Search Results (260)

The sea surface microlayer (SML) is a critical biogeochemical boundary, playing a key role in air–sea exchange processes, yet its sampling remains challenging due to potential dilution from subsurface water layers, susceptibility to contamination and labor- and time-consuming procedures. The design, development and operational verification of a research unmanned surface vehicle (USV), equipped with samplers for collecting both sea surface microlayer and subsurface water samples (SSW), are described in this study. The InterSeA autonomous vessel is of the catamaran type, equipped with an SML sampler consisting of rotating glass discs and a peristaltic pump for collecting SSW samples. Verification analysis with traditional manual sampling techniques (glass plate and mesh screen) revealed that the InterSeA achieved comparable results in terms of reproducibility and contamination control for both the inorganic and organic analytes examined. The results obtained highlight the effectiveness of autonomous platforms in achieving reliable, low-contamination SML sampling, emphasizing their suitability for broader use in marine biogeochemical research demanding high resolution and minimally disturbed interface measurements. InterSeA is one of the smallest and lightest USVs using rotating glass discs for SML sampling. Full article

To address the urgent need for high-fidelity motion data for validating navigation algorithms for Unmanned Underwater Vehicles (UUVs), this paper proposes a data generation method based on a parametric motion model. First, based on the principles of rigid body dynamics and fluid mechanics, a decoupled six-degrees-of-freedom (6-DOF) Linear and Angular Acceleration Vector (LAAV) model is constructed, establishing a dynamic mapping relationship between the rudder angle and speed setting commands and motion acceleration. Second, a segmentation–identification framework is proposed for three-dimensional trajectory segmentation, integrating Gaussian Process Regression and Ordering Points To Identify the Clustering Structure (GPR-OPTICS), along with a Dynamic Immune Genetic Algorithm (DIGA). This framework utilizes real vessel data to achieve motion segment clustering and parameter identification, completing the construction of the LAAV model. On this basis, by introducing sensor error models, highly credible Inertial Measurement Unit (IMU) data are generated, and a complete attitude, velocity, and position (AVP) motion sequence is obtained through an inertial navigation solution. Experiments demonstrate that the AVP data generated by our method achieve over 88% reliability compared with the real vessel dataset. Furthermore, the proposed method outperforms the PSINS toolbox in both the reliability and accuracy of all motion parameters. These results validate the effectiveness and superiority of our proposed method, which provides a high-fidelity data benchmark for research on underwater navigation algorithms. Full article

Deep reinforcement learning (DRL) has demonstrated considerable potential for autonomous collision avoidance in unmanned surface vessels (USVs). However, its application in complex multi-agent maritime environments is often limited by challenges such as convergence issues and high computational costs. To address these issues, this paper proposes an expert-guided DRL algorithm that integrates a Dual-Priority Experience Replay (DPER) mechanism with a Hybrid Reciprocal Velocity Obstacles (HRVO) expert module. Specifically, the DPER mechanism prioritizes high-value experiences by considering both temporal-difference (TD) error and collision avoidance quality. The TD error prioritization selects experiences with large TD errors, which typically correspond to critical state transitions with significant prediction discrepancies, thus accelerating value function updates and enhancing learning efficiency. At the same time, the collision avoidance quality prioritization reinforces successful evasive actions, preventing them from being overshadowed by a large volume of ordinary experiences. To further improve algorithm performance, this study integrates a COLREGs-compliant HRVO expert module, which guides early-stage policy exploration while ensuring compliance with regulatory constraints. The expert mechanism is incorporated into the Soft Actor-Critic (SAC) algorithm and validated in multi-vessel collision avoidance scenarios using maritime simulations. The experimental results demonstrate that, compared to traditional DRL baselines, the proposed algorithm reduces training time by 60.37% and, in comparison to rule-based algorithms, achieves shorter navigation times and lower rudder frequencies. Full article

Ship detection serves as a core foundational task for marine environmental perception. However, in real marine scenarios, dense vessel traffic often causes severe target occlusion while multi-scale targets, asymmetric vessel geometries, and harsh conditions (e.g., haze, low illumination) further degrade image quality. These factors pose significant challenges to vision-based ship detection methods. To address these issues, we propose ${\mathrm{M}}^2\mathrm{S}\text{-}\mathrm{YOLOv}8$, an improved framework based on YOLOv8, which integrates three key enhancements: First, a Multi-Scale Asymmetry-aware Parallelized Patch-wise Attention (MSA-PPA) module is designed in the backbone to strengthen the perception of multi-scale and geometrically asymmetric vessel targets. Second, a Deformable Convolutional Upsampling (DCNUpsample) operator is introduced in the Neck network to enable adaptive feature fusion with high computational efficiency. Third, a Wasserstein-Distance-Based Weighted Normalized CIoU (WA-CIoU) loss function is developed to alleviate gradient imbalance in small-target regression, thereby improving localization stability. Experimental results on the Unmanned Vessel Zhoushan Perception Dataset (UZPD) and the open-source Singapore Maritime Dataset (SMD) demonstrate that ${\mathrm{M}}^2\mathrm{S}\text{-}\mathrm{YOLOv}8$ achieves a balanced performance between lightweight design and real-time inference, showcasing strong potential for reliable deployment on edge devices of unmanned marine platforms. Full article

Traditional water quality monitoring methods are limited in providing timely chlorophyll-a (Chl-a) assessments in small inland reservoirs. This study presents a rapid Chl-a retrieval approach based on a cooperative unmanned aerial vehicle–uncrewed surface vessel (UAV–USV) framework that integrates UAV hyperspectral imaging, machine learning algorithms, and synchronized USV in situ sampling. We carried out a three-day cooperative monitoring campaign in the Longhu Reservoir of Fujian Province, during which high-frequency hyperspectral imagery and water samples were collected. An innovative median-based correction method was developed to suppress striping noise in UAV hyperspectral data, and a two-step band selection strategy combining correlation analysis and variance inflation factor screening was used to determine the input features for the subsequent inversion models. Four commonly used machine-learning-based inversion models were constructed and evaluated, with the random forest model achieving the highest accuracy and stability across both training and testing datasets. The generated Chl-a maps revealed overall good water quality, with localized higher concentrations in weakly hydrodynamic zones. Overall, the cooperative UAV–USV framework enables synchronized data acquisition, rapid processing, and fine-scale mapping, demonstrating strong potential for fast-response and emergency water-quality monitoring in small inland drinking-water reservoirs. Full article

In the dynamic marine environment, the high mobility of intrusion targets, complex interference, and insufficient multi-vessel coordination accuracy pose significant challenges to the cooperative interception mission of multiple unmanned surface vehicles (USVs). This paper proposes an adaptive dynamic prediction-based cooperative interception control algorithm and establishes a “mission planning—anti-interference control—phased coordination” system. Specifically, it ensures interception accuracy through threat-level-oriented target assignment and extended Kalman filter multi-step prediction, offsets environmental interference by separating the cooperative encirclement and anti-interference modules using an improved Two-stage architecture, and optimizes the movement of nodes to form a stable blockade through the “target navigation—cooperative encirclement” strategy. Simulation results show that in a 1000 m × 1000 m mission area, the node trajectory deviation is reduced by 40% and the heading angle fluctuation is decreased by 50, compared with the limit cycle encirclement algorithm, the average interception time is shortened by 15% and the average final distance between the intrusion target and the guarded target is increased by 20%, when the target attempts to escape, the relevant collision rates are all below 0.3%. The TFMUSV framework ensures the stable optimization of the algorithm and significantly improves the efficiency and reliability of multi-USV cooperative interception in complex scenarios. This paper provides a highly adaptable technical solution for practical tasks such as maritime security and anti-smuggling. Full article

The development of robust autonomous maritime systems is fundamentally constrained by the “data silo” problem, where valuable operational data from disparate fleets remain isolated due to privacy concerns, severely limiting the scalability of general-purpose navigation intelligence. To address this barrier, we propose a novel Federated Meta-Transfer Learning (FMTL) framework that enables collaborative evolution of unmanned surface vehicle (USV) swarms while preserving data privacy. Our hierarchical approach orchestrates three synergistic stages: (1) transfer learning pre-trains a universal “Sea-Sense” foundation model on large-scale maritime data to establish fundamental navigation priors; (2) federated learning enables decentralized fleets to collaboratively refine this model through encrypted gradient aggregation, forming a distributed cognitive network; (3) meta-learning allows for rapid personalization to individual vessel dynamics with minimal adaptation trials. Comprehensive simulations across heterogeneous fleet distributions demonstrate that our federated model achieves a 95.4% average success rate across diverse maritime scenarios, significantly outperforming isolated specialist models (63.9–73.1%), while enabling zero-shot performance of 78.5% and few-shot adaptation within 8–12 episodes on unseen tasks. This work establishes a scalable, privacy-preserving paradigm for collective maritime intelligence through swarm-based learning. Full article

With the growing demand for safe obstacle avoidance and precise trajectory tracking in the autonomous navigation of unmanned surface vessels (USVs), this paper investigates an adaptive differential evolution approach for integrated path planning and tracking control. In the path planning stage, an elite archive mechanism is first incorporated into the mutation process, and the scaling factor F and crossover rate $CR$ are adaptively adjusted to enhance population diversity and global search capability. Then, the International Regulations for Preventing Collisions at Sea (COLREGs) are embedded into the algorithmic framework to reinforce collision avoidance performance in complex encounter scenarios. A multi-objective fitness function combining six performance criteria is subsequently constructed to evaluate individual path points, thereby identifying high-quality solutions that ensure both safe navigation and route efficiency. In the tracking control stage, the optimally generated reference trajectory is then employed as the input command for the vessel’s motion control subsystem. A fuzzy logic system is introduced to approximate unknown nonlinear dynamics, and an adaptive fuzzy logic controller is designed to guarantee accurate tracking of the planned path. Finally, simulation tests are used to show the algorithm’s efficiency and usefulness. Full article

With the rapid development of unmanned surface vessels (USVs), a vector thruster was designed in this paper to meet their evolving operational demands. The anti-impact capability of the vector thruster, in which the universal joint plays a critical role in attenuating impact loads, directly governs the stability and security of power transmission in USVs. A mechanical model of the vector thruster with a universal joint was established, incorporating length and stiffness ratio coefficients to characterize its key dynamics. Based on this model, numerical simulation using the Newmark method was conducted to systematically evaluate the thruster’s mechanical characteristics, particularly the dynamic variation of the inclination angle, under various working conditions and impact loads. The results indicate that an increase in stiffness ratio amplifies the angular displacement amplitude of the driven shaft but shortens the vibration stabilization time. During the operation of the vector thruster, an increase in the inclination angle leads to greater vibration amplitude. Furthermore, systems with a higher, longer ratio exhibit a more pronounced tendency for amplitude growth as the inclination angle increases. Finally, the theoretical model was validated through a test bench, and the variation pattern of dynamic thrust under impact load was revealed. These results emphasize that the stiffness and dimensional parameters must be carefully considered in the design and control optimization of vector thrusters to ensure reliable performance under demanding operational conditions. Full article

To address trajectory tracking of underactuated unmanned surface vessels (USVs) under disturbances and model uncertainty, we propose a hierarchical control framework that combines model predictive control (MPC) with proximal policy optimization (PPO). The outer loop runs in the inertial reference frame, where an MPC planner based on a kinematic model enforces velocity and safety constraints and generates feasible body–fixed velocity references. The inner loop runs in the body–fixed reference frame, where a PPO policy learns the nonlinear inverse mapping from velocity to multi–thruster thrust, compensating hydrodynamic modeling errors and external disturbances. On top of this framework, we design a Proactive–Reactive Adaptive Reward (PRAR) that uses the MPC prediction sequence and real–time pose errors to adaptively reweight the reward across surge, sway and yaw, improving robustness and cross–model generalization. Simulation studies on circular and curvilinear trajectories compare the proposed PRAR–driven dual–loop controller (PRAR–DLC) with MPC–PID, PPO–Only, MPC–PPO and PPO variants. On the curvilinear trajectory, PRAR–DLC reduces surge MAE and maximum tracking error from 0.269 m and 0.963 m (MPC–PID) to 0.138 m and 0.337 m, respectively; on the circular trajectory it achieves about an 8.5% reduction in surge MAE while maintaining comparable sway and yaw accuracy to the baseline controllers. Real–time profiling further shows that the average MPC and PPO evaluation times remain below the control sampling period, indicating that the proposed architecture is compatible with real–time onboard implementation and physical deployment. Full article

The growing adoption of Unmanned Surface Vessels (USVs) in commercial and defense domains raises challenges for safe navigation and strict adherence to the International Regulations for Preventing Collisions at Sea (COLREGs). This paper presents the implementation and field validation of three collision-avoidance approaches on a real USV: (i) behavior-based, (ii) a modified Velocity Obstacles (VO) algorithm, and (iii) a modified A* path-planning algorithm. Field trials in Guanabara Bay (Brazil) show that the behavior-based algorithm achieved the best balance between safety and efficiency, maintaining a safe mean Closest Point of Approach (30.0 m) while minimizing operational penalties: shortest total distance (179.4 m average), lowest mission completion time (174.7 s average), and smallest trajectory deviation (27.2% average increase). The VO algorithm operated with reduced safety margins (13.0 m average CPA) at the expense of larger detours (37.6% average distance increase), while the modified A* maintained equivalent safety (30.0 m average CPA) but produced the largest deviations (46.5% average increase). The trade-off analysis reveals that algorithm selection depends on operational priorities between safety margins and route efficiency. Full article

Accurate segmentation of navigable waters and obstacles is critical for unmanned surface vessel navigation yet remains challenging in real aquatic environments characterized by complex water textures and blurred boundaries. Current models often struggle to simultaneously capture long-range contextual dependencies and fine spatial details, frequently leading to fragmented segmentation results. In order to resolve these issues, we present a novel segmentation model based on the CoAtNet architecture. Our framework employs an enhanced convolutional attention encoder, where a Fused Mobile Inverted Bottleneck Convolution (Fused-MBConv) module refines boundary features while a Convolutional Block Attention Module (CBAM) enhances feature awareness. The model incorporates a Bi-level Former (BiFormer) to enable collaborative modeling of global and local features, complemented by a Multi-scale Attention Aggregation (MSAA) module that effectively captures contextual information across different scales. The decoder, based on U-Net, restores spatial resolution gradually through skip connections and upsampling. In our experiments, the model achieves 95.15% mIoU on a self-collected dataset and 98.48% on the public MaSTr1325 dataset, outperforming DeepLabV3+, SeaFormer, and WaSRNet. These results show the model’s ability to effectively interpret complex aquatic environments for autonomous navigation. Full article

Unmanned surface vessels may encounter unknown surface obstacles when sailing. Accurate detection has a significant impact on the subsequent decision-making process. In order to deal with the complex water environment, this paper proposes an object detection framework based on the fusion of LiDAR and camera. The detection framework can achieve real-time and accurate water surface object detection without training, and has strong anti-interference ability. The detection framework achieves the data fusion of LiDAR and camera through external calibration and then uses the detection algorithm of sky–sea boundary (SSB) to establish a clear search area for LiDAR. Then, a two-stage clustering algorithm based on point cloud attributes and distribution information achieves more accurate segmentation. The region of interest (RoI) is obtained from the detection results by image projection. Finally, the region of interest is finely segmented by the saliency object detection algorithm. The experimental results show the effectiveness and robustness of the algorithm. Full article

Autonomous berthing of unmanned surface vehicles (USVs) requires high-precision positioning and accurate detection of navigable region in complex port environments. This paper presents an integrated LiDAR-based approach to address these challenges. A high-precision 3D point cloud map of the berth is first constructed by fusing LiDAR data with real-time kinematic (RTK) measurements. USV pose is then estimated by matching real-time LiDAR scans to the prior map, achieving robust, RTK-independent localization. For safe navigation, a novel navigable region detection algorithm is proposed, which combines point cloud projection, inner-boundary extraction, and target clustering. This method accurately identifies quay walls and obstacles, generating reliable navigable areas and ensuring collision-free berthing. Field experiments conducted in Ling Shui Port, Dalian, China, validate the proposed approach. Results show that the map-based positioning reduces absolute trajectory error (ATE) by 55.29% and relative trajectory error (RTE) by 38.71% compared to scan matching, while the navigable region detection algorithm provides precise and stable navigable regions. These outcomes demonstrate the effectiveness and practical applicability of the proposed method for autonomous USV berthing. Full article

With the development of artificial intelligence neural networks, semantic segmentation has received more and more attention in the field of ocean engineering, especially in the fields of unmanned vessels and drones. However, challenges such as limited open ocean datasets, insufficient feature extraction for segmentation networks, pixel pairing problem, and frequency-domain obfuscation still exist. To address these issues, we propose USVS-Net, a high-performance segmentation network for segmenting USV feasible domains and surface obstacles. To overcome the pixel pairing confusion problem, a Global Channel-Spatial Attention module (GCSA) is designed in this paper, which enhances feature interactions, suppresses redundant features, and improves pixel matching accuracy through channel shuffling strategy and large kernel spatial attention. In addition, a median-enhanced channel-spatial attention (MECS) module is proposed to enhance edge details and suppress noise by fusing the median, mean, and maximum values to facilitate cross-scale feature interactions. For evaluation, a dataset USV-DATA containing images of marine obstacles is constructed. Experiments show that USVS-Net outperforms SOTA with mIoU reaching 81.71% and mPA reaching 90.18%, which is a significant improvement over the previous methods. These findings indicate that USVS-Net has high accuracy and robustness and can provide valuable support for autonomous navigation of unmanned vessels. Full article