Search Results (551)

With the rapid advancement of maritime sports, the integration of auxiliary unmanned surface vehicles (USVs) has emerged as a promising solution to enhance the efficiency and safety of maritime education, particularly in tasks such as buoy deployment and escort operations. This paper presents a novel high-precision trajectory tracking control algorithm designed to ensure stable navigation of the USVs along predefined competition boundaries, thereby facilitating the reliable execution of buoy placement and escort missions. First, the paper proposes an improved adaptive fractional-order nonsingular fast terminal sliding mode control (AFONFTSMC) algorithm to achieve precise trajectory tracking of the reference path. To address the challenges posed by unknown environmental disturbances and unmodeled dynamics in marine environments, a nonlinear lumped disturbance observer (NLDO) with exponential convergence properties is proposed, ensuring robust and continuous navigation performance. Additionally, an artificial potential field (APF) method is integrated to dynamically mitigate collision risks from both static and dynamic obstacles during trajectory tracking. The efficacy and practical applicability of the proposed control framework are rigorously validated through comprehensive numerical simulations. Experimental results demonstrate that the developed algorithm achieves superior trajectory tracking accuracy under complex sea conditions, thereby offering a reliable and efficient solution for maritime sports education and related applications. Full article

Coastal erosion, exacerbated by climate change, poses a critical global threat to both the environment and human livelihoods. Acquiring accurate, high-resolution topo-bathymetric data is vital for understanding these dynamic environments, without underestimating the hydrodynamic and meteo-oceanographic conditions. However, traditional methods often present significant challenges in achieving comprehensive, high-resolution topo-bathymetric coverage efficiently in shallow coastal zones, leading to a notable ”white ribbon” data gap. This study introduces a novel, integrated methodology combining unmanned aerial vehicles (UAVs) for terrestrial surveys, unmanned surface vehicles (USVs) for bathymetry, and the Global Navigation Satellite System (GNSS) for ground control and intertidal gap-filling. Through this technologically rigorous approach, a seamless Bathymetry-Topography Digital Surface Model for the Guardamar del Segura dune system (Spain) was successfully elaborated using a DJI Mini 2 UAV, Leica Zeno FLX100 GNSS, and Apache 3 USV. The method demonstrated a substantial time reduction of at least 50–75% for comparable high-resolution coverage, efficiently completing the 86.4 ha field campaign in approximately 4 h. This integrated approach offers an accessible and highly efficient solution for generating detailed coastal elevation models crucial for coastal management and research. Full article

Autonomous Unmanned Surface Vehicle (USV) operations in complex ocean engineering scenarios necessitate robust navigation, guidance, and control technologies. These systems require reliable sensor-based object detection and efficient, safe, and energy-aware path planning. To address these multifaceted challenges, this paper proposes a novel synergistic AI framework. The framework integrates (1) a novel adaptation of the Swin-Transformer to generate a dense, semantic risk map from raw visual data, enabling the system to interpret ambiguous marine conditions like sun glare and choppy water, enabling real-time environmental understanding crucial for guidance; (2) a Transformer-enhanced A-star (T-ASTAR) algorithm with spatio-temporal attentional guidance to generate globally near-optimal and energy-aware static paths; (3) a domain-adapted TD3 agent featuring a novel energy-aware reward function that optimizes for USV hydrodynamic constraints, making it suitable for long-endurance missions tailored for USVs to perform dynamic local path optimization and real-time obstacle avoidance, forming a key control element; and (4) CUDA acceleration to meet the computational demands of real-time ocean engineering applications. Simulations and real-world data verify the framework’s superiority over benchmarks like A* and RRT, achieving 30% shorter routes, 70% fewer turns, 64.7% fewer dynamic collisions, and a 215-fold speed improvement in map generation via CUDA acceleration. This research underscores the importance of integrating powerful AI components within a hierarchical synergy, encompassing AI-based perception, hierarchical decision planning for guidance, and multi-stage optimal search algorithms for control. The proposed solution significantly advances USV autonomy, addressing critical ocean engineering challenges such as navigation in dynamic environments, object avoidance, and energy-constrained operations for unmanned maritime systems. Full article

Wireless sensor networks (WSNs) are used for data acquisition and transmission in unmanned surface vessels (USVs). However, the openness of wireless networks makes USVs highly susceptible to false data injection (FDI) attacks during data transmission, which affects the sensors’ ability to receive real data and leads to decision-making errors in the control center. In this paper, a novel dynamic data encryption method is proposed whereby data are encrypted prior to transmission and the key is dynamically updated using historical system data, with a view to increasing the difficulty for attackers to crack the ciphertext. At the same time, a dynamic relationship is established among ciphertext, key, and auxiliary encrypted ciphertext, and an attack detection scheme based on dynamic encryption is designed to realize instant detection and localization of FDI attacks. Further, an $H_{\infty }$ fusion filter is designed to filter external interference noise, and the real information is estimated or restored by the weighted fusion algorithm. Ultimately, the validity of the proposed scheme is confirmed through simulation experiments. Full article

In this paper, we propose a hybrid algorithm that integrates an improved artificial potential field method (IAPF), model predictive control (MPC), and an extended state observer (ESO) to address the obstacle avoidance problem in multi-unmanned surface vehicle (Multi-USV) formations, including both dynamic and static obstacles, as well as navigation through narrow waterways. Initially, the virtual structure method was applied for formation control. Next, the traditional potential field method was enhanced by employing a saturated attractive potential field and a partitioned repulsive potential field, which improve formation stability and obstacle avoidance accuracy in complex environments. The extended state observer was then employed to estimate and compensate for unknown system dynamics and external disturbances from the marine environment in real time, improving system robustness. On this basis, by leveraging the multi-step predictive optimization capabilities of model predictive control, the proposed algorithm dynamically adjusts control inputs based on the desired trajectories generated from potential field forces, which ensures the stability of formation control and effective obstacle avoidance. The simulation results demonstrate that the proposed algorithm effectively avoids both dynamic and static obstacles in multi-unmanned surface vehicle formations and enables successful navigation through narrow waterways by altering the formation. Full article

This paper studies the distributed robust predefined-time sliding mode control (DRPSC) problem for high-order heterogeneous multi-agent systems under input saturation while considering external disturbances and model uncertainties. Firstly, a distributed predefined-time state observer (PTSO) is designed for each agent to achieve individual estimation of the state information of the virtual leader within a predefined time, and the observer does not need to count on the global information of the system. Secondly, a predefined-time auxiliary dynamic system (PTADS) is developed to solve the actuator’s input saturation problem. Thirdly, a distributed predefined-time sliding mode controller (PTSMC) is proposed, which ensures that the error converges to a small region near zero within a predefined time and combines H_∞ control to deal with the lumped uncertainty disturbances in the system to improve the robustness of the system. In addition, a memory event-triggered mechanism (METM) is designed to reduce the communication frequency of the underactuated AUV-USV multi-agent system and reduce the consumption of communication resources. Finally, Lyapunov theory is employed to prove that the closed-loop system is predefined-time stable, and the simulation results demonstrate that the proposed method is effective. Full article

Unmanned surface vehicles (USVs) course control research constitutes a vital branch of ship motion control studies and serves as a key technology for the development of marine critical equipment. Aiming at the problems of model uncertainties, external marine disturbances, performance optimization, and actuator constraints encountered by the autopilot system, this paper proposes a composite disturbance cancellation optimized control method based on fuzzy reinforcement learning. Firstly, a coupling design of the finite-time disturbance observer and fuzzy logic system is conducted to estimate and reject the composite disturbance composed of internal model uncertainty and ocean disturbances. Secondly, a modified backstepping control technique is employed to design the autopilot controller and construct the error system. Based on the designed performance index function, the fuzzy reinforcement learning is utilized to propose an optimized compensation term for the error system. Meanwhile, to address the actuator saturation issue, an auxiliary system is introduced to modify the error surface, reducing the impact of saturation on the system. Finally, the stability of the autopilot system is proved using the Lyapunov stability theory. Simulation studies conducted on the ocean-going training ship “Yulong” demonstrate the effectiveness of the proposed algorithm. Under the strong and weak ocean conditions designed, this algorithm can ensure that the tracking error converges within 7 s. Full article

Unmanned Surface Vehicle (USV) offers a cost-effective platform for high-resolution marine magnetic surveys using shipborne fluxgate magnetometers. However, platform-induced magnetic interference and electromagnetic interference (EMI) can degrade data quality, even with paramagnetic hulls. This study evaluates fluxgate magnetometer data acquired from a paramagnetic-hulled USV. Noise characterization identified EMI and maneuver-induced high-frequency noise, the latter of which was effectively reduced through low-pass filtering. We compared four different correction approaches addressing both vessel attitude and magnetization. The results demonstrate that the paramagnetic hull significantly reduces magnetic interference and shortens the duration of viscous magnetization (VM) effects caused by eddy currents in the platform, compared to conventional ferromagnetic vessels. Nonetheless, residual magnetization from onboard ferromagnetic components still requires correction. A method utilizing all nine components of the susceptibility tensor demonstrated improved accuracy and stability. Despite corrections, low-frequency VM-related noise during azimuth changes and a consistent absolute offset (~200 nT) remain when compared to towed scalar magnetometer data. These findings validate the use of paramagnetic USV for vector magnetic surveys, highlighting their benefit in VM mitigation while emphasizing the need for further development in VM correction and offset correction to achieve high-precision measurements. Full article

Multibeam echosounder systems (MBESs) are widely used in unmanned surface vehicle swarms (USVs) to perform various marine bathymetry surveys because of their excellent performance. To address the challenges of systematic error superposition and edge beam error propagation in multibeam bathymetry surveying, this study proposes a novel error adjustment method integrating crossover error density clustering and beam incident angle (BIA) compensation. Firstly, a bathymetry error detection model was developed based on adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN). By optimizing the neighborhood radius and minimum sample threshold through analyzing sliding-window curvature, the method achieved the automatic identification of outliers, reducing crossover discrepancies from ±150 m to ±50 m in the deep sea at a depth of approximately 5000 m. Secondly, an asymmetric quadratic surface correction model was established by incorporating the BIA as a key parameter. A dynamic weight matrix ω = 1/(1 + 0.5θ²) was introduced to suppress edge beam errors, combined with Tikhonov regularization to resolve ill-posed matrix issues. Experimental validation in the Western Pacific demonstrated that the RMSE of crossover points decreased by about 30.4% and the MAE was reduced by 57.3%. The proposed method effectively corrects residual systematic errors while maintaining topographic authenticity, providing a reference for improving the quality of multibeam bathymetric data obtained via USVs and enhancing measurement efficiency. Full article

We investigate the affine term structure models (ATSMs) with unspanned stochastic volatility (USV). Our aim is to test their ability to generate accurate cross-sectional behavior and time-series dynamics of bond yields. Comparing the restricted models and those with USV, we test whether they produce both reasonable estimates for the short rate variance and cross-sectional fit. Essentially, a joint approach from both time series and options data for estimating risk-neutral dynamics in ATSMs should be followed. Due to the scarcity of derivative data in emerging markets, we estimate the model using only time-series of bond yields. A Bayesian estimation approach combining Markov Chain Monte Carlo (MCMC) and the Kalman filter is employed to recover the model parameters and filter out latent state variables. We further incorporate macro-economic indicators and GARCH-based volatility as external validation of the filtered latent volatility process. The $A_1\left(4\right)$USV performs better both in and out of sample, even though the issue of a tension between time series and cross-section remains unresolved. Our findings suggest that even without derivative instruments, it is possible to identify and interpret risk-neutral dynamics and volatility risk using observable time-series data. Full article

In this paper, a robust neural adaptive controller is proposed for the trajectory tracking control problem of unmanned surface vessels (USVs), considering model uncertainty, time-varying environmental disturbance, and actuator saturation. First, measurement errors in acceleration signals are eliminated through filtering techniques and a series of auxiliary variables, and after linearly parameterizing the USV dynamic model, a parameter adaptive update law is developed based on Lyapunov’s second method to estimate unknown dynamic parameters in the USV dynamics model. This parameter adaptive update law enables online identification of all USV dynamic parameters during trajectory tracking while ensuring convergence of the estimation errors. Second, a radial basis function neural network (RBF-NN) is employed to approximate unmodeled dynamics in the USV system, and on this basis, a robust damping term is designed based on neural damping technology to compensate for environmental disturbances and unmodeled dynamics. Subsequently, a trajectory tracking controller with parameter adaptation law and robust damping term is proposed using Lyapunov theory and adaptive control techniques. In addition, finite-time auxiliary variables are also added to the controller to handle the actuator saturation problem. Signal delay compensators are designed to compensate for input signal delays in the control system, thereby enhancing controller reliability. The proposed controller ensures robustness in trajectory tracking under model uncertainties and time-varying environmental disturbances. Finally, the convergence of each signal of the closed-loop system is proved based on Lyapunov theory. And the effectiveness of the control system is verified by numerical simulation experiments. Full article

Unmanned aerial vehicles (UAVs) autonomous landing plays a key role in cooperative work with other heterogeneous agents. A neglected aspect of UAV autonomous landing on a moving platform is addressed in this study. The landing process is divided into three stages: positioning, tracking, and landing. In the tracking phase, MPCs are designed to implement tracking of the target landing platform. In the landing phase, we adopt a nested Apriltags collaboration identifier combined with the Aprilatags algorithm to design a PID speed controller, thereby improving the dynamic tracking accuracy of UAVs and completing the landing. The experimental data suggested that the method enables the UAV to perform dynamic tracking and autonomous landing on a moving platform. The experimental results show that the success rate of UAV autonomous landing is as high as 90%, providing a highly feasible solution for UAV autonomous landing. Full article

Efficient and timely data collection in Underwater Acoustic Sensor Networks (UASNs) for Internet of Underwater Things (IoUT) applications remains a significant challenge due to the inherent limitations of the underwater environment. This paper presents a Value of Information (VoI)-based trajectory planning framework for a single Autonomous Underwater Vehicle (AUV) operating in coordination with an Unmanned Surface Vehicle (USV) to collect data from multiple Cluster Heads (CHs) deployed across an uneven seafloor. The proposed approach employs a VoI model that captures both the importance and timeliness of sensed data, guiding the AUV to collect and deliver critical information before its value significantly degrades. A forward Dynamic Programming (DP) algorithm is used to jointly optimize the AUV’s trajectory and the USV’s start and end positions, with the objective of maximizing the total residual VoI upon mission completion. The trajectory design incorporates the AUV’s kinematic constraints into travel time estimation, enabling accurate VoI evaluation throughout the mission. Simulation results show that the proposed strategy consistently outperforms conventional baselines in terms of residual VoI and overall system efficiency. These findings highlight the advantages of VoI-aware planning and AUV–USV collaboration for effective data collection in challenging underwater environments. Full article

Maritime vision sensing can be applied to maritime unmanned systems to perform search and rescue (SAR) missions under complex marine environments, as multiple unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) are able to conduct vision sensing through the air, the water-surface, and underwater. However, in these vision-based maritime SAR systems, collaboration between UAVs and USVs is a critical issue for successful SAR operations. To address this challenge, in this paper, we propose a long-endurance collaborative SAR scheme which exploits the complementary strengths of the maritime unmanned systems. In this scheme, a swarm of UAVs leverages a multi-agent reinforcement-learning (MARL) method and probability maps to perform cooperative first-phase search exploiting UAV’s high altitude and wide field of view of vision sensing. Then, multiple USVs conduct precise real-time second-phase operations by refining the probabilistic map. To deal with the energy constraints of UAVs and perform long-endurance collaborative SAR missions, a multi-USV charging scheduling method is proposed based on MARL to prolong the UAVs’ flight time. Through extensive simulations, the experimental results verified the effectiveness of the proposed scheme and long-endurance search capabilities. Full article

Cannabinoid receptor 1 (CB₁) signalling is critical for weight gain and for milk intake in newborn pups. This is important as in humans, low birth weight increases the risk for attention-deficit hyperactivity disorder (ADHD). Moreover, some children with ADHD also have Tourette syndrome (TS). However, it remains unclear if insufficient CB₁ receptor signalling may promote ADHD/TS-like behaviours. Here, ADHD/TS-like behaviours were studied from postnatal to adulthood by exposing postnatal wild-type CB₁ and Cannabinoid receptor 2 (CB₂) knockout mouse pups to SR141716A (rimonabant), a CB₁ receptor antagonist/inverse agonist. Postnatal disruption of the cannabinoid system by SR141716A induced vocal-like tics and learning deficits in male mice, accompanied by excessive vocalisation, hyperactivity, motor-like tics and/or high-risk behaviour in adults. In CB₁ knockouts, rearing and risky behaviours increased in females. In CB₂ knockouts, vocal-like tics did not develop, and males were hyperactive with learning deficits. Importantly, females were hyperactive but showed no vocal-like tics. The appearance of vocal-like tics depends on disrupted CB₁ receptor signalling and on functional CB₂ receptors after birth. Inhibition of CB₁ receptor signalling together with CB₂ receptor stimulation underlie ADHD/TS-like behaviours in males. This study suggests that the ADHD/TS phenotype may be a single clinical entity resulting from incorrect cannabinoid signalling after birth. Full article