLO26 models were lightweighted through structural pruning (YOLOv8) or architecture scaling (YOLO11 and YOLO26). These models were then refined through a process of fine-tuning, exported to the camera, and evaluated in terms of several metrics. The metrics encompassed training-environment accuracy, the accuracy of device-returned outputs, round-trip latency, and snapshot-based operating-load estimates. The dataset under consideration is extensive, comprising 4813 training images and 575 validation images, accompanied by 13,051 and 1615 annotations, respectively. The depth-pruned YOLOv8s variant demonstrated a significant reduction in mean round-trip latency, from 426.87 milliseconds to 231.58 milliseconds (45.75%), while the mAP@0.5 metric exhibited a decrease from 0.7018 to 0.6650, and the mAP@0.5:0.95 metric demonstrated a decline from 0.5433 to 0.5290. A class-level analysis reveals that aggregate accuracy is primarily influenced by the weaker floating-debris class, whose AP@0.5 ranges from 0.29 to 0.46, in contrast to the 0.82 to 0.94 range observed for pond/reservoir. In comparison to a matched baseline that was trained for an equivalent number of epochs with the sampler disabled, debris-biased sampling contributes 1.5 ± 0.6 mAP@0.5 points for YOLO11 and 3.6 ± 0.2 points for YOLO26 across three seed-matched pairs. The primary effect of this method is to increase floating-debris recall by 4.7–5.9 percentage points, with a concomitant small reduction in precision. The latency reduction increased the broad-inspection rate by 1.85×, provided approximately 195 milliseconds of idle margin within a 1-hertz cycle, and increased the paired far/near rate by 1.59× with two models resident on the camera. Three-seed repetitions of compact-model fine-tuning yielded 0.6717 ± 0.0033 and 0.6290 ± 0.0028 mAP@0.5. These results express detector compression in terms of operational monitoring capacity rather than model-size reduction alone, while also showing that compression by itself does not resolve the weak-class limitation that governs source-water inspection accuracy. Full article

Wireless sensing networks increasingly extend into obstacle-prone deployments, where physical blockage degrades reliability and open propagation exposes transmission activity. Intelligent reflecting surfaces (IRSs) establish programmable paths around obstacles while passive elements remain constrained by severe cascaded attenuation. To address the tradeoff between reliability and covertness, we propose an unmanned aerial vehicle (UAV) -assisted active-IRS architecture under probabilistic line-of-sight and non-line-of-sight propagation conditions that accounts for direct leakage from the transmitter to the warden together with residual jammer cancellation and always-on IRS circuit noise under a finite output power budget. Furthermore, bidirectional Kullback–Leibler analysis identifies the reverse divergence as the tighter restriction and converts the covertness requirement into conservative gain bounds under warden location uncertainty and relative phase uncertainty conditions between the direct and aggregate reflected fields. Subsequently, closed-form phase control for calibrated equal-gain elements and gain monotonicity reduce the joint design to an exhaustive search over the prescribed placement grid. The numerical results demonstrate a SINR advantage over passive reflection and single-element relaying across the evaluated settings. The finite-array and hardware analyses show that gain back-off enforces a prescribed covert-outage limit while direct leakage and residual self-interference remain explicitly controlled. Overall, the framework provides a transparent basis for reliable covert sensing through UAV-assisted active reflection. Full article

Fingertrap is a network encryption and authentication protocol that extends the X3DH and Double Ratchet frameworks with three novel mechanisms inspired by the Chinese finger trap (zhĭ wăng): a friction ratchet that exponentially increases computational cost for each failed authentication attempt; a recursive annihilation protocol that irreversibly destroys all cryptographic state after a configurable failure threshold; and a commit-then-challenge handshake that requires a counterintuitive “inward” action for legitimate authentication. A bidirectional weave hash extends the Double Ratchet’s transcript binding to cover every message in both directions. Together, these mechanisms provide per-message forward secrecy, post-compromise security (self-healing), clock-free operation, and a self-destruct capability. The individual ingredients-client puzzles, key erasure, and ratcheting-each build on established lines of work; their combination into a single stateful protocol, in which failed authentication attempts cryptographically tighten the session state and ultimately destroy it, is not to our knowledge offered by deployed transport protocols such as TLS 1.3, Signal, or WireGuard. The design targets deployments in which interception or capture of a device implies endpoint compromise, such as Unmanned Aerial Vehicle (UAV) telemetry links and body-worn sensors, where denial of exploitation requires guaranteed loss of past and future session material. We describe the full protocol, provide game-based security arguments under an explicit adversarial model, give analytic cost estimates for the friction mechanism, analyse the denial-of-service surface and a two-layer mitigation strategy, and specify a post-quantum extension using hybrid $\mathsf{X25519}\text{/}\mathsf{ML}\text{-}\mathsf{KEM}\text{-}\mathsf{768}$ ratcheting. Full article

Autonomous acoustic remote sensing of underwater infrastructure is challenging due to the physical characteristics of 3D sonar and the geometric degeneracy commonly encountered in feature-poor underwater environments. Accurate localization is essential for integrating sequential sonar observations into globally consistent 3D maps; however, existing odometry methods often rely on isotropic noise assumptions despite the highly directional nature of acoustic sensing. This mismatch may cause unreliable measurements to be over-trusted, leading to severe trajectory drift and distortion in sonar-derived 3D reconstructions. To address these challenges, we propose RA-SIDO, a robust and adaptive tightly coupled 3D sonar–inertial–depth odometry framework based on the Error-State Iterated Kalman Filter (ESIKF), which fuses measurements from a 3D sonar, an inertial measurement unit (IMU), and a depth sensor for reliable underwater acoustic mapping. The proposed method introduces two mechanisms to handle sonar-specific uncertainties: (1) a physics-based anisotropic acoustic measurement model that distinguishes high-resolution radial range measurements from highly uncertain cross-range angular measurements; (2) an online degeneracy-awareness module that continuously evaluates the minimum eigenvalue of the translational information matrix and dynamically adjusts sensor fusion weights to avoid over-trusting ill-conditioned constraints. Real-world experiments were conducted with an unmanned surface vehicle in underwater infrastructure inspection scenarios. RA-SIDO achieved an ATE RMSE of $0.8924\mathrm{m}$, reducing the error by $16.8\%$ compared with SIDO, the strongest baseline. In addition, the proposed method effectively suppresses longitudinal slip and produces globally consistent 3D acoustic maps of submerged structures. These results validate the potential of RA-SIDO as a robust localization and mapping solution for underwater remote sensing, infrastructure inspection, and acoustic 3D reconstruction in challenging aquatic environments. Full article

This study addresses target-to-ROV relative localization in an unmanned surface platform-assisted UAV–ROV search-and-recovery system. Because the submerged ROV is not assumed to be visible from the air, the UAV observes the floating target and a GNSS-equipped ROV-associated surface buoy in the same image. The buoy position and target-to-buoy image displacement are combined to construct a world-frame target-position measurement, whose covariance accounts for buoy GNSS uncertainty and correlated image-projection errors. An upward-looking ROV imaging sonar provides range–bearing measurements. A delay-aware extended Kalman filter fuses the asynchronous observations using sea-state- and confidence-dependent covariance adaptation and normalized-innovation gating. ROV acoustic/inertial navigation uncertainty is propagated into the sonar measurement covariance and the reported relative-state covariance, avoiding duplication of the same navigation error in the aerial channel. The method is evaluated using a JONSWAP-based temporal disturbance model, Monte Carlo simulations, and single-factor and joint sea-state–occlusion–delay sensitivity tests. Under the nominal sea-state-5 condition, the proposed method achieves a mean ROV-frame relative RMSE of 0.992 m, compared with 1.083 m for ROV-only localization and 1.054 m for fixed-covariance fusion, with no run exceeding the 5 m divergence threshold. The results demonstrate improved relative-localization robustness within the simulated environment. Full article

Addressing the challenges of low environmental modeling accuracy and inadequate obstacle avoidance precision in complex obstacle scenarios in unmanned aerial vehicle (UAV) 3D path planning, this study proposes a UAV 3D path planning method that integrates the truncated signed distance field (TSDF) with an improved particle swarm optimization algorithm (IPSO). A unified planning space integrating a voxel occupancy grid with a truncated signed distance field is constructed offline: the Euclidean distance to obstacle surfaces is truncated and confined within an effective band, whose extent is coordinated with the UAV safety distance threshold determined by physical dimensions and task requirements, thereby preserving the continuous geometric information needed for safety assessment. On this basis, the continuous distance and gradient information provided by the truncated distance field are utilized to formulate a piecewise continuous, distance-based threat cost function, replacing traditional binary collision detection; the distance and gradient are further embedded into the initialization, fitness evaluation, and velocity update procedures of the particle swarm. Moreover, an adaptive inertia weight and a Lévy escape mechanism are introduced to improve search efficiency and global exploration capability. Experimental results demonstrate that under dense discrete safety verification, the proposed method achieves a 100% success rate in complex unstructured environments and that the safety distance threshold can be flexibly adjusted according to task requirements while consistently satisfying the specified safety requirement. The resulting paths achieve a favorable balance among length, smoothness, and controllable safety margin, validating the effectiveness of the proposed method. Full article

To address the scarcity of bridge defect data in the harsh environments of cold regions, as well as the parameter redundancy and edge platform deployment challenges of existing algorithms, this paper proposes an intelligent inspection system for bridge surface defects in cold regions based on parameter sharing and feature enhancement. The system first constructs a large-scale dataset called CRBD (Cold-Region Bridge Defect), which contains 10,129 high-resolution images and finely classifies defects into four standardized categories: Crack, Spalling, Patch, and Seepage. Subsequently, a lightweight detection network called BridgeNet is designed. Its core parameter sharing and feature enhancement detection head stabilizes training via group normalization, significantly reduces the parameter count through cross-scale global sharing and structural reparameterization, and improves bounding-box regression accuracy by incorporating a distribution focal loss mechanism. On this basis, an airborne real-time image processing and intelligent perception pipeline is constructed, which establishes the complete workflow for autonomous unmanned aerial vehicle inspections. The experimental results demonstrate that with a lightweight architecture of only 2.26 M parameters and a model size of 4.98 M, BridgeNet achieves a mean Average Precision of 61.4% and an F1 Score of 60.9%. Furthermore, it exhibits excellent real-time inference speed on heterogeneous edge mobile platforms and maintains robust overall perception stability under various extreme physical disturbances. Full article

Achieving high-accuracy path following remains challenging for an unmanned surface vehicle (USV) in narrow waterways with time-varying curvature and straight–curve transitions; fixed-parameter line-of-sight (LOS) guidance can cause delayed response, overshoot, and steady-state cross-track error. This paper proposes a curvature-coupled adaptive vector-field integral LOS (AVFILOS) guidance law. It incorporates curvature-adaptive guidance: a lookahead distance regulated by curvature and cross-track error and a field-source radius that contracts with curvature to strengthen centripetal correction in high-curvature regions. A fuzzy adaptive proportional–integral–derivative (PID) controller tracks surge speed and heading. A stability analysis establishes local exponential stability for straight and constant-curvature paths and local ISS with local uniform ultimate boundedness for time-varying curvature under a bounded-rate condition. Across six elliptical and sinusoidal cases, AVFILOS achieved an average root mean square error (RMSE($y_e$)) of 0.1325 m, reducing RMSE($y_e$) by 90.6%, 63.6%, and 37.2% compared with LOS, time-varying LOS (TLOS), and vector-field integral LOS (VFILOS), respectively. Its average maximum absolute cross-track error (Max($|y_e|$)) was 0.3478 m, with reductions of 88.2%, 48.2%, and 30.7%. The ablation and sensitivity results indicate that coupled adaptive mechanisms improve curved-path tracking and reduce overshoot. The simulations indicate that AVFILOS is promising for cross-track-error-sensitive USV navigation. Full article