Search Results (1,268)

Detecting changes in coral and seagrass habitat composition from satellite imagery places exceptionally high demands on sensor design due to low underwater reflectance signals and variable water column conditions. Recent multispectral satellite-based attempts to assess such changes across large spatial extents illustrate this challenge through an inability to reliably distinguish live coral from algae, often resulting in broad confidence intervals. This study quantifies the radiometric sensitivity requirements for detecting 10% absolute changes in live coral and seagrass fractional cover from spaceborne imaging spectroscopy using representative parameters for an aquatic imaging spectrometer. The analysis combined representative benthic spectra with realistic, management-relevant co-occurrence scenarios informed by extensive regional knowledge and field measurements from Fiji, Australia, and the Solomon Islands to evaluate detection performance across depths from 0 to 30 m. We show that live coral is the most demanding of the benthic targets evaluated because of its low reflectance and spectral similarity to algal cover types, requiring SNRs of approximately 300–700 to detect 10% absolute changes in cover at depths up to 10 m. In contrast, the greater spectral separation between seagrass and adjacent sandy substrates allows detection of 10% absolute changes in cover to depths exceeding 20 m in clear water. These results highlight the importance of high radiometric sensitivity and contiguous spectral sampling for future aquatic imaging spectrometers intended to monitor benthic change. Approaches that increase effective SNR, such as ground motion compensation (GMC), can extend the depth and confidence with which changes in benthic composition are detected, supporting a transition from broad-area habitat mapping toward quantitative monitoring of benthic change from space. Full article

Carrier-based fixed-wing UAV landing is challenged by deck motion, carrier airwake, gust disturbances, strong trajectory–attitude coupling, and actuator constraints. To address these issues, this paper proposes a Predefined-Time Direct Lift/Side-Force-Integrated Approach Landing (PTDIAL) method. An integrated direct-force architecture is constructed using the trailing-edge flap for direct lift and the spoiler for direct side force, thereby reducing the dependence of trajectory correction on angle-of-attack- and bank-angle/sideslip-mediated regulation. A preview-based reference glide slope is generated from the predicted Ideal Touch Point (ITP) sequence to improve the response to deck motion. Predefined-time control laws are developed for the cascaded position, trajectory, attitude, angular rate, and velocity loops, with prescribed-performance constraints imposed on the attitude response. A predefined-time disturbance observer is introduced to estimate the lumped aerodynamic disturbances, while an auxiliary anti-saturation mechanism compensates for the effect of trailing-edge flap saturation. Lyapunov analysis establishes the practical predefined-time stability of the closed-loop system under bounded disturbances and actuator constraints. Various simulations demonstrate that the proposed architecture improves lateral and vertical tracking while preserving the UAV attitude, and Monte Carlo simulations further confirm the robustness of PTDIAL. Full article

This paper formulates a self-organized dynamic fencing problem for multiple AUVs under finite target-sensing range and bounded nonuniform acoustic communication delays. Here, self-organization means that the fence is generated by local interactions without assigning fixed angular slots, virtual leaders, or persistent vehicle roles. A minimal observer-assisted attraction-repulsion fencing controller is proposed for AUV implementation, comprising target-AUV radial attraction–repulsion, AUV–AUV distance attraction–repulsion computed from timestamp-aligned delayed neighbor states, a state-only target-motion observer, and a label-free bearing-coverage repulsion that acts only on oversized target-centered angular gaps. To address acoustic delay without delaying physical execution, each AUV stores its own state history and evaluates pairwise relative geometry at the timestamp carried by the received neighbor packet. The resulting command is applied at the current time. The analysis shows that timestamp alignment converts acoustic delay into a bounded geometric perturbation and establishes collision avoidance, radial confinement, angular-gap contraction, target tracking, and packet-range preservation on a locally order-consistent regular fencing interval. The theorem does not claim global entry from arbitrary non-enclosing configurations. Numerical simulations, including a five-degree-of-freedom ocean-current robustness test without current feedforward compensation and an evasive-target stress test with four rapid finite-acceleration turns, demonstrate self-organized entry and maintenance of compact convex-hull fencing in the tested cases. Full article

This paper proposes a time-varying grouping containment control strategy for multi-unmanned surface vehicle (USV) systems navigating through narrow waterways with mid-channel obstacles. First, a virtual-leader-based grouping mechanism is developed to decompose the original global containment hull into multiple time-varying sub-convex hulls, enabling different follower subgroups to pass through separated navigable regions. Subsequently, a local algebraic-connectivity-based topology reconfiguration strategy is introduced within each subgroup to regulate follower-to-follower coupling while preserving subgroup connectivity. Moreover, a fuzzy adaptive compensator is incorporated into the distributed containment control protocol to compensate for matched unknown hydrodynamic nonlinearities and environmental disturbances. A Lyapunov-based analysis demonstrates that the containment errors and adaptive parameters are uniformly ultimately bounded under admissible local topology switching and bounded virtual-leader motion. Finally, numerical simulations with four actual leaders and six followers indicate that, in the considered scenario, the proposed method maintains positive obstacle clearance, regulates local algebraic connectivity, and improves the robustness of grouping containment control. Full article

To address the dynamic communication topology switching, asynchronous perception information, and uncertainty caused by vehicle mobility in the cooperative control of a vehicle-charger pile-grid under industrial Internet environments, this paper proposes a cooperative optimal control method based on decentralized holistic sensing graph-based estimation. First of all, this method constructs a time-varying weighted directed graph by using decentralized holistic sensing data obtained from the industrial Internet to characterize the dynamic evolution of communication topologies in real time. Secondly, a distributed graph estimator relying solely on local perception information is designed, enabling each agent to predict online its neighbor set and link reliability over a short future horizon based on its own position, the motion trends of nearby objects, and historical link states. On this basis, the graph-based estimation results are embedded as a feedforward compensation term into the consensus control law, forming a predictive graph consensus control algorithm that enables the system to proactively adjust control inputs before topology switching occurs, achieving a paradigm shift from “passive response” to “active pre-compensation.” Meanwhile, an Age of Information (AoI)-aware event-triggered mechanism is introduced, where broadcasting is triggered when the state error exceeds a threshold or the AoI approaches its upper bound, significantly reducing communication load while ensuring control accuracy. Finally, simulations are conducted on a modified IEEE 33-bus distribution system comprising 61 agents (20 electric vehicles, eight charging stations, and 33 grid nodes). The results show that, compared to the event-triggered consensus method without prediction, the proposed method reduces the steady-state error by 40.1%, shortens the convergence time by 40.5%, and decreases the number of broadcasts by 36.5%. In a large-scale system with 169 agents, the proposed method still maintains the highest accuracy, the fastest convergence speed, and the lowest communication overhead, while meeting real-time computational requirements. This method can fully exploit the spatiotemporal redundancy of decentralized holistic sensing, offering a new solution for efficient, robust, and low-cost cooperative control of “vehicle–charger–grid” under industrial Internet environments. Full article

An underwater towed vehicle serves as an effective and widely applicable mobile marine observation platform. Existing towed vehicles rely heavily on cables for depth adjustment. They also suffer from poor hydrodynamic efficiency and insufficient instrument space. To address these limitations, this study developed a novel compensation control system. This system regulates the vehicle’s vertical movement and cable deployment by controlling the attack angles of its front and rear hydrofoils. The Myring profile was selected as the base design for the towed vehicle, offering excellent hydrodynamic performance, ample internal space, and cost-effectiveness. To verify the system’s reliability, critical components were meticulously designed and calibrated. Hydrodynamic simulations confirmed that adjusting the hydrofoil angle effectively controls vertical motion, with stress and deformation in the lifting mechanism and cable connectors meeting design specifications. Additionally, the overall drag resistance remains low, while the lift generated by both hydrofoils satisfies depth adjustment requirements. This research provides robust numerical foundations for developing vertical control strategies, optimizing operational conditions, and conducting subsequent sea trials of towed vehicles. Quantitative comparison with the conventional scheme indicates that the proposed structure cuts total drag by 21.6%, boosts depth adjustment efficiency by 47.3%, and achieves a 32% higher hydrofoil lift-drag ratio, accompanied by a structural safety factor of 1.8 and maximum deformation of only 1.711 mm under rated working conditions. Full article

Surface deformation in rapidly urbanizing coastal cities is often shaped by the interplay between hydrogeological setting and development disturbance, yet these controls remain insufficiently constrained in tropical coastal environments. Using 119 ascending Sentinel-1A scenes acquired between September 2020 and August 2025, we derived a high-density line-of-sight (LOS) deformation field over Haikou, China, through the combined use of PS-InSAR and DS-InSAR time-series analysis. The results show pronounced spatial heterogeneity, with negative LOS anomalies concentrated in reclaimed coastal sectors, port-adjacent zones, and Jiangdong New District, where cumulative LOS displacement locally approaches 90 mm. DS-InSAR increased the number of valid observations to 764,573, approximately 2.8 times that of PS-InSAR, and showed good internal consistency with collocated PS estimates (R² = 0.8048). Positive LOS zones are present but are generally weaker and more spatially diffuse than the major negative anomalies, indicating that the near-zero city-wide mean partly reflects compensation between localized negative and positive signals. Spatial attribution analysis suggests that groundwater type, aquifer water-yield property, and geological zoning provide the main hydrogeological background associated with deformation, whereas human activity intensity, road density, and land-use intensity are associated with stronger negative deformation where hydrogeological conditions are susceptible. Because the dataset is limited to a single ascending viewing geometry, the reported deformation is interpreted as LOS motion rather than a fully resolved vertical subsidence field. These results support a cautiously framed interpretation in which coastal urban deformation in Haikou is hydrogeologically conditioned and development-amplified, providing a basis for targeted monitoring and risk-informed planning in newly developed coastal districts. Full article

Piezoelectric ceramic actuators are widely used in precision positioning and sensor-integrated micro-motion systems, but their accuracy is limited by asymmetric, rate-dependent hysteresis and by residual disturbances that remain after feedforward linearization. This study develops a self-contained modeling and control framework that combines an explicit rising/falling branch polynomial model, frequency-dependent coefficient maps, direct inverse feedforward compensation, and disturbance-observer-based adaptive sliding-mode feedback. The actuator is represented as a multilayer piezoelectric stack coupled to an equivalent electrical-mechanical-sensing plant. A branch-state logic resolves the multivalued inverse mapping, and a numerical order-sensitivity study shows that the seventh-order model provides the lowest validation RMSE while avoiding the endpoint growth observed at higher orders. Laboratory measurements at 1, 5, 10, 20, 50, and 100 Hz, together with attenuated, triangular, random-amplitude, step, and 2 Hz sinusoidal tests, are used for validation. The proposed branch model reduces static maximum relative fitting error from 4.50–6.21% for the classical P-I model to 1.28–2.58%. Direct inverse compensation reduces linearity error from 8.56–13.88% to 0.53–1.024%, and the hybrid controller achieves a 1% settling time of 8.6 ms, a maximum tracking error of 0.0051 micrometers, and an RMSE of 0.0012 micrometers. The results demonstrate an embedded-oriented compromise between model accuracy, online computational simplicity, and robust closed-loop precision. Full article

The concept of Meta-Action Units (MAUs) has emerged as a promising paradigm for decomposing machine tool motion into fundamental action units, providing new insights into error propagation and accuracy stability in CNC machine tools. This paper presents a comprehensive review of accuracy stability from the MAU perspective. Fluctuation mechanisms induced by geometric errors, thermal effects, load-dependent deformations and wear-related degradation are systematically reviewed. Existing modeling, identification, and compensation methods are critically analyzed. A key contribution is the synthesis of a novel MAU-centric taxonomy integrating research on key MAU identification, precision remaining useful life prediction under incomplete maintenance, cascading fault propagation and reliability coupling mechanisms. The integration of screw theory, multi-body systems, and active learning Kriging is examined, along with hybrid approaches combining physics-based models with machine learning. The alignment of MAU-based digital twins with Industry 4.0 and Industry 5.0 is discussed. MAU decomposition provides a physically interpretable framework for accuracy formation. Hybrid Wiener–GPIM models achieve PRUL prediction errors below ten percent. Five research gaps are identified: uncertainty propagation, robust parameter identification, benchmark datasets, cost–benefit frameworks, and transfer learning. Addressing these gaps will guide the development of next-generation high-accuracy and intelligent CNC machine tools. Full article

To address the reduction in nozzle row-following accuracy caused by sensing–execution latency during corn row-following operations, a delay compensation method based on machine vision and dynamic region of interest (ROI) adjustment was proposed. The method integrates real-time forward-velocity information from a global navigation satellite system/inertial measurement unit (GNSS/IMU), decomposes the delays associated with image processing, command transmission, and actuator motion, and calculates a visual look-ahead distance from the total response delay and robot forward velocity. Inverse-perspective mapping was used to establish the relationship between pixel and world coordinates, and the ROI position was dynamically shifted to synchronize the sensing–execution process. Indoor bench tests showed that, under variable conveyor-belt speeds ranging from 0 to 0.25 m/s, the algorithm achieved a row-following accuracy of 93.75% and a lateral mean absolute error of 0.019 m; compared with the average result of the three fixed-ROI tests, the lateral mean absolute error was reduced by 24.8%. Whole-machine tests showed that, under random platform forward speeds of 0–1.00 m/s, the row-following accuracy remained above 85.71%, with a lateral mean absolute error of 0.034 m. The results indicate that the proposed method effectively compensates for system delay under different speed conditions and reduces lateral tracking errors caused by longitudinal spatiotemporal mismatch, providing technical support for the development of precision corn row-following spraying equipment. Full article

During offshore wind turbine maintenance operations, maintenance vessels typically rely on transfer gangways to achieve safe transfer of personnel from the vessel to the wind turbine. With the development of offshore wind farms toward far sea areas, working sea state conditions have become increasingly complex. In recent years, the application of large-scale active wave compensation (AWC) transfer gangways has mitigated the adverse effects of ship motions on transfer operations to a certain extent, but the problem of limited motion ranges of each gangway joint has also emerged as a prominent issue. Under complex sea state conditions, if the station-keeping position of the maintenance vessel is improperly selected, joint limit violations may occur during the gangway compensation process, thereby posing safety risks. To address the above problems, this paper develops a novel geometric-envelope-based analytical method for operational sea states and ranges that explicitly accounts for gangway joint constraints and wave-induced vessel motions to meet the safety requirements of large-scale gangways. Subject to the motion constraints of gangway joints, to ensure that the initially selected station-keeping position of the vessel satisfies the requirements for safe gangway transfer, the feasible domain and motion range of the gangway base point under different sea states are obtained. By solving for the solutions where the motion range of the base point lies entirely within the feasible domain, feasible schemes for vessel station-keeping operations under different sea states are derived. The results show that the gangway can operate safely under sea states 3 and 4 but fails under sea state 5. The allowable height difference between the transfer point and the gangway base is [−2.7 m, 8.7 m] for sea state 3 and [1.8 m, 3.5 m] for sea state 4. The operational area on the horizontal plane presents a partial ring shape, and the ring width first increases and then decreases with increasing height difference. Finally, random numerical examples are designed to demonstrate the applicability and internal consistency of the proposed analytical method. Full article

With the rapid expansion of offshore wind farms into deeper waters, large-scale wave-compensated transfer gangways have become critical equipment for ensuring safe personnel transfer between maintenance vessels and wind turbines. However, the increased mass and inertia of large gangways introduce significant control delays in hydraulic actuation, data acquisition, and compensation calculation processes, which severely degrade compensation accuracy and threaten operational safety. This paper proposes a predictive active wave compensation control scheme based on Back Propagation (BP) neural network to address this challenge. First, a ship vertical-plane kinematic model was established to derive the mapping relationship between ship motions (roll, pitch, heave) and gangway end-point positions. The impact of 3–5 s control delays on gangway stability was systematically analyzed, revealing that uncompensated delays can cause end-point deviations exceeding 4 m. Then, an optimized BP neural network model was constructed for short-term ship six-degree-of-freedom (6-DOF) motion prediction, with key parameters (input duration, hidden layer nodes, random seed) tuned to achieve high prediction accuracy. Finally, the proposed predictive control scheme was validated through numerical simulations. The results demonstrate that the scheme can reduce the maximum end-point deviations by more than 80% for delays up to 5 s, effectively mitigating the adverse effects of control delays. This research provides a technical solution for improving the safety and reliability of large offshore transfer gangways. Full article

In this paper, the concept of symmetry is applied to design active trajectory tracking control of autonomous distributed drive electric vehicles considering energy efficiency—that is, the construction and solution of active trajectory tracking controllers are symmetrical. This paper proposes a hierarchical control strategy consisting of upper-level control and lower-level control to improve trajectory tracking accuracy of DDEVs considering energy-efficiency characteristics. In the upper-layer control, a sliding mode active disturbance rejection (ADRC) controller is developed to control the front wheel steering angle and active yaw moment to achieve tracking of the desired trajectory, in which an extended state observer (ESO) is synthesized to estimate and compensate for internal model uncertainties and external environmental disturbances. In the lower-layer control, a multi-objective optimization algorithm based on Karush–Kuhn–Tucker (KKT) conditions is designed to realize the torque distribution control for improving energy efficiency and vehicle stability of the distributed drive electric vehicle. Finally, a joint simulation platform based on Matlab/Simulink-CarSim (version 2019) is established for simulation verification. The performances of ADRC, linear quadratic regulator controller (LQR), and model predictive controller (MPC) are compared in snake-like and double-lane-change maneuvers. Simulation results show that the proposed controller can effectively reduce motor energy consumption while maintaining trajectory tracking accuracy and handling stability. This work provides a certain engineering design solution for motion control of intelligent electric vehicles. Full article

Visual simultaneous localization and mapping in indoor dynamic environments remains challenging because moving objects introduce unreliable correspondences, whilst removing dynamic feature points often leaves insufficient static features in low-texture regions. This paper proposes SFSE-SLAM (Semantic Feature Selection and Enhancement SLAM), a robust visual SLAM framework that combines semantic-geometric feature filtering with texture-aware feature compensation to improve pose estimation under dynamic interference. The framework first identifies potentially dynamic regions through pixel-level semantic segmentation and removes features associated with highly dynamic objects. To reduce over-filtering and address semi-static objects, depth variation and multi-view geometric consistency are further used to distinguish static and moving feature points across consecutive frames. After dynamic filtering, a learned local feature extractor is introduced to improve descriptor discriminability and feature density in reliable static regions. Two additional modules, semantic confidence weighting and static region feature compensation, adaptively adjust feature extraction thresholds so that low-texture but geometrically useful areas can contribute more stable correspondences. The proposed system is evaluated on public dynamic RGB-D benchmarks, including the TUM RGB-D dataset (featuring high-dynamic human motions) and the Bonn dataset (containing complex non-human dynamic objects such as thrown balloons and moved boxes). Experimental results indicate that the method improves localization robustness in high-dynamic scenarios and reduces trajectory error compared with conventional ORB-based SLAM and several dynamic SLAM baselines. Specifically, on the highly dynamic TUM fr3-walking-xyz sequence, our method achieves an ATE RMSE of 0.0148 m, representing a 97.9% reduction compared to ORB-SLAM3. The study demonstrates the potential of combining semantic priors, geometric verification and adaptive feature enhancement for dynamic indoor localization. Full article

Multi-exposure is an efficient way to capture real-world high-dynamic-range (HDR) scenes. However, HDR imaging suffers from severe ghosting artifacts in dynamic scenes due to the temporal gap between sequential exposures. In this article, we categorize the literature on two important topics on HDR imaging: multi-exposure fusion (MEF) and ghost removal. Conventional filter-based and data-driven methods are studied in pixel space and feature space. For popular deep learning-based approaches, we provide a granular taxonomy based on their alignment and fusion domains: pixel-space methods, which typically employ explicit motion compensation such as optical flow or spatial transformers, and feature-space methods, which leverage implicit alignment through deformable convolutions, attention mechanisms, or latent representation merging. Representative works are compared across different supervision settings, and key design principles are summarized. In addition, this survey summarizes commonly used datasets and evaluation metrics, discussing their applicability under diverse output forms. Finally, major bottlenecks and promising directions for future research are outlined. Full article