Docking method and system for unmanned surface vessel and docking station

Abstract

The invention provides a docking method and system for an unmanned surface vessel and a docking station, and relates to the field of autonomous docking of unmanned surface vessels, and the method comprises the steps: respectively obtaining the state data of the unmanned surface vessel and the docking station when the distance between the unmanned surface vessel and the docking station is smaller than a set value; performing data fusion on the real-time state data and predicted state data calculated based on historical data to generate target optimal state data; constructing a gravitational field taking the front end of the docking station as a gravitational source and a repulsive force field taking the rear end of the docking station and an obstacle as repulsive force sources according to the target optimal state data; calculating the relative position of the unmanned surface vessel and the docking station; determining a control mode according to the relative position, and generating a virtual force field by combining a gravitational field, a repulsive force field and a mode algorithm; and generating a motion path according to the direction and size of the virtual force field and controlling docking. Therefore, through cooperative acquisition of the double-end state data, the docking deviation is effectively reduced; and in combination with a path planning strategy of dynamic mode switching, the obstacle avoidance success rate is remarkably improved, so that the docking efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of autonomous docking of unmanned surface vessels, and in particular to a docking method and system for an unmanned surface vessel and a docking station. Background Art

[0002] Unmanned surface vehicles (USVs) are increasingly used in marine surveillance and security patrols, but autonomous docking with docking stations in complex marine environments presents numerous challenges. Existing technologies typically utilize single-ended positioning for docking, but this approach cannot effectively eliminate docking station motion or displacement errors, resulting in significant docking errors. Furthermore, traditional static path planning algorithms suffer from low obstacle avoidance success rates in complex scenarios, large path oscillations, and a high incidence of local minima, which can compromise docking efficiency and stability. Summary of the Invention

[0003] The present application provides a docking method and system for an unmanned surface vessel and a docking station, so as to at least solve the above technical problems existing in the prior art.

[0004] According to a first aspect of the present application, a method for docking an unmanned surface vessel with a docking station is provided, the method comprising: When the distance between the unmanned surface vessel and the docking station is less than a first set distance, obtaining status data of the unmanned surface vessel and the docking station respectively, the status data including coordinates, attitude, acceleration and angular velocity; fusing the status data of the unmanned surface vessel and the docking station and the predicted status data of the unmanned surface vessel and the docking station to generate target status data of the unmanned surface vessel and the docking station, wherein the predicted status data is obtained based on the historical status data of the unmanned surface vessel and the docking station; Based on the target state data of the unmanned surface vessel and the docking station, a gravitational field with the front end of the docking station as the gravitational source and a repulsive field with the rear end of the docking station and obstacles as the repulsive sources are constructed; Calculating the relative position of the unmanned surface vessel and the docking station based on the target state data of the unmanned surface vessel and the docking station; According to the relative position of the unmanned surface vehicle and the docking station, the control mode of the unmanned surface vehicle is determined, and the virtual force field of the unmanned surface vehicle is generated according to the gravitational field, repulsive field, control mode and their corresponding algorithms; According to the direction and size of the virtual force field, the motion path of the unmanned surface vessel is determined, and the unmanned surface vessel is controlled to dock with the docking station according to the motion path of the unmanned surface vessel.

[0005] In one embodiment, obtaining predicted status data based on historical status data of the unmanned surface vessel and the docking station includes: Based on the angular velocity and acceleration shown in the historical state data at the previous set time, the intermediate predicted state data at the current time is predicted by integrating the angular velocity of the inertial measurement unit; The intermediate prediction state data and the historical state data are fused to obtain the prediction state data at the current moment.

[0006] In one embodiment, data fusion is performed on the status data of the unmanned surface vessel and the docking station and the predicted status data of the unmanned surface vessel and the docking station, including: Obtaining the state error covariance corresponding to the predicted state data of the unmanned surface vehicle and the docking station; Convert the global coordinates in the global coordinate system of the status data of the unmanned surface vehicle and the docking station into Cartesian coordinates in the Cartesian local coordinate system, where the global coordinates include longitude, latitude and elevation; The converted Cartesian coordinates and the heading angle shown in the state data are used as the actual observation vector, and the observation equation is constructed based on the actual observation vector to obtain the deviation, which is used to show the degree of deviation between the predicted state data and the actual observation vector; Calculating a Kalman gain based on the state error covariance, the observation matrix, and a preset measurement noise covariance; Target optimal state data is calculated based on the Kalman gain, the deviation and the predicted state data of the unmanned surface vessel and the docking station, and the target optimal state data is used as the predicted state data at the next set moment.

[0007] In one embodiment, the repulsive field includes a first repulsive field and a second repulsive field; accordingly, According to the target state data of the unmanned surface vessel and the docking station, a gravitational field with the front end of the docking station as the gravitational source and a repulsive field with the rear end of the docking station and obstacles as the repulsive sources are constructed, including: Using the coordinates of the front end of the docking station shown in the target state data of the unmanned surface vessel and the docking station as the center of the gravity source, a gravitational field is constructed, wherein the gravitational field is used to determine the gravitational force and gravitational direction according to the gravitational function; Using the coordinates of the rear end of the docking station shown in the target state data of the unmanned surface vessel and the docking station as the center of the repulsive force source, a first repulsive force field is constructed, wherein the first repulsive force field is used to determine a first repulsive force and a first repulsive force direction according to a first repulsive force function; The coordinates of all obstacles of the unmanned surface vessel are obtained, and a second repulsive field is constructed according to the coordinates of all obstacles, wherein the second repulsive field is used to determine a second repulsive force and a repulsive direction according to a second repulsive function.

[0008] In one embodiment, the relative position includes the relative distance, relative azimuth, and relative heading deviation between the unmanned surface vessel and the docking station; determining the control mode of the unmanned surface vessel based on the relative position of the unmanned surface vessel and the docking station includes: When the relative distance between the unmanned surface vessel and the docking station meets the preset contact range and the relative heading deviation between the unmanned surface vessel and the docking station is less than the set deviation range, determining the control mode to be the forward alignment mode; When the relative azimuth angle between the unmanned surface vessel and the docking station is greater than the set azimuth angle and the relative distance between the unmanned surface vessel and the docking station is less than a set multiple of the docking station length, determining the control mode to be the rearward bypass mode; When the relative heading deviation between the unmanned surface vessel and the docking station is greater than the set deviation range, the control mode of the unmanned surface vessel is determined to be the alignment mode.

[0009] In one embodiment, a virtual force field of the unmanned surface vehicle is generated according to the gravitational field, the repulsive field, the control mode and the corresponding algorithm, including: When the control mode is the forward alignment mode, a gravitational vector is calculated based on the gravitational function of the gravitational field, a first repulsive vector is calculated based on the first repulsive function of the first repulsive field, and a second repulsive vector is calculated based on the second repulsive function of the second repulsive field; after adjusting the weight of the gravitational vector using a gravitational line alignment algorithm, the resultant force vector is superimposed on the first repulsive vector and the second repulsive vector to obtain a resultant force vector; and a virtual force field is constructed based on the resultant force vector. When the control mode is the rear bypass mode, a gravitational vector is calculated based on the gravitational function of the gravitational field, a first repulsive vector is calculated based on the first repulsive function of the first repulsive field, and a second repulsive vector is calculated based on the second repulsive function of the second repulsive field; after adjusting the weight of the first repulsive vector using a repulsion point guidance algorithm, the resultant force vector is superimposed with the gravitational vector and the second repulsive vector to obtain a resultant force vector; and a virtual force field is constructed based on the resultant force vector. When the control mode is the alignment mode, the gravitational vector is calculated based on the gravitational function of the gravitational field, the first repulsive vector is calculated based on the first repulsive function of the first repulsive field, and the second repulsive vector is calculated based on the second repulsive function of the second repulsive field; after adjusting the lateral weight and forward weight of the gravitational vector using the gravitational line alignment algorithm, the resultant force vector is superimposed with the first repulsive vector and the second repulsive vector to obtain a resultant force vector; and a virtual force field is constructed based on the resultant force vector.

[0010] In one possible implementation, determining the motion path of the unmanned surface vehicle based on the direction and magnitude of the virtual force field includes: Determining a generation rule of the path points according to the direction and size of the virtual force field, wherein the generation rule includes the spacing and direction of the path points; In each control cycle, the next path point is generated according to the direction and magnitude of the virtual force field based on the corresponding generation rules; Connect a series of path points in sequence to generate the motion path of the unmanned surface vehicle.

[0011] In one embodiment, controlling the unmanned surface vessel to dock with the docking station according to the motion path of the unmanned vessel includes: According to the motion path, the acceleration and angular velocity required by the unmanned boat at each point on the path are calculated, and corresponding motion instructions are generated; Decomposing the motion command into propeller differential and rudder angle control variables, and performing anti-saturation optimization in combination with the unmanned boat dynamics model; The optimized motion instructions are sent to the unmanned surface vessel, driving the motor and servo of the unmanned surface vessel to perform docking with the docking station.

[0012] According to a second aspect of the present application, a docking system for an unmanned surface vessel and a docking station is provided, the system comprising: Unmanned surface vehicle, equipped with a dual antenna system and inertial measurement unit to obtain its own status data; The docking station is configured with a dual antenna system and an inertial measurement unit for obtaining its own status data; the control system is deployed on the unmanned surface vessel and includes a processor and a memory in communication with the processor, the memory storing instructions executable by the processor, the instructions being executed by the processor so that the processor can perform the above method; An inter-device communication system, configured to control data communication between the unmanned surface vessel and the docking station through a communication server and an encryption algorithm; A remote monitoring communication system is used to monitor data of the communication server and the front end through the communication server and encryption algorithm.

[0013] In one embodiment, the unmanned surface vessel is further equipped with a laser radar for detecting obstacles during docking between the unmanned surface vessel and the docking station and triggering an emergency stop when it is detected that the distance to the docking station is less than a second set distance; The unmanned surface vessel is also used to perform dynamic interception through a command verification module; the unmanned surface vessel performs dynamic interception through a command verification module, including: when it is detected that the speed is greater than a first set speed or the angular velocity is greater than a first set angular velocity, the speed is limited by the command over-limit interception function of the command verification module.

[0014] The docking method and system of the unmanned surface vessel and the docking station of the present application respectively obtains the status data of the unmanned surface vessel and the docking station when the distance between the unmanned surface vessel and the docking station is less than a first set distance, and the status data include coordinates, posture, acceleration and angular velocity; the status data of the unmanned surface vessel and the docking station and the predicted status data of the unmanned surface vessel and the docking station are fused to generate target optimal status data of the unmanned surface vessel and the docking station, and the predicted status data are obtained based on the historical status data of the unmanned surface vessel and the docking station; according to the target optimal status data of the unmanned surface vessel and the docking station Based on the state data of the docking station, a gravitational field is constructed with the front end of the docking station as the gravitational source, and a repulsive field with the rear end of the docking station and obstacles as the repulsive sources. The relative positions of the unmanned surface vehicle and the docking station are calculated based on the target optimal state data of the unmanned surface vehicle and the docking station. Based on the relative positions of the unmanned surface vehicle and the docking station, the control mode of the unmanned surface vehicle is determined, and a virtual force field for the unmanned surface vehicle is generated based on the gravitational field, repulsive field, control mode, and their corresponding algorithms. The motion path of the unmanned surface vehicle is determined based on the direction and magnitude of the virtual force field, and the unmanned surface vehicle is controlled to dock with the docking station based on the motion path of the unmanned surface vehicle. Thus, through the coordinated acquisition of state data from both ends, docking deviation is effectively reduced. Combined with a path planning strategy that uses dynamic mode switching, the obstacle avoidance success rate is significantly improved, thereby enhancing docking efficiency.

[0015] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The above and other objects, features and advantages of the exemplary embodiments of the present application will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present application are shown in an illustrative and non-limiting manner, in which: In the drawings, the same or corresponding reference numerals denote the same or corresponding parts.

[0017] Figure 1 A schematic diagram of the structure of a docking system between an unmanned surface vessel and a docking station provided in an embodiment of the present application is shown; Figure 2 A schematic diagram of the implementation process of the docking method between the unmanned surface vessel and the docking station provided in an embodiment of the present application is shown; Figure 3 A schematic diagram of the implementation flow of the force field construction operation of the docking method between the unmanned surface vessel and the docking station provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0018] In order to make the purpose, features, and advantages of this application more obvious and easy to understand, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of this application.

[0019] Figure 1 A schematic diagram of the structure of a docking system between an unmanned surface vessel and a docking station provided in an embodiment of the present application is shown.

[0020] refer to Figure 1 An embodiment of the present application provides a docking system for an unmanned surface vehicle (USV) and a docking station. The system includes: an USV equipped with a dual antenna system and an inertial measurement unit (IMU) for acquiring its own status data; a docking station equipped with a dual antenna system and an IMU for acquiring its own status data; a control system deployed on the USV, including a processor and a memory in communication with the processor, the memory storing instructions executable by the processor, the instructions executed by the processor enabling the processor to execute a docking method between the USV and the docking station; an inter-device communication system (not shown in the figure) for completing data communication between the USV and the docking station via a communication server and an encryption algorithm; and a remote monitoring communication system (not shown in the figure) for completing data monitoring between the communication server and the front end via a communication server and an encryption algorithm. The docking station is a stopover point for the USV and may include, but is not limited to, a docking station, a communication relay station, an energy supply station, an emergency shelter, etc.

[0021] Specifically, to achieve dual-end coordinate synchronization and matching, both the unmanned surface vehicle and the docking station are equipped with an SGEllipse-D dual-antenna GPS RTK (Real-Time Kinematic) system. This system acquires three-dimensional coordinates in the WGS-84 (World Geodetic System-1984) coordinate system in real time, with an update frequency of 10Hz. Specific information includes longitude, latitude, and elevation. The USV performs Euclidean distance matching based on the docking station coordinates preset for the mission, and calculates the relative distance by calculating the coordinate difference. When the relative distance is less than 50 meters, the docking method of the unmanned surface vehicle and the docking station of this application is triggered.

[0022] During docking between the USV and the docking station, the USV's dual-antenna GPS and RTK system provides centimeter-level real-time positioning, including information on longitude, latitude, and elevation. Furthermore, the IMU (Inertial Measurement Unit) measures attitude, angular velocity, and acceleration to compensate for dynamic errors. The docking station also continuously transmits its own precise status information, including coordinates, attitude, acceleration, and angular velocity, through GPS, RTK, and IMU, for the USV to use. Secure wireless communication synchronizes data between the two, ensuring sub-meter relative positioning in complex environments and enabling stable closed-loop control, ultimately completing high-precision autonomous docking.

[0023] In terms of communication, the entire system is divided into two parts: inter-device communication and remote monitoring communication. During inter-device communication, the docking station uses UDP (User Datagram Protocol) encrypted with AES-256 (Advanced Encryption Standard-256, 256-bit) to send an identity packet containing the device's MAC address (6-byte, 48-bit hexadecimal number), a timestamp (accurate to the millisecond level), and a digital signature. Upon receiving this information, the unmanned surface vehicle uses the SHA-256 (Secure Hash Algorithm-256, 256-bit) hash algorithm to calculate a 256-bit, 32-byte hexadecimal string. This hash value verifies the docking station's legitimacy and prevents interference from malicious impersonators.

[0024] The USV and docking station use WebSocket as a bridge to transmit data to the Robot Operating System 2 (ROS2), the control system deployed by the unmanned surface vehicle. This data includes the real-time posture (position and orientation) of the USV and docking station, motion status (angular velocity and acceleration), and environmental data (such as obstacle information). The USV also receives information from ROS2 via WebSocket, including path planning and control commands.

[0025] During remote monitoring communications, TLS (Transport Layer Security) version 1.2 / 1.3 is used to encrypt WebSockets (wss: / / ), with the underlying AES-256-GCM (Advanced Encryption Standard-256 bit Galois / Counter Mode) algorithm ensuring secure transmission. A browser can also connect to the WebSocket server and subscribe to ROS2 topics. The server pushes ROS2 data in real time to the frontend in JSON (JavaScript Object Notation), displaying information such as the USV's trajectory (sequence of longitude and latitude) and docking station status (dynamically updated coordinates and heading angles). This facilitates accurate monitoring of normal operation. Communication interruption protection is also enabled during communication; if no new commands are received for more than 100ms, the USV enters hover mode.

[0026] In one embodiment of the present application, the unmanned surface vessel is also equipped with a laser radar for obstacle detection during docking between the unmanned surface vessel and the docking station and for triggering an emergency stop when it is detected that the distance to the docking station is less than a second set distance; the unmanned surface vessel is also used for dynamic interception through a command verification module; the unmanned surface vessel performs dynamic interception through the command verification module, including: when it is detected that the speed is greater than the first set speed or the angular velocity is greater than the first set angular velocity, the speed is limited by the command over-limit interception function of the command verification module.

[0027] Specifically, the unmanned surface vehicle is equipped with a laser radar (LiDAR) that triggers an emergency stop when it detects the distance from the docking station is less than a second set distance (e.g., 10 cm). Furthermore, the unmanned surface vehicle has a command over-limit interception function: when it detects that the speed exceeds a first set speed (e.g., 1.3 m / s) or the angular velocity exceeds a first set angular velocity (e.g., 0.8 rad / s), it automatically limits the speed. The second set distance, first set speed, or angular velocity can be configured based on actual circumstances and are not specifically limited in this application.

[0028] It should be noted that the description of the system in the embodiment of the present application is similar to the description of the method embodiment described below, and has similar beneficial effects as the method embodiment, so it will not be repeated here. Any unfinished technical details of the docking system between the unmanned surface vessel and the docking station provided in the embodiment of the present application can be understood based on the description of the docking method between the unmanned surface vessel and the docking station described below.

[0029] Figure 2A schematic diagram of the implementation flow of the docking method between the unmanned surface vessel and the docking station provided in an embodiment of the present application is shown.

[0030] refer to Figure 2 The embodiment of the present application provides a method for docking an unmanned surface vessel with a docking station, the method comprising: Operation 101 : When the distance between the unmanned surface vessel and the docking station is less than a first set distance, status data of the unmanned surface vessel and the docking station are respectively obtained, where the status data includes coordinates, attitude, acceleration, and angular velocity.

[0031] The USV performs Euclidean distance matching based on the docking station coordinates (docking station longitude and latitude) preset for the mission in real time, calculates the relative distance through the coordinate difference, and triggers the initial docking process when the relative distance is less than the first set distance (for example, 50m), and begins to obtain the status data of both parties respectively.

[0032] Status data includes coordinates, attitude, acceleration, and angular velocity. Coordinates are three-dimensional coordinates in the WGS-84 global positioning system (GPS), acquired by the USV and docking station using the SBG Ellipse-D dual-antenna GPS RTK system. They include longitude, latitude, and elevation information. Attitude is the roll, pitch, and yaw angles measured by the IMU of the USV and docking station, reflecting their tilt and steering status. Acceleration is the linear acceleration measured by the IMU of the USV and docking station, including acceleration information in the x, y, and z directions. Angular velocity is the angular velocity of the attitude measured by the IMU of the USV and docking station, including angular velocity information about the x, y, and z axes.

[0033] Operation 102 is to fuse the status data of the unmanned surface vessel and the docking station and the predicted status data of the unmanned surface vessel and the docking station to generate target optimal status data of the unmanned surface vessel and the docking station. The predicted status data is obtained based on the historical status data of the unmanned surface vessel and the docking station.

[0034] In order to generate more accurate status data, the real-time status data of the unmanned surface vessel and the docking station are also integrated with the predicted status data to generate the target optimal status data.

[0035] In one embodiment of the present application, predicted state data is obtained based on the historical state data of the unmanned surface vessel and the docking station, including: predicting the intermediate predicted state data at the current moment by integrating the angular velocity of the inertial measurement unit based on the angular velocity and acceleration shown in the historical state data at the last set moment; and fusing the intermediate predicted state data with the historical state data to obtain the predicted state data at the current moment.

[0036] Specifically, based on the historical status data of the unmanned surface vessel and the docking station, a certain algorithm is used to predict the current state data, wherein the historical state data is the state data at the previous set time before the current time.

[0037] In one embodiment of the present application, the data fusion method may be an Extended Kalman Filter (EKF) algorithm or a Particle Filter (PF) algorithm.

[0038] Operation 103 : constructing a gravitational field with the front end of the docking station as a gravitational source and a repulsive field with the rear end of the docking station and obstacles as repulsive sources based on the target optimal state data of the unmanned surface vessel and the docking station.

[0039] To guide the USV to move safely and efficiently to the docking station and avoid collisions, a virtual force field environment needs to be provided for the USV, including: Constructing a gravitational field: Designing a gravitational field with the front end of the docking station as the center as the gravitational source. The direction of gravity always points to the front end of the docking station, and the magnitude changes with the distance, which is used to attract the unmanned surface vehicle to move towards the docking station; Construct a repulsive field: A repulsive field is constructed with the rear end of the docking station as the repulsive source. The direction is away from the rear end, and the size varies with the distance. A Gaussian distribution repulsive field is generated for each obstacle. The repulsive field is used to repel the unmanned surface vehicle and avoid collision.

[0040] In this way, the USV is guided to move toward the docking station through the gravitational field, while the repulsive field is used to prevent it from colliding with obstacles, thereby improving the safety and effectiveness of path planning.

[0041] Operation 104 : Calculate the relative position of the unmanned surface vessel and the docking station based on the target optimal state data of the unmanned surface vessel and the docking station.

[0042] Accurate relative position information is fundamental to determining control modes and generating virtual force fields. Therefore, the relative positions of the USV and docking station must be calculated. Specifically, the relative position between the USV and docking station is calculated based on their target optimal state data. This relative position includes relative coordinates (Δx, Δy, Δz), relative distance (calculated using the Euclidean distance formula), relative azimuth, and relative heading deviation.

[0043] Operation 105 : determining a control mode of the unmanned surface vessel according to the relative position of the unmanned surface vessel and the docking station, and generating a virtual force field of the unmanned surface vessel according to the gravitational field, the repulsive field, the control mode and their corresponding algorithms.

[0044] To address the path oscillation, local minima, and environmental adaptability issues of existing static path planning algorithms in complex dynamic environments, the present embodiment adopts a dynamic path planning method. Based on the different relative positions of the USV and the docking station, different control modes, such as front alignment and rear detour, are activated. This method adjusts the gravitational and repulsive fields in real time according to the corresponding algorithm to generate a virtual force field to guide the USV's movement, thereby ensuring a smooth docking process. This effectively reduces the path oscillation amplitude, reduces the incidence of local minima, adapts to complex marine environments, and improves the success rate and efficiency of docking.

[0045] Operation 106 : determining a motion path of the unmanned surface vessel according to the direction and size of the virtual force field, and controlling the unmanned surface vessel to dock with the docking station according to the motion path of the unmanned surface vessel.

[0046] To achieve precise docking of the USV, the guidance of the virtual force field must be translated into a specific motion path and control instructions. Specifically, based on the direction and magnitude of the virtual force field, the next path point is generated along the direction of the virtual force field, starting from the current USV position. By continuously iteratively updating the path points, a dynamic and smooth motion path is formed. Finally, the USV is controlled to move along the motion path to achieve docking with the docking station.

[0047] In this way, the embodiment of the present application accurately obtains and integrates the status data of the unmanned surface vessel USV and the docking station, constructs a dynamic virtual force field to guide path planning, and adopts different control modes to ensure the smooth docking, thereby achieving high-precision and high-reliability autonomous docking, effectively improving the path smoothness and safety, and reducing the incidence of path oscillation and local minimum problems.

[0048] In one embodiment of the present application, in the above operation 102, the status data of the unmanned surface vessel and the docking station and the predicted status data of the unmanned surface vessel and the docking station are fused, including: obtaining the state error covariance corresponding to the predicted status data of the unmanned surface vessel and the docking station; converting the global coordinates in the global coordinate system of the status data of the unmanned surface vessel and the docking station into Cartesian coordinates in the Cartesian local coordinate system, and the global coordinates include longitude, latitude and elevation; using the converted Cartesian coordinates and the heading angle shown in the status data as the actual observation vector, and constructing an observation equation based on the actual observation vector to obtain the deviation, and the deviation is used to show the degree of deviation between the predicted status data and the actual observation vector; calculating the Kalman gain according to the state error covariance, the observation matrix and the preset measurement noise covariance; calculating the target optimal state data according to the Kalman gain, the deviation and the predicted state data of the unmanned surface vessel and the docking station, and using the target optimal state data as the predicted state data at the next set moment.

[0049] Specifically, the data fusion process can be regarded as: 1) Obtain the state error covariance of the predicted state data. This variance is used to describe the uncertainty and error distribution of the predicted state data, reflecting the possible deviation between the predicted state data and the actual situation; 2) Convert the coordinate format in the status data to a set unified format. For example, if the original coordinates are global coordinates in the global coordinate system, they can be converted to Cartesian coordinates in the local Cartesian (East-North-Up) coordinate system to facilitate subsequent calculations and processing, so that coordinate data from different sources can be integrated in the same reference system. 3) Using the converted Cartesian coordinates and the heading angle shown in the state data as the actual observation vector, an observation equation is constructed based on the actual observation vector to obtain the deviation. The observation equation is used to show the mathematical relationship between the actual measured state data and the predicted state data, and the deviation is used to show the degree of deviation between the predicted state data and the actual observation vector. 4) Based on the state prediction deviation, state error covariance, observation matrix and preset noise covariance, the Kalman gain is further calculated. The Kalman gain is used to show the correction strength of the actual measurement data to the predicted state during the fusion process.

[0050] 6) Based on the calculated Kalman gain, state prediction deviation, and predicted state data, the fused target optimal state data is determined.

[0051] Furthermore, to facilitate understanding of the data fusion process of this application, a specific application example is used below to illustrate. In this specific application example, data fusion can be understood as performing EKF filtering on the data, specifically including: 1) Initial setting parameters Before starting the EKF filtering process, the initial parameters are set, including the observation matrix, state transfer matrix, process noise covariance, measurement noise covariance, and state error covariance at time 0. The state transfer matrix is ​​determined by the motion model of the unmanned surface vehicle, and the measurement noise covariance is pre-set through sensor calibration or experience.

[0052] 2) Get the initial state At t=0, the initial state information of the USV and the docking station is obtained, including position (x, y, z), attitude (roll, pitch, yaw), linear velocity (v x , v y , v z ) and angular velocity (ω x ,ω y ,ω z ).

[0053] 3) Prediction stage In the prediction stage, the angular velocity and acceleration data of the IMU are used to predict the pose x at the next moment by integration. k|k-1 Convert GPS coordinates (longitude, latitude, elevation) to Cartesian coordinates (x, y, z) in the ENU local coordinate system, and use the converted ENU coordinates as part of the actual observation vector. The observation vector contains the ENU coordinates and the GPSRTK heading angle ψ, i.e., y k . Construct the observation equation: y k =Hx k|k-1 +v k , where H is the observation matrix, used to map the state vector to the measurement space, v k The deviation reflects the degree of deviation between the predicted state and the actual measurement.

[0054] 4) Calculate Kalman gain According to the predicted state error covariance P k|k-1 , the observation matrix H and the measurement noise covariance R, calculate the Kalman gain Kk, the calculation formula is K k =P k|k-1 H T (HP k|k-1 H T +R) -1 Among them, P k|k-1 It is through the formula P k|k-1 =FP k-1|k-1 F T +Q is obtained in the prediction stage, F is the state transfer matrix, and Q is the process noise covariance. 0|0 For the unit array.

[0055] 5) Update phase In the update phase, the Kalman gain K is used k Correct the predicted state and obtain the optimal state estimate x after fusion of the measured values k|k , the calculation formula is x k|k =x k|k-1 +K k v k At the same time, update the state error covariance P k|k , the calculation formula is P k|k =(IK k H)P k|k-1 .

[0056] Repeat the above steps 1)-5) to continuously optimize the state estimation and output the optimal estimated state, that is, the target optimal state data and the error covariance.

[0057] In this way, through the above-mentioned EKF filtering process, the position accuracy can be improved to ±2cm, and the attitude angle accuracy can be improved to ±0.3°, achieving centimeter-level docking requirements.

[0058] Figure 3 A schematic diagram of the implementation flow of the force field construction operation of the docking method between the unmanned surface vessel and the docking station provided in an embodiment of the present application is shown.

[0059] refer to Figure 3 In one embodiment of the present application, the repulsive field includes a first repulsive field and a second repulsive field. The operation 103, based on the target optimal state data of the unmanned surface vessel and the docking station, constructs a gravitational field with the front end of the docking station as a gravitational source and a repulsive field with the rear end of the docking station and the obstacle as a repulsive source, including: Operation 201 : Using the coordinates of the front end of the docking station shown in the target optimal state data of the unmanned surface vessel and the docking station as the center of the gravity source, a gravity field is constructed. The gravity field is used to determine gravity and gravity direction according to a gravity function.

[0060] Specifically, the front end of the docking station is used as the target point to generate gravity pointing to the docking station. The gravitational field is designed to use the center of the front end of the docking station as the gravity source. The gravitational function is as follows:

[0061] in, k att is the gravitational coefficient (usually configurable to 10N / m), q is the Cartesian coordinate of the unmanned surface vehicle, q dock is the Cartesian coordinate of the docking station, and the direction of gravity always points to the target point.

[0062] In operation 202 , the coordinates of the rear end of the docking station shown in the target optimal state data of the unmanned surface vessel and the docking station are used as the center of the repulsive force source to construct a first repulsive force field for determining a first repulsive force and a first repulsive force direction according to a first repulsive force function.

[0063] The rear end of the docking station is set as the repulsive force source to construct the first repulsive force field. The corresponding first repulsive force function is:

[0064] Among them, K rep is the repulsion coefficient (can be configured to 50N / m), d (q) is the distance between the USV and the repulsion source, and d0 (configurable to 10m) is the repulsion radius.

[0065] Operation 203 : Acquire the coordinates of all obstacles of the unmanned surface vessel, and construct a second repulsive field according to the coordinates of all obstacles. The second repulsive field is used to determine a second repulsive force and a repulsive direction according to a second repulsive force function.

[0066] According to the LiDAR point cloud clustering results, that is, the ENU coordinates and distance of each obstacle relative to the USV, a Gaussian distribution repulsion field (second repulsion field) is generated for each obstacle. The corresponding second repulsion function is:

[0067] Among them, k obs is the obstacle repulsion coefficient, q obs are the coordinates of the obstacle, and σ is the obstacle influence range (usually 5~10 meters).

[0068] In one embodiment of the present application, in the above operation 105, the relative position includes the relative distance, relative azimuth, and relative heading deviation between the unmanned surface vessel and the docking station; determining the control mode of the unmanned surface vessel based on the relative position of the unmanned surface vessel and the docking station includes: When the relative distance between the unmanned surface vessel and the docking station meets the preset contact range and the relative heading deviation between the unmanned surface vessel and the docking station is less than the set deviation range, the control mode is determined to be the forward alignment mode.

[0069] Specifically, when the USV enters the preset contact range (usually 10-50) in front of the docking station and the relative heading deviation is less than the set deviation range (such as 15°), it indicates that the USV's heading is basically consistent with the docking station axis, and the conditions for direct docking are met. Therefore, the control mode at this time is determined to be FRONT, allowing the USV to smoothly approach and dock along the docking station axis. The relative heading deviation refers to the angle between the USV's heading and the docking station axis, which can be calculated from the heading angle indicated by the USV and docking station postures.

[0070] When the relative azimuth angle between the unmanned surface vessel and the docking station is greater than the set azimuth angle and the relative distance between the unmanned surface vessel and the docking station is less than a set multiple of the docking station length, determining the control mode to be the rearward detour mode; When the USV is behind the docking station and the relative azimuth angle is greater than the set azimuth angle threshold (usually 180°±30°), and the relative distance is less than a set multiple of the docking station length (usually 3 times), the USV is in an area close to the docking station. At this time, if it continues to sail in a straight line, it may collide with the docking station or fall into a local minimum. Therefore, the control mode is determined to be the rear bypass mode REAR, guiding the USV to bypass to the front of the docking station to increase the success rate of docking.

[0071] When the relative heading deviation between the unmanned surface vessel and the docking station is greater than the set deviation range, the control mode of the unmanned surface vessel is determined to be the alignment mode.

[0072] When the relative heading deviation between the USV and the docking station exceeds the set deviation range (usually 15°), it indicates that there is a significant deviation between the USV's heading and the docking station's axis. At this time, directly docking according to the normal path may cause path oscillation or docking failure. Therefore, the control mode is determined to be ALIGN, or alignment mode, which enables the USV to quickly adjust its heading to align with the docking station's axis and prepare for subsequent docking. In alignment mode, if the relative heading deviation is reduced to 10°, ALIGN mode is exited and switched to FRONT mode.

[0073] In one embodiment of the present application, in the above operation 105, generating a virtual force field of the unmanned surface vehicle according to the gravitational field, the repulsive field, the control mode and their corresponding algorithms includes: When the control mode is the forward alignment mode, the gravitational vector is calculated based on the gravitational function of the gravitational field, the first repulsive vector is calculated based on the first repulsive function of the first repulsive field, and the second repulsive vector is calculated based on the second repulsive function of the second repulsive field; after adjusting the weight of the gravitational vector using the gravitational line alignment algorithm, it is superimposed with the first repulsive vector and the second repulsive vector to obtain a resultant force vector; and a virtual force field is constructed based on the resultant force vector.

[0074] In the forward alignment mode, the algorithm used is the gravity alignment algorithm, which forces the USV heading to align with the docking station axis through the gravity alignment algorithm to reduce the path oscillation amplitude. The gravity alignment algorithm can be specifically:

[0075] in, is the USV heading normal vector, is the USV heading unit vector, k 11 is the lateral alignment factor (usually 0.8), k 12 is the forward thrust coefficient (typically 1.2).

[0076] Specifically, the virtual force field of this application is constructed based on the Artificial Potential Field Algorithm (APF). The principle is to use artificial potential field functions (gravitational function, repulsive function) to calculate the "virtual force" (the vector sum of gravity and repulsion) acting on the USV in real time, namely the virtual force field. The direction of the virtual force within the virtual force field is the target motion direction of the USV, and the magnitude of the resultant force corresponds to the target motion speed of the USV. The construction process of the virtual force field is as follows: 1) Potential field synthesis: The gravitational function outputs the gravitational vector, the first repulsive function outputs the first repulsive vector, and the second repulsive function outputs the second repulsive vector. The force vector includes the direction and magnitude of the force. The farther the distance between the unmanned surface vehicle and the docking station, the greater the gravitational force. The gravitational vector can be expressed by the gravitational coefficient k attAdjust the intensity. The closer the distance between the unmanned surface vehicle and the docking station, the greater the repulsive force. The repulsive force vector can be expressed by the repulsive force coefficient k. rep And the effective radius d0 limits the impact range.

[0077] 2) Calculate the resultant force vector acting on the USV: attraction vector + first repulsion vector + second repulsion vector.

[0078] When the control mode is the forward alignment mode, the calculation of the resultant force vector on the USV in step 2) can be regarded as adjusting the weight of the gravity vector using the gravity line alignment algorithm according to the relative distance and relative heading deviation, thereby increasing the lateral alignment coefficient k. 11 and forward thrust coefficient k 12 , add the adjusted attraction vector to the first repulsion vector and the second repulsion vector to obtain a virtual force (resultant force vector), and generate a virtual force field corresponding to the virtual force.

[0079] When the control mode is the rear bypass mode, the gravitational vector is calculated based on the gravitational function of the gravitational field, the first repulsive vector is calculated based on the first repulsive function of the first repulsive field, and the second repulsive vector is calculated based on the second repulsive function of the second repulsive field; after adjusting the weight of the first repulsive vector using the repulsion point guidance algorithm, it is superimposed with the gravitational vector and the second repulsive vector to obtain a resultant force vector; and a virtual force field is constructed based on the resultant force vector.

[0080] When the control mode is the rear bypass mode, the repulsion guidance algorithm is used, and the repulsion guidance algorithm is:

[0081] Among them, k 21 is the repulsive strength coefficient (usually 50Nm 2 ), k 22 is the heading hold factor (usually 1.0).

[0082] Specifically, the virtual force field construction process in the current mode remains consistent with the above-mentioned front alignment mode, and only the resultant force vector calculation process is redesigned. In the rear bypass mode, after the gravity function and the repulsion function are used to calculate the gravity vector, the first repulsion vector and the second repulsion vector, the process of calculating the resultant force vector can be regarded as adjusting the weight of the first repulsion vector through the repulsion guidance algorithm, increasing the repulsion strength coefficient and the heading keeping coefficient, and adding the adjusted gravity vector to the first repulsion vector and the second repulsion vector to obtain the virtual force (resultant force vector) so that the USV maintains a safe distance from the rear end of the docking station during the bypass process and gradually turns to the front of the docking station.

[0083] When the control mode is the alignment mode, the gravitational vector is calculated based on the gravitational function of the gravitational field, the first repulsive vector is calculated based on the first repulsive function of the first repulsive field, and the second repulsive vector is calculated based on the second repulsive function of the second repulsive field; after adjusting the lateral weight and forward weight of the gravitational vector using the gravitational line alignment algorithm, it is superimposed with the first repulsive vector and the second repulsive vector to obtain a resultant force vector; and a virtual force field is constructed based on the resultant force vector.

[0084] Specifically, the virtual force field construction process in the current mode remains consistent with the above-mentioned front alignment mode, only the resultant force vector calculation process is redesigned, and the lateral alignment coefficient k of the gravity line alignment algorithm is changed to 11 and forward thrust coefficient k 12 The values ​​of are swapped to increase the weight of the lateral gravity term by 50%, forcing the USV to turn toward the docking station axis. This reduces the forward thrust and reduces the USV's forward speed, thus avoiding untimely course corrections due to high-speed travel. In alignment mode, after using the gravity and repulsion functions to calculate the gravity vector, the first repulsion vector, and the second repulsion vector, the process of calculating the resultant force vector can be seen as adjusting the weight of the gravity vector using the gravity line alignment algorithm, adding the lateral alignment coefficient k11 and the forward thrust coefficient k12, and then adding the adjusted gravity vector to the first and second repulsion vectors to obtain a virtual force (resultant force vector), thereby generating a virtual force field corresponding to the virtual force.

[0085] In one embodiment of the present application, the repulsion coefficient k is adjusted according to the wave height during the docking process between the USV and the docking station. att and the gravitational coefficient k rep .

[0086] Specifically, in practical applications, adjusting the attraction and repulsion coefficients according to different sea conditions (such as wave height) can help the USV better adapt to environmental changes. For example, when wave heights are low (<0.5 meters), the default attraction and repulsion coefficients (1.0 times) can be used. When wave heights are higher (0.5-1.0 meters or 1.0-1.5 meters), the repulsion coefficient can be appropriately increased. For wave heights of 0.5-1.0 meters, the repulsion coefficient can be adjusted to 1.2 times, and for wave heights of 1.0-1.5 meters, the repulsion coefficient can be adjusted to 1.5 times to enhance obstacle avoidance capabilities. At the same time, the attraction coefficient can be appropriately reduced. For wave heights of 0.5-1.0 meters, the repulsion coefficient can be adjusted to 0.9 times, and for wave heights of 1.0-1.5 meters, the repulsion coefficient can be adjusted to 0.8 times, to prevent excessive attraction from causing instability in the USV.

[0087] In one embodiment of the present application, the above-mentioned operation 106 determines the motion path of the unmanned surface vessel based on the direction and size of the virtual force field, including: determining a generation rule of the path point based on the direction and size of the virtual force field, the generation rule including the spacing and direction of the path point; within each control cycle, based on the corresponding generation rule, generating the next path point according to the direction and size of the virtual force field; and connecting a series of path points in sequence to generate the motion path of the unmanned surface vessel.

[0088] Specifically, starting from the current USV position, the next path point is generated along the direction of the virtual force field. The distance of the next path point is determined by the target linear velocity shown by the virtual force field and the processing cycle (≤8ms). For example, when the linear velocity is 1m / s, a path point with a step length of 0.008m is generated every 8ms. Through continuous iteration (for example, updating the virtual force field and path points every 8ms), these continuous path points are connected in series to form a dynamic and smooth motion path, ensuring that the path oscillation amplitude is controlled within ±0.3m. Among them, the direction of the virtual force field is the direction of the generation rule, and the distance of the next path point is the spacing of the generation rule.

[0089] In one embodiment of the present application, the unmanned surface vessel is controlled to dock with the docking station according to the motion path of the unmanned boat, including: calculating the acceleration and angular velocity required for the unmanned boat at each point on the path according to the motion path, and generating corresponding motion instructions; decomposing the motion instructions into propeller differential and rudder angle control quantities, and performing anti-saturation optimization in combination with the unmanned boat dynamics model; sending the optimized motion instructions to the unmanned surface vessel to drive the motor and servo of the unmanned boat to perform the docking action with the docking station.

[0090] Specifically, for each pathpoint in the motion path, the APF algorithm converts the corresponding motion requirements into motion commands including linear velocity and angular velocity (such as forward velocity along the direction of the resultant force and angular velocity for adjusting heading). These motion commands are published as a ROS2Topic ( / cmd_vel topic) and transmitted to the USV in real time via a TLS-encrypted WebSocket (wss: / / ). The USV controls the linear velocity and angular velocity using an adaptive proportional-integral-derivative controller (PID) to decompose the linear velocity and angular velocity into propeller differential (for steering control) and rudder angle control (for forward direction control). These are ultimately sent to the USV's ArduPilot autopilot via the MAVLink protocol, driving the motors and servos to execute the commands, completing path tracking and achieving docking with the docking station.

[0091] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this application can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this application can be achieved. This is not a limitation herein.

[0092] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. Throughout the description of this application, "plurality" means two or more, unless otherwise specifically defined.

[0093] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for docking an unmanned surface vessel with a docking station, characterized in that: The method comprises: When the distance between the unmanned surface vessel and the docking station is less than a first set distance, obtaining status data of the unmanned surface vessel and the docking station respectively, the status data including coordinates, attitude, acceleration and angular velocity; fusing the status data of the unmanned surface vessel and the docking station and the predicted status data of the unmanned surface vessel and the docking station to generate target optimal status data of the unmanned surface vessel and the docking station, wherein the predicted status data is obtained based on historical status data of the unmanned surface vessel and the docking station; Based on the target optimal state data of the unmanned surface vessel and the docking station, a gravitational field with the front end of the docking station as the gravitational source and a repulsive field with the rear end of the docking station and obstacles as the repulsive sources are constructed; Calculating the relative positions of the unmanned surface vessel and the docking station based on the target optimal state data of the unmanned surface vessel and the docking station; According to the relative position of the unmanned surface vehicle and the docking station, the control mode of the unmanned surface vehicle is determined, and the virtual force field of the unmanned surface vehicle is generated according to the gravitational field, repulsive field, control mode and their corresponding algorithms; According to the direction and size of the virtual force field, the motion path of the unmanned surface vessel is determined, and the unmanned surface vessel is controlled to dock with the docking station according to the motion path of the unmanned surface vessel.

2. The method according to claim 1, characterized in that Obtain predicted status data based on historical status data of the USV and docking station, including: Based on the angular velocity and acceleration shown in the historical state data at the previous set time, the intermediate predicted state data at the current time is predicted by integrating the angular velocity of the inertial measurement unit; The intermediate prediction state data and the historical state data are fused to obtain the prediction state data at the current moment.

3. The method according to claim 2, characterized in that The state data of the unmanned surface vessel and the docking station and the predicted state data of the unmanned surface vessel and the docking station are integrated, including: Obtaining the state error covariance corresponding to the predicted state data of the unmanned surface vehicle and the docking station; Convert the global coordinates in the global coordinate system of the status data of the unmanned surface vehicle and the docking station into Cartesian coordinates in the Cartesian local coordinate system, where the global coordinates include longitude, latitude and elevation; The converted Cartesian coordinates and the heading angle shown in the state data are used as the actual observation vector, and the observation equation is constructed based on the actual observation vector to obtain the deviation, which is used to show the degree of deviation between the predicted state data and the actual observation vector; Calculating a Kalman gain based on the state error covariance, the observation matrix, and a preset measurement noise covariance; Target optimal state data is calculated based on the Kalman gain, the deviation and the predicted state data of the unmanned surface vessel and the docking station, and the target optimal state data is used as the predicted state data at the next set moment.

4. The method according to claim 1, wherein The repulsive field includes a first repulsive field and a second repulsive field; accordingly, Based on the target optimal state data of the unmanned surface vessel and the docking station, a gravitational field with the front end of the docking station as the gravitational source and a repulsive field with the rear end of the docking station and obstacles as the repulsive sources are constructed, including: Using the coordinates of the front end of the docking station shown in the target optimal state data of the unmanned surface vehicle and the docking station as the center of the gravity source, a gravitational field is constructed, wherein the gravitational field is used to determine the gravitational force and gravitational direction according to the gravitational function; Using the coordinates of the rear end of the docking station shown in the target optimal state data of the unmanned surface vessel and the docking station as the center of the repulsive force source, a first repulsive force field is constructed, wherein the first repulsive force field is used to determine a first repulsive force and a first repulsive force direction according to a first repulsive force function; The coordinates of all obstacles of the unmanned surface vessel are obtained, and a second repulsive field is constructed according to the coordinates of all obstacles, wherein the second repulsive field is used to determine a second repulsive force and a repulsive direction according to a second repulsive function.

5. The method according to claim 4, characterized in that The relative position includes the relative distance, relative azimuth, and relative heading deviation between the unmanned surface vessel and the docking station; determining the control mode of the unmanned surface vessel based on the relative position of the unmanned surface vessel and the docking station includes: When the relative distance between the unmanned surface vessel and the docking station meets the preset contact range and the relative heading deviation between the unmanned surface vessel and the docking station is less than the set deviation range, determining the control mode to be the forward alignment mode; When the relative azimuth angle between the unmanned surface vessel and the docking station is greater than the set azimuth angle and the relative distance between the unmanned surface vessel and the docking station is less than a set multiple of the docking station length, determining the control mode to be the rearward detour mode; When the relative heading deviation between the unmanned surface vessel and the docking station is greater than the set deviation range, the control mode of the unmanned surface vessel is determined to be the alignment mode.

6. The method according to claim 5, characterized in that The virtual force field of the unmanned surface vehicle is generated according to the gravitational field, repulsive field, control mode and their corresponding algorithms, including: When the control mode is the forward alignment mode, a gravitational vector is calculated based on the gravitational function of the gravitational field, a first repulsive vector is calculated based on the first repulsive function of the first repulsive field, and a second repulsive vector is calculated based on the second repulsive function of the second repulsive field; after adjusting the weight of the gravitational vector using a gravitational line alignment algorithm, the resultant force vector is superimposed on the first repulsive vector and the second repulsive vector to obtain a resultant force vector; and a virtual force field is constructed based on the resultant force vector. When the control mode is the rear bypass mode, a gravitational vector is calculated based on the gravitational function of the gravitational field, a first repulsive vector is calculated based on the first repulsive function of the first repulsive field, and a second repulsive vector is calculated based on the second repulsive function of the second repulsive field; after adjusting the weight of the first repulsive vector using a repulsion point guidance algorithm, the resultant force vector is superimposed with the gravitational vector and the second repulsive vector to obtain a resultant force vector; and a virtual force field is constructed based on the resultant force vector. When the control mode is the alignment mode, the gravitational vector is calculated based on the gravitational function of the gravitational field, the first repulsive vector is calculated based on the first repulsive function of the first repulsive field, and the second repulsive vector is calculated based on the second repulsive function of the second repulsive field; after adjusting the lateral weight and forward weight of the gravitational vector using the gravitational line alignment algorithm, the resultant force vector is superimposed with the first repulsive vector and the second repulsive vector to obtain a resultant force vector; and a virtual force field is constructed based on the resultant force vector.

7. The method according to claim 1, characterized in that According to the direction and size of the virtual force field, the motion path of the unmanned surface vehicle is determined, including: Determining a generation rule of the path points according to the direction and size of the virtual force field, wherein the generation rule includes the spacing and direction of the path points; In each control cycle, the next path point is generated according to the direction and magnitude of the virtual force field based on the corresponding generation rules; Connect a series of path points in sequence to generate the motion path of the unmanned surface vehicle.

8. The method according to claim 7, characterized in that Controlling the unmanned surface vessel to dock with the docking station according to the motion path of the unmanned vessel includes: According to the motion path, the acceleration and angular velocity required by the unmanned boat at each point on the path are calculated, and corresponding motion instructions are generated; Decomposing the motion command into propeller differential and rudder angle control variables, and performing anti-saturation optimization in combination with the unmanned boat dynamics model; The optimized motion instructions are sent to the unmanned surface vessel, driving the motor and servo of the unmanned surface vessel to perform docking with the docking station.

9. A docking system for an unmanned surface vessel and a docking station, characterized in that: The system includes: Unmanned surface vehicle, equipped with a dual antenna system and inertial measurement unit to obtain its own status data; A docking station, configured with a dual antenna system and an inertial measurement unit, for obtaining its own status data; a control system, deployed on the unmanned surface vessel, comprising a processor and a memory in communication with the processor, the memory storing instructions executable by the processor, the instructions being executed by the processor so that the processor can perform the method according to any one of claims 1 to 8; An inter-device communication system, configured to control data communication between the unmanned surface vessel and the docking station through a communication server and an encryption algorithm; A remote monitoring communication system is used to monitor data of the communication server and the front end through the communication server and encryption algorithm.

10. The system according to claim 9, characterized in that The unmanned surface vessel is further equipped with a laser radar for detecting obstacles during docking between the unmanned surface vessel and the docking station and for triggering an emergency stop when it is detected that the distance to the docking station is less than a second set distance; The unmanned surface vessel is also used to perform dynamic interception through a command verification module; The unmanned surface vessel performs dynamic interception through a command verification module, including: when it is detected that the speed is greater than a first set speed or the angular velocity is greater than a first set angular velocity, the speed is limited by the command over-limit interception function of the command verification module.

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