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Ensuring representativeness of collected samples is the most critical requirement of water sampling. Unmanned surface vehicles (USVs) have been widely adopted in water sampling, but current USV sampling path planning tend to overemphasize path optimization, neglecting the representative samples collection. This study proposed a modified A* algorithm that combined remote sensing technique while considering both path length and the representativeness of collected samples. Water quality parameters were initially retrieved using satellite remote sensing imagery and a deep belief network model, with the parameter value incorporated as coefficient Q in the heuristic function of A* algorithm. The adjustment coefficient k was then introduced into the coefficient Q to optimize the trade-off between sampling representativeness and path length. To evaluate the effectiveness of this algorithm, Chlorophyll-a concentration (Chl-a) was employed as the test parameter, with Chaohu Lake as the study area. Results showed that the algorithm was effective in collecting more representative samples in real-world conditions. As the coefficient k increased, the representativeness of collected samples enhanced, indicated by the Chl-a closely approximating the overall mean Chl-a and exhibiting a gradient distribution. This enhancement was also associated with increased path length. This study is significant in USV water sampling and water environment protection. [ABSTRACT FROM AUTHOR]

The goal of this paper is to establish a motion control system for unmanned surface vehicles (USVs) that enables point-to-point tracking and dynamic positioning. This includes the heading control and path following control of USVs. A hardware and software platform for USVs using microcontrollers is designed. This paper presents the construction of a kinematics and dynamics model for an unmanned catamaran. The motion process is divided into two segments. In the target point tracking segment, the heading coordinate system and the ship coordinate system are established. Based on these, a control method using differential steering to track the desired yaw angle is designed to improve the tracking efficiency. And the accuracy of heading keeping and path following is improved by combining the cascade PID controller. In the dynamic positioning segment, a self-adjusting mechanism is designed, thereby enhancing the flexibility of thrust distribution and improving the accuracy of the USV's positioning retention in wind and wave environments. Finally, experimental validation is carried out to verify the effectiveness of the design proposed in this paper by issuing control commands and saving the return data through the upper computer, and then analyzing the return data with MATLAB (R2022b, MathWorks, Natick, MA, USA). [ABSTRACT FROM AUTHOR]

This article shows that the PID control, applied in linear systems, is introduced to handle the adaptive fixed-time PID-based control issue of uncertain nonlinear systems. For handling smooth complex unknown nonlinearities existing in the considered system, neural networks will be introduced to estimate these functions. Furthermore, by integrating the self-tuning gain PID controller, an actual controller is proposed. The stability analysis and the simulations all demonstrate the utilisability of the given scheme. [ABSTRACT FROM AUTHOR]

When navigating dynamic ocean environments characterized by significant wave and wind disturbances, USVs encounter time-varying external interferences and underactuated limitations. This results in reduced navigational stability and increased difficulty in trajectory tracking. Controllers based on deterministic models or non-adaptive control parameters often fail to achieve the desired performance. To enhance the adaptability of USV motion controllers, this paper proposes a trajectory tracking control algorithm that calculates PID control parameters using an improved Deep Deterministic Policy Gradient (DDPG) algorithm. Firstly, the maneuvering motion model and parameters for USVs are introduced, along with the guidance law for path tracking and the PID control algorithm. Secondly, a detailed explanation of the proposed method is provided, including the state, action, and reward settings for training the Reinforcement Learning (RL) model. Thirdly, the simulations of various algorithms, including the proposed controller, are presented and analyzed for comparison, demonstrating the superiority of the proposed algorithm. Finally, a maneuvering experiment under wave conditions was conducted in a marine tank using the proposed algorithm, proving its feasibility and effectiveness. This research contributes to the intelligent navigation of USVs in real ocean environments and facilitates the execution of subsequent specific tasks. [ABSTRACT FROM AUTHOR]

Formation obstacle avoidance is an essential attribute of the cooperative task in unmanned surface vehicle (USV) formation. In real-world scenarios involving multiple USVs, both formation obstacle avoidance and formation recovery after obstacle avoidance play a critical role in ensuring the success of collaborative missions. In this study, an Interfered Fluid Dynamic System (IFDS) algorithm was used for obstacle avoidance due to its excellent robustness, high computational efficiency and path smoothness. The algorithm can provide good local path planning for USVs. However, the use of the IFDS on USVs still has the defect of local extreme values, which has been effectively modified to obtain an enhanced IFDS (EIFDS). In formation, based on the leader–follower method, the virtual leader was used to determine the desired position of USVs in formation, and the streamlines generated by the EIFDS guided the USVs. In order to make the formation converge to the desired formation better, the vector and scalar of the EIFDS algorithm were uncoupled, and different designs were made to achieve convergence to the desired formation. The interfered residue of the IFDS is not suitable for addressing collision avoidance between USVs in practice. Therefore, the vector field method was employed to tackle the issue, with some enhancements made to optimize its performance. Subsequently, a weighted separation method was applied to combine the vector field and EIFDS, resulting in a composite field solution. Finally, the formation obstacle avoidance strategy based on composite fields was formed. The feasibility of this scheme was verified by simulation, and compared with the single IFDS formation method, the pairwise spacing of USVs behind obstacles could be increased, and the reliability of formation obstacle avoidance was increased. [ABSTRACT FROM AUTHOR]

Multimodal sensors are often employed by USVs (unmanned surface vehicles) to enhance situational awareness, and the fusion of LiDAR and monocular vision is widely used in near-field perception scenarios. However, this strategy of fusing data from LiDAR and monocular vision may lead to the incorrect matching of image targets and LiDAR point cloud targets when targets occlude one another. To address this issue, a target matching network with an attention module was developed to process occlusion information. Additionally, an image target occlusion detection branch was incorporated into YOLOv9 to extract the occlusion relationships of the image targets. The introduction of the attention module and the occlusion detection branch allows for the consideration of occlusion information in matching point cloud and image targets, thereby achieving more accurate target matching. Based on the target matching network, a method for water surface target detection and multi-target tracking was proposed. This method fuses LiDAR point cloud and image data while considering occlusion information. Its effectiveness was confirmed through experimental verification. The experimental results show that the proposed method improved the correct matching rate in complex scenarios by 13.83% compared to IoU-based target matching methods, with an MOTA metric of 0.879 and an average frame rate of 21.98. The results demonstrate that the method effectively reduces the mismatch rate between point cloud and image targets. The method's frame rate meets real-time requirements, and the method itself offers a promising solution for unmanned surface vehicles (USVs) to perform water surface target detection and multi-target tracking. [ABSTRACT FROM AUTHOR]

ObjectiveIn order to improve the combined navigation accuracy of unmanned surface vehicles (USVs) by strapdown inertial navigation system (SINS) and doppler range finder (DVL), the DVL error needs to be accurately calibrated. MethodsThis paper carries out error modeling of the installation error angle, lever arm, and scale factor of the DVL, and focuses on the analysis of the impact of the lever arm error term on the combined navigation positioning in the vessels, pointing out the necessity of its calibration; proposes a DVL error calibration method based on the parameter estimation between two point sets, which treats the SINS/GNSS navigation parameters and DVL output parameters as two point sets, converting the calibration problem into an estimation problem of the difference between the two point sets, and using the Kalman filter to estimate the error parameters between the two point sets; using the SVD（singular value decomposition）-based observability analysis to quantify the observability of the filter in different motion condtions, giving the motion recommendations of the carrier during the calibration process.ResultsThe results of mathematical simulation and real ship experiments show that the global relative accuracy of the calibrated SINS/DVL algorithm can reach a voyage of 0.122%. During cornering maneuvers, the lever arm causes abrupt changes in SINS/DVL positioning. After compensating for the lever arm, the positioning errors become smoother and the overall positioning error is reduced.ConclusionThe proposed method provides a feasible way to calibrate each DVL error including the lever arm.

In this paper, targeting the problem that it is difficult to deal with the time-varying sideslip angle of an underactuated unmanned surface vehicle (USV), a line–of–sight (LOS) guidance law based on an improved extended state observer (ESO) is proposed. A reduced-order ESO is introduced into the identification of the sideslip angle caused by the environmental disturbance, which ensures a fast and accurate estimation of the sideslip angle. This enables the USV to follow the reference path with high precision, despite external disturbances from wind, waves, and currents. These unknown disturbances are modeled as drift, which the modified ESO-based LOS guidance law compensates for using the ESO. In the guidance subsystem incorporating the reduced-order state observer, the observer estimation and track errors are proved uniformly ultimately bounded. Simulation and experimental results are presented to validate the effectiveness of the proposed method. The simulation and comparison results demonstrate that the proposed ELOS guidance can help a USV track different types of paths quickly and smoothly. Additionally, the experimental results confirm the feasibility of the method. [ABSTRACT FROM AUTHOR]

Unmanned surface vehicles (USVs) in nearshore areas are prone to environmental occlusion and electromagnetic interference, which can lead to the failure of traditional satellite-positioning methods. This paper utilizes a visual simultaneous localization and mapping (vSLAM) method to achieve USV positioning in nearshore environments. To address the issues of uneven feature distribution, erroneous depth information, and frequent viewpoint jitter in the visual data of USVs operating in nearshore environments, we propose a stereo vision SLAM system tailored for nearshore conditions: SP-SLAM (Segmentation Point-SLAM). This method is based on ORB-SLAM2 and incorporates a distance segmentation module, which filters feature points from different regions and adaptively adjusts the impact of outliers on iterative optimization, reducing the influence of erroneous depth information on motion scale estimation in open environments. Additionally, our method uses the Sum of Absolute Differences (SAD) for matching image blocks and quadric interpolation to obtain more accurate depth information, constructing a complete map. The experimental results on the USVInland dataset show that SP-SLAM solves the scaling constraint failure problem in nearshore environments and significantly improves the robustness of the stereo SLAM system in such environments. [ABSTRACT FROM AUTHOR]

In this paper, an innovative method is proposed to improve the global path planning of Unmanned Surface Vehicles (USV) in complex sea areas, combining fluid mechanic calculations with an improved A* algorithm. This method not only generates smooth paths but also ensures feasible global solutions, significantly enhancing the efficiency and safety of path planning. Firstly, in response to the water depths limitation, this study set up safe water depths, providing strong guarantees for the safe navigation of USVs in complex waters. Secondly, based on the hydrological and geographical characteristics of the study sea area, an accurate ocean environment model was constructed using Ansys Fluent software and computational fluid dynamics (CFD) technology, thus providing USVs with a feasible path solution on a global scale. Then, the local sea area with complex obstacles was converted into a grid map to facilitate detailed planning. Meanwhile, the improved A* algorithm was utilized for meticulous route optimization. Furthermore, by combining the results of local and global planning, the approach generated a comprehensive route that accounts for the complexities of the maritime environment while avoiding local optima. Finally, simulation results demonstrated that the algorithm proposed in this study shows faster pathfinding speed, shorter route distances, and higher route safety compared to other algorithms. Moreover, it remains stable and effective in real-world scenarios. [ABSTRACT FROM AUTHOR]

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Unmanned surface vehicles (USVs) offer significant value through their capability to undertake hazardous and time-consuming missions across water surfaces. Recently, as the application of USVs has been extended to nearshore waterways, object tracking is vital to the safe navigation of USVs in offshore scenes. However, existing tracking systems for USVs are mainly based on cameras or LiDAR sensors, which suffer from drawbacks such as lack of depth perception or high deployment costs. In contrast, millimeter-wave (MMW) radar offers advantages in terms of low cost and robustness in all weather and lighting conditions. In this work, to construct a robust and low-cost tracking system for USVs in complex offshore scenes, we propose a novel MMW radar-based object tracking method (ROTracker). The proposed ROTracker combines the physical properties of MMW radar with traditional tracking systems. Specifically, we introduce the radar Doppler velocity and a designed motion discriminator to improve the robustness of the tracking system toward low-speed targets. Moreover, we conducted real-world experiments to validate the efficacy of the proposed ROTracker. Compared to other baseline methods, ROTracker achieves excellent multiple object tracking accuracy in terms of 91.9% in our collected dataset. The experimental results demonstrated that the proposed ROTracker has significant application potential in both accuracy and efficiency for USVs, addressing the challenges posed by complex nearshore environments. [ABSTRACT FROM AUTHOR]

Ensuring representativeness of collected samples is the most critical requirement of water sampling. Unmanned surface vehicles (USVs) have been widely adopted in water sampling, but current USV sampling path planning tend to overemphasize path optimization, neglecting the representative samples collection. This study proposed a modified A* algorithm that combined remote sensing technique while considering both path length and the representativeness of collected samples. Water quality parameters were initially retrieved using satellite remote sensing imagery and a deep belief network model, with the parameter value incorporated as coefficient Q in the heuristic function of A* algorithm. The adjustment coefficient k was then introduced into the coefficient Q to optimize the trade-off between sampling representativeness and path length. To evaluate the effectiveness of this algorithm, Chlorophyll-a concentration (Chl-a) was employed as the test parameter, with Chaohu Lake as the study area. Results showed that the algorithm was effective in collecting more representative samples in real-world conditions. As the coefficient k increased, the representativeness of collected samples enhanced, indicated by the Chl-a closely approximating the overall mean Chl-a and exhibiting a gradient distribution. This enhancement was also associated with increased path length. This study is significant in USV water sampling and water environment protection.

Recent developments in condition-based maintenance (CBM) have helped make it a promising approach to maintenance cost avoidance in engineering systems. By performing maintenance based on conditions of the component with regards to failure or time, there is potential to avoid the large costs of system shutdown and maintenance delays. However, CBM requires a large investment cost compared to other available maintenance strategies. The investment cost is required for research, development, and implementation. Despite the potential to avoid significant maintenance costs, the large investment cost of CBM makes decision makers hesitant to implement. This study is the first in the literature that attempts to address the problem of conducting a cost-benefit analysis (CBA) for implementing CBM concepts for unmanned systems. This paper proposes a method for conducting a CBA to determine the return on investment (ROI) of potential CBM strategies. The CBA seeks to compare different CBM strategies based on the differences in the various maintenance requirements associated with maintaining a multi-component, unmanned system. The proposed method uses modular dynamic fault tree analysis (MDFTA) with Monte Carlo simulations (MCS) to assess the various maintenance requirements. The proposed method is demonstrated on an unmanned surface vessel (USV) example taken from the literature that consists of 5 subsystems and 71 components. Following this USV example, it is found that selecting different combinations of components for a CBM strategy can have a significant impact on maintenance requirements and ROI by impacting cost avoidances and investment costs.

AbstractPonds are a unique agricultural irrigation method with a long history in the Taoyuan area. However, most of them lack accurate spatial information, such as water depth, water surface area, and effective water storage capacity. In this study, the water depth of pond was measured using an unmanned surface vehicle, which has the advantage of being able to operate in shallow waters and in areas that are inaccessible to manned boats. The results show that it can rapidly provide 3D point cloud and an underwater 50 cm gridded digital elevation model with an accuracy of 6.7 cm, which can be used to identify structural defects at the pond bed and the embankment and to accurately estimate the water surface area and effective water storage capacity. The nests with diameters of 30 to 70 cm dug by male tilapia to attract female tilapia for spawning can also be found. In addition, the ground-penetrating radar and electrical resistivity tomography were carried out to successfully investigate the electricity-related properties of the internal structure of the embankment and the leakage locations of pond. In conjunction with the maintenance experience of the Taoyuan Management Office, a risk reference index was proposed for the safety management.

The detection of objects on water surfaces is a pivotal technology for the perceptual systems of unmanned surface vehicles (USVs). This paper proposes a novel real-time target detection system designed to address the challenges posed by indistinct bottom boundaries and foggy imagery. Our method enhances the YOLOv8s model by incorporating the convolutional block attention module (CBAM) and a self-attention mechanism, examining their impact at various integration points. A dynamic sample assignment strategy was introduced to enhance the precision of our model and accelerate its convergence. To address the challenge of delineating bottom boundaries with clarity, our model employs a two-strategy approach: a threshold filter and a feedforward neural network (FFN) that provides targeted guidance for refining these boundaries. Our model demonstrated exceptional performance, achieving a mean average precision (mAP) of 47.1% on the water surface object dataset, which represents a 1.7% increase over the baseline YOLOv8 model. The dynamic sample assignment strategy contributes a 1.0% improvement on average precision at the intersection over union (IoU) threshold of 0.5 (AP0.5), while the FFN strategy fine-tunes the bottom boundaries and achieves an additional 0.8% improvement in average precision at IoU threshold of 0.75 (AP0.75). Furthermore, ablation studies have validated the versatility of our approach, confirming its potential for integration into various detection frameworks. [ABSTRACT FROM AUTHOR]

This article studies the distributed formation control problem for multiple unmanned surface vehicles (USVs) considering uncertain coefficient matrixes, unmeasurable velocities, and time-varying disturbances. The main contributions are as follows: First, a global coordinate translation is proposed to partially linearize the nonlinear dynamic model equipped with the unmeasurable velocity. Second, based on the global coordinate translation, a novel type of fixed-time extended two-state observer (FTETSO) is developed to estimate unmeasurable velocities and total disturbances for each vehicle. Wherein, the estimation errors will converge to zero within a fixed time. Meanwhile, considering estimation accuracy, a two-state extension is proposed to replace a single-state extension. Third, using a sliding model-based control technique, an FTETSO-based distributed global output-feedback fixed-time formation controller (GOFFC) is elaborately developed. Based on the proposed controller, the fixed-time convergence of the closed-loop system is ensured. Finally, the validity and stability of the proposed control approach are verified by simulations. • A newly introduced decoupling technique can guarantee global stability. • A novel fixed-time extended two-state observer is proposed. • A distributed global output-feedback fixed-time formation control scheme is proposed. • The observation errors and tracking errors converge to zero in a fixed time. [ABSTRACT FROM AUTHOR]

Owing to the intricate and ever-changing nature of the marine environment, traditional marine survey methods are subject to numerous limitations. Unmanned surface vehicles (USVs) have gained significant popularity for their role in automatically identifying and positioning targets in the ocean. To enhance the environmental perception capabilities of USVs in complex marine environments, vision-based sea surface object detection algorithms have emerged as a crucial technological approach. In response to the unique challenges of sea surface object detection tasks and the various extreme situations encountered by USVs in real sea environments, YOLOv7 was chosen as the baseline model due to its excellent trade-off between speed and accuracy. We propose Efficient Multi-Scale Pyramid Attention Networks, which enable easy and rapid multi-scale feature fusion. Additionally, we improve the boundary box loss function. Building upon these optimizations, we develop a new detector called Marit-YOLO, which consistently achieves better accuracy and efficiency than the prior art across a wide spectrum of resource constraints. Marit-YOLO achieved a 5.0% increase in the average precision (AP) value on the independently collected Ocean Buoys Dataset. During generalization validation, the AP value also saw a 7.7% increase on the open-source Singapore Maritime Dataset. The Marit-YOLO algorithm achieves a real-time inference speed of 69 frames per second on a single RTX2080, enabling accurate and real-time target detection in complex sea environments. [ABSTRACT FROM AUTHOR]

Path planning is crucial for unmanned surface vehicles (USVs) to navigate and avoid obstacles efficiently. This study evaluates and contrasts various USV path-planning algorithms, focusing on their effectiveness in dynamic obstacle avoidance, resistance to water currents, and path smoothness. Meanwhile, this research introduces a novel collective intelligence algorithm tailored for two-dimensional environments, integrating dynamic obstacle avoidance and smooth path optimization. The approach tackles the global-path-planning challenge, specifically accounting for moving obstacles and current influences. The algorithm adeptly combines strategies for dynamic obstacle circumvention with an eight-directional current resistance approach, ensuring locally optimal paths that minimize the impact of currents on navigation. Additionally, advanced artificial bee colony algorithms were used during the research process to enhance the method and improve the smoothness of the generated path. Simulation results have verified the superiority of the algorithm in improving the quality of USV path planning. Compared with traditional bee colony algorithms, the improved algorithm increased the length of the optimization path by 8%, shortened the optimization time by 50%, and achieved almost 100% avoidance of dynamic obstacles. [ABSTRACT FROM AUTHOR]

The article focuses on developing a sea surface target detection algorithm for Unmanned Surface Vehicles (USVs). Topics include challenges of sea surface target detection due to variable target sizes and complex environments, improving the YOLO model for USVs through CSPLSPPF for multi-size feature extraction, PANP for feature fusion, and LCFA for efficient attention, and successful object detection on Ocean Buoys and Seaships datasets.

ObjectiveAiming at the problem of ship hull number recognition, this paper proposes a real-time ship's hull number recognition method for unmanned surface vehicles (USVs). MethodsBased on a one-stage object detection model (e.g. YOLO), the attention mechanism is introduced to make the network more sensitive to the target area by the spatial information interaction module and divided attention method. Considering the effect of prior knowledge on accuracy, the adaptive anchor method and positive sample assignment strategy are utilized to improve the accuracy of regression. Aiming to resolve the problem of slow convergence at the beginning, the loss function is redesigned to speed up the convergence and enhance the stability of the network in the training phase. Finally, the proposed method is deployed in a USV to validate the availability of the recognition performance. ResultsThe results shows that the proposed method can achieve the recognition of ships and hull numbers simultaneously under Sea State 3 conditions, and has a 14% improvement in mean average precision (mAP) compared with the original model, with the ability to perform recognition in real time. ConclusionThe results of this study indicate that the proposed method can be applied to USVs to perform hull number recognition, even under complex ocean conditions.

ObjectiveAs the complicated and changing environment on the water imposes uncertain interference on the control of unmanned surface vehicles (USVs), a heading and speed controller for USVs with a Type-2 fuzzy control algorithm is proposed. On this basis, an intelligent autonomous and robust control system is developed. MethodsFirst, a motion characteristic model for heading and speed is constructed. The recursive least square (RLS) method is then used to identify the parameters of the model. Finally, based on the identified model, a Type-2 fuzzy control algorithm and sliding mode control algorithm are designed and validated through actual ship experiments.ResultsAs the experimental results indicate, compared with the sliding mode control algorithm, the Type-2 fuzzy control algorithm demonstrates better robustness and ideal anti-interference ability in controlling USVs, but its real-time response is slightly longer. ConclusionThe results of this study can provide references for the motion control of USVs subjected to uncertain interference.

ObjectiveA finite-time disturbance observer-based optimal backstepping control (FDO-OBC) method is proposed for the issue of unknown disturbances and formation changes in the formation trajectory tracking of unmanned surface vehicles (USVs).MethodsFirst, the USV formation control framework is established on the basis of the virtual structure method, and the kinematics and dynamics formation controllers are designed. Second, a finite-time disturbance observer is introduced to estimate and compensate for unknown environmental disturbances in real time. Further, a dynamic trajectory optimization strategy based on optimal backstepping control is proposed for the trajectory tracking issue of formation change, and the information of the disturbance observer is used to calculate the optimal control input and achieve the dynamic optimization of USV formation trajectory tracking. Finally, Lyapunov stability theory is used to demonstrate the stability of the designed formation control method.ResultsThe simulation results indicate that the proposed control strategy can effectively improve the accuracy and robustness of the USV formation system.ConclusionThe FDO-OBC method provides a new technical means for the design of USV formation control systems under unknown environmental disturbances.

ObjectiveTo address the problem of visual target tracking failure caused by significant wave interference and severe camera shaking in unmanned surface vehicles (USVs), a multi-feature fusion long-term correlation robust tracking algorithm is proposed. MethodsFirst, the multi-feature fusion technique is used to enhance the expression of target features and improve the robustness of the target model. Then, high-dimensional feature dimension reduction and response map sub-grid interpolation are utilized to improve the efficiency and accuracy of target tracking. After that, a mechanism for water surface target re-identification is designed to address the issue of stable tracking when the target is completely out of sight. Finally, the proposed algorithm is validated and compared through multiple representative video datasets. Results The experimental results show that compared with traditional long-term correlation tracking algorithms, the average success rate is improved by 15.7%, the average distance precision index is improved by 30.3% and the F-score index is improved by 7.0%. ConclusionThe proposed algorithm can handle target tracking failure in harsh marine environments and has important technical support significance for improving the intelligent perception capability of USVs and ocean robots.

Unmanned surface vehicles (USVs) often encounter unknown obstacles, adverse weather, and emergency situations during missions, especially external disturbances such as wind, waves, and ocean currents at sea, which pose challenges for operators to respond promptly. To solve this issue, a Fixed-time prescribed performance adaptive state feedback shared control method for unmanned surface vehicles is proposed in this paper. The method comprises two parts: autonomous control based on Fixed-time prescribed performance adaptive state feedback for unmanned surface vehicles and shared control with adaptive state feedback, combining direct control commands with autonomous control. To ensure the convergence of tracking errors, a predefined performance control function is designed to guarantee transient error of the tracking error. Subsequently, an error transformation function is introduced for convenient controller design, and a nonlinear filter is used to compensate for boundary errors. Finally, utilizing the backstepping method, a controller is designed, and a Lyapunov function is formulated to verify the stability of unmanned surface vehicles using the designed transformation error. A state feedback shared control function is also designed to implement the shared control algorithm. Experimental results demonstrate the feasibility and effectiveness of this approach.

Maritime operations, such as ship hull inspections, infrastructure assessments, and rapid response to emergencies, often expose humans to hazardous environments. The deployment of unmanned vehicles offers a potential solution to mitigate risks and reduce operational costs in such scenarios. Collaborative efforts between Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vehicles (USVs) can enhance mission endurance and portability. This paper introduces a cooperative UAV-USV system designed to expand the operational coverage area. It leverages off-the-shelf satellite-based technology to establish robust communication links between UAVs, USVs, and the ground station. This connection facilitates the development of an autonomous launch and recovery system, relying on GPS and visual servoing. Furthermore, the system incorporates a Battery Hot Swap-system (BHS) on the USV, allowing the UAV to resume its mission within 3 minutes. The effectiveness of this proposed solution was demonstrated through successful inspection trials conducted at the port of Hamburg, Germany.

When navigating dynamic ocean environments characterized by significant wave and wind disturbances, USVs encounter time-varying external interferences and underactuated limitations. This results in reduced navigational stability and increased difficulty in trajectory tracking. Controllers based on deterministic models or non-adaptive control parameters often fail to achieve the desired performance. To enhance the adaptability of USV motion controllers, this paper proposes a trajectory tracking control algorithm that calculates PID control parameters using an improved Deep Deterministic Policy Gradient (DDPG) algorithm. Firstly, the maneuvering motion model and parameters for USVs are introduced, along with the guidance law for path tracking and the PID control algorithm. Secondly, a detailed explanation of the proposed method is provided, including the state, action, and reward settings for training the Reinforcement Learning (RL) model. Thirdly, the simulations of various algorithms, including the proposed controller, are presented and analyzed for comparison, demonstrating the superiority of the proposed algorithm. Finally, a maneuvering experiment under wave conditions was conducted in a marine tank using the proposed algorithm, proving its feasibility and effectiveness. This research contributes to the intelligent navigation of USVs in real ocean environments and facilitates the execution of subsequent specific tasks.

The goal of this paper is to establish a motion control system for unmanned surface vehicles (USVs) that enables point-to-point tracking and dynamic positioning. This includes the heading control and path following control of USVs. A hardware and software platform for USVs using microcontrollers is designed. This paper presents the construction of a kinematics and dynamics model for an unmanned catamaran. The motion process is divided into two segments. In the target point tracking segment, the heading coordinate system and the ship coordinate system are established. Based on these, a control method using differential steering to track the desired yaw angle is designed to improve the tracking efficiency. And the accuracy of heading keeping and path following is improved by combining the cascade PID controller. In the dynamic positioning segment, a self-adjusting mechanism is designed, thereby enhancing the flexibility of thrust distribution and improving the accuracy of the USV’s positioning retention in wind and wave environments. Finally, experimental validation is carried out to verify the effectiveness of the design proposed in this paper by issuing control commands and saving the return data through the upper computer, and then analyzing the return data with MATLAB (R2022b, MathWorks, Natick, MA, USA).

This study addresses the dynamic object detection problem for Unmanned Surface Vehicles (USVs) in marine environments, which is complicated by boat tilting and camera illumination sensitivity. A novel pipeline named “Seal” is proposed to enhance detection accuracy and reliability. The approach begins with an innovative preprocessing stage that integrates data from the Inertial Measurement Unit (IMU) with LiDAR sensors to correct tilt-induced distortions in LiDAR point cloud data and reduce ripple effects around objects. The adjusted data are grouped using clustering algorithms and bounding boxes for precise object localization. Additionally, a specialized Kalman filter tailored for maritime environments mitigates object discontinuities between successive frames and addresses data sparsity caused by boat tilting. The methodology was evaluated using the VRX simulator, with experiments conducted on the Volga River using real USVs. The preprocessing effectiveness was assessed using the Root Mean Square Error (RMSE) and tracking accuracy was evaluated through detection rate metrics. The results demonstrate a 25% to 30% improvement in detection accuracy and show that the pipeline can aid industry even with sparse object representation across different frames. This study highlights the potential of integrating sensor fusion with specialized tracking for accurate dynamic object detection in maritime settings, establishing a new benchmark for USV navigation systems’ accuracy and reliability.

Formation obstacle avoidance is an essential attribute of the cooperative task in unmanned surface vehicle (USV) formation. In real-world scenarios involving multiple USVs, both formation obstacle avoidance and formation recovery after obstacle avoidance play a critical role in ensuring the success of collaborative missions. In this study, an Interfered Fluid Dynamic System (IFDS) algorithm was used for obstacle avoidance due to its excellent robustness, high computational efficiency and path smoothness. The algorithm can provide good local path planning for USVs. However, the use of the IFDS on USVs still has the defect of local extreme values, which has been effectively modified to obtain an enhanced IFDS (EIFDS). In formation, based on the leader–follower method, the virtual leader was used to determine the desired position of USVs in formation, and the streamlines generated by the EIFDS guided the USVs. In order to make the formation converge to the desired formation better, the vector and scalar of the EIFDS algorithm were uncoupled, and different designs were made to achieve convergence to the desired formation. The interfered residue of the IFDS is not suitable for addressing collision avoidance between USVs in practice. Therefore, the vector field method was employed to tackle the issue, with some enhancements made to optimize its performance. Subsequently, a weighted separation method was applied to combine the vector field and EIFDS, resulting in a composite field solution. Finally, the formation obstacle avoidance strategy based on composite fields was formed. The feasibility of this scheme was verified by simulation, and compared with the single IFDS formation method, the pairwise spacing of USVs behind obstacles could be increased, and the reliability of formation obstacle avoidance was increased.

Multimodal sensors are often employed by USVs (unmanned surface vehicles) to enhance situational awareness, and the fusion of LiDAR and monocular vision is widely used in near-field perception scenarios. However, this strategy of fusing data from LiDAR and monocular vision may lead to the incorrect matching of image targets and LiDAR point cloud targets when targets occlude one another. To address this issue, a target matching network with an attention module was developed to process occlusion information. Additionally, an image target occlusion detection branch was incorporated into YOLOv9 to extract the occlusion relationships of the image targets. The introduction of the attention module and the occlusion detection branch allows for the consideration of occlusion information in matching point cloud and image targets, thereby achieving more accurate target matching. Based on the target matching network, a method for water surface target detection and multi-target tracking was proposed. This method fuses LiDAR point cloud and image data while considering occlusion information. Its effectiveness was confirmed through experimental verification. The experimental results show that the proposed method improved the correct matching rate in complex scenarios by 13.83% compared to IoU-based target matching methods, with an MOTA metric of 0.879 and an average frame rate of 21.98. The results demonstrate that the method effectively reduces the mismatch rate between point cloud and image targets. The method’s frame rate meets real-time requirements, and the method itself offers a promising solution for unmanned surface vehicles (USVs) to perform water surface target detection and multi-target tracking.

The ARGO-USV (Unmanned Surface Vehicle for ARchaeological GeO-application) is a technological project involving a marine drone aimed at devising an innovative methodology for marine geological and geomorphological investigations in shallow areas, usually considered critical areas to be investigated, with the help of traditional vessels. The methodological approach proposed in this paper has been implemented according to a multimodal mapping technique involving the simultaneous and integrated use of both optical and geoacoustic sensors. This approach has been enriched by tools based on artificial intelligence (AI), specifically intended to be installed onboard the ARGO-USV, aimed at the automatic recognition of submerged targets and the physical characterization of the seabed. This technological project is composed of a main command and control system and a series of dedicated sub-systems successfully tested in different operational scenarios. The ARGO drone is capable of acquiring and storing a considerable amount of georeferenced data during surveys lasting a few hours. The transmission of all acquired data in broadcasting allows the cooperation of a multidisciplinary team of specialists able to analyze specific datasets in real time. These features, together with the use of deep-learning-based modules and special attention to green-compliant construction phases, are the particular aspects that make ARGO-USV a modern and innovative project, aiming to improve the knowledge of wide coastal areas while minimizing the impact on these environments. As a proof-of-concept, we present the extensive mapping and characterization of the seabed from a geoarchaeological survey of the underwater Roman harbor of Puteoli in the Gulf of Naples (Italy), demonstrating that deep learning techniques can work synergistically with seabed mapping methods. [ABSTRACT FROM AUTHOR]

Maritime transport, responsible for delivering over eighty percent of the world's goods, is the backbone of the global delivery industry. However, it also presents considerable environmental risks, particularly regarding aquatic contamination. Nearly ninety percent of marine oil spills near shores are attributed to human activities, highlighting the urgent need for continuous and effective surveillance. To address this pressing issue, this paper introduces a novel technique named OS-BREEZE. This method employs an Unmanned Surface Vehicle (USV) for assessing the extent of oil pollution on the sea surface. The OS-BREEZE algorithm directs the USV along the spill edge, facilitating rapid and accurate assessment of the contaminated area. The key contribution of this paper is the development of this novel approach for monitoring and managing marine pollution, which significantly reduces the path length required for mapping and estimating the size of the contaminated area. Furthermore, this paper presents a scale model experiment executed at the Coastal and Marine Engineering Research Institute (CAMERI). This experiment demonstrated the method's enhanced speed and efficiency compared to traditional monitoring techniques. The experiment was methodically conducted across four distinct scenarios: the initial and advanced stages of an oil spill at the outer anchoring, as well as scenarios at the inner docking on both the stern and port sides. [ABSTRACT FROM AUTHOR]

In the paper, a backstepping control strategy based on a fractional-order finite-time command filter and a fractional-order finite-time disturbance observer is proposed for the trajectory tracking control of an unmanned surface vehicle. A fractional-order finite-time command filter is presented to estimate the derivatives of the intermediate control, which cannot be directly calculated, thereby reducing the chattering generated by the integer-order command filter. The fractional-order finite-time disturbance observer is presented to approximate and compensate for the model uncertainty and unknown external disturbances in the system. Subsequently, the globally asymptotically stable nature of the closed-loop system is proved based on the Lyapunov method. The effectiveness of the method is proven by simulation experiments on unmanned surface vehicles. [ABSTRACT FROM AUTHOR]

In view of the complex marine environment of navigation, especially in the case of multiple static and dynamic obstacles, the traditional obstacle avoidance algorithms applied to unmanned surface vehicles (USV) are prone to fall into the trap of local optimization. Therefore, this paper proposes an improved artificial potential field (APF) algorithm, which uses 5G communication technology to communicate between the USV and the control center. The algorithm introduces the USV discrimination mechanism to avoid the USV falling into local optimization when the USV encounter different obstacles in different scenarios. Considering the various scenarios between the USV and other dynamic obstacles such as vessels in the process of performing tasks, the algorithm introduces the concept of dynamic artificial potential field. For the multiple obstacles encountered in the process of USV sailing, based on the International Regulations for Preventing Collisions at Sea (COLREGS), the USV determines whether the next step will fall into local optimization through the discrimination mechanism. The local potential field of the USV will dynamically adjust, and the reverse virtual gravitational potential field will be added to prevent it from falling into the local optimization and avoid collisions. The objective function and cost function are designed at the same time, so that the USV can smoothly switch between the global path and the local obstacle avoidance. The simulation results show that the improved APF algorithm proposed in this paper can successfully avoid various obstacles in the complex marine environment, and take navigation time and economic cost into account. [ABSTRACT FROM AUTHOR]

In a competitive scenario, to effectively utilise the resources and battery power, the communication and control update has to be minimised to a great extent. In this work, an event-triggered coupled controller with linearly varying event threshold is proposed for setpoint tracking of USV, which optimally utilises the resources with the help of a simple interactive control loop in a classical control framework. The gains of coupled controller are optimised using differential evolution (DE) algorithm. This research analyses the effect of various classes of event thresholds on the number of triggering instants and proposes a linear event threshold (LET) for a reduced number of control triggers. In addition, the relative percentage difference in energy consumption of the USV for different cases of event-triggered control are compared with that of the USV without event-triggered control. The simulation results establish the superiority of the linear event-triggered control in reducing the control update significantly with satisfactory performance in setpoint tracking without compromising stability. [ABSTRACT FROM AUTHOR]

To solve the problem of multi-target hunting by an unmanned surface vehicle (USV) fleet, a hunting algorithm based on multi-agent reinforcement learning is proposed. Firstly, the hunting environment and kinematic model without boundary constraints are built, and the criteria for successful target capture are given. Then, the cooperative hunting problem of a USV fleet is modeled as a decentralized partially observable Markov decision process (Dec-POMDP), and a distributed partially observable multitarget hunting Proximal Policy Optimization (DPOMH-PPO) algorithm applicable to USVs is proposed. In addition, an observation model, a reward function and the action space applicable to multi-target hunting tasks are designed. To deal with the dynamic change of observational feature dimension input by partially observable systems, a feature embedding block is proposed. By combining the two feature compression methods of column-wise max pooling (CMP) and column-wise average-pooling (CAP), observational feature encoding is established. Finally, the centralized training and decentralized execution framework is adopted to complete the training of hunting strategy. Each USV in the fleet shares the same policy and perform actions independently. Simulation experiments have verified the effectiveness of the DPOMH-PPO algorithm in the test scenarios with different numbers of USVs. Moreover, the advantages of the proposed model are comprehensively analyzed from the aspects of algorithm performance, migration effect in task scenarios and self-organization capability after being damaged, the potential deployment and application of DPOMH-PPO in the real environment is verified. [ABSTRACT FROM AUTHOR]

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Abstract The detection of river boundaries is a crucial branch of the intelligent perception of unmanned surface vehicles (USVs), it can be used to determine the driving areas of USVs, and also to ensure driving safety by limiting the effective drivable areas of USVs in the river areas. Aiming to detect the boundaries of incompletely structured river channels, this study proposes a real‐time detection method for river boundaries based on a Light Detection and Ranging (LiDAR) sensor. The point clouds that are disturbed by the water surface noise are filtered firstly, and then the spatial and geometric features are extracted separately from the point cloud detected above the water surface. To prevent the error detection and missing detection, the boundary point information is predicted and calibrated in real time by Extended Kalman Filter (EKF). A planning track generation algorithm for coastal autonomous cruise without relying on high‐precision maps, and a heading and distance adaptive control method by Proportional‐Integral‐Derivative (PID), and different driving line generation methods for driving along the narrow river and wide river are proposed respectively. The experimental data verification of river boundary detection shows that the algorithm is accurate, real‐time, and robust.

In this paper, an innovative method is proposed to improve the global path planning of Unmanned Surface Vehicles (USV) in complex sea areas, combining fluid mechanic calculations with an improved A* algorithm. This method not only generates smooth paths but also ensures feasible global solutions, significantly enhancing the efficiency and safety of path planning. Firstly, in response to the water depths limitation, this study set up safe water depths, providing strong guarantees for the safe navigation of USVs in complex waters. Secondly, based on the hydrological and geographical characteristics of the study sea area, an accurate ocean environment model was constructed using Ansys Fluent software and computational fluid dynamics (CFD) technology, thus providing USVs with a feasible path solution on a global scale. Then, the local sea area with complex obstacles was converted into a grid map to facilitate detailed planning. Meanwhile, the improved A* algorithm was utilized for meticulous route optimization. Furthermore, by combining the results of local and global planning, the approach generated a comprehensive route that accounts for the complexities of the maritime environment while avoiding local optima. Finally, simulation results demonstrated that the algorithm proposed in this study shows faster pathfinding speed, shorter route distances, and higher route safety compared to other algorithms. Moreover, it remains stable and effective in real-world scenarios.

In this paper, targeting the problem that it is difficult to deal with the time-varying sideslip angle of an underactuated unmanned surface vehicle (USV), a line–of–sight (LOS) guidance law based on an improved extended state observer (ESO) is proposed. A reduced-order ESO is introduced into the identification of the sideslip angle caused by the environmental disturbance, which ensures a fast and accurate estimation of the sideslip angle. This enables the USV to follow the reference path with high precision, despite external disturbances from wind, waves, and currents. These unknown disturbances are modeled as drift, which the modified ESO-based LOS guidance law compensates for using the ESO. In the guidance subsystem incorporating the reduced-order state observer, the observer estimation and track errors are proved uniformly ultimately bounded. Simulation and experimental results are presented to validate the effectiveness of the proposed method. The simulation and comparison results demonstrate that the proposed ELOS guidance can help a USV track different types of paths quickly and smoothly. Additionally, the experimental results confirm the feasibility of the method.

The detection of river boundaries is a crucial branch of the intelligent perception of unmanned surface vehicles (USVs), it can be used to determine the driving areas of USVs, and also to ensure driving safety by limiting the effective drivable areas of USVs in the river areas. Aiming to detect the boundaries of incompletely structured river channels, this study proposes a real‐time detection method for river boundaries based on a Light Detection and Ranging (LiDAR) sensor. The point clouds that are disturbed by the water surface noise are filtered firstly, and then the spatial and geometric features are extracted separately from the point cloud detected above the water surface. To prevent the error detection and missing detection, the boundary point information is predicted and calibrated in real time by Extended Kalman Filter (EKF). A planning track generation algorithm for coastal autonomous cruise without relying on high‐precision maps, and a heading and distance adaptive control method by Proportional‐Integral‐Derivative (PID), and different driving line generation methods for driving along the narrow river and wide river are proposed respectively. The experimental data verification of river boundary detection shows that the algorithm is accurate, real‐time, and robust. [ABSTRACT FROM AUTHOR]

Unmanned Surface Vehicles (USVs) are nowadays used in various applications for security, inspection and delivery among others. To operate in dynamic and complex environments efficiently demands an optimal path planning where multiple factors should be taken into account. In this paper, the multi-objective path planning problem of USV is formulated aiming to minimize the traveled distance maximizing in parallel the trajectory smoothness and energy efficiency. To address this multi-objective path planning problem with contradicting terms, the popular Ant Colony Optimization (ACO) algorithm is employed enhanced with the proposed Fuzzy Pareto framework. In particular, ACO is used to solve the problem by finding the Pareto solutions optimizing each single objective. Then these solutions are evaluated via the Mamdani fuzzy inference system to identify the optimal one. The ranking of the solutions is based on the defuzzification values. A case study is performed in a simulation area based on Saronic Gulf topology. The results showed that depending the needs of an operation and the conditions of the area of operations (number of obstacles, currents, and distance from the initial to the target point), each objective can impact the path quality differently. [ABSTRACT FROM AUTHOR]