An unmanned surface vessel system for coral reef monitoring

Abstract

This application discloses an unmanned surface vessel (USV) system for coral reef monitoring, relating to the field of USV technology for coral reef monitoring. The system includes a hull structure, a power system, a control system, a protection system, observation equipment, and a ground control station. The hull structure adopts a detachable modular design, supporting the power system, control system, protection system, and observation equipment to perform coral reef monitoring tasks. The power system provides propulsion and electrical energy to the USV. The control system enables autonomous navigation, path planning, and obstacle avoidance. The protection system monitors the operational status of each system on the USV and provides fault warnings and emergency response. The observation equipment collects coral reef monitoring data. The ground control station communicates with the USV to achieve remote control and data exchange. This USV system is suitable for coral reef monitoring scenarios and can improve the efficiency and accuracy of coral reef monitoring.

Description

Technical Field

[0001] This application relates to the field of unmanned surface vessel (USV) technology for coral reef monitoring, and in particular to an USV system for coral reef monitoring. Background Technology

[0002] Coral reef ecosystems are an important component of the marine ecological environment. Their investigation and exploration are fundamental to studying the stability, dynamic evolution, and future development of coral reefs, and are of great significance for the planning, management, development, utilization, and protection of coral reef resources, as well as coral reef engineering construction. However, coral reefs are located at the coupling point of the terrestrial-oceanic-atmosphere ecosystem, far from the mainland and affected by tides, making traditional monitoring methods significantly limited.

[0003] Among existing coral reef monitoring methods, while airborne remote sensing is suitable for large-area population monitoring, it lacks the capability for refined studies such as small-area community structure identification and interspecific differentiation. Underwater manual surveys are time-consuming, labor-intensive, and inefficient, making it difficult to achieve large-scale repeated measurements. Underwater manned propulsion vehicles improve efficiency to some extent but still cannot meet the needs of large-area surveys and precise route re-measurement. Underwater robots are highly efficient but lack flexibility and cannot reach shallow water areas. Conventional unmanned surface vessels (USVs), while capable of carrying multiple devices for multi-element measurements, lack a dedicated design for coral reef monitoring and are ill-suited to the shallow water environment, simultaneous multi-parameter acquisition, and long-term dynamic observation requirements of coral reef monitoring. Therefore, there is an urgent need to develop a dedicated USV system for coral reef monitoring to address the shortcomings of existing technologies and improve the efficiency and accuracy of coral reef monitoring. Summary of the Invention

[0004] The purpose of this application is to provide an unmanned surface vessel system for coral reef monitoring, which can improve the efficiency and accuracy of coral reef monitoring.

[0005] To achieve the above objectives, this application provides the following solution.

[0006] This application provides an unmanned surface vessel system for coral reef monitoring, which includes: a hull structure, a power system, a control system, a protection system, observation equipment, and a ground control station.

[0007] The power system, the control system, the protection system, and the observation equipment are all mounted on the hull structure, and the control system is connected to the power system, the protection system, the observation equipment, and the ground control station, respectively.

[0008] The hull structure adopts a detachable modular design and is used to carry the power system, the control system, the protection system and the observation equipment to perform coral reef monitoring tasks.

[0009] The power system is used to provide navigation power and electrical energy for the unmanned vessel.

[0010] The control system is used to enable autonomous navigation, path planning, and obstacle avoidance of the unmanned vessel.

[0011] The protection system is used to monitor the operating status of various systems of the unmanned vessel and to provide early warning of malfunctions and emergency response.

[0012] The observation equipment is used to collect coral reef monitoring data.

[0013] The ground control station is used to communicate with the unmanned vessel to achieve remote control and data interaction.

[0014] According to the specific embodiments provided in this application, this application has the following technical effects.

[0015] This application provides an unmanned surface vessel (USV) system for coral reef monitoring, suitable for coral reef monitoring scenarios. The system includes a hull structure, a power system, a control system, a protection system, observation equipment, and a ground control station. The hull structure employs a detachable modular design, facilitating transportation, assembly, and maintenance. It stably supports the power system, control system, protection system, and observation equipment, providing a solid foundation for the coordinated operation of these systems and ensuring the USV can successfully reach the target area to perform coral reef monitoring tasks. The power system simultaneously provides the USV with propulsion and electrical energy, ensuring its navigation capability to reach the coral reef monitoring area and supplying power to other systems (such as the control system and observation equipment), guaranteeing continuous and stable operation of each system during monitoring and supporting the complete execution of the monitoring task. The control system's control functions enable autonomous navigation, reducing reliance on manual piloting and lowering the risks for personnel operating in complex marine environments (such as the complex nearshore waters where coral reefs are located). Path planning capabilities allow for the planning of optimal monitoring paths, improving the coverage efficiency of the coral reef monitoring area. Obstacle avoidance capabilities allow the USV to avoid obstacles (such as reefs and other vessels) during monitoring, ensuring the safety of the USV and the continuity of the monitoring task. The protection system monitors the operational status of each system in real time, enabling timely detection of system anomalies and providing early warnings of potential problems for prompt troubleshooting. In the event of a malfunction, emergency response can be implemented to prevent the unmanned surface vessel (USV) from escalating into a disaster or disrupting the monitoring mission, ensuring the USV's navigation safety and mission stability during coral reef monitoring. The observation equipment is specifically designed to collect coral reef monitoring data, providing direct data support for subsequent analysis of coral reef health and distribution. This is crucial for the USV to achieve its core coral reef monitoring objectives, ensuring the acquisition of the necessary and effective data. Remote control of the USV is achieved through communication with a ground control station, allowing operation from safe shore-based areas without the need for onboard personnel, reducing operational risks. Simultaneously, data exchange is possible, enabling real-time reception of precise monitoring data from the USV for timely understanding of the monitoring situation. Furthermore, instructions can be sent to the USV to adjust the monitoring task, enhancing the flexibility and controllability of the monitoring process. This unmanned surface vessel (USV) system is a dedicated USV system developed for coral reef monitoring. It can automatically monitor coral reefs, achieving efficient, accurate, and long-term dynamic monitoring of coral reefs. It effectively improves the efficiency and accuracy of coral reef monitoring and solves the problems of low efficiency, poor adaptability, and insufficient precision of existing coral reef monitoring methods. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is an overall system block diagram of an unmanned surface vessel system for coral reef monitoring, provided as an embodiment of this application.

[0018] Figure 2 This is a schematic diagram of the overall hull structure provided in one embodiment of this application.

[0019] Figure 3 This is a multi-angle schematic diagram of a ship's hull structure provided according to an embodiment of this application. Figure 3 (a) in the image is the left view. Figure 3 (b) in the diagram is the front view. Figure 3 (c) in the diagram is the right view. Figure 3 (d) in the diagram is the top view.

[0020] Figure 4 This is a schematic diagram of the underwater rotation mechanism provided in one embodiment of this application.

[0021] Figure 5 This is a schematic diagram of the power supply system provided in one embodiment of this application.

[0022] Figure 6 This is a navigation control logic diagram of a control system provided in an embodiment of this application.

[0023] Figure 7 This is a schematic diagram of data fusion logic provided in an embodiment of this application.

[0024] Figure 8 A schematic diagram of the decision-level fusion recognition principle provided in an embodiment of this application.

[0025] Figure 9 A schematic diagram of the detection principle of an unmanned surface vessel system provided in an embodiment of this application.

[0026] Figure 10 This is a schematic diagram of the obstacle avoidance decision-making principle of a collision decision-making unit provided in an embodiment of this application.

[0027] Figure 11 This is a schematic diagram of obstacle avoidance for an unmanned vessel system provided in an embodiment of this application.

[0028] Figure 12 This is a system block diagram of an execution unit provided in an embodiment of this application.

[0029] Figure 13 This is a system block diagram of a control module provided in one embodiment of this application.

[0030] Figure 14 A schematic diagram of a tracking control algorithm based on closed-loop feedback provided in an embodiment of this application.

[0031] Figure 15 A schematic diagram illustrating the speed tracking and control principle of an unmanned vessel provided in an embodiment of this application.

[0032] Figure 16 This is a schematic diagram of an expert PID control provided in an embodiment of this application.

[0033] Figure 17 This is a schematic diagram of the structure of a closed-loop system provided in an embodiment of this application.

[0034] Figure 18 This is a schematic diagram of the CMAC model structure provided in an embodiment of this application.

[0035] Figure 19 A block diagram of a CMAC model and PID composite control structure provided in an embodiment of this application. Detailed Implementation

[0036] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0037] The purpose of this application is to provide an unmanned surface vessel system for coral reef monitoring, which can be applied to scenarios such as coral reef monitoring, coral reef health assessment and bleaching early warning, marine protected area ecological monitoring, artificial coral reef restoration effect assessment, coastal zone environmental change research, and climate change impact assessment on marine ecosystems. It can solve the problems of low efficiency, poor adaptability and insufficient precision of existing coral reef monitoring methods, and achieve efficient, accurate and long-term dynamic monitoring of coral reefs.

[0038] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0039] like Figure 1 As shown in the figure, this embodiment provides an unmanned surface vessel system for coral reef monitoring. The unmanned surface vessel system for coral reef monitoring includes: hull structure, power system, control system, protection system, observation equipment, data processing and comprehensive analysis system, and ground control station.

[0040] The propulsion system, control system, protection system, and observation equipment are all mounted on the hull structure. The control system is connected to the propulsion system, protection system, observation equipment, and ground control station. The data processing and comprehensive analysis system is mounted on the hull structure and is connected to the control system and observation equipment.

[0041] The hull structure adopts a detachable modular design and is used to carry the power system, the control system, the protection system and the observation equipment to perform coral reef monitoring tasks.

[0042] The power system is used to provide navigation power and electrical energy for the unmanned vessel.

[0043] The control system is used to enable autonomous navigation, path planning, and obstacle avoidance of the unmanned vessel.

[0044] The protection system is used to monitor the operating status of various systems of the unmanned vessel and to provide early warning of malfunctions and emergency response.

[0045] The observation equipment is used to collect coral reef monitoring data.

[0046] The ground control station is used to communicate with the unmanned vessel to achieve remote control and data interaction.

[0047] As an optional implementation, the hull structure includes: a front frame, a middle frame, a power frame, a cross-float support, an underwater rotation mechanism, a pylon support, inflatable pontoons, equipment plates, a battery box, and an instrument box.

[0048] The front frame, the middle frame, and the power frame are connected by connecting pipes to form a frame assembly; the two inflatable floats are evenly fixed to the bottom of the frame assembly by hooks.

[0049] The trans-floating support is fixed above the front frame, the underwater rotating mechanism is fixed to the front of the front frame, the battery box is fixed above the power frame, and the instrument box is fixed above the middle frame; the battery box is used to house batteries, and the instrument box is used to house various equipment and instruments.

[0050] The underwater rotating mechanism includes: a hand-cranked winch, a fixed pulley, a fixed pipe frame, a rotating support frame, and an extension pipe. One end of the fixed pipe frame is connected to one end of the extension pipe through the rotating support frame. The other end of the fixed pipe frame is fixed with the hand-cranked winch and the fixed pulley. The other end of the extension pipe is fixed to the equipment plate.

[0051] The hand-cranked winch is wound with a steel wire rope, which passes around the fixed pulley and connects to the other end of the extension tube. The extension tube is folded 90 degrees to the fixed tube frame by the rotating support frame, so as to realize the deployment and retrieval of the observation equipment.

[0052] As an optional implementation, the observation device is fixed to the device plate by a custom clamp. The device plate has a number of holes for installing the custom clamp, and the position and size of the holes are determined according to the size of each observation device.

[0053] As an optional implementation, the power system includes a thruster and a power supply component.

[0054] The thrusters are at least two in number and are controlled by PWM signals. The unmanned vessel can be turned by differential speed.

[0055] The power supply assembly includes at least two sets of battery packs. One set of battery packs is used to power the thruster, and the other set of battery packs is used to power the control system and load equipment. An isolated voltage regulator is configured at the front end of the DC load, and an inverter is connected to power the AC load.

[0056] As an optional implementation, the control system includes: an environmental perception unit, a collision avoidance decision unit, and an execution unit.

[0057] The environmental perception unit is used to acquire information about the unmanned vessel's own status and surrounding environment by using multi-sensor data fusion.

[0058] The collision avoidance decision unit is used to formulate navigation strategies using path planning algorithms and obstacle avoidance algorithms.

[0059] The execution unit includes a control module and an execution module. The control module is used to receive decision instructions issued by the ground control station and convert the decision instructions into control signals. The execution module is used to control the throttle, gear, and steering of the unmanned vessel according to the control signals.

[0060] As an optional implementation, the environmental perception unit includes: navigation radar, visual sensors, and AIS (Automatic Identification System) equipment.

[0061] The navigation radar is used to detect static obstacles and dynamic targets around the unmanned vessel, and to obtain three-dimensional spatial information of the target and the unmanned vessel.

[0062] The visual sensor is used to collect photoelectric image information around the unmanned vessel.

[0063] The AIS device is used to receive AIS information broadcast by other ships in the vicinity.

[0064] The environmental perception unit is also used to achieve decision-level fusion of static obstacle and dynamic target information, three-dimensional spatial information, photoelectric image information and AIS information through spatiotemporal registration and correlation decision.

[0065] As an optional implementation, the path planning algorithm is the A* algorithm, and the obstacle avoidance algorithm is the artificial potential field method.

[0066] As an optional implementation, the protection system includes: a status monitoring module, a fault log module, and an emergency response module.

[0067] The status monitoring module is used to monitor the operating status of various systems of the unmanned vessel in real time.

[0068] The fault log module is used to record the operating status of each system at a preset frequency, distinguish between warnings and faults, and mark the fault identification code.

[0069] The emergency response module is used to perform power outage, position holding, or automatic return-to-home operations when the status monitoring module detects an anomaly.

[0070] As an optional implementation, the observation equipment includes: a multibeam measurement system, a spectrometer, an underwater camera, and a multi-parameter water quality meter.

[0071] The multibeam measurement system is used to monitor the spatial coverage, morphological characteristics, and distribution location information of coral reefs.

[0072] The spectrometer is used to collect reflectance spectral information of coral reefs.

[0073] The underwater camera is used to collect visual image information of coral reefs.

[0074] The multi-parameter water quality meter is used to collect water quality information in the growth environment of coral reefs.

[0075] As an optional implementation, the data processing and comprehensive analysis system is used to process the coral reef monitoring data with unified timestamps and spatial coordinates, construct a spatiotemporal database, support periodic repeated observations and establish time series, and assess the ecological evolution trend of the environment surrounding the coral reef.

[0076] This application proposes an unmanned surface vessel (USV) system for coral reef monitoring. Primarily used for coral reef monitoring, it includes a hull structure, power system, protection system, control system, observation equipment, data processing and comprehensive analysis system, and a ground control station. The system is equipped with a small multibeam bathymetry system, a high-resolution spectrometer, an underwater camera, and a multi-parameter water quality meter to comprehensively monitor coral reef substrate, habitat, and spectral conditions. This allows for the analysis and assessment of coral reef health status, detailed distribution, and water quality conditions, establishing a long-term, refined monitoring system for coral reef ecosystems and contributing to marine ecological protection, climate change research, and ecological restoration projects.

[0077] To make the technical solution of this application clearer, the specific structure of the unmanned vessel system of this application will be described in detail below by way of example.

[0078] This embodiment proposes an unmanned surface vessel (USV) system for coral reef monitoring, which mainly includes a hull structure, power system, protection system, control system, observation equipment, data processing and comprehensive analysis system, and ground control station. The overall USV structure is based on a modular design concept, with each module possessing advantages such as detachability, portability, and quick and convenient assembly. The USV structure consists of six main modules: a front frame, a middle frame, a power frame, a cross-float support, an underwater rotation mechanism, and a pylon support. In addition to these modules, it also includes inflatable buoys, a battery box, an instrument box, and a propulsion system. The hull structure is as follows: Figure 2 As shown.

[0079] In this embodiment, the front frame, middle frame, and power frame are reinforcedly connected by connecting pipes (such as square tubes). The cross-buoy support is fixed above the front frame, the underwater rotating mechanism is fixed at the front of the front frame, the two inflatable floats are fixed to the bottom of both sides of the frame by hooks, the battery box is fixed above the power frame, the instrument box is fixed above the middle frame, and the two thrusters are fixed to the tail of the power frame.

[0080] Figure 3 These are multi-angle schematic diagrams of the ship's hull structure, in which... Figure 3 (a) in the image is the left view. Figure 3 (b) in the diagram is the front view. Figure 3 (c) in the diagram is the right view. Figure 3(d) is a top view. The front frame is welded from 304 stainless steel square tubing (25mm long × 25mm wide × 1mm thick), with an overall length of 1500mm and a width of 1070mm. The middle frame is welded from 304 stainless steel square tubing (25mm long × 25mm wide × 1mm thick), with an overall length of 1500mm and a width of 975mm. The power frame is welded from 304 stainless steel square tubing (25mm long × 25mm wide × 1mm thick), with an overall length of 1500mm and a width of 975mm. At the rear, to improve the strength of the thruster connection, square tubing is stacked and welded. Guardrails are welded to the left and right sides to prevent the battery box from accidentally slipping. Square mounting plates are welded to the front and rear positions of the middle of the power frame for mounting various antennas.

[0081] In this embodiment, the trans-floating support is welded from 304 stainless steel square tubing (25mm in length × 25mm in width × 1mm in thickness), with an overall length of 1500mm, a width of 940mm, and a height of 687.5mm. The trans-floating support is a three-dimensional frame, with the square tubing on the bottom left and right sides matching and fixedly connected to the square tubing on the front frame on the left and right sides. A mounting plate is fixed on top of it for mounting various antennas or supports.

[0082] like Figure 4 As shown, the underwater rotating mechanism consists of a hand-cranked winch, a fixed pulley block, a fixed pipe frame, a rotating support frame, and an extension pipe. The steel wire rope inside the hand-cranked winch passes around the fixed pulley and is then connected to the bottom of the extension pipe. In other words, the extension pipe can be folded 90 degrees through the rotating support frame using the hand-cranked winch, thus realizing the function of launching and retrieving the underwater equipment.

[0083] In this embodiment, the underwater equipment plate is cut from 304 stainless steel plate (length 560mm × width 300mm × thickness 3mm). The equipment plate is used to install observation equipment such as small multibeam bathymetry systems, multi-parameter water quality meters, high-resolution spectrometers, and underwater cameras, and the hole positions are designed according to the size of each device.

[0084] In this embodiment, the hull structure adopts a catamaran structure, which refers to an integral structure formed by two parallel inflatable pontoons connected by a connecting frame (such as a cross-buoy support). Each inflatable pontoon is 4m long and 35cm in diameter, made of PVC, and features a dual-chamber design, providing excellent impact resistance, wear resistance, and puncture resistance, making it suitable for use in harsh sea conditions and complex environments. The intermediate connecting bottom plate is made with a 304 stainless steel frame and an aviation-grade aluminum alloy panel, which not only accommodates the installation of detection instruments but also supports manned navigation and provides good corrosion resistance.

[0085] In this embodiment, the power system uses two 160-pound 24V thrusters, which can generate a maximum thrust of 3000 kg, enabling the unmanned surface vessel to reach a speed of 7 knots. The thrusters are equipped with brushless motors controlled by PWM signals. The unmanned surface vessel's steering is controlled by the differential speed of the two engines, allowing it to achieve a 360-degree turn on the spot.

[0086] In this embodiment, the power supply system uses two 24V, 300AH ternary lithium battery packs. These ternary lithium battery packs have relatively large capacity, light weight, small size, and good discharge performance, enabling them to accommodate more output power and provide long battery life. They also exhibit good low-temperature performance, are resistant to low temperatures, adapt to all-weather conditions, and have a built-in protection board for stable, reliable, and safe protection.

[0087] like Figure 5 As shown, one battery pack (power battery) supplies power to two thrusters (left and right thrusters) and is directly connected to the thrusters. Another battery pack (load battery) supplies power to the control system and load equipment. To avoid interference from the common negative terminal, an isolated voltage regulator is configured at the front end of the DC load, and a 220V inverter is connected to supply power to the 220V load.

[0088] In this embodiment, the control system is fundamental for the unmanned surface vessel (USV) platform to navigate through complex waters and ensure safe and efficient mission execution. It not only needs to process data from various sensors in real time but also needs to monitor the USV's current navigation attitude in real time to achieve precise control over the USV's course and speed.

[0089] In this embodiment, the navigation control logic diagram of the control system is as follows: Figure 6 As shown, the control system can be divided into an environmental perception unit, a collision avoidance decision-making unit, and an execution unit. The environmental perception unit acquires information such as the current location, direction of travel, and pitch status of the unmanned surface vessel (USV). The decision-making unit determines the execution commands for the USV in the next cycle. The execution unit, in addition to executing the commands issued by the decision-making module, also provides feedback on the current navigation status. The decision-making and execution units operate in a closed-loop feedback manner to precisely control the USV.

[0090] In this embodiment, the control system has functions such as path planning, intelligent control, power control and track tracking. Based on the perception unit, the unmanned vessel collects static and dynamic obstacle information and nautical chart information between the current position and the destination. The decision unit formulates the optimal path through intelligent control, automatic path planning and track tracking control functions. Finally, the execution unit controls the power and steering device to make the unmanned vessel automatically travel to the destination.

[0091] In this embodiment, the environmental perception unit is the foundation for the autonomous and safe navigation of the unmanned vessel. The system uses a variety of sensors and corresponding information processing equipment to acquire data about the vessel's internal environment and the surrounding navigation environment, thereby enabling the unmanned vessel to achieve safe, reliable, and autonomous navigation.

[0092] In this embodiment, the environmental perception unit mainly includes an environmental perception module and an autonomous obstacle avoidance sub-module. The environmental perception module employs a fusion method of navigation radar, photoelectric image information, and AIS ship data. This includes using navigation radar to detect maritime targets, acquiring distance and 3D information of surrounding targets; and using a visual sensor to detect maritime targets (detecting photoelectric image data), acquiring rich feature information of the targets, such as color and texture. A decision-level fusion approach is adopted, using navigation radar data, photoelectric image data, and AIS ship data to establish multi-sensor maritime target detection models. Spatial calibration of the multi-sensor system is completed through intrinsic parameter calibration of the photoelectric camera and joint calibration of the radar, photoelectric, and AIS systems. Simultaneously, a nearest neighbor time synchronization method is used to ensure that the radar, photoelectric, and AIS systems match the same target at the same time. After completing the spatiotemporal synchronization of the sensors, a target detection box overlap model is established, and finally, the target detection results from navigation radar, photoelectric image information, and AIS ship data are fused at the decision level.

[0093] In this embodiment, in the object detection task, whether it's a single-stage model like YOLO (You Only Look Once) and SSD (Single Shot MultiBox Detector) or a two-stage model like Faster R-CNN (Faster Region-based Convolutional Neural Network), for the same object, the model may predict multiple overlapping detection boxes with different confidence levels. Directly outputting all these boxes would result in a very messy outcome. Therefore, this embodiment requires a post-processing step to filter out the most accurate and unique boxes and eliminate redundant detection results. This post-processing step is the box overlap model.

[0094] In this embodiment, the navigation radar is primarily responsible for providing parameters such as the target's distance, bearing, and speed; the photoelectric imagery is responsible for providing elements such as the target's visual appearance, type, outline, and behavior; and the AIS ship data provides precise data such as the target's identity, accurate latitude and longitude, speed, heading, and size. The specific fusion logic is as follows: Figure 7 As shown, the principle of decision-level fusion recognition is as follows: Figure 8 As shown, it mainly includes the following contents.

[0095] (1) Spatiotemporal registration. First, the data from different sensors are compared under the same time reference and spatial coordinate system. The polar coordinates of the radar and the geographic coordinates of the AIS are uniformly transformed to the same coordinate system. At the same time, since the data from each sensor arrives at different times, it is also necessary to extrapolate all the information to the same time based on the target's motion model.

[0096] (2) Association Decision. Radar and AIS Association: This is the most direct and reliable association. Calculate the spatial distance between the radar target and the AIS reported target. If the distance is less than the association threshold and the heading and speed are roughly the same, they are considered successfully associated. This is equivalent to attaching an "identity card" (MMSI) to the radar target. Electro-optical and Radar / AIS Association: Match the target location in the electro-optical image (calculated through image pixels and camera parameters) with the location of the radar or associated AIS target. In addition to location, visual information can also be used for auxiliary association: for example, if the AIS reported target is an "oil tanker" and the outline identified by the electro-optical image is indeed that of an oil tanker, the association confidence level is greatly increased.

[0097] (3) Fusion Decision and Conflict Resolution. Based on the association results, the final decision output is made. Case 1: Successful association (complementary fusion). Identity / Type: The ship type and identity provided by AIS are used, or the identification results of electro-optical images are used for confirmation. Position and Motion Status: Weighted fusion is used. Generally, the latitude and longitude of AIS (especially at high update rates) and true north heading are more trusted, but the radial velocity measurement of radar is also very accurate. A fused track can be generated, whose position and velocity are the weighted optimal estimates of AIS and radar data (e.g., using Kalman filtering). Target Profile: The accurate profile provided by electro-optical images is used to enrich the target information, which is of great value for collision avoidance and situation assessment. Case 2: Unsuccessful association (conflict resolution and independent target handling). Radar target present, no AIS signal: The logical judgment is that it is a "non-cooperative target". It may be a small fishing boat, yacht, or wooden boat that has not been forced to install AIS, or a suspicious vessel that has deliberately turned off AIS. The decision is to mark it as a potential threat or a target that needs to be focused on. The system should issue a warning. At this point, if the electro-optical image can capture the target and identify its type (e.g., "fishing boat"), it greatly enhances situational awareness. If there is an AIS signal but no radar echo: the logical conclusion is that the target is outside the radar detection range (but AIS can transmit further via VHF signals), is obscured by islands or large vessels, or is an AIS spoofing (false report). The decision is to mark it as an "AIS-only target" and prompt the operator to verify. A long-range observation with an electro-optical sensor aimed at the location reported by the AIS can be attempted for confirmation. If there is an electro-optical target but no radar or AIS: the logical conclusion is usually that it is a very small non-metallic target (such as a sailboat, wooden boat, or buoy) or a target within the radar's blind spot. The decision is to mark it as a "purely visual target" and treat it as a close-range collision avoidance hazard.

[0098] In this embodiment, when the unmanned vessel arrives at a certain sea area, the environmental perception unit preliminarily determines the static marine environmental information within the area to be detected based on existing hydrological, island, and reef information. The location of reefs is automatically marked using image algorithms, and the type of land-water boundary line is labeled, such as reefs, beaches, and mudflats. Then, the information in the image is projected onto a planar coordinate system to initially establish a static marine perception map.

[0099] like Figure 9 As shown, the detection principle of the unmanned surface vessel (USV) system includes camera detection, lidar detection, AIS detection, and fusion detection. After acquiring various types of data, data fusion is performed to achieve temporal and spatial synchronization of multi-dimensional data. Target matching is then performed, and the final fusion detection result is output. After multi-sensor target fusion, field tests can be conducted in a certain sea area. The final image can be distinguished by different colors; for example, green targets represent targets obtained after data fusion, red circular targets represent targets detected solely by navigation radar without fusion, and red triangles represent target information reported by AIS without fusion.

[0100] like Figure 10 As shown, the obstacle avoidance decision unit receives fused environmental situational information from the environmental perception module, currently executing task data, and real-time status information of the unmanned vessel (task data is sent from the shore, and real-time status data is collected by sensors), performs obstacle avoidance decisions, and calculates the obstacle avoidance path, direction, and speed. The obstacle avoidance planning algorithm adopts the A* path planning algorithm and the artificial potential field method.

[0101] For local path planning, this embodiment employs the artificial potential field method. The artificial potential field includes an attractive field and a repulsive field. The target path point exerts an attractive force on the object, guiding it towards the target. Obstacles exert a repulsive force on the object, preventing collisions. The net force on the object at each point on the path is equal to the sum of all attractive and repulsive forces at that point. The gravitational potential can be expressed by the following formula.

[0102] (1).

[0103] in, Indicates the current position of the unmanned vessel. gravitational field potential, For the target path point Current position of the unmanned vessel distance, Indicates the target safety distance threshold. This is the gravitational gain coefficient. Gravity can be expressed by the following formula.

[0104] (2).

[0105] in, Indicates the current position of the unmanned vessel. Gravity.

[0106] Since obstacles beyond a certain safe distance do not need to be considered during obstacle avoidance, the repulsive potential in this embodiment adopts the following formula.

[0107] (3).

[0108] in, Indicates the current position of the unmanned vessel. Repulsive force, The repulsive gain coefficient is... Current position of obstacles and unmanned vessels distance, This represents the safe distance threshold from the obstacle. The derivative of the repulsive potential is the repulsive force: (4).

[0109] in, Indicates the current position of the unmanned vessel. The repulsive force.

[0110] When there are multiple obstacles near the unmanned vessel, the sum of the repulsive forces is given by the following formula.

[0111] (5).

[0112] in, This represents the sum of repulsive forces. Indicates the first An obstacle affects the current position of the unmanned vessel. The repulsive force, This represents the number of obstacles in the current environment that affect the unmanned vessel.

[0113] Since gravity only considers the next path point, the resultant force of gravity and repulsion is as follows.

[0114] (6).

[0115] in, This represents the resultant force, and the direction of the resultant force is the obstacle avoidance direction of the unmanned vessel at this time.

[0116] Obstacle avoidance diagram as follows Figure 11 As shown, the orange polygonal pattern represents the unmanned boat, the black ellipse represents the obstacle, the black arrow represents the repulsive force generated by the obstacle, the orange circle represents the target path point, the orange arrow represents the gravitational force generated by the target path point, the blue dashed line represents the original route, and the blue solid line represents the artificial potential energy field obstacle avoidance route.

[0117] However, when using the traditional artificial potential field method for path planning, it is easy to get stuck in a local minimum of the resultant force, that is, when the resultant force of attraction and repulsion is zero, the unmanned surface vessel (USV) cannot obtain an obstacle avoidance direction. Generally, the USV does not stop instantly; it will leave the position due to inertia. However, when the obstacle, the waypoint, and the USV are aligned, the USV will either oscillate back and forth or stop at the point of minimum resultant force, unable to navigate normally. When the above situation occurs, an artificial lateral force is added to move the USV away from the local minimum. According to the relevant provisions of the International Maritime Collision Avoidance Regulations, the direction of the added lateral force in this embodiment is to the right, thereby moving the USV away from the point of minimum resultant force.

[0118] In this embodiment, when the unmanned vessel operates within a certain range for a long time, it is determined that the artificial potential field method causes the unmanned vessel to fall into a local optimum for obstacle avoidance, and the path planning is re-performed using the A* algorithm.

[0119] In this embodiment, the execution unit mainly includes two modules: a control module and an execution module. The control module includes speed control and heading control; the execution module includes throttle control, gear control, and steering control. A detailed system block diagram is shown below. Figure 12 As shown. The control module is mainly responsible for converting the desired actions output by the upper-level decision-making body, such as desired speed, desired path, reversing, turning and other basic actions, into control commands for each actuator, and transmitting them to each actuator module through the CAN bus via the agreed data interface format; after receiving the commands from the control module, the actuator module accurately controls each underlying control object (steering, throttle, gear) according to the commands.

[0120] In this embodiment, the execution module's function is to transform the desired actions generated by the upper-level decision-making system into actions of various execution mechanisms, and control each execution mechanism to complete the corresponding actions, achieving accurate and stable tracking of the heading and speed. It is the lowest level of the entire unmanned surface vessel (USV) control system, composed of a series of traditional control laws and logical reasoning algorithms, including speed control, steering wheel control, and gear control. Based on the given desired speed, the control system selects the appropriate control law and controls its acceleration under certain safety constraints, establishing corresponding gear and throttle control rules to achieve stable speed tracking by the USV. Based on the given desired heading, the control system selects the appropriate control law and controls its acceleration under certain safety constraints, i.e., by calculating the deviation between the desired heading and the actual heading of the USV, and determining the steering wheel angle signal based on the current speed of the USV, causing the USV to move in the specified heading. By changing the deviation between the desired heading and the actual heading of the USV, this process is repeated to achieve heading control of the USV. The execution module is responsible for controlling various actuators according to the received control commands, mainly including the steering mechanism, throttle control and gear shifting mechanism.

[0121] In this embodiment, the control module is mainly responsible for converting the desired actions output by the upper-level decision-making body, such as desired speed, desired path, reversing, turning, and other basic actions, into control commands that the execution module can execute, and for tracking and controlling the speed and heading. Figure 13 As shown. The control module mainly includes speed tracking control and heading tracking control, and adopts a tracking control algorithm based on closed-loop feedback, such as... Figure 14 As shown.

[0122] This embodiment employs a closed-loop feedback-based tracking control algorithm, decomposing the motion control of the unmanned surface vessel (USV) into lateral control and longitudinal control. Lateral control controls the turning angle of the USV's direction, enabling it to travel along the desired path—essentially steering control. Longitudinal control manages the throttle and gear position, aiming to maintain the USV at the desired speed. For longitudinal control / speed and gear control, the focus is on controlling the USV's speed, i.e., controlling its speed to the desired level, and controlling its longitudinal acceleration. A closed-loop design is used to control the speed, maintaining it at a specific value. Figure 15 The diagram illustrates the speed tracking control principle of the unmanned surface vessel.

[0123] In this embodiment, the speed controller, based on traditional control algorithms, incorporates expert control methods to control the speed of the unmanned vessel (UAV), establishing parameter tuning rules for the speed control system. This addresses the control accuracy issues arising from the highly nonlinear and complex interference conditions of the UAV's transmission system. This embodiment employs an expert PID-based speed controller. Because the engine power and braking friction of the UAV exhibit nonlinear characteristics, the controlled object is a nonlinear system with varying parameters. This embodiment mimics human thinking to solve the control problem of this large-inertia, time-varying, and highly nonlinear system. A rule-based expert controller is used, establishing a knowledge base based on human understanding of the speed control mechanism of UAV driving. Based on experimental experience, the system measures the UAV's speed setpoint, speed deviation, and the process information of speed deviation changes. The control quantity is derived from the rules in the knowledge base, achieving real-time control of the UAV's longitudinal speed. The knowledge base rule table is a set of typical rules based on human driving experience. The rule format is: if E is true and EC is true, then U is true. This can be clearly displayed and explained using a rule table. The rules in the knowledge base are shown in Table 1.

[0124] Table 1 Knowledge Base Rules

[0125] The conventional PID control algorithm can be expressed as follows.

[0126] (7).

[0127] in, and These are the integral time constant and the derivative time constant of the controller, respectively. , , These are the proportional coefficient, integral coefficient, and derivative coefficient of the controller, respectively. This is the output of the controller, which is the throttle command ultimately sent to the thruster; This is the deviation value.

[0128] When incremental control is used, the mathematical recursive expression of its algorithm is as follows.

[0129] (8).

[0130] in, For the first The change in control quantity at the next sampling time For the first The control quantity at the next sampling, and , , , For the first The change in control deviation during the next sampling. For the first The change in control deviation at -1 sampling time Indicates the first Control deviation value at the next sampling time Indicates the first Control deviation value at -1 sampling time Indicates the first -2 control deviation values ​​during sampling.

[0131] Conventional PID control typically tunes the controller parameters based on the dynamic and steady-state characteristics of the control process. However, when the controlled object changes over time and space, or is affected by disturbances, the PID controller struggles to achieve the expected optimal setpoint tracking and disturbance suppression, and may even oscillate. To address these shortcomings of conventional control, designing an expert PID controller incorporating expert control system concepts is an effective approach.

[0132] An expert control system is an automatic control system that, based on expert systems, utilizes the expert's existing knowledge and learns the control methods and processes of domain experts to perform rule-based reasoning. Combining expert control with a PID controller creates an expert PID controller.

[0133] For the expert control design of speed control, this embodiment designs an expert controller to simulate human behavior to achieve speed control of the unmanned vessel. The controller is designed to control the throttle and gear of the unmanned vessel to achieve fast and efficient driving behavior to maintain a constant speed or quickly track changes in the desired speed. Therefore, the two parameters used as inputs to the controller are as follows.

[0134] Speed ​​error: The difference between the current speed of the unmanned vessel and the set expected speed.

[0135] Acceleration: The time derivative of the unmanned vessel's current velocity.

[0136] The desired speed value is provided by the decision-making system, while the current speed and acceleration of the unmanned vessel can be obtained through a sensor signal system. The controller will provide two output signals, one for throttle and the other for gear.

[0137] In this embodiment, the expert control method makes the speed control process of the unmanned vessel more similar to the behavior of a human pilot. Expert control establishes control rules and parameter tuning rules based on the experience of actual pilots' speed control characteristics, making it highly targeted and thus achieving significantly better control results than conventional control. Expert control does not require the mathematical model of the controlled object to be highly accurate. The expert PID control structure diagram is shown below. Figure 16 As shown.

[0138] In this embodiment, for lateral control / steering control, the main focus of the unmanned vessel's lateral control is on how to ensure the vessel travels accurately along the course planned by the upper-level decision-making system while guaranteeing its safety. First, a dynamics and kinematic model of the unmanned vessel is established, and dynamics and mechanics analysis are performed. Based on the open-loop driving mode of a human driver, a control model using the "pre-aiming-following" theory is designed to create an open-loop steering controller for the unmanned vessel. Then, based on the traditional controller, a composite control system based on a cerebellar model neural network (CMAC) and PID controller is designed.

[0139] Figure 17 The structure of the closed-loop system is shown, where P represents the transfer function between the unmanned vessel's steering angle input and the system output y, and G represents the transfer function used for the controller. External environmental factors such as wind, waves, and currents affect the system dynamics through the transfer function G. The signal n represents the sensor noise affecting the system.

[0140] This embodiment aims to maintain the expected performance of the system even when the model is not very accurate or other variable factors exist. For control systems with time delays and time-varying parameters, the PID parameters can only achieve optimal control performance by continuously and adaptively self-tuning or adjusting according to changes in the object parameters. Therefore, this embodiment considers designing a controller that combines a cerebellar model neural network with PID control, enabling the controller to have self-learning capabilities and automatically compensate for unforeseen changes in the controlled model and input signals.

[0141] In this embodiment, an adaptive controller based on a combination of a cerebellar model neural network and PID is designed. CMAC is a lookup table-type adaptive neural network that can accurately describe the characteristics of nonlinear functions. It also has a learning function, allowing it to change the table content based on the learning results and distinguish and store information. The cerebellar model neural network is a locally approximating neural network with associative memory capabilities. The input and output of each neuron have a linear relationship, but overall it is a table system expressing nonlinear mappings. Its structure is as follows... Figure 18 As shown, the input vector of the input layer, i.e., the "U input space," mainly consists of key continuous parameters for the unmanned vessel's steering control. These parameters are weighted and summed by the AC (accumulator) module, then hashed by the AP (address pointer) module, ultimately outputting the final calculation result of CMAC (i.e., the feedforward control quantity). This result, combined with the output of the PID controller, drives the unmanned vessel's steering actuator (such as the steering motor), achieving precise steering adjustment. The block diagram of the CMAC and PID composite control structure is shown below. Figure 19 As shown.

[0142] This application develops a CMAC-PID composite control algorithm based on the existing CMAC-PID composite control system. The CMAC control loop learns and trains the dynamic characteristics of the controlled object to obtain the relationship between the system control input and state variables. This CMAC-PID composite control system achieves closed-loop control through the combined control of CMAC and PID. PID, as a feedback loop, ensures the accuracy and stability of the control and reduces the impact of disturbances. The CMAC loop, as a feedforward loop, approximates the inverse dynamic model of the unmanned vessel's lateral motion, reducing steady-state error and minimizing system overshoot and response time. In this CMAC-PID composite control, the PID control enhances the system's robustness, while the CMAC control enhances its adaptability. When PID control acts alone, the values ​​of its parameters determine the control effect; changes in the control system's operating conditions require retuning of the system parameters. However, with the combined control of PID and CMAC, the control effect is no longer limited to the values ​​of the PID parameters. When the object or environment changes, the CMAC function allows the system's control performance to automatically adjust within a certain range.

[0143] This embodiment employs a composite control algorithm combining expert PID and CMAC-PID, along with multi-sensor data fusion technology, to improve the navigation stability and control accuracy of the unmanned surface vessel system in complex sea conditions, ensuring precise route accuracy and controllable data acquisition location during monitoring, thereby enhancing the reliability of monitoring data.

[0144] In this embodiment, the execution module is mainly used to drive the actuator to perform actions according to control instructions, and mainly includes the following control contents.

[0145] 1) Throttle control.

[0146] The main purpose of throttle control is to enable the unmanned vessel to track the desired speed. That is, when the actual speed is lower or higher than the desired speed, the controller will provide appropriate acceleration; when the speed reaches the desired speed, the controller will provide stable power to maintain the current speed.

[0147] 2) Gear control.

[0148] The main purpose of gear control is to change the direction of travel of the unmanned surface vessel (USV). When the gear is in forward gear, the USV moves forward; when the gear is in reverse gear, the USV moves backward. Reverse gear is often used for reversing. The gears are controlled by relays.

[0149] 3) Steering control.

[0150] The main purpose of steering control is to change the heading of the unmanned surface vessel (USV), bringing the actual heading closer to the desired heading. Specifically, when a right turn command is sent to the lower-level mechanism, the USV turns right; when a left turn command is sent, the USV moves to the left. Steering is controlled by a motor.

[0151] In this embodiment, the ground control station is a crucial module for controlling the unmanned vessel. The ground control station employs a shore-based ground station system, which is a self-developed portable ground control box containing a battery, communication radio, gear and direction control levers, an industrial display screen, and a keyboard. It has an outdoor continuous operating time of ≥10 hours. The communication radio is a 20W broadband image transmission radio with a maximum transmission rate of 70Mbps and a single communication distance of 100-300km (line-of-sight) and 1-30km (urban area).

[0152] In this embodiment, the broadband radio provides broadband networking between the ground station and the unmanned vessel. Control data, video data, and observation data are all transmitted using network protocols. The underlying modules (RS232 / 485 / PWM signals, etc.) are also converted into network interfaces and connected to the control system through various conversion modules.

[0153] In this embodiment, the protection system has system status monitoring and safety protection functions. It mainly realizes real-time monitoring and analysis of the various systems, equipment and navigation status of the unmanned vessel, and generates monitoring reports and early warnings. When a major failure occurs, the security system automatically starts to operate and executes corresponding security strategies to ensure the navigation safety of the unmanned vessel.

[0154] In this embodiment, the protection system has a system status monitoring function, which monitors the real-time status of the unmanned vessel by monitoring the dynamics of its navigation equipment, power equipment, and electrical attributes. This includes monitoring the data transmission status and electrical attributes of the unmanned vessel's navigation equipment (including navigation radar, AIS, inertial navigation, and navigation lights), determining the connectivity of data transmission links and whether the equipment is powered on, thereby determining whether the unmanned vessel can achieve autonomous navigation; dynamic monitoring of the power equipment mainly involves monitoring the engine's operating status and the acquisition sensors used to collect signals related to the engine's operation, as well as the control logs and signal transmission data logs of the relevant power equipment; monitoring the unmanned vessel's control system mainly involves monitoring 12V voltage, charging / discharging current, 24V voltage, charging / discharging current, 220V voltage, and system power levels to determine whether the unmanned vessel can operate normally.

[0155] In this embodiment, the protection system also has a fault log recording function, which records the link transmission status and electrical attribute status of the system self-test equipment once per second. The fault log is distinguished by warning and serious fault, and different levels of logs are distinguished by fault identification codes. Among them, faults that affect navigation and faults that affect power operation are serious faults, and other faults are warnings. The types and contents of faults are shown in Table 2.

[0156] Table 2 Fault Types and Fault Descriptions

[0157] In addition to generating logs on the unmanned surface vessel (USV), the USV control system reports to the shore-based ground station system once per second, which then displays warnings and serious fault information.

[0158] Unmanned surface vessels (USVs) may encounter various problems during navigation, such as communication interruptions, abnormal sensor data, and uncontrollable actuators. The protection system has functions such as status monitoring, fault self-diagnosis, fault reporting, and emergency response. The protection system monitors the operating status of each module in real time. When an anomaly occurs, it executes corresponding operations according to the anomaly handling algorithm and the configured strategies, such as power failure to stop navigation, position holding, and automatic return to shore. At the same time, it transmits alarm information to the shore-based surface station system.

[0159] In this embodiment, when the main control unit detects an anomaly, it executes corresponding actions according to a pre-configured strategy. For example, if it detects a loss of connection with the shore-based ground station system and fails to restore the connection within the expected time (adjustable 5 minutes), the main control unit controls the power system to shut down or autonomously return to a pre-set autonomous return point. In automatic return mode, the system automatically plans a path back to the set point, and the unmanned surface vessel (USV) enters path tracking mode. Position holding mode can be operated when a fault occurs, or the USV can be remotely controlled to enter this mode, primarily using a soft-control parking method. Using high-precision BeiDou positioning as a reference, the system collects data from the navigation module and corrects the USV's heading and speed in real time, controlling the bow to maintain its alignment with the satellite. This allows the USV to accurately return to the reference point after drifting away from the designated area. Actual testing shows that the position holding deviation of this vessel type is less than or equal to 18 meters in sea state 1.

[0160] In this embodiment, the unmanned surface vessel (USV) instruments and equipment mainly include: a small multibeam bathymetry system, a high-resolution spectrometer, an underwater camera, and a multi-parameter water quality meter, etc. The equipment is fixed to the mounting backplate using customized clamps. An instrument and equipment mounting plate is designed at the bottom of the USV for mounting observation and monitoring equipment.

[0161] This application proposes an unmanned surface vessel (USV) system for coral reef monitoring. Employing a comprehensive design concept of "modular catamaran structure + multi-sensor integration + intelligent control + remote monitoring," it is specifically tailored for "coral reef ecological monitoring in complex shallow water environments," possessing high stability, strong adaptability, long endurance, and multi-source data fusion capabilities. The hull structure is modular, lightweight, and maintainable; the modular design facilitates transportation, assembly, and maintenance. The "catamaran + inflatable pontoons" structure ensures stability and shallow water adaptability. Underwater equipment is retractable to prevent damage from reef impact. The power system is efficient, long-endurance, and powerful; electric drive results in zero emissions and low noise, making it environmentally friendly. Dual power supply isolation design enhances system stability and safety. The navigation and control system is intelligent, autonomous, and highly precise; intelligent control algorithms improve anti-interference capabilities. Remote control and automatic task execution reduce reliance on manual intervention. The protection and safety system achieves full-state monitoring, fault warning, and emergency response, constructing a "monitoring-early warning-response" closed loop, significantly improving the safety of USV operations in complex sea conditions. The observation equipment system enables multi-parameter, high-precision, and collaborative operation, achieving integrated five-dimensional monitoring of "topography-substrate-optics-biology-environment," with strong data complementarity. The data processing and comprehensive analysis system unifies the timestamps and spatial coordinates of multi-source sensor data (acoustic, optical, water quality, and imagery) to construct a spatiotemporal database. It supports periodic repeated observations, establishes time series, and assesses ecological evolution trends.

[0162] Currently, traditional coral reef monitoring methods generally use research vessels / divers, which have disadvantages such as deep draft, inability to enter shallow reef areas, high cost, low efficiency, high risk, single or scattered monitoring parameters, difficulty in repeating fixed-point observations, easy disturbance to coral ecology (noise, anchors), and long data acquisition cycles. In contrast, the unmanned surface vessel system proposed in this application has a shallow draft (<0.5m), can penetrate deep into shallow coral areas, operates automatically, is low-cost, highly safe, collects multiple parameters simultaneously, and has strong data synergy; it can accurately repeat measurements of the same area, supports long-term dynamic analysis; it is electrically driven, anchorless, and eco-friendly; and it transmits data in real time, allowing for rapid response to environmental changes. Future development directions include: by carrying underwater unmanned aerial vehicles (UUVs), small ROVs (remotely operated underwater vehicles) can be deployed to enter complex areas such as reef caves and fissures for three-dimensional monitoring. By integrating an eDNA (environmental DNA) sampling module, water samples can be collected for environmental DNA analysis, improving the accuracy of biodiversity monitoring. Through a hybrid power supply of solar energy and lithium batteries, the operating time can be extended to more than 24 hours, supporting continuous day and night operation. The AI ​​edge computing module enables image recognition and anomaly warning on board, reducing the pressure on data backhaul. The BeiDou short message communication system allows for basic status reporting in areas without public network coverage, improving communication reliability.

[0163] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0164] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. An unmanned surface vessel system for coral reef monitoring, characterized in that, The unmanned surface vessel system for coral reef monitoring includes: hull structure, power system, control system, protection system, observation equipment, and ground control station; The power system, the control system, the protection system, and the observation equipment are all mounted on the hull structure, and the control system is connected to the power system, the protection system, the observation equipment, and the ground control station, respectively. The hull structure adopts a detachable modular design and is used to carry the power system, the control system, the protection system and the observation equipment to perform coral reef monitoring tasks. The power system is used to provide navigation power and electrical energy for the unmanned vessel; The control system is used to enable autonomous navigation, path planning, and obstacle avoidance of the unmanned vessel; The protection system is used to monitor the operating status of various systems of the unmanned vessel and to realize fault early warning and emergency response; The observation equipment is used to collect coral reef monitoring data; The ground control station is used to communicate with the unmanned vessel to achieve remote control and data interaction; The hull structure includes: a front frame, a middle frame, a power frame, a cross-float support, an underwater rotation mechanism, a lifting support, inflatable buoys, equipment plates, a battery box, and an instrument box. The front frame, the middle frame, and the power frame are connected by connecting pipes to form a frame assembly; the two inflatable floats are evenly fixed to the bottom of the frame assembly by hooks. The trans-floating support is fixed above the front frame, the underwater rotating mechanism is fixed to the front of the front frame, the battery box is fixed above the power frame, and the instrument box is fixed above the middle frame; the battery box is used to house batteries, and the instrument box is used to house various equipment and instruments. The underwater rotating mechanism includes: a hand-cranked winch, a fixed pulley, a fixed pipe frame, a rotating support frame, and an extension pipe. One end of the fixed pipe frame is connected to one end of the extension pipe through the rotating support frame. The other end of the fixed pipe frame is fixed with the hand-cranked winch and the fixed pulley. The other end of the extension pipe is fixed to the equipment plate. The hand-cranked winch is wound with a steel wire rope, which passes around the fixed pulley and connects to the other end of the extension tube. The extension tube is folded 90 degrees to the fixed tube frame by the rotating support frame, so as to realize the deployment and retrieval of the observation equipment.

2. The unmanned surface vessel system for coral reef monitoring according to claim 1, characterized in that, The observation equipment is fixed to the equipment plate by a custom clamp. The equipment plate has several holes for installing the custom clamp. The position and size of the holes are determined according to the size of each observation equipment.

3. The unmanned surface vessel system for coral reef monitoring according to claim 1, characterized in that, The power system includes: a thruster and a power supply assembly; At least two thrusters are installed, controlled by PWM signals, and the unmanned vessel can be turned by differential speed. The power supply assembly includes at least two sets of battery packs. One set of battery packs is used to power the thruster, and the other set of battery packs is used to power the control system and load equipment. An isolated voltage regulator is configured at the front end of the DC load, and an inverter is connected to power the AC load.

4. The unmanned surface vessel system for coral reef monitoring according to claim 1, characterized in that, The control system includes: an environmental perception unit, a collision avoidance decision unit, and an execution unit; The environmental perception unit is used to acquire information about the unmanned vessel's own status and surrounding environment by using multi-sensor data fusion. The collision avoidance decision unit is used to formulate navigation strategies using path planning algorithms and obstacle avoidance algorithms; The execution unit includes a control module and an execution module. The control module is used to receive decision instructions issued by the ground control station and convert the decision instructions into control signals. The execution module is used to control the throttle, gear, and steering of the unmanned vessel according to the control signals.

5. The unmanned surface vessel system for coral reef monitoring according to claim 4, characterized in that, The environmental perception unit includes: navigation radar, visual sensors, and AIS equipment; The navigation radar is used to detect static obstacles and dynamic targets around the unmanned vessel and to obtain three-dimensional spatial information of the target and the unmanned vessel. The visual sensor is used to collect photoelectric image information around the unmanned vessel; The AIS device is used to receive AIS information broadcast by other nearby vessels; The environmental perception unit is also used to achieve decision-level fusion of static obstacle and dynamic target information, three-dimensional spatial information, photoelectric image information and AIS information through spatiotemporal registration and correlation decision.

6. The unmanned surface vessel system for coral reef monitoring according to claim 4, characterized in that, The path planning algorithm is A. The obstacle avoidance algorithm is an artificial potential field method.

7. The unmanned surface vessel system for coral reef monitoring according to claim 1, characterized in that, The protection system includes: a status monitoring module, a fault log module, and an emergency response module; The status monitoring module is used to monitor the operating status of various systems of the unmanned vessel in real time. The fault log module is used to record the operating status of each system at a preset frequency, distinguish between warnings and faults, and mark the fault identification code. The emergency response module is used to perform power outage, position holding, or automatic return-to-home operations when the status monitoring module detects an anomaly.

8. The unmanned surface vessel system for coral reef monitoring according to claim 1, characterized in that, The observation equipment includes: a multibeam measurement system, a spectrometer, an underwater camera, and a multi-parameter water quality meter; The multibeam measurement system is used to monitor the spatial coverage, morphological characteristics, and distribution location information of coral reefs; The spectrometer is used to collect reflectance spectral information of coral reefs; The underwater camera is used to collect visual image information of the coral reef; The multi-parameter water quality meter is used to collect water quality information in the growth environment of coral reefs.

9. The unmanned surface vessel system for coral reef monitoring according to claim 1, characterized in that, The unmanned surface vessel system for coral reef monitoring also includes: a data processing and comprehensive analysis system; The data processing and comprehensive analysis system is connected to both the control system and the observation equipment. The data processing and comprehensive analysis system is used to process the coral reef monitoring data with unified timestamps and spatial coordinates, construct a spatiotemporal database, support periodic repeated observations and establish time series, and assess the ecological evolution trend of the environment surrounding the coral reef.

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