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This paper investigates the motion state estimation problem of the unmanned surface vehicle (USV) steering system in wireless sensor networks based on the binary coding scheme (BCS). In response to the presence of bandwidth constraints and mixed cyber-attacks in USV communication networks, this paper proposes an improved set-membership state estimation algorithm based on BCS. This algorithm partially addresses the problem of degraded performance in USV steering motion state estimation caused by mixed cyber-attacks and bandwidth constraints. Furthermore, this paper proposes a robust resilient filtering framework considering the possible occurrence of unknown but bounded (UBB) noises, model parameter uncertainties, and estimator gain perturbations in practical scenarios. The proposed framework can accurately estimate the sway velocity, yaw velocity, and roll velocity of the USV under the concurrent presence situation of mixed cyber-attacks, communication capacity constraints, UBB noises, model parameter uncertainties, and estimator gain perturbations. This paper first utilizes mathematical induction to provide the sufficient conditions for the existence of the desired estimator, and obtains the estimator gain by solving a set of linear matrix inequalities. Then, a recursive optimization algorithm is utilized to achieve optimal estimation performance. Finally, the effectiveness of the proposed estimation algorithm is verified through a simulation experiment. [ABSTRACT FROM AUTHOR]

The coastal zone is constantly exposed to marine erosion, rising water levels, waves, tides, sea currents, and debris transport. As a result, there are dynamic changes in the coastal zone topography, which may have negative effects on the aquatic environment and humans. Therefore, in order to monitor the changes in landform taking place in the coastal zone, periodic bathymetric and photogrammetric measurements should be carried out in an appropriate manner. The aim of this review is to develop a methodology for performing bathymetric and photogrammetric measurements using an Unmanned Aerial Vehicle (UAV) and an Unmanned Surface Vehicle (USV) in a coastal zone. This publication shows how topographic and bathymetric monitoring should be carried out in this type of zone in order to obtain high-quality data that will be used to develop a Digital Terrain Model (DTM). The methodology for performing photogrammetric surveys with the use of a drone in the coastal zone should consist of four stages: the selection of a UAV, the development of a photogrammetric flight plan, the determination of the georeferencing method for aerial photos, and the specification as to whether there are meteorological conditions in the studied area that enable the implementation of an aerial mission through the use of a UAV. Alternatively, the methodology for performing bathymetric measurements using a USV in the coastal zone should consist of three stages: the selection of a USV, the development of a hydrographic survey plan, and the determination of the measurement conditions in the studied area and whether they enable measurements to be carried out with the use of a USV. As can be seen, the methodology for performing bathymetric and photogrammetric measurements using UAV and USV vehicles in the coastal zone is a complex process and depends on many interacting factors. The correct conduct of the research will affect the accuracy of the obtained measurement results, the basis of which a DTM of the coastal zone is developed. Due to dynamic changes in the coastal zone topography, it is recommended that bathymetric measurements and photogrammetric measurements with the use of UAV and USV vehicles should be carried out simultaneously on the same day, before or after the vegetation period, to enable the accurate measurement of the shallow waterbody depth. [ABSTRACT FROM AUTHOR]

ObjectivesThis paper proposes a multi-source observation-based recovery bucket guidance docking strategy for the reliable recovery observation and motion goal tracking of unmanned surface vessels (USVs). MethodsDuring the entire docking process, the interference zone of the mother ship's wake is first avoided in order to complete the rough alignment of the USV route; the heading tracking guidance line is then maintained to prepare for terminal docking recovery adjustments; finally, the data obtained by the visual sensor and inertial navigation sensor is filtered and fused to calculate the recovery guidance line, which is then transmitted to the USV. The USV completes the tracking of the terminal guidance line and docking recovery task through its own guidance and control systems. A USV docking recovery system is simultaneously designed on the basis of visual and integrated navigation fusion, the hardware and software of the real boat is independently designed, and lake field tests are conducted to verify the feasibility of the system design and docking strategy.ResultsThe experimental results show that the success rate of the USV in performing autonomous docking tasks reaches 91.6%. The proposed docking strategy can meet the high-precision docking and recovery requirements of USVs. ConclusionsThe findings of this study can provide critical technical support for USV recovery operations.

In contrast to classic hydrographic cutters, unmanned surface vehicles, due to their size, ease of transport and the equipment installed, enable the performance of quick and cost-effective bottom inspections in various water areas. Thanks to their shallow draught and high manoeuvrability, hydrographic drones are capable of the bathymetric exploration of shallow waters such as harbours, hydrotechnical structures and the areas where classic naval vessels could encounter implementation difficulties. The aim of this paper is to demonstrate, using a selected practical example, the specific ability of an unmanned surface vehicle (USV) to carry out the urgent and immediate inspection of the bottom of a specific water area. The freedom to move between restricted areas, the ease of transport and the satisfactory quality of the surveys make hydrographic drones ideal tools for projects of this type. The referenced study produced a bathymetric map of a section of the seabed adjacent to the quay at which a Search and Rescue (SAR) vessel is moored and regularly, at its permanent fixed location, actuates its propellers. The effect of its propellers is the local deepening of the bottom in two places. The research showed a local decrease in the depth from 5.5 m to less than 7 m, which may threaten the stability of the quay structure. In addition, it was noted that the washed bottom material had been moved approximately 10 m from the quay, causing shallowing in two places and reducing the depth from 5.5 m to 4.7 m. This study demonstrated that the use of USVs for applications of this type is very effective in terms of the implementation time and is economically justified. [ABSTRACT FROM AUTHOR]

This article introduces the Lake Environmental Data Harvester (LED) System, a robotic platform designed for the development of an innovative solution for monitoring remote alpine lakes. LED is intended as the first step in creating portable robotic tools that are lightweight, cost-effective, and highly reliable for monitoring remote water bodies. The LED system is based on the Shallow-Water Autonomous Multipurpose Platform (SWAMP), a groundbreaking Autonomous Surface Vehicle (ASV) originally designed for monitoring wetlands. The objective of LED is to achieve the comprehensive monitoring of remote lakes by outfitting the SWAMP with a suite of sensors, integrating an IoT infrastructure, and adhering to FAIR principles for structured data management. SWAMP's modular design and open architecture facilitate the easy integration of payloads, while its compact size and construction with a reduced weight ensure portability. Equipped with four azimuth thrusters and a flexible hull structure, SWAMP offers a high degree of maneuverability and position-keeping ability for precise surveys in the shallow waters that are typical of remote lakes. In this project, SWAMP was equipped with a suite of sensors, including a single-beam dual-frequency echosounder, water-quality sensors, a winch for sensor deployment, and AirQino, a low-cost air quality analysis system, along with an RTK-GNSS (Global Navigation Satellite System) receiver for precise positioning. Utilizing commercial off-the-shelf (COTS) components, a Multipurpose Data-Acquisition System forms the basis for an Internet of Things (IoT) infrastructure, enabling data acquisition, storage, and long-range communication. This data-centric system design ensures that acquired variables from both sensors and the robotic platform are structured and managed according to the FAIR principles. [ABSTRACT FROM AUTHOR]

In response to the inefficiencies and high costs associated with manual buoy inspection, this paper presents the design and testing of an Autonomous Navigation Unmanned Surface Vehicle (USV) tailored for this purpose. The research is structured into three main components: Firstly, the hardware framework and communication system of the USV are detailed, incorporating the Robot Operating System (ROS) and additional nodes to meet practical requirements. Furthermore, a buoy tracking system utilizing the Kernelized Correlation Filter (KCF) algorithm is introduced. Secondly, buoy image training is conducted using the YOLOv7 object detection algorithm, establishing a robust model for accurate buoy state recognition. Finally, an improved Line-of-Sight (LOS) method for USV path tracking, assuming the presence of an attraction potential field around the inspected buoy, is proposed to enable a comprehensive 360-degree inspection. Experimental testing includes validation of buoy image target tracking and detection, assessment of USV autonomous navigation and obstacle avoidance capabilities, and evaluation of the enhanced LOS path tracking algorithm. The results demonstrate the USV's efficacy in conducting practical buoy inspection missions. This research contributes insights and advancements to the fields of maritime patrol and routine buoy inspections. [ABSTRACT FROM AUTHOR]

One of the main methods of the path localization of moving objects is positioning using Global Navigation Satellite Systems (GNSSs) in cooperation with Inertial Navigation Systems (INSs). Its basic task is to provide high availability, in particular in areas with limited access to satellite signals such as forests, tunnels or urban areas. The aim of the article is to carry out the testing and analysis of selected navigation parameters (3D position coordinates (Northing, Easting, and height) and Euler angles (pitch and roll)) of the GNSS/INS system for Unmanned Surface Vehicle (USV) path localization during inland hydrographic surveys. The research used the Ellipse-D GNSS/INS system working in the Real Time Kinematic (RTK) mode in order to determine the position of the "HydroDron" Autonomous Surface Vehicle (ASV). Measurements were conducted on four representative routes with a parallel and spiral arrangement of sounding profiles on Lake Kłodno (Poland). Based on the obtained research results, position accuracy measures of the "HydroDron" USV were determined using the Ellipse-D GNSS/INS system. Additionally, it was determined whether USV path localization using a GNSS/INS system working in the RTK mode meets the positioning requirements for inland hydrographic surveys. Research has shown that the Ellipse-D system operating in the RTK mode can be successfully used to position vessels when carrying out inland hydrographic surveys in all International Hydrographic Organization (IHO) Orders (Exclusive, Special, 1a/1b and 2) even when it does not work 100% correctly, e.g., loss of RTK corrections for an extended period of time. In an area with limited coverage of the mobile network operator (30–40% of the time the receiver operated in the differential mode), the positioning accuracy of the "HydroDron" USV using the Ellipse-D GNSS/INS system working in the RTK mode was from 0.877 m to 0.941 m for the R95(2D) measure, depending on the route travelled. Moreover, research has shown that if the Ellipse-D system performed GNSS/INS measurements using the RTK method, the pitch and roll error values amounted to approx. 0.06°, which is almost identical to that recommended by the device manufacturer. However, when working in the differential mode, the pitch and roll error values increased from 0.06° to just over 0.2°. [ABSTRACT FROM AUTHOR]

Decarbonization is a trend in the maritime industry and may include the use of alternative energy sources on ships. At the same time, autonomous ships are under development. In the future, the two technologies may be combined. The objective of this study is to identify possible hazards related to the operation of autonomous vessels using green energy sources. An extended and holistic Systems-Theoretic Process Analysis (STPA) based approach is proposed, where both safety and security is considered. Changes in level of autonomy during operation are considered, and an extension of the STPA method is proposed to highlight the interaction between the system and external energy source. A solar-powered and wave-propelled unmanned surface vehicle is analysed. The results show that mission performance may be affected by both safety and security issues, and that considering influences from the environment and the autonomous functionalities of the system together, contributes to identifying hazards. The results are compared to operational experience from multiple field campaigns. The case study focuses on a relatively simple autonomous vehicle, but some functionalities may be shared with Maritime Autonomous Surface Ships (MASS). Hence, implications for utilisation of alternative energy sources on MASS, and effects on risks, are discussed. [ABSTRACT FROM AUTHOR]

The monocular camera can both perceive the surroundings and measure the motion states of an unmanned surface vehicle (USV) by imitating the functions of human vision, such that the monocular visual servo is an important means of improving the navigation perception and control autonomy of USVs. Starting from basic principles, the classification of visual servo methods, perspective projection model of the camera and mathematical model of the USV are briefly described so as to provide a fundamental framework for the literature review. According to task complexity, the research progress and challenges of monocular visual servo-based USV control are then summarized in four typical scenarios, namely course control, stabilization control, trajectory tracking control and swarm control. Finally, the future trends of the monocular visual servo-based autonomous control of USVs are systematically summarized.

This paper investigates the motion state estimation problem of the unmanned surface vehicle (USV) steering system in wireless sensor networks based on the binary coding scheme (BCS). In response to the presence of bandwidth constraints and mixed cyber-attacks in USV communication networks, this paper proposes an improved set-membership state estimation algorithm based on BCS. This algorithm partially addresses the problem of degraded performance in USV steering motion state estimation caused by mixed cyber-attacks and bandwidth constraints. Furthermore, this paper proposes a robust resilient filtering framework considering the possible occurrence of unknown but bounded (UBB) noises, model parameter uncertainties, and estimator gain perturbations in practical scenarios. The proposed framework can accurately estimate the sway velocity, yaw velocity, and roll velocity of the USV under the concurrent presence situation of mixed cyber-attacks, communication capacity constraints, UBB noises, model parameter uncertainties, and estimator gain perturbations. This paper first utilizes mathematical induction to provide the sufficient conditions for the existence of the desired estimator, and obtains the estimator gain by solving a set of linear matrix inequalities. Then, a recursive optimization algorithm is utilized to achieve optimal estimation performance. Finally, the effectiveness of the proposed estimation algorithm is verified through a simulation experiment.

The coastal zone is constantly exposed to marine erosion, rising water levels, waves, tides, sea currents, and debris transport. As a result, there are dynamic changes in the coastal zone topography, which may have negative effects on the aquatic environment and humans. Therefore, in order to monitor the changes in landform taking place in the coastal zone, periodic bathymetric and photogrammetric measurements should be carried out in an appropriate manner. The aim of this review is to develop a methodology for performing bathymetric and photogrammetric measurements using an Unmanned Aerial Vehicle (UAV) and an Unmanned Surface Vehicle (USV) in a coastal zone. This publication shows how topographic and bathymetric monitoring should be carried out in this type of zone in order to obtain high-quality data that will be used to develop a Digital Terrain Model (DTM). The methodology for performing photogrammetric surveys with the use of a drone in the coastal zone should consist of four stages: the selection of a UAV, the development of a photogrammetric flight plan, the determination of the georeferencing method for aerial photos, and the specification as to whether there are meteorological conditions in the studied area that enable the implementation of an aerial mission through the use of a UAV. Alternatively, the methodology for performing bathymetric measurements using a USV in the coastal zone should consist of three stages: the selection of a USV, the development of a hydrographic survey plan, and the determination of the measurement conditions in the studied area and whether they enable measurements to be carried out with the use of a USV. As can be seen, the methodology for performing bathymetric and photogrammetric measurements using UAV and USV vehicles in the coastal zone is a complex process and depends on many interacting factors. The correct conduct of the research will affect the accuracy of the obtained measurement results, the basis of which a DTM of the coastal zone is developed. Due to dynamic changes in the coastal zone topography, it is recommended that bathymetric measurements and photogrammetric measurements with the use of UAV and USV vehicles should be carried out simultaneously on the same day, before or after the vegetation period, to enable the accurate measurement of the shallow waterbody depth.

Due to the complicated and changing circumstances of the sea environment, path planning technology is essential for unmanned surface vehicles (USVs) to fulfill search tasks. In most cases, the location of the underwater target is unknown, so it is necessary to completely cover the search area. In this paper, the global static path is planned using a parallel line scan search. When encountering unknown obstacles, the improved Bug algorithm is used for local dynamic path planning according to the sensor detection information. This paper first sets up the safe expansion area to ensure the safety of the USV during the obstacle avoidance process and optimizes the movement direction considering the operation and behavior characteristics of the USV. To meet the requirement of USV steering, the Bezier curve is used to smooth the path points, which greatly improves the smoothness of the path. In this paper, the multi-mode switching strategy of the Bug algorithm based on obstacle boundary width obtained by the sensor is proposed, which ensures no area omissions and meets the requirement of search area coverage during the process of bypassing obstacles. The simulation results show that the improved Bug algorithm can maintain a safe distance along the obstacle boundary to bypass the obstacle. Moreover, the improved Bug algorithm effectively improves the path oscillation phenomenon of traditional Bug and shortens the path length and operating time. Finally, through the global search path planning simulation and comparison experiments, the effectiveness of the proposed method is verified. [ABSTRACT FROM AUTHOR]

In order to solve the problem of many constraints and a complex navigation environment in the path planning of unmanned surface vehicles (USV), an improved sparrow search algorithm combining cubic chaotic map and Gaussian random walk strategy was proposed to plan it. Firstly, in the population initialisation stage, cubic chaotic map was used to replace the random generation method of the traditional sparrow search algorithm to optimise the uneven initial distribution of the population and improve the global search ability of the population. Secondly, in the late iteration of the algorithm, the standard deviation of fitness is introduced to determine whether the population is trapped in the local optimum. If true, the Gaussian random walk strategy is used to perturb the optimal individual and assist the algorithm to escape the local optimum. Thirdly, the chosen water environment is modelled, and the navigation information of the original inland electronic navigation chart (ENC) is preprocessed, gridised, and the obstacle swelling is processed. Finally, the path planning experiments of USV are carried out in an inland ENC grid environment. The experimental results show that, compared with the traditional sparrow search algorithm, the average fitness value of the path planned by improved sparrow search algorithm is reduced by 14.8% and the variance is reduced by 49.9%. The path planned by the algorithm is of good quality and high stability and, combined with ENC, it can provide a reliable path for USV. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Facing the three-dimensional, diverse, and complex underwater detection task, this paper proposed an unmanned surface vehicle-autonomous underwater vehicle (USV-AUV) cross-domain heterogeneous cooperative control strategy based on the leader-follower method. First, the control strategy takes into account the dynamic modeling error caused by factors such as unknown environmental disturbances, and designs a non-linear disturbance observer to estimate the influence. Then, the dynamic adaptive look-ahead distance is used to improve the LOS method, which can solve the formation control problem of underactuated vehicles. The USV-AUV formation control problem is rebuilt based on the cascaded system. The AUV following controller is designed through feedback linearization. Finally, the whole system is improved uniformly, globally, and asymptoticly stable by the Lyapunov theory. The USV-AUV lake test verifies the effectiveness of the formation control strategy.

In contrast to classic hydrographic cutters, unmanned surface vehicles, due to their size, ease of transport and the equipment installed, enable the performance of quick and cost-effective bottom inspections in various water areas. Thanks to their shallow draught and high manoeuvrability, hydrographic drones are capable of the bathymetric exploration of shallow waters such as harbours, hydrotechnical structures and the areas where classic naval vessels could encounter implementation difficulties. The aim of this paper is to demonstrate, using a selected practical example, the specific ability of an unmanned surface vehicle (USV) to carry out the urgent and immediate inspection of the bottom of a specific water area. The freedom to move between restricted areas, the ease of transport and the satisfactory quality of the surveys make hydrographic drones ideal tools for projects of this type. The referenced study produced a bathymetric map of a section of the seabed adjacent to the quay at which a Search and Rescue (SAR) vessel is moored and regularly, at its permanent fixed location, actuates its propellers. The effect of its propellers is the local deepening of the bottom in two places. The research showed a local decrease in the depth from 5.5 m to less than 7 m, which may threaten the stability of the quay structure. In addition, it was noted that the washed bottom material had been moved approximately 10 m from the quay, causing shallowing in two places and reducing the depth from 5.5 m to 4.7 m. This study demonstrated that the use of USVs for applications of this type is very effective in terms of the implementation time and is economically justified.

This article introduces the Lake Environmental Data Harvester (LED) System, a robotic platform designed for the development of an innovative solution for monitoring remote alpine lakes. LED is intended as the first step in creating portable robotic tools that are lightweight, cost-effective, and highly reliable for monitoring remote water bodies. The LED system is based on the Shallow-Water Autonomous Multipurpose Platform (SWAMP), a groundbreaking Autonomous Surface Vehicle (ASV) originally designed for monitoring wetlands. The objective of LED is to achieve the comprehensive monitoring of remote lakes by outfitting the SWAMP with a suite of sensors, integrating an IoT infrastructure, and adhering to FAIR principles for structured data management. SWAMP’s modular design and open architecture facilitate the easy integration of payloads, while its compact size and construction with a reduced weight ensure portability. Equipped with four azimuth thrusters and a flexible hull structure, SWAMP offers a high degree of maneuverability and position-keeping ability for precise surveys in the shallow waters that are typical of remote lakes. In this project, SWAMP was equipped with a suite of sensors, including a single-beam dual-frequency echosounder, water-quality sensors, a winch for sensor deployment, and AirQino, a low-cost air quality analysis system, along with an RTK-GNSS (Global Navigation Satellite System) receiver for precise positioning. Utilizing commercial off-the-shelf (COTS) components, a Multipurpose Data-Acquisition System forms the basis for an Internet of Things (IoT) infrastructure, enabling data acquisition, storage, and long-range communication. This data-centric system design ensures that acquired variables from both sensors and the robotic platform are structured and managed according to the FAIR principles.

The coastal zone is an area that includes the sea coast and adjacent parts of the land and sea, where the mutual interaction of these environments is clearly marked. Hence, the modelling of the land and seabed parts of the coastal zone is crucial and necessary in order to determine the dynamic changes taking place in this area. The accurate determination of the terrain in the coastal zone is now possible thanks to the use of Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vehicles (USVs). The aim of this article is to present land and seabed surface modelling in the coastal zone using UAV/USV-based data integration. Bathymetric and photogrammetric measurements were carried out on the waterbody adjacent to a public beach in Gdynia (Poland) in 2022 using the DJI Phantom 4 Real Time Kinematic (RTK) UAV and the AutoDron USV. As a result of geospatial data integration, topo-bathymetric models in the coastal zone were developed using the following terrain-modelling methods: Inverse Distance to a Power (IDP), kriging, Modified Shepard's Method (MSM) and Natural Neighbour Interpolation (NNI). Then, the accuracies of the selected models obtained using the different interpolation methods, taking into account the division into land and seabed parts, were analysed. Research has shown that the most accurate method for modelling both the land and seabed surfaces of the coastal zone is the kriging (linear model) method. The differences between the interpolated and measurement values of the R95 measurement are 0.032 m for the land part and 0.034 m for the seabed part. It should also be noted that the data interpolated by the kriging (linear model) method showed a very good fit to the measurement data recorded by the UAVs and USVs. [ABSTRACT FROM AUTHOR]

For the current aquaculture process of water quality monitoring method has low efficiency, poor realtime, working area is restricted and other shortcomings, designed a water quality monitoring unmanned boat system based on STM32 and Raspberry Pi. The system uses STM32 single chip microcomputer and Raspberry Pi 4B as the controller, combines GPS module and electronic compass to realize the navigation control of the unmanned surface vehicle, uses camera, pH sensor, turbidity sensor and temperature sensor to collect water environment data and water surface image data in real time. Data transmission is realized through 4G communication module. Deploy the upper computer software developed based on the . NET Framework on Alibaba cloud server to implement the functions of issuing commands to unmanned surface vehicle and visualizing the collected data. The results show that the maximum and minimum positional errors between the actual measurement area and the preset area reached by the unmanned water quality monitoring boat for aquaculture are 4. 3 meters and 3. 4 meters, and the accuracy range meets the actual needs. The average time for water quality measurement is around 7 minutes and 28 seconds, which significantly improves the operational efficiency compared to traditional manual detection methods. The research shows that the system is stable, easy to operate, and can obtain the data of water body in breeding waters in a timely and effective manner, which improves the efficiency of breeding personnel and reduces the cost of breeding, and has certain application prospects. [ABSTRACT FROM AUTHOR]

The operation of offshore structures at sea requires the implementation of advanced systems for their permanent monitoring. There is a set of novel technologies that could be implemented to deliver a higher level of effective and safe operation of these systems. A possible novel solution may be the application of a new maritime unmanned (USV) surface and underwater vehicles/platforms (AUV). Application of such vehicles/platforms may increase the level of operability and safety of the offshore structures and installations. Practical use of these platforms requires the application of advanced forms of different technologies to platform structures, materials, strength of structures, applied propulsion systems, energy supply sources, dynamics of platforms, control, and communication systems. The new control and communication systems may require an application of AI (artificial intelligence) technology to obtain more advanced offshore monitoring systems. The application of the USV/AUV platforms for monitoring offshore structures may provide an increase in the functionality, performance, and safety levels of those structures in operation. This paper presents a concept for an offshore monitoring system based on the application of the USV/AUV platforms. The complexity of the research is shown by presenting the performance-oriented risk-based method. The basic information on the platforms is given. The main drivers that determine the functionality, performance, and safety of the USV/AUV platforms are introduced. A brief description of the primary operational characteristics of the platforms is presented. An example of a simple operational procedure (scenario) during the monitoring of an offshore structure is described. In the final part of this paper, conclusions are given. [ABSTRACT FROM AUTHOR]

This publication is aimed at developing a concept of an innovative system for dimensioning and predicting changes in the coastal zone topography using Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vehicles (USVs). The 4DBatMap system will consist of four components: 1. Measurement data acquisition module. Bathymetric and photogrammetric measurements will be carried out with a specific frequency in the coastal zone using a UAV equipped with a Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS), Light Detection And Ranging (LiDAR) and a photogrammetric camera, as well as a USV equipped with a GNSS Real Time Kinematic (RTK) receiver and a MultiBeam EchoSounder (MBES). 2. Multi-sensor geospatial data fusion module. Low-altitude aerial imagery, hydrographic and LiDAR data acquired using UAVs and USVs will be integrated into one. The result will be an accurate and fully covered with measurements terrain of the coastal zone. 3. Module for predicting changes in the coastal zone topography. As part of this module, a computer application will be created, which, based on the analysis of a time series, will determine the optimal method for describing the spatial and temporal variability (long-term trend and seasonal fluctuations) of the coastal zone terrain. 4. Module for imaging changes in the coastal zone topography. The final result of the 4DBatMap system will be a 4D bathymetric chart to illustrate how the coastal zone topography changes over time. [ABSTRACT FROM AUTHOR]

In response to the inefficiencies and high costs associated with manual buoy inspection, this paper presents the design and testing of an Autonomous Navigation Unmanned Surface Vehicle (USV) tailored for this purpose. The research is structured into three main components: Firstly, the hardware framework and communication system of the USV are detailed, incorporating the Robot Operating System (ROS) and additional nodes to meet practical requirements. Furthermore, a buoy tracking system utilizing the Kernelized Correlation Filter (KCF) algorithm is introduced. Secondly, buoy image training is conducted using the YOLOv7 object detection algorithm, establishing a robust model for accurate buoy state recognition. Finally, an improved Line-of-Sight (LOS) method for USV path tracking, assuming the presence of an attraction potential field around the inspected buoy, is proposed to enable a comprehensive 360-degree inspection. Experimental testing includes validation of buoy image target tracking and detection, assessment of USV autonomous navigation and obstacle avoidance capabilities, and evaluation of the enhanced LOS path tracking algorithm. The results demonstrate the USV’s efficacy in conducting practical buoy inspection missions. This research contributes insights and advancements to the fields of maritime patrol and routine buoy inspections.

One of the main methods of the path localization of moving objects is positioning using Global Navigation Satellite Systems (GNSSs) in cooperation with Inertial Navigation Systems (INSs). Its basic task is to provide high availability, in particular in areas with limited access to satellite signals such as forests, tunnels or urban areas. The aim of the article is to carry out the testing and analysis of selected navigation parameters (3D position coordinates (Northing, Easting, and height) and Euler angles (pitch and roll)) of the GNSS/INS system for Unmanned Surface Vehicle (USV) path localization during inland hydrographic surveys. The research used the Ellipse-D GNSS/INS system working in the Real Time Kinematic (RTK) mode in order to determine the position of the “HydroDron” Autonomous Surface Vehicle (ASV). Measurements were conducted on four representative routes with a parallel and spiral arrangement of sounding profiles on Lake Kłodno (Poland). Based on the obtained research results, position accuracy measures of the “HydroDron” USV were determined using the Ellipse-D GNSS/INS system. Additionally, it was determined whether USV path localization using a GNSS/INS system working in the RTK mode meets the positioning requirements for inland hydrographic surveys. Research has shown that the Ellipse-D system operating in the RTK mode can be successfully used to position vessels when carrying out inland hydrographic surveys in all International Hydrographic Organization (IHO) Orders (Exclusive, Special, 1a/1b and 2) even when it does not work 100% correctly, e.g., loss of RTK corrections for an extended period of time. In an area with limited coverage of the mobile network operator (30–40% of the time the receiver operated in the differential mode), the positioning accuracy of the “HydroDron” USV using the Ellipse-D GNSS/INS system working in the RTK mode was from 0.877 m to 0.941 m for the R95(2D) measure, depending on the route travelled. Moreover, research has shown that if the Ellipse-D system performed GNSS/INS measurements using the RTK method, the pitch and roll error values amounted to approx. 0.06°, which is almost identical to that recommended by the device manufacturer. However, when working in the differential mode, the pitch and roll error values increased from 0.06° to just over 0.2°.

With the continued development of artificial intelligence technology, unmanned surface vehicles (USVs) have attracted the attention of countless domestic and international specialists and academics. In particular, path planning is a core technique for the autonomy and intelligence process of USVs. The current literature reviews on USV path planning focus on the latest global and local path optimization algorithms. Almost all algorithms are optimized by concerning metrics such as path length, smoothness, and convergence speed. However, they also simulate environmental conditions at sea and do not consider the effects of sea factors, such as wind, waves, and currents. Therefore, this paper reviews the current algorithms and latest research results of USV path planning in terms of global path planning, local path planning, hazard avoidance with an approximate response, and path planning under clustering. Then, by classifying USV path planning, the advantages and disadvantages of different research methods and the entry points for improving various algorithms are summarized. Among them, the papers which use kinematic and dynamical equations to consider the ship's trajectory motion planning for actual sea environments are reviewed. Faced with multiple moving obstacles, the literature related to multi-objective task assignment methods for path planning of USV swarms is reviewed. Therefore, the main contribution of this work is that it broadens the horizon of USV path planning and proposes future directions and research priorities for USV path planning based on existing technologies and trends. [ABSTRACT FROM AUTHOR]

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Geospatial data obtained using Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vehicles (USVs) are increasingly used to model the terrain in the coastal zone, in particular in shallow waterbodies (with a depth of up to 1 m). In order to generate a terrain relief, it is important to choose a method for modelling that will allow it to be accurately projected. Therefore, the aim of this article is to present a method for accuracy assessment of topo-bathymetric surface models based on geospatial data recorded by UAV and USV vehicles. Bathymetric and photogrammetric measurements were carried out on the waterbody adjacent to the public beach in Gdynia (Poland) in 2022 using a DJI Phantom 4 RTK UAV and an AutoDron USV. The geospatial data integration process was performed in the Surfer software. As a result, Digital Terrain Models (DTMs) in the coastal zone were developed using the following terrain modelling methods: Inverse Distance to a Power (IDP), Inverse Distance Weighted (IDW), kriging, the Modified Shepard's Method (MSM) and Natural Neighbour Interpolation (NNI). The conducted study does not clearly indicate any of the methods, as the selection of the method is also affected by the visualization of the generated model. However, having compared the accuracy measures of the charts and models obtained, it was concluded that for this type of data, the kriging (linear model) method was the best. Very good results were also obtained for the NNI method. The lowest value of the Root Mean Square Error (RMSE) (0.030 m) and the lowest value of the Mean Absolute Error (MAE) (0.011 m) were noted for the GRID model interpolated with the kriging (linear model) method. Moreover, the NNI and kriging (linear model) methods obtained the highest coefficient of determination value (0.999). The NNI method has the lowest value of the R68 measure (0.009 m), while the lowest value of the R95 measure (0.033 m) was noted for the kriging (linear model) method. [ABSTRACT FROM AUTHOR]

In ocean environments with unknown complex disturbances, the control accuracy for an unmanned surface vehicle (USV) is severely challenged with an increase in task complexity. As the foundation for executing complex tasks, it is particularly important to control a USV to navigate along a safe trajectory that has been set. In order to effectively handle the trajectory tracking problem, an innovative USV tracking control strategy with high accuracy is proposed by combining the integral sliding-mode and disturbance observer technologies, and these are effectively extended to a scenario with the cooperative trajectory tracking of multiple USVs in this study. Specifically, unknown disturbances are treated as lumped uncertainties, and a novel fixed-time stable-convergence disturbance observer (FT-DO) is proposed to effectively observe and approximate the lumped uncertainties. Then, in order to quickly reach and steadily navigate along the desired trajectory, an effective fixed-time stable-convergence fast integral sliding mode is modified, and on this basis, an accurate trajectory tracking controller (FTFISM-TTC) for a single USV and a cooperative trajectory tracking controller for multiple USVs are meaningfully proposed. Finally, the stability of FT-DO and FTFISM-TTC was rigorously proven by using the Lyapunov approach, and a comprehensive simulation of current advanced tracking control methods was conducted by using Matlab, which proved the reliability of the proposed trajectory tracking control strategy and further eliminated the impact of the initial state on the tracking accuracy. [ABSTRACT FROM AUTHOR]

ObjectivesA multiple unmanned surface vehicle (USV) cooperative hunting algorithm based on the double layer switching strategy is proposed to cope with the difficulties of USVs in hunting intelligent escaped targets. MethodsSpecifically, the first hunting strategy adopts the improved potential point method. The Hungarian algorithm is employed in order to dynamically allocate potential points for USVs, and the optimization goal is applied to minimize the total linear distance between USVs and potential points. In this process, the artificial potential field method is used to achieve cooperative collision avoidance. The second hunting strategy takes advantage of the nature of the Apollonius circle to tighten the surrounding area, i.e. two USVs go to the target point of the escaped target to intercept it, while the remaining USVs maintain the same direction as the escaped target. Moreover, in order to deal with the different escape strategies of targets, the first and second layers of hunting strategy can be transformed into each other.ResultsNumerical simulation shows that the proposed algorithm can reduce the hunting time to less than or equal to that of the sequential distribution potential point algorithm and polar angle distribution potential point algorithm.ConclusionsThe results of this study prove the effectiveness and progressiveness of the proposed algorithm.

ObjectivesIn order to solve the problems of safety and smoothness in the path planning of an unmanned surface vehicle (USV), a path planning method with a controllable distance from obstacles is proposed.MethodsFirst, the raster environment information is generated in combination with the radar image, and the Voronoi field algorithm (VFA) is used to add the danger potential field to each grid and establish the navigation boundary; second, the risk function associated with the navigation boundary is established to improve the evaluation function of the A-star algorithm, and the improved A-star algorithm is used for path planning; finally, for the problem of the large course altering of the navigation path, the gradient descent method (GDM) is used to plot a continuous smooth navigation path that satisfies the actual navigation requirements of the USV.ResultsThe simulation results show that the proposed path planning method can control the distance between the path and obstacles by setting different navigation boundaries, and the smoothness meets the navigation requirements. ConclusionsThe method proposed herein is reasonable and effective in the path planning process of USVs, and can provide references for USV autonomous obstacle avoidance decision-making.

ObjectivesThis paper studies a three-dimensional (3D) cooperative path-following control problem in the process of maritime search and rescue for a heterogeneous unmanned cluster system composed of unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs).MethodsFirst, kinematic models of the UAVs and USVs are established under a fixed coordinate system and body coordinate system. In order to design a 3D path-following controller suitable for motion control, an air coordinate system is established, and the path tracking error models of the UAVs and USVs are established in the Serret-Frenet coordinate system. Next, a 3D line-of-sight (LOS) guidance law is designed at the kinematic level, and a cooperative path-following control method suitable for heterogeneous clusters of marine vehicles is proposed, allowing the UAVs and USVs to track the preset parameterized path. Finally, the stability of the control system is analyzed based on the Lyapunov stability theory.ResultsThe simulation results verify the effectiveness of the proposed cooperative path-following control method for heterogeneous clusters of marine vehicles.ConclusionsThe results of this study can provide references for maritime search and rescue by using the proposed cooperative path-following control method.

In order to solve the problem of many constraints and a complex navigation environment in the path planning of unmanned surface vehicles (USV), an improved sparrow search algorithm combining cubic chaotic map and Gaussian random walk strategy was proposed to plan it. Firstly, in the population initialisation stage, cubic chaotic map was used to replace the random generation method of the traditional sparrow search algorithm to optimise the uneven initial distribution of the population and improve the global search ability of the population. Secondly, in the late iteration of the algorithm, the standard deviation of fitness is introduced to determine whether the population is trapped in the local optimum. If true, the Gaussian random walk strategy is used to perturb the optimal individual and assist the algorithm to escape the local optimum. Thirdly, the chosen water environment is modelled, and the navigation information of the original inland electronic navigation chart (ENC) is preprocessed, gridised, and the obstacle swelling is processed. Finally, the path planning experiments of USV are carried out in an inland ENC grid environment. The experimental results show that, compared with the traditional sparrow search algorithm, the average fitness value of the path planned by improved sparrow search algorithm is reduced by 14.8% and the variance is reduced by 49.9%. The path planned by the algorithm is of good quality and high stability and, combined with ENC, it can provide a reliable path for USV.

Due to the complicated and changing circumstances of the sea environment, path planning technology is essential for unmanned surface vehicles (USVs) to fulfill search tasks. In most cases, the location of the underwater target is unknown, so it is necessary to completely cover the search area. In this paper, the global static path is planned using a parallel line scan search. When encountering unknown obstacles, the improved Bug algorithm is used for local dynamic path planning according to the sensor detection information. This paper first sets up the safe expansion area to ensure the safety of the USV during the obstacle avoidance process and optimizes the movement direction considering the operation and behavior characteristics of the USV. To meet the requirement of USV steering, the Bezier curve is used to smooth the path points, which greatly improves the smoothness of the path. In this paper, the multi-mode switching strategy of the Bug algorithm based on obstacle boundary width obtained by the sensor is proposed, which ensures no area omissions and meets the requirement of search area coverage during the process of bypassing obstacles. The simulation results show that the improved Bug algorithm can maintain a safe distance along the obstacle boundary to bypass the obstacle. Moreover, the improved Bug algorithm effectively improves the path oscillation phenomenon of traditional Bug and shortens the path length and operating time. Finally, through the global search path planning simulation and comparison experiments, the effectiveness of the proposed method is verified.

Unmanned surface vehicle (USV) is the future development direction of ships, but few studies have focused on USV’s energy optimization based on visual perception. An energy optimization strategy based on visual object detection is developed for USV. A visual target recognition method is proposed by combining YOLOv5 and DeepSORT. Visual recognition results are fused with radar targets to support route plan for energy optimization of USV. By dynamically adjusting the threshold of visual target recognition with the target number provided by radar, the target detection result is more accurate. Experimental results show that the proposed target detection method has the best performance than other commonly used methods, MOTA of the proposed method reaches 87.40%, and the YOLOv4 method, CenterTrack and FairMOT are 85.18%, 64.97% and 46.39% respectively. And the energy consumption optimization can be dynamically achieved by continuously analyzing the speed and path of the USV and predicting fuel consumption.

Optimal path planning is required for unmanned surface vehicle (USV) operation. This paper proposes two new path planning methods based on ant colony algorithm considering variables such as minimum energy consumption, lowest collision probability with fixed and moving obstacles and the least travelling time. These two methods are called ant colony optimization local search (ACOLS) Out Teta and ACOLS Curve Path. In the ACOLS Out Teta, path is optimized using a new pheromone updating method based on distance with obstacles. In the ACOLS Curve Path method, an innovative way is presented to eliminate curvatures of the path, which reduces the robot's maneuverability, decreasing the path length, energy consumption and travel time. In this paper, collision probability variable is used, providing the ability to present methods for providing an optimal path by paying a reasonable fee. Their performance is compared with each other, and with ACO and PSO. [ABSTRACT FROM AUTHOR]

As the research concerning unmanned surface vehicles (USVs) intensifies, research on swarm operations is also being actively conducted. A swarm operation imitates the appearance of nature, such as ants, bees, and birds, in forming swarms, moving, and attacking in the search for food. However, several problems are encountered in the USV swarm operation. One of these is the problem of collisions between USVs. A conflict between agents in a swarm can lead to operational failure and property loss. This study attempted to solve this problem. In this study, a virtual matrix approach was applied as a swarm operation. Velocity obstacle (VO) and artificial potential field (APF) methods were used and compared as algorithms for collision avoidance for USVs in a swarm when the formation is changed. For effective collision avoidance, evasive maneuvers should be performed at an appropriate time and location. Therefore, a closest point of approach (CPA)-based method, which considers both temporal and spatial factors, was used. The swarm operation was verified through a large-scale simulation in which 30 USVs changed their formation seven times in 3400 s. When comparing the averages of the distance, error to waypoint, and battery usage, no significant differences were noticed between the VO and APF methods. However, when comparing the cumulative time using the minimum distance, VO was demonstrably safer than APF, and VO completed the formation faster. In conclusion, both the APF and VO methods can evidently perform swarm operations without collisions. [ABSTRACT FROM AUTHOR]

Due to limitations in maneuverability, Unmanned Surface Vehicle (USV) often struggles to achieve automatic unberthing through a single cohesive operation in constricted harbors. To address this problem, this paper proposed a novel Two-Layer Trajectory Planning Method (TL-TPM) and two Model Predictive Control (MPC) methods. Throughout the trajectory planning process, the TL-TPM considers the USV dynamic constraints along with the cumulative risk in viable areas. It incorporates the residual velocity and yaw angle deviation from the backward phase to derive the USV's retreating trajectory and optimizes sailing time in the forward phase for a smooth forward trajectory, forming a complete automatic unberthing trajectory. The Efficiency-Priority MPC (EP-MPC) and Accuracy-Priority MPC (AP-MPC) are designed for trajectory tracking, considering tracking positional deviations, yaw angle deviations and surge force variations. The proposed EP-MPC demonstrates high computational efficiency and robustness, while AP-MPC shows superior control accuracy under various environmental disturbances. Extensive simulations validate the effectiveness of these methods in achieving automatic unberthing under wind and current impacts. • A TL-TPM integrated with USV model predicts trajectories in constricted harbors. • EP-MPC and AP-MPC controllers are designed and their performances are compared. • Accounting for uncertainties and environment boosts unberthing system effectiveness. [ABSTRACT FROM AUTHOR]

The Unmanned Surface Vehicles (USVs) cluster shows significant potential in executing complex maritime tasks. However, existing task decision-making approaches often overlook inherent constraints of tasks such as executable time and operational area, leading to inefficiency during execution. This paper addresses the task decision-making problem for USV clusters by a bi-layer framework and introduces a novel Multi-Constrained Task Point Adaptive Self-Organizing Map (MCTP-ASOM) algorithm to improve the task execution efficiency within these clusters. In the task planning layer, the MCTP-ASOM algorithm incorporates the dynamic time window and perceptual distance constraints of task points. It adapts neuron movement by incorporating task waiting time and perceptual distance into the SOM algorithm's WTA and Kohonen's rules, enabling the planning of task execution sequences and optimization of execution positions, effectively reducing average waiting time and navigation costs during task completion. In the multi-task allocation layer, the K-means algorithm is utilized to minimize the inter-task distance assigned to each USV, thereby optimizing the cluster efficiency. Extensive simulations across various scenarios confirm the effectiveness of this approach, with an average reduction in task waiting time by 18.34%, per USV waiting time by 18.86%, and navigation cost by 16.3%, particularly beneficial in multi-USV operations with various constraints. • A novel MCTP-ASOM algorithm is proposed for task planning of multi-constrained tasks. • Time window constraint of tasks is considered by MCTP-ASOM in task sequence planning. • Execution position is adaptively decided by MCTP-ASOM with reduced navigation costs. • The solution complexity of task decision-making is reduced by a bi-layer framework. • Cluster performance is optimized by efficient multi-task allocation using K-means. [ABSTRACT FROM AUTHOR]

This paper presents the development of autonomy capability for an unmanned surface vehicle (USV). The development mainly focuses on the high-level autonomy on perception, path planning, guidance and control to achieve real sea applications of the USV. First, visual recognition and point cloud data processing techniques are utilized to achieve a real-time perception of the object in the sea environment. Second, detailed path planning strategies are illustrated to plan the easily reachable path for different missions, and the classic guidance and heading controller are adopted to implement the path following algorithm. Subsequently, these autonomy algorithms run in the high-level computer and render the actuator commands for the low-level embedded control system. Finally, sea trials of the USV are conducted by attending the 2020 Zhuhai Wanshan International Intelligent Vessel Competition (IIVC) in Dong Ao Island of South China Sea. The USV accomplish three missions: 1) path following, 2) navigating around the obstacle, and 3) rescuing the drowning. Sea trial results verify the autonomy of the USV in terms of the achieved performances.

Indonesia is a maritime country that has vast coastal resources and biodiversity. To support the Indonesian maritime program, a topography mapping tool is needed. The ideal topography mapping tool is the Unmanned Surface Vehicle (USV). This paper proposes the design, manufacture, and development of an affordable autonomous USV. The USV which is composed of thruster and rudder is quite complicated to build. This study employs rudderless and double thrusters as the main actuators. PID compensator is utilized as the feedback control for the autonomous USV. Energy consumption is measured when the USV is in autonomous mode. The Dynamics model of USV was implemented to study the roll stability of the proposed USV. Open-source Mission Planner software was selected as the Ground Control Station (GCS) software. Performance tests were carried out by providing the USV with an autonomous mission to follow a specific trajectory. The results showed that the developed USV was able to complete autonomous mission with relatively small errors, making it suitable for underwater topography mapping.

Objectives The problem of autonomous obstacle avoidance of unmanned surface vehicles (USV) under dense obstacle sea conditions is studied. Methods An improved dual-window dynamic window approach (DWA) obstacle avoidance algorithm based on a fuzzy control strategy is proposed; that is, a sensing window based on an onboard sensor is designed on the basis of the conventional speed window, and a dual-window model composed of the speed window and sensing window is further optimized. The speed space is constrained and the weight of the evaluation function is dynamically adjusted based on the fuzzy control strategy in accordance with the obstacle distribution state and distance between the USV and obstacles. Results The simulation experimental results show that, compared with the original DWA algorithm, the path planned by the improved algorithm under unknown dense obstacle sea conditions is smoother and more reasonable, which not only solves the problem of USVs detouring outside dense obstacle groups, but also improves the safety of obstacle avoidance navigation and reduces the number of iterations and running time by more than 20%. Conclusions The results of this study have certain reference value for research on the autonomous obstacle avoidance technology of USVs.

ObjectivesIn order to meet the needs of long-distance sea target recognition and tracking through the navigation radar and integrated optoelectronic equipment of an unmanned surface vehicle (USV) operating in a marine environment, a novel USV autonomous sensing system is developed with heterogeneous sensor association, target track prediction and photoelectric camera attitude compensation.MethodsBy using the target track prediction algorithm based on a Kalman filter for the target position information output by the navigation radar, target positioning accuracy can be improved and real-time target information can be provided to the photoelectric camera. The posture compensation algorithm based on the ship's posture in the photoelectric camera is used to complete the tasks of target image collection, recognition and tracking. An USV equipped with our proposed sensing system has completed dynamic target recognition and tracking tasks under Sea State 3 conditions.ResultsThe target tracking error is reduced by 6% and the target recognition success rate is increased to 96.25%, which verifies the environmental adaptability of this sensing system. ConclusionsThrough testing experiments, the proposed recognition and tracking system can effectively solve the problems of difficult image acquisition and poor recognition effects of sea surface targets, effectively improving the success rate of target recognition.