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This study is structured to address the problem of mapping the bottom of shallow and ultra-shallow inland water bodies using high-resolution satellite imagery. These environments, with their diverse distribution of optically relevant components, pose a challenge to traditional mapping methods. The study was conducted on several research issues, each focusing on a specific aspect of the SDB, related to the selection of spectral bands and regression models, regression models creation, evaluation of the influence of the number and spatial distribution of reference soundings, and assessment of the quality of the bathymetric surface, with a focus on microtopography. The study utilized basic empirical techniques, incorporating high-precision reference data acquired via an unmanned surface vessel (USV) integrated with a single-beam echosounder (SBES), and Global Navigation Satellite System (GNSS) receiver measurements. The performed investigation allowed the optimization of a methodology for bathymetry acquisition of such areas by identifying the impact of individual processing components. The first results indicated the usefulness of the proposed approach, which can be confirmed by the values of the obtained RMS errors of elaborated bathymetric surfaces in the range of up to several centimeters in some study cases. The work also points to the problematic nature of this type of study, which can contribute to further research into the application of remote sensing techniques for bathymetry, especially during acquisition in optically complex waters. [ABSTRACT FROM AUTHOR]

Under the action of waves, a small unmanned surface vehicle (USV) will experience continuous oscillation, significantly impacting its photovoltaic power generation system. This paper proposes a USV photovoltaic power generation simulation model, and the efficiency of photovoltaic MPPT control under wave action is studied. A simulation model for solar irradiance on solar panels of USV under wave action is established based on CFD and solar irradiation models. The dynamic changes in irradiance of USV solar panels under typical wave conditions are analyzed. The MPPT efficiency of USV photovoltaic power generation devices under continuously changing irradiance conditions is studied on this basis. The simulation research results indicate that waves and solar altitude angles significantly impact the instantaneous irradiation energy of USV photovoltaic devices. However, the impact of waves on the average irradiance is relatively tiny. The sustained oscillation of irradiance poses certain requirements for the Maximum Power Point Tracking (MPPT) control frequency of USV photovoltaic systems; a disturbance control frequency of no less than 50 Hz is proposed. [ABSTRACT FROM AUTHOR]

Neuroinflammation during the neonatal period has been linked to disorders such as autism and epilepsy. In this study, we investigated the early life behavioral consequences of a single injection of lipopolysaccharide (LPS) at postnatal day 10 (PD10) in mice. To assess deficits in communication, we performed the isolation‐induced ultrasonic vocalizations (USVs) test at PD12. To determine if early life immune stimulus could alter seizure susceptibility, latency to flurothyl‐induced generalized seizures was measured at 4 hours (hrs), 2 days, or 5 days after LPS injections. LPS had a sex‐dependent effect on USV number. LPS‐treated male mice presented significantly fewer USVs than LPS‐treated female mice. However, the number of calls did not significantly differ between control and LPS for either sex. In male mice, we found that downward, short, and composite calls were significantly more prevalent in the LPS treatment group, while upward, chevron, and complex calls were less prevalent than in controls (p < 0.05). Female mice that received LPS presented a significantly higher proportion of short, frequency steps, two‐syllable, and composite calls in their repertoire when compared with female control mice (p < 0.05). Seizure latency was not altered by early‐life inflammation at any of the time points measured. Our findings suggest that early‐life immune stimulation at PD10 disrupts vocal development but does not alter the susceptibility to flurothyl‐induced seizures during the neonatal period. Additionally, the effect of inflammation in the disruption of vocalization is sex‐dependent. [ABSTRACT FROM AUTHOR]

The study presents an optimized Unmanned Surface Vehicle (USV) collision avoidance decision-making strategy in restricted waters based on the improved Proximal Policy Optimization (PPO) algorithm. This approach effectively integrates the ship domain, the action area of restricted waters, and the International Regulations for Preventing Collisions at Sea (COLREGs), while constructing an autonomous decision-making system. A novel set of reward functions are devised to incentivize USVs to strictly adhere to COLREGs during autonomous decision-making. Also, to enhance convergence performance, this study incorporates the Gated Recurrent Unit (GRU), which is demonstrated to significantly improve algorithmic efficacy compared to both the Long Short-Term Memory (LSTM) network and traditional fully connected network structures. Finally, extensive testing in various constrained environments, such as narrow channels and complex waters with multiple ships, validates the effectiveness and reliability of the proposed strategy. [ABSTRACT FROM AUTHOR]

The detection algorithms for water surface objects considerably assist unmanned surface vehicles in rapidly perceiving their surrounding environment, providing essential environmental information and evaluating object attributes. This study proposes a lightweight water surface target detection algorithm called YOLO-WSD (water surface detection), based on YOLOv8n, to address the need for real-time, high-precision, and lightweight target detection algorithms that can adapt to the rapid changes in the surrounding environment during specific tasks. Initially, we designed the C2F-E module, enriched in gradient flow compared to the conventional C2F module, enabling the backbone network to extract richer multi-level features while maintaining lightness. Additionally, this study redesigns the feature fusion network structure by introducing low-level features and achieving multi-level fusion to enhance the network's capability of integrating multiple levels. Meanwhile, it investigates the impact of channel number differences in the Concat module fusion on model performance, thereby optimizing the neural network structure. Lastly, it introduces the WIOU localization loss function to bolster model robustness. Experiments demonstrated that YOLO-WSD achieves a 4.6% and 3.4% improvement in mAP0.5 on the water surface object detection dataset and Seaship public dataset, respectively, with recall rates improving by 5.4% and 8.5% relative to the baseline YOLOv8n model. The model's parameter size is 3.3 M. YOLO-WSD exhibits superior performance compared to other mainstream lightweight algorithms. [ABSTRACT FROM AUTHOR]

Monitoring of volcanic eruptions, the atmosphere, and the ocean, along with their scientific understanding, can be achieved through multifaceted observations that include camera images, topographic deformations, elastic waves, geology, and the chemical constituents and temperatures of air and water. However, regions of increased volcanic activity and/or shallow waters are difficult to access by crewed ship due to danger or grounding risk. To overcome these difficulties, we used an uncrewed surface vessel (USV), Bluebottle, to operate multiple observation around oceanic volcanoes in the Bonin Island arc in May 2023. Even under adverse sea conditions, the USV successfully reached a remote observation site, Nishinoshima volcano, which is about 130 km away from Chichijima Island where the USV started out from. The USV conducted five days of observations at shallower than 500 m water depth around Nishinoshima Island, the first time after violent eruptions of Nishinoshima in June 2020. The USV is equipped with various sensors and data collection technologies, including a single-beam echosounder, oceanographic and meteorological sensors, an Acoustic Doppler Current Profiler, and a time-lapse camera. These tools have provided a multi-dimensional view of the underwater landscape and marine conditions near the volcano for the first time in the world. We obtained new bathymetric data, sub-bottom images, and measurements of temperature, salinity, and pH. This study leverages advanced technologies and innovative methodologies to enhance our understanding of marine and geological phenomena. [ABSTRACT FROM AUTHOR]

This article studies the problem of real‐time path planning for unmanned surface vessels (USVs) in complex environments. Although this problem has received increasing research attention recently, it is still unsolved to a large extent due to the complexity of obstacles and their high dynamics. In particular, the existing path planning approaches are only able to solve the simplest scenarios. The main contribution of this article is to propose a new dynamic artificial potential field to represent the influence of obstacles on the danger degree of the surrounding environment, which is an important step towards the uniform and appropriate description of dynamic and static obstacles in real‐time path planning. At the same time, based on state constraints of USV, the obstacle avoidance strategy consistent with the International Regulations for Preventing Collisions at Sea (COLREGs, short for Collision Regulations) is set up, and the optimal path is solved by genetic algorithm. Simulation results are presented to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Unmanned surface vehicles (USVs) often operate in real-world environments with long voyage distances and complex routes. The use of a single-grid map model presents challenges, such as the high computational costs for high-resolution maps and loss of environmental information for low-resolution maps. This article proposes an environmental modeling method using a hybrid map that combines topology units and grids. The approach involves calibrating key nodes based on the watershed skeleton line, constructing a topology map using these nodes, decomposing the original map into unit maps, converting each unit map into a grid map, and creating a hybrid map environment model that comprises topology maps, unit map sets, and grid map sets. Then, the article introduces an improved genetic algorithm, called Genetic Algorithm with Feedback (FGA), to address path planning in hybrid maps. Experimental results demonstrate that FGA has better computational efficiency than other algorithms in similar experimental environments. In hybrid maps, path planning with FGA reduces the path lengths and time consumption, and the paths are more logical, smooth, and continuous. These findings contribute to enhancing the quality of path planning and the practical value of USVs. [ABSTRACT FROM AUTHOR]

This article describes the design and construction journey of a self-developed unmanned surface vehicle (USV). In order to increase the accessibility and lower the barrier of entry we propose a low-cost (under EUR 1000) approach to the vessel construction with great adaptability and customizability. This design prioritizes minimal power consumption as a key objective. It focuses on elucidating the intricacies of both the design and assembly processes involved in creating an economical USV. Utilizing easily accessible components, the boat outlined in this study has been already participated in the 1st Aegean Ro-boat Race 2023 competition and is tailored for entry into similar robotic competitions. Its primary functionalities encompass autonomous sea navigation coupled with sophisticated collision avoidance capabilities. Finally, we studied reinforcement learning strategies for constructing a robust intelligent controller for the task of USV navigation under disturbances and we show some preliminary simulation results we have obtained. [ABSTRACT FROM AUTHOR]

Aiming at the unmanned surface vehicle (USV) trajectory tracking control under complex time-varying environment, a predefined-time convergence sliding mode disturbance observer (PTC-SMO) is introduced to effectively handle the internal parameter uncertainties and external environmental disturbances, thereby guaranteeing precise compensation of the lumped disturbance term within a set time. Then, in order to achieve precise tracking of the desired trajectory using USV under a predetermined time constraint, a novel fast trajectory tracking control strategy with predefined-time convergence (PTC-FTTCS) is established to improve tracking performance and ensure that the trajectory tracking error converges quickly in the predefined time. Through rigorous comparative simulation under ideal conditions and time-varying disturbances, the results demonstrate reliable trajectory tracking and disturbance handling effects, and the tracking performance and disturbance observation performance are significantly better than state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Unmanned surface vehicles (USVs) are crucial in ensuring maritime safety and observation, attracting widespread attention and research. However, a single USV exhibits limited performance and cannot effectively observe complex marine environments. In contrast, clusters of USVs can collaborate to execute complex maritime tasks, thereby enhancing the overall operational efficiency. USVs typically form heterogeneous clusters by combining vehicles with varying maneuverabilities and communication network capabilities. This has sparked an increased interest in cooperative communication research within heterogeneous USV clusters. The heterogeneous USVs discussed in this paper share the same dynamic model; however, they differ in dynamic parameters and communication capabilities. First, this study establishes a three-degree-of-freedom motion mathematical model for an underdriven USV considering environmental interference. It estimates the dynamic parameters of four USVs and evaluates their communication abilities, laying the foundation for researching the cooperative control of heterogeneous USV clusters and their application in Ocean Observation. Next, the communication capability of the USVs is assessed by studying the communication mode and signal transmission loss within the USV clusters. This study investigates the problem of cooperative communication in USV cluster formation, starting with the communication delay of USV clusters under a directed switching topology. Finally, a coherent formation controller is designed under a switching communication topology to address the dynamic transformation of communication topologies within heterogeneous USV clusters. This verifies that heterogeneous USV clusters can seamlessly form and maintain formation shapes during communication topology transformations through formation simulation experiments involving four heterogeneous USVs was 22% higher than that of dispersed control topology structures. This study provides a solid foundation for future investigations into the cooperative control of heterogeneous USV clusters and their applications in marine observations. [ABSTRACT FROM AUTHOR]

Unmanned surface vehicles (USVs) are intelligent platforms for unmanned surface navigation based on artificial intelligence, motion control, environmental awareness, and other professional technologies. Obstacle avoidance is an important part of its autonomous navigation. Although the USV works in the water environment (e.g. monitoring and tracking, search and rescue scenarios), the dynamic and complex operating environment makes the traditional methods not suitable for solving the obstacle avoidance problem of the USV. In this paper, to address the issue of poor convergence of the Twin Delayed Deep Deterministic policy gradient (TD3) algorithm of Deep Reinforcement Learning (DRL) in an unstructured environment and wave current interference, random walk policy is proposed to deposit the pre-exploration policy of the algorithm into the experience pool to accelerate the convergence of the algorithm and thus achieve USV obstacle avoidance, which can achieve collision-free navigation from any start point to a given end point in a dynamic and complex environment without offline trajectory and track point generation. We design a pre-exploration policy for the environment and a virtual simulation environment for training and testing the algorithm and give the reward function and training method. The simulation results show that our proposed algorithm is more manageable to converge than the original algorithm and can perform better in complex environments in terms of obstacle avoidance behavior, reflecting the algorithm's feasibility and effectiveness. [ABSTRACT FROM AUTHOR]

This paper proposes a fixed-time prescribed performance control technique to address the challenge of precise trajectory tracking control for unmanned surface vessels (USVs) in the presence of external time-varying disturbances and input saturation. To begin with, a fixed-time disturbance observer is created to handle the time-varying external interference. The observer can accurately estimate and compensate for the disturbance in a fixed time, which effectively improves the robustness of the system. Furthermore, to guarantee both the transient and steady-state response of the system, we employed a specific control technology that ensures the trajectory tracking error remains within a preset bounded range. Then, combined with the fixed-time disturbance observer, the command filter, the prescribed performance control technology, and the fixed-time stability theory, a fixed-time trajectory tracking control law is designed to make the trajectory tracking error of the system converge in a fixed time. Finally, an experiment was designed to validate the suggested control scheme. The results show that under the same conditions, compared with the nonlinear controller and the finite-time controller, the absolute error tracking index of this paper is the lowest, which means that the presented control scheme has higher accuracy. [ABSTRACT FROM AUTHOR]

The development of dynamic positioning (DP) algorithms for an unmanned surface vehicle (USV) is attracting great interest, especially in support of complex missions such as sea rescue. In order to improve the simplicity of the algorithm, a DP algorithm based on its own path following control ability is proposed. The algorithm divides the DP problem into two parts: path generation and path following. The key contribution is that the DP ability can be realized only by designing the path generation method, rather than a whole complex independent DP controller. This saves the computing power of the USV onboard computer and can effectively reduce the complexity of the algorithm. In addition, the fixed-time LOS guidance law is designed to improve the convergence rate of the system state in path-following control. The reasonable selection of speed and a heading controller ensures that the number of design parameters to be determined is at a low level. The above algorithms have been thoroughly evaluated and validated through extensive computer simulations, demonstrating their effectiveness in simulated and real marine environments. The simulation results verify the ability of the proposed algorithm to realize the dynamic positioning of USVs, and provide a practical scheme for the design of the dynamic positioning controller of USVs. [ABSTRACT FROM AUTHOR]

Excellent self-righting performance is important to guarantee the normal navigation of Unmanned Surface Vehicles (USVs) after overturning, and the restoring time is an important index in design requirements. Traditionally, the static stability method and experiments on full-scale vehicles were used to analyze the large-angle stability of the USV. However, when it comes to the analysis of self-righting performance, the traditional static stability method will cause improper integration, and experiments are not convenient. To solve these problems, an improved static stability method was proposed, and a whole self-righting process simulation of a physical model was finished. The numerical simulation method was used to predict the self-righting process of a USV under four working conditions. Firstly, a midpoint average method based on the static stability theory was adopted to compute the static restoring time, and the results were compared with the results of the references, which verifies the effectiveness of the midpoint average method. Also, the midpoint average method is convenient because it only needs the restoring arm curve, the width and the gravity center height. Then, a numerical simulation of a physical model in static water was finished, and an experiment for a physical model in a towing tank was conducted. Comparing the restoring time of the midpoint average method, the numerical simulation and the experiment, the results show that the numerical simulation has high accuracy. Moreover, the numerical simulation was used to predict the self-righting process and analyze the self-righting performance of a USV under four working conditions. [ABSTRACT FROM AUTHOR]

ObjectiveIn order to tackle the issue of the poor navigation stability of unmanned surface vehicles (USVs) under interference conditions, an intelligent control parameter adjustment strategy based on the deep reinforcement learning (DRL) method is proposed. MethodA dynamic model of a USV combining the line-of-sight (LOS) method and PID navigation controller is established to conduct its navigation control tasks. In view of the time-varying characteristics of PID parameters for course control under interference conditions, the DRL theory is introduced. The environmental state, action and reward functions of the intelligent agent are designed to adjust the PID parameters online. An improved deep deterministic policy gradient (DDPG) algorithm is proposed to increase the convergence speed and address the issue of the occurrence of local optima during the training process. Specifically, the original experience pool is separated into success and failure experience pools, and an adaptive sampling mechanism is designed to optimize the experience pool playback structure. ResultsThe simulation results show that the improved algorithm converges rapidly with a slightly improved average return in the later stages of training. Under interference conditions, the lateral errors and heading angle deviations of the controller based on the improved DDPG algorithm are reduced significantly. Path tracking can be maintained more steadily after fitting the desired path faster.ConclusionThe improved algorithm greatly reduces the cost of training time, enhances the steady-state performance of the agent in the later stages of training and achieves more accurate path tracking.

ObjectiveAiming at the encirclement tactics adopted by enemy ships, this study focuses on the problem of planning an escape strategy when an unmanned surface vehicle (USV) is surrounded by enemy ships. MethodsA hybrid sampling deep Q-network (HS-DQN) reinforcement learning algorithm is proposed which gradually increases the playback frequency of important samples and retains a certain level of exploration to prevent it from falling into local optimization. The state space, action space and reward function are designed to obtain the USV's optimal escape strategy, and its performance is compared with that of the deep Q-network (DQN) algorithm in terms of reward and escape success rate. ResultsThe simulation results show that using the HS-DQN algorithm for training increases the escape success rate by 2% and the convergence speed by 20%. ConclusionsThe HS-DQN algorithm can reduce the number of useless explorations and speed up the convergence of the algorithm. The simulation results verify the effectiveness of the USV escape strategy.

Most of the previous research about unmanned surface vehicle (USV) control system just focuses on reaching the target but not considers berthing. Moreover, computation cost is rarely concerned. For automatic target arriving and berthing of USV in presence of multi-shape convex obstacles and disturbances, this paper designs a composite control system including trajectory optimization and trajectory tracking. In order to generate a smooth and optimal trajectory, two objectives including minimum running time and energy consumption are proposed, and their priority requirement is presented. Fuzzy satisfactory optimization is introduced to reformulate the objective functions, and the membership degree difference is used to model priority between two objectives. The obstacle avoidance constraint is developed based on the improved P-criterion. The comprehensive optimization model is established to obtain the maximum membership degree. Further, the priority optimization model is proposed to expand the feasible domain by relaxing the comprehensive optimization result. Two models are discretized by Gaussian pseudospectral method to find the optimal solution. To achieve good trajectory tracking under disturbances, two first-order active disturbance rejection control (ADRC) controllers are designed to follow the desired longitudinal velocity and course angular velocity. All the velocities of USV can converge. The simulation proves the effectiveness of the proposed co-designed system by comparing with other methods.

In order to realize waypoint-based automatic berthing alongside or astern, a generation algorithm of dynamic virtual ships is proposed to guide the underactuated surface vehicle (USV). In this paper, a nonlinear model predictive control (NMPC) problem is set up with multiple shooting method, which lifts the system and improves convergency. Measuring error and disturbances are reduced by moving horizon estimation (MHE) where the weights of sensors are auto-tuned according to their variation. Simulations of berthing alongside and astern of ship BAY CLASS illustrates the position error is smaller than 0.6 m and course error is smaller than 0.5°. Compared to adaptive sliding mode control, proposed method has advantage on state smoothness and the control performance meets the requirements of berthing accuracy.

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.

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This work investigates the cooperative formation control problem of unmanned surface vehicle-remotely operated vehicles (USV-ROVs) subject to uncertainties under deceptive attacks. In the control design, with the utilization of the desired formation as well as the geometric position between USV and ROVs, a geometric transformation approach is developed and a geometric constraint relationship of governing formation positions is derived. Under the terminal sliding mode control (TSMC) design framework, a novel terminal sliding surface is crafted to circumvent the singularity issue. To further bolster robustness, using the sliding mode damper concept, a variable damping reaching law is devised. To refrain from the effectiveness of attacks and uncertainties, the adaptive technique is integrated into the TSMC framework. To save the communication resources, an event-triggering mechanism is established between the distributed controller and ROVs. Then, an event-triggered adaptive finite-time cooperative formation control scheme is developed for the USV-ROVs. The Lyapunov theory analysis shows that the cooperative formation control issue of USV-ROVs is realized and the deceptive attack can be suppressed efficaciously. The simulation, comparison, and quantitative analysis demonstrate the relative effectiveness and superiority of the developed scheme. [ABSTRACT FROM AUTHOR]

When fuel cells are used to power mobile applications, such a vehicles, hybridization with batteries is normally required. Depending on the electronic coupling between the energy sources the power plants can have passive or active configurations. Hybrid fuel cell-battery power plants with active power control flow have some advantages. For example, they can decrease the total energy losses, while improving the fuel cell performance, extending its lifetime. Power plants with DC/DC converters show low specific energy ratios, but with a superior energy management. In the present research, the hybrid power plant for an unmanned aquatic surface vehicle (USV) based on a PEM fuel cell and a Li-ion battery is developed. Active (with DC–DC converters) or passive architectures are analyzed by numerical simulations and experimental tests. Good results are obtained for the active power plant, where the peak power demands are managed by the battery pack while the fuel cell power remains constant thanks to the DC-converter control. The study shows that a simple control algorithm (no optimal) can help to extend the USV autonomy above 12 h in calm waters with a specific energy of 85.6 W h kg-1. • Hybrid fuel cell-battery power plants with active power control flow can decrease total energy losses. • Power plants with DC/DC converters have lower specific energy ratio. • However, their better energy management enables achieving a longer endurance. [ABSTRACT FROM AUTHOR]

The context surrounding vocal communication can have a strong influence on how vocal signals are perceived. The serotonergic system is well-positioned for modulating the perception of communication signals according to context, because serotonergic neurons are responsive to social context, influence social behavior, and innervate auditory regions. Animals like lab mice can be excellent models for exploring how serotonin affects the primary neural systems involved in vocal perception, including within central auditory regions like the inferior colliculus (IC). Within the IC, serotonergic activity reflects not only the presence of a conspecific, but also the valence of a given social interaction. To assess whether serotonin can influence the perception of vocal signals in male mice, we manipulated serotonin systemically with an injection of its precursor 5-HTP, and locally in the IC with an infusion of fenfluramine, a serotonin reuptake blocker. Mice then participated in a behavioral assay in which males suppress their ultrasonic vocalizations (USVs) in response to the playback of female broadband vocalizations (BBVs), used in defensive aggression by females when interacting with males. Both 5-HTP and fenfluramine increased the suppression of USVs during BBV playback relative to controls. 5-HTP additionally decreased the baseline production of a specific type of USV and male investigation, but neither drug treatment strongly affected male digging or grooming. These findings show that serotonin modifies behavioral responses to vocal signals in mice, in part by acting in auditory brain regions, and suggest that mouse vocal behavior can serve as a useful model for exploring the mechanisms of context in human communication. [ABSTRACT FROM AUTHOR]

The development of intelligent task allocation and path planning algorithms for unmanned surface vehicles (USVs) is gaining significant interest, particularly in supporting complex ocean operations. This paper proposes an intelligent hybrid algorithm that combines task allocation and path planning to improve mission efficiency. The algorithm introduces a novel approach based on a self-attention mechanism (SAM) for intelligent task allocation. The key contribution lies in the integration of an adaptive distance field, created using the locking sweeping method (LSM), into the SAM. This integration enables the algorithm to determine the minimum practical sailing distance in obstacle-filled environments. The algorithm efficiently generates task execution sequences in cluttered maritime environments with numerous obstacles. By incorporating a safety parameter, the enhanced SAM algorithm adapts the dimensional influence of obstacles and generates paths that ensure the safety of the USV. The algorithms have been thoroughly evaluated and validated through extensive computer-based simulations, demonstrating their effectiveness in both simulated and practical maritime environments. The results of the simulations verify the algorithm's capability to optimize task allocation and path planning, leading to improved performance in complex and obstacle-laden scenarios. [ABSTRACT FROM AUTHOR]

Seamless integration of both terrestrial and non-terrestrial networks is crucial to providing full-dimensional wireless and ubiquitous coverage, particularly catering to those engaged in marine activities. Compared to terrestrial networks, wireless communications in the marine domain are still not satisfactory for ubiquitous connectivity. Featuring agile maneuverability and strong adaptive capability, autonomous marine vehicles (AMVs) play a pivotal role in enhancing communication coverage by relaying or collecting data. However, path planning for maritime data harvesting is one of the most critical issues to enhance transmission efficiency while ensuring safe sailing for AMVs; yet it has rarely been discussed under this context. This paper provides a comprehensive and holistic overview of path-planning techniques custom-tailored for the purpose of maritime data collection. Specifically, we commence with a general portrayal of fundamental models, including system architectures, problem formulations, objective functions, and associated constraints. Subsequently, we summarize the various algorithms, methodologies, platforms, tools, coding environments, and their practical implementations for addressing these models. Furthermore, we delve into the burgeoning applications of path planning in the realm of maritime data harvesting and illuminate potential avenues for upcoming research endeavors. We believe that future research may focus on developing techniques to adapt more intricate and uncertain scenarios, such as sensor failures, inaccurate state estimations, complete modeling of communication channels, ocean dynamics, and application of heterogeneous systems. [ABSTRACT FROM AUTHOR]

The control problem of avoidance-path-following is a critical consideration in the research of unmanned surface vehicle (USV) navigation control, and it holds great significance for the navigation safety of USVs. A guidance and control scheme based on finite-distance convergence is proposed in this paper. First, the requirements for the USV to avoid obstacles from the perspective of path-following lateral error are analyzed. Then, a new performance function with finite-distance convergence is proposed to constrain the lateral error. Based on this, a heading guidance law and a backstepping controller are designed to ensure that the lateral error converges to a steady-state value within the prescribed navigation distance and that the stability is maintained, satisfying the requirements of obstacle avoidance for the USV. In addition, an adaptive velocity command is designed to adjust the velocity with the lateral error, which, to a certain extent, avoids the saturation of the heading actuator caused by the large lateral error. Finally, it is proven through theory and simulation that the control algorithm can guide the USV to achieve avoidance-path-following within a limited distance and to avoid obstacles effectively. [ABSTRACT FROM AUTHOR]

This study investigates the guidance and control of an autonomous swarm of surface watercraft with focusing on distributed control laws in scenarios with and without boat dynamics. Through comprehensive simulations, we explore the behavior of boats under varying conditions in terms of analyzing their trajectories, velocities, yaw rates, and angles. The inclusion of boat dynamics significantly impacts these parameters for affecting the efficiency and convergence of boat trajectories towards the homing point. We observe that boats operating under dynamic conditions show much smoother convergence and demonstrate improved collision avoidance behaviors although their movements are slower due to the increased complexities introduced. The introduction of noise in simulations enhances realism and robustness regarding mimicking real-world scenarios. Our findings emphasize the importance of considering boat dynamics in autonomous systems where offering insights into energy-efficient control strategies for surface watercraft. This research contributes to the understanding of swarm behavior and creates a path for practical applications in marine robotics.

Aiming at the lack of simulation test means for unmanned equipment at the firing range, this paper designs a simulation test training system for unmanned vehicle motion control based on CoppeliaSim simulation software and on the basis of the construction of unmanned vehicle dynamics model and motion model, and constructs three controllers, PID, synovium and fuzzy PID. The Navmesh routing algorithm is selected to realize the path planning of the unmanned boat simulation platform. The system fills the gap in the simulation test of unmanned equipment at the firing range, solves the problem of the lack of simulation training means of unmanned boat, and can provide basic support for the digital test evaluation of unmanned equipment.

The study presents an optimized Unmanned Surface Vehicle (USV) collision avoidance decision-making strategy in restricted waters based on the improved Proximal Policy Optimization (PPO) algorithm. This approach effectively integrates the ship domain, the action area of restricted waters, and the International Regulations for Preventing Collisions at Sea (COLREGs), while constructing an autonomous decision-making system. A novel set of reward functions are devised to incentivize USVs to strictly adhere to COLREGs during autonomous decision-making. Also, to enhance convergence performance, this study incorporates the Gated Recurrent Unit (GRU), which is demonstrated to significantly improve algorithmic efficacy compared to both the Long Short-Term Memory (LSTM) network and traditional fully connected network structures. Finally, extensive testing in various constrained environments, such as narrow channels and complex waters with multiple ships, validates the effectiveness and reliability of the proposed strategy.

Under the action of waves, a small unmanned surface vehicle (USV) will experience continuous oscillation, significantly impacting its photovoltaic power generation system. This paper proposes a USV photovoltaic power generation simulation model, and the efficiency of photovoltaic MPPT control under wave action is studied. A simulation model for solar irradiance on solar panels of USV under wave action is established based on CFD and solar irradiation models. The dynamic changes in irradiance of USV solar panels under typical wave conditions are analyzed. The MPPT efficiency of USV photovoltaic power generation devices under continuously changing irradiance conditions is studied on this basis. The simulation research results indicate that waves and solar altitude angles significantly impact the instantaneous irradiation energy of USV photovoltaic devices. However, the impact of waves on the average irradiance is relatively tiny. The sustained oscillation of irradiance poses certain requirements for the Maximum Power Point Tracking (MPPT) control frequency of USV photovoltaic systems; a disturbance control frequency of no less than 50 Hz is proposed.

Aiming at the lack of simulation test means for unmanned equipment at the firing range, this paper designs a simulation test training system for unmanned vehicle motion control based on CoppeliaSim simulation software and on the basis of the construction of unmanned vehicle dynamics model and motion model, and constructs three controllers, PID, synovium and fuzzy PID. The Navmesh routing algorithm is selected to realize the path planning of the unmanned boat simulation platform. The system fills the gap in the simulation test of unmanned equipment at the firing range, solves the problem of the lack of simulation training means of unmanned boat, and can provide basic support for the digital test evaluation of unmanned equipment. [ABSTRACT FROM AUTHOR]

To solve the problems of path planning and dynamic obstacle avoidance for an unmanned surface vehicle (USV) in a locally observable non-dynamic ocean environment, a visual perception and decision-making method based on deep reinforcement learning is proposed. This method replaces the full connection layer in the Proximal Policy Optimization (PPO) neural network structure with a convolutional neural network (CNN). In this way, the degree of memorization and forgetting of sample information is controlled. Moreover, this method accumulates reward models faster by preferentially learning samples with high reward values. From the USV-centered radar perception input of the local environment, the output of the action is realized through an end-to-end learning model, and the environment perception and decision are formed as a closed loop. Thus, the proposed algorithm has good adaptability in different marine environments. The simulation results show that, compared with the PPO algorithm, Soft Actor–Critic (SAC) algorithm, and Deep Q Network (DQN) algorithm, the proposed algorithm can accelerate the model convergence speed and improve the path planning performances in partly or fully unknown ocean fields. [ABSTRACT FROM AUTHOR]

Reliable nutrient load estimation of a reservoir is challenging due to inconsistent spatial extent and temporal frequency of water quality and quantity measurements. This study aims to collect consistent spatial extent and temporal frequency of water depths and nitrate concentrations of a reservoir in South Korea using uncrewed surface vehicle (USV). In this study, reservoir nitrate loads were estimated using four methods to examine how spatial variation in water depth and nitrate concentrations affected load estimates. Based on dual measurements of water depth and nitrate concentration, reservoir nitrate loads across 30 sampling dates (0.7 million tons of fresh water on average) ranged from one to four tons. Results showed that a point measurement of water depths and nitrate concentrations can cause up to −17% of underestimation of nitrate loads, particularly after intense rainfall events. This study highlights potential opportunities and challenges of the USV‐based dual monitoring systems for water quality and quantity. Plain Language Summary: Water quantity and quality are monitored at different spatial extent and temporal frequency. This study used an uncrewed boat to measure the water depth and nitrate concentration of a reservoir in the mid‐eastern Korean Peninsula at considering the spatial component and temporal components. This uncrewed boat was equipped with water depth and nitrate concentration sensors. During the study period (2021–2022), uncrewed boats conducted 30 surveys. We found strong seasonal variations in nitrate load estimates in the reservoir, particularly during the wet season These results suggest that estimating nitrate loads from depth measurements at a point measurement in a reservoir can lead to underestimates. This study is a case study how the cutting‐edge technologies like our uncrewed boat equipped with environmental sensors can be used for the next‐generation water monitoring system. Key Points: An uncrewed surface vehicle (USV) was used to map water depth and nitrate concentration at a 10‐m resolutionNutrient load estimates varied up to 17% when comparing the USV method to a point‐measurement methodLimitations and challenges of USV‐based surveys for water quantity and quality were discussed [ABSTRACT FROM AUTHOR]

A methodology of mathematical testing is proposed for a ship with twin azimuth thrusters based on numerical calculations. An unmanned surface vessel (USV) powered by two azimuth thrusters is considered, which is a model-scale configuration. The Ship Maneuvering Mathematical Model Group (MMG) model is introduced to describe forces on the hull and propellers. A set of captive tests (planar motion mechanism (PMM) and open-water tests) were simulated using STAR-CCM+ (16.06.008-R8) software to obtain hull hydrodynamic derivatives and azimuth thruster hydrodynamic coefficients. A maneuvering test of the model-scale ship with two azimuth thrusters is built based on numerical results, and numerical results are compared with the model-scale experimental data to validate the feasibility of numerical methods. The findings show that the usability of the developed mathematical test in predicting the maneuvering ability of ships with two azimuth thrusters is confirmed through numerical calculations. [ABSTRACT FROM AUTHOR]

The aim of the research is to identify the optimal method for smoothing the surface of a hybrid digital bathymetric model (HDBM). The initiation of this research is justified by the fact that a model created from diverse types of data may have different surface textures and outliers. This diversity may cause problems in subsequent data processing stages, such as generating depth contours. As part of the adopted research methodology, fifteen filters were analysed. Filtering techniques were examined for filter type, the number of iterations, weights, and window size. The result is the adopted research methodology, which enabled the selection of the optimal filtering method. The research undertaken in this work is an extension of the methodology for developing an HDBM. An important aspect of the research is the approach to elaborating on such kinds of models in shallow and ultra-shallow waters adjacent to the land, as well as the use of data obtained by modern measurement platforms, such as unmanned surface vehicles (USV) and unmanned aerial vehicles (UAV). The studies fit into the general context of works related to the development of this type of model and undoubtedly provide a solid reference for further development or improvement of similar methods. [ABSTRACT FROM AUTHOR]

Path planning is crucial in the automatic navigation of USVs (unmanned underwater vehicles), which directly affects the operational efficiency and safety of USVs. In this paper, we propose a path-planning algorithm based on DDPG (Deep Deterministic Policy Gradient) and make a detailed comparison with the traditional A-Star algorithm and the recent Actor–Critical algorithm. Through a series of simulation experiments, it can be observed that the optimal path for USVs found by the DDPG-based path planning algorithm is faster and more accurate than that found by the other two methods. The experimental results show that the DDPG algorithm has a significant advantage in processing time and better performance in terms of path quality and safety. These results provide a strong reference for future research on automatic navigation for USVs and demonstrate the potential of DDPG-based path planning for USVs. [ABSTRACT FROM AUTHOR]

Uncrewed surface vehicles (USVs) have gained significant attention in the past decade due to their potential commercial applications and a vast global market. An essential capability of USVs is autonomous obstacle avoidance in oceanic environments to prevent collisions. This study focuses on investigating and benchmarking deep learning-based real-time object detection algorithms for collision avoidance in USVs. The candidate algorithms were trained and evaluated using a maritime dataset provided by Marine Thinking Inc. Among the algorithms tested, You Only Look Once (YOLO) v5m demonstrated outstanding performance (mAP50 > 0.84 and mAP50:95 > 0.69) and high frame per second (FPS > 60) in realtime scenarios. Subsequently, the pre-trained YOLO v5m algorithm was converted into a TensorRT engine and deployed within a DeepStream pipeline on an onboard Nvidia Jetson for real-time collision avoidance tests. The results indicate that the YOLO v5m algorithm successfully detected obstacles and provided feedback to the ArduPilot autonomous suite. The ArduPilot, in turn, adjusted the control signal of the USV motor based on the Bendy Ruler obstacle avoidance algorithm, resulting in the successful avoidance of detected obstacles, and preventing collisions. This study presents an efficient, production-ready, and cost-effective autonomous collision avoidance methodology for USVs. [ABSTRACT FROM AUTHOR]

The context surrounding vocal communication can have a strong influence on how vocal signals are perceived. The serotonergic system is well-positioned for modulating the perception of communication signals according to context, because serotonergic neurons are responsive to social context, influence social behavior, and innervate auditory regions. Animals like lab mice can be excellent models for exploring how serotonin affects the primary neural systems involved in vocal perception, including within central auditory regions like the inferior colliculus (IC). Within the IC, serotonergic activity reflects not only the presence of a conspecific, but also the valence of a given social interaction. To assess whether serotonin can influence the perception of vocal signals in male mice, we manipulated serotonin systemically with an injection of its precursor 5-HTP, and locally in the IC with an infusion of fenfluramine, a serotonin reuptake blocker. Mice then participated in a behavioral assay in which males suppress their ultrasonic vocalizations (USVs) in response to the playback of female broadband vocalizations (BBVs), used in defensive aggression by females when interacting with males. Both 5-HTP and fenfluramine increased the suppression of USVs during BBV playback relative to controls. 5-HTP additionally decreased the baseline production of a specific type of USV and male investigation, but neither drug treatment strongly affected male digging or grooming. These findings show that serotonin modifies behavioral responses to vocal signals in mice, in part by acting in auditory brain regions, and suggest that mouse vocal behavior can serve as a useful model for exploring the mechanisms of context in human communication.