Wave adaptive catamaran unmanned ship for ocean surveying and mapping and control method thereof

Abstract

The application relates to the technical field of marine surveying and mapping, and discloses a wave adaptive catamaran unmanned ship for marine surveying and mapping and a control method thereof. The control method combines an A* algorithm and a third-order Bezier curve to generate a complete and smooth initial path, simultaneously uses the effective band strip coverage width of a multi-beam sonar to perform constraint, accurately adapts to the full coverage requirement of marine surveying and mapping, and realizes path scene customization. Dynamic correlation of ship speed adjustment, conversion of LOS forward-looking distance PID and differential thrust of the unmanned ship propeller, realization of customization optimization for the dynamic characteristics of the unmanned ship, determination of the dominant mode of rolling by using the vertical velocity difference between the left and right pontoons, calculation of the preliminary expected force corresponding to the left and right electromagnetic damping mechanisms, matching of the fuzzy value of the damping coefficient by using the hierarchical priority level of the preset safety rule model, improvement of control accuracy, defuzzification of the fuzzy value to obtain the accurate value of the damping coefficient, more accurate adjustment of the damping field strength of the electromagnetic damping mechanism, and improvement of the stability of the ship body in waves.

Description

Technical Field

[0001] This application relates to the field of marine surveying technology, and for example to a wave-adaptive catamaran unmanned surface vessel for marine surveying and its control method. Background Technology

[0002] Marine surveying is fundamental to marine resource development and marine engineering construction. Unmanned surface vessels (USVs) are increasingly used in marine surveying due to their advantages such as autonomous navigation and strong environmental adaptability. However, existing marine surveying USVs have shortcomings. Their hulls lack stability in waves, making them susceptible to turbulence that affects surveying accuracy and equipment lifespan. Traditional catamarans are mostly rigidly connected, allowing wave impacts to be directly transmitted to the hull platform, causing severe shaking of onboard equipment and affecting operations.

[0003] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general commentary, nor is it intended to identify key / important components or describe the scope of protection of these embodiments, but rather as a prelude to the detailed description that follows.

[0005] This disclosure provides a wave-adaptive catamaran unmanned surface vessel for marine mapping and its control method to improve the stability of the vessel in waves.

[0006] In some embodiments, the control method for a wave-adaptive catamaran unmanned surface vessel (USV) used for ocean mapping includes: constructing a two-dimensional cost map of the mapping area; generating an initial path for the USV on the two-dimensional cost map by invoking the A* algorithm and a third-order Bézier curve based on the effective strip coverage width of the multibeam sonar; determining a starting point and a line-of-sight point on the initial path; calculating a differential thrust torque based on the line-of-sight point and the current position of the USV; and converting the differential thrust torque into target thrust for the left and right thrusters; and collecting the operating parameters of the USV to calculate... The vertical velocity difference between the left and right floats is obtained. The dominant mode of oscillation is determined based on the vertical velocity difference. The initial expected force corresponding to the electromagnetic damping mechanism is determined based on the dominant mode. The operating parameters are matched with each of the preset safety rule models one by one according to the priority of multiple preset safety rule models to obtain the fuzzy value of the damping adjustment coefficient. The precise value of the damping adjustment coefficient is obtained based on the fuzzy value of the damping adjustment coefficient. The control current is calculated based on the precise value of the damping adjustment coefficient and the initial expected force. The damping field strength of the corresponding electromagnetic damping mechanism is adjusted based on the control current.

[0007] In some embodiments, the wave-adaptive catamaran unmanned surface vessel (USV) for ocean mapping employs the control method for wave-adaptive catamarans for ocean mapping described above. The USV includes: a payload platform for carrying mapping equipment; two pontoons symmetrically distributed along the roll axis of the USV and connected to the payload platform via two electromagnetic damping mechanisms; two thrusters mounted at the sterns of the two pontoons; a frameless motor mounted at the bottom of the payload platform; a multibeam sonar connected to a mounting frame, which is connected to a connecting rod; the frameless motor is driven by the connecting rod to drive the multibeam sonar to rotate; a binocular camera and a lidar are mounted on the payload platform; wherein, both pontoons and the payload platform are equipped with IMUs.

[0008] The wave-adaptive catamaran unmanned surface vessel and its control method for marine mapping provided in this disclosure can achieve the following technical effects:

[0009] Combining the A* algorithm and third-order Bézier curves to generate a complete and smooth initial path on a two-dimensional cost map, while utilizing the effective strip coverage width of multibeam sonar for constraint, it accurately adapts to the full coverage requirements of marine mapping and is a scenario-based customization of a general path algorithm.

[0010] Dynamically correlated ship speed adjustment LOS forward sight distance (gain coefficient) k (Adaptation) The conversion between PID and differential thrust of unmanned vessel propulsion system enables customized optimization for the dynamic characteristics of catamaran unmanned vessels.

[0011] The dominant oscillation mode is determined by the vertical velocity difference between the left and right floats, and the initial expected force corresponding to the left and right electromagnetic damping mechanisms is then calculated. The fuzzy values ​​of the damping coefficients are matched using the hierarchical priority levels of a preset safety rule model to improve control accuracy.

[0012] By defuzzifying the fuzzy values, the precise value of the damping coefficient can be obtained, allowing for more accurate adjustment of the damping field strength of the electromagnetic damping mechanism and improving the stability of the ship in waves.

[0013] The above general description and the description below are exemplary and illustrative only and are not intended to limit this application. Attached Figure Description

[0014] One or more embodiments are illustrated by way of example with reference to the accompanying drawings. These illustrations and drawings do not constitute a limitation on the embodiments. Elements having the same reference numerals in the drawings are shown as similar elements. The drawings are not to be scaled. And wherein:

[0015] Figure 1 This is a schematic diagram of the structure of a catamaran unmanned surface vessel (with multibeam sonar deployed) provided in an embodiment of this disclosure;

[0016] Figure 2 This is a schematic diagram of the structure of a catamaran unmanned surface vessel (with multibeam sonar raised) provided in an embodiment of this disclosure;

[0017] Figure 3 This is a schematic diagram of the structure of the catamaran unmanned surface vessel without floats and multibeam sonar provided in the embodiments of this disclosure;

[0018] Figure 4 This is a schematic diagram showing the connection between the multibeam sonar, the mounting frame, and the connecting rod provided in the embodiments of this disclosure;

[0019] Figure 5 This is a schematic diagram of the first control method for a wave-adaptive catamaran unmanned surface vessel for marine mapping provided in this disclosure embodiment;

[0020] Figure 6 This is a schematic diagram of a second control method for a wave-adaptive catamaran unmanned surface vessel for marine mapping provided in this disclosure embodiment;

[0021] Figure 7 This is a schematic diagram comparing the roll angle response of the vibration reduction scheme provided in this embodiment with that of the traditional rigid connection vibration reduction scheme under sea state 5.

[0022] Figure 8 This is a schematic diagram comparing the damping adjustment scheme provided in this embodiment with the traditional PID damping control scheme in terms of damping force tracking error.

[0023] Figure 9 This is a schematic diagram comparing the damping adjustment scheme provided in this embodiment with the traditional PID damping control scheme in terms of expected damping force and actual output.

[0024] Figure 10 This is a comparative diagram of the path smoothing scheme provided in this embodiment and the traditional smoothing scheme;

[0025] Figure 11 This is a schematic diagram of the path planning results provided in this embodiment;

[0026] Figure 12 yes Figure 11 Enlarged view of a section of the mid-turn area.

[0027] Figure label:

[0028] 1. Float; 2. Electromagnetic damping mechanism; 3. Mounting frame; 4. Frameless motor; 5. Binocular camera; 6. Load platform; 7. LiDAR; 8. Thruster; 9. Mounting base; 10. Magnetorheological damper; 11. Support arm; 12. Front arch; 13. Rear arch; 14. Connecting rod; 15. Multibeam sonar. Detailed Implementation

[0029] To provide a more detailed understanding of the features and technical content of the embodiments of this disclosure, the implementation of the embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. The accompanying drawings are for illustrative purposes only and are not intended to limit the embodiments of this disclosure. In the following technical description, for ease of explanation, several details are used to provide a full understanding of the disclosed embodiments. However, one or more embodiments may still be implemented without these details. In other cases, well-known structures and devices may be simplified in their depiction to simplify the drawings.

[0030] The terms "first," "second," etc., used in the specification and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this disclosure described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion.

[0031] Unless otherwise stated, the term "multiple" means two or more.

[0032] In this embodiment of the disclosure, the character " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B means: A or B.

[0033] The term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0034] The term "correspondence" can refer to an association or binding relationship. The correspondence between A and B means that there is an association or binding relationship between A and B.

[0035] Combination Figures 1 to 4As shown, this disclosure discloses a wave-adaptive catamaran unmanned surface vessel (hereinafter referred to as "USV") for marine mapping. The USV includes: a payload platform 6, two pontoons 1 (left and right), two electromagnetic damping mechanisms 2, two thrusters 8, a multibeam sonar 15, frameless motors 4, a binocular camera 5, and a radar. The payload platform 6 carries the mapping equipment. The two pontoons 1 are symmetrically distributed along the roll axis of the USV and are connected to the payload platform 6 via the two electromagnetic damping mechanisms 2. The two thrusters 8 are respectively installed at the stern of the left and right pontoons, providing propulsion for the USV's navigation. The two frameless motors 4 are installed at the bottom of the payload platform. The multibeam sonar 15 is connected to a mounting frame 3, which is connected to a connecting rod 14. The two frameless motors 4 are drive-driven to the connecting rod 14 to rotate the connecting rod 14 along its own axis, thereby causing the multibeam sonar 15 to rotate. The binocular camera 5 and the lidar 7 are installed on the payload platform 6. The left float 1, the right float 1, and the load platform 6 are all equipped with IMUs (Inertial Measurement Units).

[0036] Each electromagnetic damping mechanism 2 includes a magnetorheological damper 10, a mounting base 9, and a support arm 11. Two mounting bases 9 are symmetrically arranged and connected to the corresponding floats 1. The magnetorheological damper 10 and the support arm 11 are connected to the load platform 6 via a front arch 12, and the tails of the two mounting bases 9 are connected to the load platform 6 via a rear arch 13.

[0037] The controller is communicatively connected to the IMU, the electromagnetic damping mechanism 2, and the thruster 8 to receive data and adjust the damping force of the electromagnetic damping mechanism 2 and the thrust of the thruster 8.

[0038] In this way, the catamaran structure combined with the suspension-type semi-active shock absorption mechanism greatly improves the stability of the hull in waves, has strong wave adaptability, and ensures the accuracy and lifespan of the surveying equipment; the flip-up mounting bracket 3 allows the multibeam sonar 15 to be flexibly switched between underwater surveying and surface storage and maintenance, taking into account both operational needs and equipment maintenance.

[0039] Based on the aforementioned catamaran unmanned surface vessel, combined with Figure 5 As shown, this disclosure provides a control method for a wave-adaptive catamaran unmanned surface vessel (USV) used for ocean mapping, including:

[0040] S10: Construct a two-dimensional cost map of the survey area. Based on the effective strip coverage width of the multibeam sonar and by calling the A* algorithm and the third-order Bézier curve, generate the initial path of the catamaran unmanned vessel on the two-dimensional cost map.

[0041] S20: Determine the starting point and line-of-sight point on the initial path, calculate the differential thrust torque based on the line-of-sight point and the current position of the catamaran unmanned vessel, and convert the differential thrust torque into the target thrust of the left and right thrusters;

[0042] S30: Collect the operating parameters of the catamaran unmanned surface vessel to calculate the vertical velocity difference between the left and right pontoons. Determine the dominant sway mode based on the vertical velocity difference, and determine the preliminary expected force corresponding to the electromagnetic damping mechanism based on the dominant mode. According to the priority of multiple preset safety rule models, match the operating parameters with each preset safety rule model one by one to obtain the fuzzy value of the damping adjustment coefficient. Obtain the precise value of the damping adjustment coefficient based on the fuzzy value of the damping adjustment coefficient. Calculate the control current based on the precise value of the damping adjustment coefficient and the preliminary expected force, and adjust the damping field strength of the corresponding electromagnetic damping mechanism based on the control current.

[0043] 1. The unmanned surface vessel (USV) generates a full-coverage initial path based on the A* algorithm and Bézier curves. The A* algorithm is used to find the optimal path, and then Bézier curves are used to smooth the inflection points of the optimal path. Specifically, this includes:

[0044] S11. First, the surveying task area is rasterized based on high-precision electronic nautical chart information to construct a two-dimensional cost map. Each grid cell in the map is assigned a different cost value to represent navigability. Known static obstacles (such as coastlines, docks, and marked shoals) are marked as high-cost or impassable areas. Known safe areas have a cost value of 10, indicating safe passage. Unknown areas also have a cost value of 10, and their safety is ensured by subsequent local obstacle avoidance modules.

[0045] S12. Based on the effective strip coverage width W of the multibeam sonar, determine the spacing between adjacent mapping routes according to the following formula.

[0046] ;

[0047] Where D represents the distance between flight routes. To ensure seamless coverage, a certain degree of overlap is allowed, ranging from 10% to 20%.

[0048] S13. Based on the boundary shape of the surveyed area (usually a rectangle or polygon), the system automatically generates a series of parallel straight line segments, which are the surveying track lines. On each surveying track line, guide points are placed evenly at certain intervals (this interval mainly affects the density of path points, not coverage, and can be set to 5 to 10 meters). These guide points are the sequence of target points that the A* algorithm needs to visit sequentially.

[0049] S14: Set the first guiding point as the starting point Start, and the second guiding point as the first sub-goal Goal_1. Call the standard A* algorithm to find the lowest-cost path from Start to Goal_1 on the constructed 2D cost map. The A* algorithm maintains an open list and a closed list, and uses the Euclidean distance heuristic to evaluate the total cost of each node.

[0050] f(n) = g(n) + h(n);

[0051] Here, g(n) is the actual cost from the starting point to the current node, and h(n) is the estimated cost from the current node to the target, thus efficiently searching for the optimal path. The open list is used to store candidate nodes that have completed path evaluation but have not yet explored adjacent nodes, while the closed list is responsible for maintaining the set of nodes that have completed expansion processing.

[0052] The iterative process of the A* algorithm follows the following mechanism:

[0053] In each search iteration, the system first sorts and calculates the comprehensive evaluation value of all nodes in the open list. Then, it selects the node with the minimum value as the current expansion node, removes it from the open list, and marks it in the closed list. The algorithm then expands the neighborhood of this node, adding new nodes that meet the criteria to the open list. This process continues iterating until the target point Goal_1 is successfully extracted.

[0054] Save the sequence of path points from Start to Goal_1 found by the A* algorithm. Then, set Goal_1 as the new starting point and Goal_2 (the third guide point) as the new goal, and run the A* algorithm again. Repeat this process until all guide points are connected. The final result is an initial full-coverage path consisting of straight line segments connecting all guide points.

[0055] S15. The path obtained above ensures complete coverage of the surveyed area, but it has sharp turns at the guide points, making it unsuitable for direct use in ship navigation. Therefore, a multi-constraint Bézier curve is introduced for path smoothing optimization. The Bézier curve is used to traverse the initial path, identifying all turning points. These points are where smoothing is required. Simultaneously, the two adjacent path points before and after each turning point are recorded as the starting point of the generated Bézier curve. P 0 and the endpoint P 3.

[0056] The parametric equation for a third-order Bézier curve is:

[0057] ;

[0058] in, t It is an independent variable with a range of (0,1). It is the first on the Bézier curve One control point.

[0059] Curvature continuity constraint: The third-order Bézier curve has the continuity of the second derivative, which can ensure that the curvature function of adjacent path segments does not change abruptly at the connecting node, effectively suppressing the instantaneous surge of robot steering angle acceleration.

[0060] Slope continuity constraint: To ensure that there is no abrupt change in direction at the connection point between the smoothed path and the original straight line segment, the Bézier curve must be forced to maintain a continuous slope. P 0 and P The tangent at point 3 is in the same direction as the straight line segment of the original path.

[0061] Turning radius constraint: Third-order Bézier curves under arbitrary parameters t curvature at It can be represented as:

[0062] ;

[0063] The curvature at any point on the trajectory should be less than the minimum turning radius of the unmanned vessel. The reciprocal of, that is:

[0064] ;

[0065] The above smoothing process is performed at each turning point, replacing the original turning path with a newly generated Bézier curve that satisfies all constraints, and then splicing it with the unmodified path to form a complete and smooth initial path.

[0066] 2. The path tracking control of the unmanned catamaran adopts a composite control architecture of "LOS+PID" to control the thrusters. The system obtains the current position of the unmanned vessel in real time from GNSS-RTX (Global Navigation Satellite System Real-time Precise Positioning Service) and IMU. and current heading angle The system loads a complete, smooth initial path from the global path planning module. This path consists of a series of discrete path points P0, P1, ..., P... n The system consists of points connected by multi-constrained Bézier curves, forming a continuous trajectory with a gently changing curvature. A starting point and a line-of-sight (LOS) point are determined on the initial path; the LOS point is the next point to be reached. Based on the LOS point and the current position of the unmanned surface vessel (USV), the differential thrust torque of the left and right thrusters is calculated and converted into the target thrust of the left and right thrusters, enabling the USV to move from the starting point to the LOS point along the initial path. Specifically, this includes:

[0067] S21, the controller first finds the distance to the current ship position by traversing all waypoints on the initial path and calculating the Euclidean distance. nearest path .

[0068] S22, dynamically calculates the forward sight distance based on the current ship speed. :

[0069] ;

[0070] in, This is the current ship speed; k It is an adjustable gain coefficient that increases the forward sight distance at higher ship speeds to avoid over-steering, and decreases the forward sight distance at lower speeds to improve tracking accuracy.

[0071] S23, using the nearest path point Starting from the initial path, explore forward until the cumulative path length equals the forward sight distance. The point is denoted as the line-of-sight point. If the remaining path length is insufficient If so, the endpoint of the initial path is directly taken as the line of sight.

[0072] S24, Connect to the current ship position and line of sight This forms a line-of-sight vector, and the angle of this vector relative to true north is calculated, which is the desired heading angle. :

[0073] ;

[0074] After obtaining the desired heading angle, the real-time heading angle deviation can be calculated. :

[0075]

[0076] S25, the PID controller adjusts the real-time heading angle deviation... Based on the historical heading angle deviation, the control output is calculated. This output represents the differential thrust required to eliminate the deviation, and its standard form is:

[0077] ;

[0078] Among them, the proportional term Provides an instantaneous response proportional to the real-time heading angle deviation; integral term Used to eliminate steady-state errors in the system (such as persistent yaw caused by crosswinds or waves); differential term It predicts future deviation trends, provides damping, and makes the system response smoother.

[0079] get This then needs to be converted into specific left and right thruster commands. Assume the total thrust requirement is... The differential thrust output by the PID controller is The target thrust of the left and right thrusters are respectively:

[0080] ;

[0081] ;

[0082] Thruster controller receives and The command is executed precisely by adjusting the motor speed, generating the required thrust to drive the hull to rotate at the desired bow angle.

[0083] 3. Semi-active vibration reduction of unmanned ships based on fuzzy collaborative control.

[0084] S31: Collect operational parameters of the unmanned surface vessel, including motion data of various moving components. Simultaneously collect three sets of raw data from the left float IMU, right float IMU, and payload platform IMU. Perform coordinate transformation and filtering on the raw data to calculate the following key state variables: absolute velocity of the payload platform. angular velocity of the load platform Absolute speed of left and right floats , Relative displacement of left and right shock absorbers , and relative velocity , .

[0085] To ensure that the data collected by the three inertial measurement units (IMUs) accurately reflects the actual motion state of the unmanned aircraft, a high-precision IMU data synchronization and coordinate unification method is adopted. This method includes three key steps: hardware synchronization mechanism, software filtering processing, and coordinate transformation matrix calibration, as detailed below:

[0086] S311, hardware synchronization mechanism

[0087] IMU data synchronization is achieved through hardware triggering, using the PPS (Pulse Per Second) signal as a time base to ensure that all IMUs sample at the same time. The specific steps are as follows:

[0088] PPS signal source configuration: A high-precision GPS module is integrated in the main controller to output a stable PPS pulse signal with a frequency of 1Hz and a jitter of less than ±50ns;

[0089] IMU Synchronization Interface Configuration: Each IMU is equipped with a hardware interrupt pin, connected to the PPS signal output port of the main controller. Each time a PPS pulse arrives, the IMU is triggered to perform a synchronization sample.

[0090] Data tag appending: During each sampling, the current system timestamp (with microsecond precision) is appended to the raw IMU data packet to ensure accurate matching of data frames from different IMUs during subsequent processing.

[0091] S312, software filtering

[0092] To eliminate sensor noise and improve data quality, this invention employs an adaptive Kalman filter to preprocess the raw IMU data. The filter parameters are dynamically adjusted according to the actual sea conditions to ensure optimal filtering performance under various operating conditions.

[0093] State equation establishment: Define state vector ,in 'a' represents the velocity component, and 'a' represents the acceleration component.

[0094] Construction of observation equations: Based on the acceleration and angular velocity values ​​directly measured by the IMU, and combined with the ship motion model, an observation matrix H is constructed.

[0095] Filtering parameter settings: Initial covariance matrix Let it be the identity matrix; process noise covariance matrix. Q and observation noise covariance matrix R Based on experience, it is set as follows and It will automatically adjust based on the residuals during operation.

[0096] S313, Coordinate Transformation Matrix Calibration

[0097] Because the IMU installation location may vary, it needs to be calibrated using a rigid body transformation matrix to ensure that all IMU data can be represented in a unified load platform coordinate system. The specific steps are as follows:

[0098] Calibration equipment preparation: Use a high-precision laser rangefinder or 3D scanner to measure the precise position and orientation of the IMU relative to the load platform coordinate system;

[0099] Transformation matrix calculation: Based on the measurement results, calculate the rotation and translation matrix from each IMU to the load platform coordinate system. ,in For rotation matrix, It is a translation vector;

[0100] Error compensation: In practical applications, considering installation errors and long-term drift, the transformation matrix is ​​periodically recalibrated and minor deviations are compensated online.

[0101] S314, Data Fusion and Consistency Verification

[0102] To ensure the accuracy and reliability of the final merged data, a consistency verification mechanism needs to be introduced to ensure that the data from different IMUs conforms to physical laws. Specific measures include:

[0103] Speed ​​Consistency Check: The speed difference between the IMUs of the left and right pontoons reflects the hull's rolling motion (such as roll and pitch) in the waves. When the hull rolls, the left and right pontoons will move vertically in opposite or the same direction, and the speed difference is a direct reflection of the rolling intensity. The speed change trend of the load platform mainly reflects the overall heave motion (up and down) of the hull. Ideally, the overall heave motion of the hull should be consistent with the average motion trend of the two pontoons.

[0104] Based on this, a pre-established expected relationship based on the ship dynamics model is used to characterize the correspondence between the velocity difference of the IMUs of the left and right floats and the velocity change trend of the load platform under normal conditions. For example, during severe rolling, the velocity difference between the left and right floats is large, but the vertical velocity change of the load platform may be relatively gradual; while when large waves cause overall heave, the velocity change trends of the two floats and the load platform should be highly consistent. If the real-time calculated correspondence between the velocity difference of the IMUs of the left and right floats and the velocity change trend of the load platform does not meet the expected relationship, that is, it deviates significantly from this expected relationship (for example, the velocity difference between the IMUs of the left and right floats is small, but the velocity of the load platform changes drastically), it indicates that at least one IMU's data is abnormal (such as sensor drift, communication error, or loose installation). At this time, an alarm is triggered and the coordinate transformation matrix is ​​recalibrated. This is because the IMU data needs to be unified to the "load platform coordinate system" for effective fusion and analysis. This unification process relies on an accurate coordinate transformation matrix T. When data consistency verification fails, the most likely reason is that this transformation matrix T is no longer accurate, so the coordinate transformation matrix needs to be recalibrated.

[0105] Angular velocity correlation analysis: The correlation between the angular velocities of the left and right floats and the roll angular velocity of the load platform is evaluated using the Pearson correlation coefficient to assist in the identification of the dominant mode.

[0106] S32, calculate the vertical velocity difference between the left and right buoys using the absolute velocities of the left and right buoys, and determine the dominant mode of the unmanned surface vessel's rolling motion based on the vertical velocity difference between the left and right buoys. Optionally, the dominant mode includes roll and pitch.

[0107] S321 uses the Pearson correlation coefficient to quantify the linear correlation between the vertical velocity difference and the roll angular velocity of the load platform.

[0108] S322, when the absolute value of the correlation coefficient is greater than a preset threshold, determine the vertical velocity difference and the roll angular velocity of the load platform. Strong correlation.

[0109] S323, when the vertical velocity difference is greater than the velocity threshold, and at the same time, the vertical velocity difference is strongly correlated with the roll angular velocity of the load platform, the roll is determined to be the dominant mode.

[0110] S324, when the vertical velocities of the left and right pontoons are in the same direction and the amplitude is greater than the amplitude threshold (large amplitude), the pitch is determined to be the dominant mode.

[0111] S33, determine the initial expected force corresponding to the electromagnetic damping mechanism based on the dominant mode.

[0112] S331, Generate virtual attitude control torque :

[0113] ;

[0114] in, The virtual ceiling torsional damping coefficient.

[0115] S332, regardless of whether the current wave disturbance is dominated by roll or pitch, the following four types of force components are continuously calculated:

[0116] : The left-side control force derived from the virtual attitude control torque;

[0117] : The right-side control force derived from the virtual attitude control torque;

[0118] : Damping force on the left side of the ground canopy generated based on the vertical velocity of the load platform;

[0119] : Damping force of the right-side canopy generated based on the vertical velocity of the load platform.

[0120] in, and The direction and amplitude switch dynamically according to the dominant mode, specifically:

[0121] When roll is the dominant mode, a differential mode is used to form a damping couple;

[0122] When pitch or heave is the dominant mode, use the same-direction mode to provide net vertical force.

[0123] and The following formula is used to calculate the load platform's vertical stability while avoiding coupling interference with attitude control.

[0124] ;

[0125] ;

[0126] in, To adjust the coefficient, denoted as the vertical velocity of the load platform.

[0127] S333, Calculate the differential compensation force acting on the left and right magnetorheological dampers. and :

[0128] ;

[0129] ;

[0130] in, This is the differential compensation gain coefficient. The vertical velocity of the left pontoon is... This represents the vertical velocity of the right-side pontoon.

[0131] S334, the initial expected force of the left magnetorheological damper is: .

[0132] The initial expected force of the magnetorheological damper on the right is: .

[0133] S34, according to the priority of multiple preset safety rule models, matches the operating parameters with each preset safety rule model one by one to obtain the fuzzy value of the damping adjustment coefficient.

[0134] Independent fuzzy controllers are established for the left and right magnetorheological dampers. The fuzzy controllers are used to dynamically adjust the damping force of the magnetorheological dampers based on real-time sensing conditions. Their input variables include the following six items, all normalized to the interval [-1, 1]:

[0135] 1. Vertical velocity of the load platform;

[0136] 2. Relative velocity of the shock absorber piston;

[0137] 3. Relative displacement of the shock absorber;

[0138] 4. Initial expectations;

[0139] 5. The roll mode energy ratio was obtained by short-time Fourier transform and mode energy decomposition using three-channel IMU data;

[0140] 6. The pitch mode energy ratio was obtained by short-time Fourier transform and mode energy decomposition of three-channel IMU data.

[0141] Furthermore, the fuzzy controller employs a Mamdani-type inference mechanism and has a rule base containing 32 control rules; this rule base is divided into five preset safety rule models according to execution priority from high to low:

[0142] (1) Security protection rule model (priority 0)

[0143] When the system is at its mechanical travel limit or subjected to a severe impact, it is forced to output maximum damping to ensure structural safety, specifically including:

[0144] 1. If the relative displacement of the shock absorber is "near limit positive" (PE) or "near limit negative" (NE), then α' is "positive maximum" (PBE);

[0145] 2. If the absolute value of the relative velocity of the shock absorber piston is greater than the first preset velocity and the absolute value of the relative displacement is greater than the preset displacement, then α' is the "positive maximum" (PBE); optionally, the first preset velocity is 0.8 m / s and the preset displacement is 0.7 m / s;

[0146] 3. If the vertical acceleration of the load platform exceeds the preset acceleration, then α' is the "positive maximum" (PBE). Optionally, the preset acceleration is 1.2 m / s².

[0147] (2) Modality-dominated rule model (priority 1)

[0148] The dominant motion mode is determined based on the proportion of energy in the roll or pitch modes, and control commands matching the modal characteristics are generated, specifically including:

[0149] 1. If the roll mode energy ratio is "high" (H), and the vertical velocity of the load platform and the relative velocity of the piston are both "positive" (PB), then α' is "positive maximum" (PBE).

[0150] 2. If the roll mode energy ratio is "high" (H), and the vertical velocity of the load platform and the relative velocity of the piston are both "negatively large" (NB), then α' is "negatively large" (NBE).

[0151] 3. If the pitch mode energy ratio is "high" (H), and the vertical velocity of the load platform is in the same direction as the relative velocity of the piston and the amplitude is greater than the preset amplitude (large amplitude), then α' is "positive maximum" (PBE) or "negative maximum" (NBE);

[0152] 4. If the roll mode energy ratio is "high" (H) and the relative displacement of the shock absorber is "near limit positive" (PE) or "near limit negative" (NE), then α' is "positive maximum" (PBE) or "negative maximum" (NBE) respectively.

[0153] (3) Ceiling-floor synergistic rule model (priority 2)

[0154] Based on the classical physical logic of semi-active damping, the absolute motion of the load platform and the relative motion of the pontoons are coordinated, specifically including:

[0155] 1. If the vertical velocity of the load platform and the relative velocity of the piston are in the same direction and both are "positive" (PB) or "negative" (NB), then α' is "positive" (PB) or "negative" (NB);

[0156] 2. If the two are in opposite directions, then α' is either "negative small" (NS) or "positive small" (PS);

[0157] 3. If the load platform speed is close to zero and the piston speed is greater than the second preset speed (high speed piston movement), then α' is "positive center" (PM) or "negative center" (NM).

[0158] (4) Compensation and Integration Rule Model (Priority 3)

[0159] Integrating preliminary expectation force with multivariable states enhances the reliability of model-driven control, specifically including:

[0160] 1. If the initial expected force is "positive" (PB) and the vertical velocity of the load platform is "positive" (PB), then α' is "positive" (PB);

[0161] 2. If all key state variables are in the central region (i.e., the linguistic value is "zero" or "center"), then α' is "zero" (ZO).

[0162] 3. When the energy ratios of roll and pitch are both "medium" (M) and the relative velocity is close to zero, α' is "zero" (ZO).

[0163] (5) Default fallback rule model (priority 4)

[0164] When none of the above rules are effectively activated, the neutral damping coefficient is output to maintain a smooth system transition. That is, if the membership degree of all input variables is lower than the preset threshold of 0.1, then α' is "zero" (ZO).

[0165] During fuzzy inference, the system first checks whether the security protection rule with priority 0 is satisfied; if not, it checks the rules with priorities 1 to 4 in sequence, and terminates the subsequent rule evaluation once a match is found.

[0166] The hierarchical priority rule base design effectively solves the control failure problem caused by incomplete rule coverage, logical conflicts or modal misjudgment in traditional fuzzy control, and improves the shock absorption robustness and operational reliability of unmanned catamarans in complex sea conditions.

[0167] S35, the precise value of the damping adjustment coefficient is obtained based on the fuzzy value of the damping adjustment coefficient.

[0168] After a successful match and the fuzzy controller receives the precise input value, it converts it into the membership degrees of each linguistic variable through the fuzzification interface. Based on the aforementioned rule base, it performs Mamdani-type fuzzy inference and defuzzification operations. The centroid method is then used to defuzzify the fuzzy value of the damping adjustment coefficient, outputting the precise value α of the damping adjustment coefficient. .

[0169] S36, the target damping force ultimately acting on the magnetorheological damper. for:

[0170] ;

[0171] in, This represents initial expectations.

[0172] S37, based on the inverse dynamic model of the magnetorheological damper, the target damping force Mapped to the corresponding control current The calculated left and right current commands are sent to the corresponding current drivers to adjust the damping field strength of the left and right shock absorbers in real time.

[0173] Combination Figure 6 As shown, this disclosure provides another control method for a wave-adaptive catamaran unmanned surface vessel used for ocean mapping, including:

[0174] S10: Construct a two-dimensional cost map of the survey area. Based on the effective strip coverage width of the multibeam sonar and by calling the A* algorithm and the third-order Bézier curve, generate the initial path of the catamaran unmanned vessel on the two-dimensional cost map.

[0175] S20: Determine the starting point and line-of-sight point on the initial path, calculate the differential thrust torque based on the line-of-sight point and the current position of the catamaran unmanned vessel, and convert the differential thrust torque into the target thrust of the left and right thrusters;

[0176] S30: Collect the operating parameters of the catamaran unmanned vessel to calculate the vertical velocity difference between the left and right floats. Determine the dominant sway mode based on the vertical velocity difference, and determine the initial expected force corresponding to the electromagnetic damping mechanism based on the dominant mode. According to the priority of multiple preset safety rule models, match the operating parameters with each preset safety rule model one by one to obtain the fuzzy value of the damping adjustment coefficient. Obtain the precise value of the damping adjustment coefficient based on the fuzzy value of the damping adjustment coefficient. Calculate the control current based on the precise value of the damping adjustment coefficient and the initial expected force, and adjust the damping field strength of the corresponding electromagnetic damping mechanism based on the control current.

[0177] S40, determine whether obstacle avoidance is triggered, and generate a local path when obstacle avoidance is triggered; control the catamaran to move forward according to the local path, and after successfully bypassing the obstacle and meeting the preset conditions, control the catamaran to return to the initial path.

[0178] The local dynamic obstacle avoidance of the unmanned vessel is designed around the TEB to ensure that the unmanned vessel can avoid sudden obstacles and return to the original global mapping path after obstacle avoidance.

[0179] Time synchronization and coordinate calibration: The unmanned catamaran is equipped with a binocular camera and radar. A hardware trigger signal enables the binocular camera and radar to synchronously acquire one frame of data every 20ms. The system pre-calibrates the intrinsic parameters of the binocular camera and the extrinsic parameters of the binocular-radar system, obtaining the rigid body transformation matrix from the radar coordinate system to the left camera coordinate system.

[0180] Point cloud generation: The binocular vision module performs stereo matching on the left and right images (using the SGM algorithm) to generate a disparity map. Using the camera intrinsic matrix and baseline, the disparity map is converted into a dense 3D point cloud. The radar module outputs a raw point cloud, containing range, azimuth, elevation, and radial velocity information. This raw point cloud is then transformed back to the camera coordinate system using a rigid body transformation matrix from the radar coordinate system to the left camera coordinate system. .

[0181] Multi-source point cloud fusion and obstacle extraction: Constructing a unified point cloud Furthermore, voxel mesh downsampling is performed to improve subsequent processing efficiency.

[0182] Sea surface segmentation: The RANSAC plane fitting algorithm is applied to separate sea surface points from obstacle points in the point cloud. Euclidean clustering is then performed on the obstacle points to form independent targets.

[0183] Dynamic obstacle avoidance triggering and local trajectory planning: A dynamic safety corridor is constructed in front of the global path. If the center point of any clustered obstacle enters the corridor, obstacle avoidance is triggered.

[0184] After obstacle avoidance is triggered, the current ship position, heading, speed, and local target points accumulated along the global path are obtained.

[0185] The obstacle point cloud obtained from binocular vision processing is used as the obstacle input for TEB, and these obstacles are expanded to form a safety boundary.

[0186] The physical limitations of the unmanned catamaran are input into the TEB as constraints, including maximum linear velocity, minimum linear velocity, maximum angular velocity, minimum turning radius, etc.

[0187] TEB represents the trajectory to be optimized as a series of poses with timestamps. ,in From arrive The time interval.

[0188] TEB solves a complex nonlinear optimization problem while minimizing the following types of costs:

[0189] Trajectory smoothness cost: Penalizes drastic changes between adjacent poses to make the trajectory as smooth as possible;

[0190] Obstacle avoidance cost: Penalty for the trajectory point being too close to the obstacle;

[0191] The cost of dynamic feasibility: penalizing excessively high lateral acceleration;

[0192] Time-optimal cost: Encourage trajectories to reach local target points in the shortest possible time while satisfying all constraints.

[0193] TEB repeats the above optimization within a short control cycle, using the solution from the previous cycle as an initial guess, and quickly converges to a new local safety trajectory that adapts to the latest environmental perception.

[0194] After TEB optimization is complete, it will output the entire local trajectory. The path tracking controller will no longer track the global path, but will instead track the local trajectory output by TEB.

[0195] After successfully bypassing the obstacle, the system needs to determine when to return to the initial path, which requires the following three conditions to be met:

[0196] 1. After successfully bypassing the obstacle, the binocular camera confirms that the obstacle that triggered the obstacle avoidance is no longer in the safe corridor and there are no new threats within a certain distance ahead;

[0197] 2. The current position of the unmanned catamaran has moved to a pre-defined regression window near the global reference path. This window is defined as a strip-shaped area centered on the global path and with a width of 8 meters;

[0198] 3. If the angle between the current heading angle of the unmanned vessel and the tangent direction of the global path at the current position is less than a threshold, such as 15°, it indicates that the vessel's attitude is basically aligned with the global path direction.

[0199] Once all three conditions are met, the system will perform a smooth transition. The line-of-sight point in the LOS algorithm is no longer a simple global path point or a simple TEB trajectory point, but a weighted average of the two. The weight gradually transitions from biased towards the TEB to completely biased towards the initial path over time.

[0200] It should be noted that the specific methods of S10 to S30 can be found in the previous text and will not be repeated here.

[0201] The wave-adaptive catamaran unmanned surface vessel and its control method for marine mapping provided in this disclosure, combined with the A* algorithm and third-order Bézier curves, generate a complete and smooth initial path on a two-dimensional cost map. Simultaneously, it utilizes the effective strip coverage width of multibeam sonar for constraint, accurately adapting to the full coverage requirements of marine mapping. This represents a scenario-based customization of a general path algorithm. Dynamic correlation between vessel speed and LOS (look-forward distance) (gain coefficient) is used to adjust the forward look-through distance. k (Adapted) The conversion between PID control and differential thrust of the unmanned surface vessel (USV) propulsion system enables customized optimization for the dynamic characteristics of the USV. The dominant sway mode is determined by the vertical velocity difference between the left and right floats, allowing for the calculation of the initial desired force corresponding to the left and right electromagnetic damping mechanisms. The fuzzy values ​​of the damping coefficient are matched using the hierarchical priority level of a preset safety rule model, improving control accuracy. By defuzzifying the fuzzy values, the precise value of the damping coefficient is obtained, enabling more accurate adjustment of the damping field strength of the electromagnetic damping mechanism and enhancing the stability of the hull in waves.

[0202] Comprehensive performance analysis:

[0203] To fully verify the performance of the control method proposed in this application, this embodiment compares it with traditional methods in three dimensions: unmanned vessel stability, damping control accuracy, and path planning.

[0204] (1) Stability verification: combined with Figure 7 As shown in Table 1, under the excitation of sinusoidal waves in sea state 5, both schemes exhibit stable periodic oscillation characteristics in their roll angle response. Compared to the traditional rigid connection scheme, the adaptive damping control scheme proposed in this embodiment shows a smaller roll amplitude in the steady-state phase. This embodiment reduces both the steady-state peak value and the steady-state root mean square value (RMS), indicating that it can effectively suppress the roll energy of the hull under continuous wave action and improve the operational stability of the catamaran unmanned surface vessel in complex sea states.

[0205] Table 1 Comparison of Roll Angle Response Indicators under Sea State 5

[0206]

[0207] (2) Verification of damping control accuracy: Figure 8The results compare the damping force tracking errors of the traditional PID damping control method and the fuzzy rule-based damping adjustment method proposed in this embodiment under the desired input condition of sinusoidal damping force. As shown in the figure, the errors of both control methods exhibit a periodic characteristic consistent with the change in the desired damping force throughout the simulation time, indicating that the error mainly originates from the system's dynamic lag effect. Compared to the traditional PID control method, the fuzzy rule control proposed in this embodiment can effectively suppress error peaks in each period, significantly reducing the amplitude of its tracking error fluctuations and eliminating persistent positive and negative bias phenomena, resulting in a more stable overall error distribution.

[0208] Figure 9 The comparison between the expected damping force and the actual damping force output under the two control methods is presented. It can be seen that traditional PID control has a certain degree of insufficient amplitude and phase lag during the rapid change phase of damping force. However, the fuzzy rule control method proposed in this embodiment can better follow the trend of expected damping force changes while ensuring system stability. Its output waveform maintains a high degree of consistency with the expected damping force in terms of both amplitude and phase.

[0209] Table 2 presents a comparison of the error evaluation indices of the two control methods under the damping force tracking task. It can be seen that, compared with the traditional PID control method, the fuzzy rule-based damping adjustment method proposed in this embodiment achieves smaller values ​​for both the maximum absolute error and the root mean square error, indicating that this method has superior control performance in terms of fine-tuning of damping force and dynamic tracking accuracy.

[0210] Table 2 Statistical results of damping force tracking error

[0211]

[0212] (3) Path planning verification:

[0213] Combination Figure 10 As shown, the path generated by the traditional A* algorithm exhibits abrupt curvature changes at turns, while the path processed by the method in this embodiment shows more continuous curvature changes.

[0214] Combination Figure 11 and Figure 12 As shown, by zooming in on the turning area, it can be seen that the method in this embodiment effectively reduces the abruptness of the path turning while maintaining path feasibility, making the path turning process smoother and suitable for actual navigation of unmanned vessels.

[0215] The above are merely preferred embodiments of this application and are not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0216] While the specific embodiments of the present invention have been described above, they are not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A control method for a wave-adaptive catamaran unmanned surface vessel used for marine mapping, characterized in that, The catamaran unmanned surface vessel includes floats respectively disposed on both sides of the payload platform and connected to the payload platform via an electromagnetic damping mechanism, thrusters mounted on the floats, and multibeam sonar mounted on the payload platform; the control method includes: A two-dimensional cost map of the survey area is constructed. Based on the effective strip coverage width of the multibeam sonar, the A* algorithm and a third-order Bézier curve are invoked to generate the initial path of the catamaran unmanned surface vessel (USV) on the two-dimensional cost map. This initial path generation involves: assigning different values ​​to each grid in the two-dimensional cost map to represent drivability; determining the spacing between adjacent routes based on the effective strip coverage width of the multibeam sonar; generating a series of parallel straight line segments representing the survey track lines based on the boundary shape of the survey area, and setting multiple guide points at intervals along each survey track line according to the effective strip coverage width, serving as a sequence of target points visited sequentially by the A* algorithm; using the first guide point as the starting point and the second guide point as the first sub-target, invoking the A* algorithm to search for the optimal path on the two-dimensional cost map; and smoothing the optimal path using the third-order Bézier curve to form the initial path. The process involves determining a starting point and a line-of-sight point on the initial path, calculating a differential thrust torque based on the line-of-sight point and the current position of the unmanned catamaran, and converting the differential thrust torque into target thrust for the left and right thrusters. Specifically, determining the starting point and line-of-sight point on the initial path and calculating the differential thrust torque based on the line-of-sight point and the current position of the unmanned catamaran includes: traversing all path points on the initial path and calculating Euclidean distances to find the path point closest to the current position; calculating the forward sight distance based on the current ship speed and adjustable gain coefficient; using the closest path point as the starting point, searching forward along the initial path for a line-of-sight point with a cumulative path length equal to the forward sight distance; calculating the desired heading angle based on the current position and the line-of-sight point, and calculating the real-time heading angle deviation based on the desired heading angle and the current heading angle; and calculating the differential thrust torque based on the real-time heading angle deviation and the historical heading angle deviation. The operating parameters of the catamaran unmanned surface vessel are collected to calculate the vertical velocity difference between the left and right pontoons. Based on the vertical velocity difference, the dominant oscillation mode is determined, and based on the dominant mode, the initial expected force corresponding to the electromagnetic damping mechanism is determined. According to the priority of multiple preset safety rule models, the operating parameters are matched with each preset safety rule model one by one to obtain the fuzzy value of the damping adjustment coefficient. Based on the fuzzy value of the damping adjustment coefficient, the precise value of the damping adjustment coefficient is obtained. Based on the precise value of the damping adjustment coefficient and the initial expected force, the control current is calculated, and the damping field strength of the corresponding electromagnetic damping mechanism is adjusted based on the control current.

2. The control method for a wave-adaptive catamaran unmanned surface vessel for marine mapping according to claim 1, characterized in that, The determination of the dominant mode of oscillation based on the vertical velocity difference includes: The linear correlation between the vertical velocity difference and the roll angular velocity of the load platform was quantitatively evaluated using the Pearson correlation coefficient. When the absolute value of the correlation coefficient is greater than a preset threshold, it is determined that there is a strong correlation between the vertical velocity difference and the roll angular velocity of the load platform. When the vertical velocity difference is greater than the velocity threshold and the vertical velocity difference is strongly correlated with the roll angular velocity of the load platform, roll is the dominant mode. When the vertical velocities of the left and right pontoons are in the same direction and the amplitude is greater than the amplitude threshold, the pitch is the dominant mode.

3. The control method for a wave-adaptive catamaran unmanned surface vessel for marine mapping according to claim 1, characterized in that, The determination of the preliminary expected force corresponding to the electromagnetic damping mechanism based on the dominant mode includes: A virtual attitude control torque is generated, and the virtual attitude control torque is decomposed into control forces acting on the left and right electromagnetic damping mechanisms; wherein the direction and amplitude of the control forces are dynamically switched according to the dominant mode; Based on the ground cover logic, the ground cover damping forces corresponding to the left and right electromagnetic damping mechanisms are generated respectively; The differential compensation force acting on the left and right electromagnetic damping mechanisms is calculated based on the vertical velocity of the left and right floats. The initial expected force of each electromagnetic damping mechanism is the sum of its corresponding side control force, ground damping force, and differential compensation force.

4. The control method for a wave-adaptive catamaran unmanned surface vessel for marine mapping according to claim 1, characterized in that, The step of obtaining the precise value of the damping adjustment coefficient based on the fuzzy value of the damping adjustment coefficient, and calculating the control current based on the precise value of the damping adjustment coefficient and the preliminary expected force, includes: The fuzzy value of the damping adjustment coefficient is defuzzified using the center-of-gravity method to obtain the precise value of the damping adjustment coefficient; wherein, the precise value of the damping adjustment coefficient... ; Based on the precise value of the damping adjustment coefficient and the preliminary expected force, the target damping force acting on the electromagnetic damping mechanism is calculated. The target damping force is converted into a control current.

5. The control method for a wave-adaptive catamaran unmanned surface vessel for marine mapping according to claim 1, characterized in that, The operating parameters of the catamaran unmanned surface vessel are collected synchronously in the following ways: Using the PPS signal as a time reference, whenever a PPS pulse arrives, the IMUs of the left and right floats and the payload platform are synchronously sampled, and the current system timestamp is appended to the raw data packet of each IMU during each sampling. The raw data for each IMU is preprocessed using an adaptive Kalman filter; Measure the position and orientation of each IMU relative to the load platform coordinate system, calculate the rotation and translation matrix of each IMU to the load platform coordinate system based on the measured position and orientation, and recalibrate the transformation matrix at a preset period. Calculate the velocity difference between the IMUs of the left and right floats and the velocity change trend of the load platform. If the correspondence between the velocity difference between the IMUs of the left and right floats and the velocity change trend of the load platform does not meet the pre-established expected relationship, an alarm is triggered and the coordinate transformation matrix is ​​recalibrated. Evaluate the correlation between the angular velocities of the left and right floats and the roll angular velocity of the load platform to help identify the dominant mode.

6. The control method for a wave-adaptive catamaran unmanned surface vessel for marine mapping according to claim 1, characterized in that, The preset security rule model includes five types of models in descending order of priority: security protection rule model, modality-dominant rule model, ceiling-floor collaborative rule model, compensation and integration rule model, and default rule model; The safety protection rule model includes: if the relative displacement of the electromagnetic damping mechanism is "near limit positive" or "near limit negative", then α' is "positive maximum"; the modal-dominant rule model includes: if the absolute value of the relative velocity of the piston of the electromagnetic damping mechanism is greater than the first preset velocity and the absolute value of the relative displacement is greater than the preset displacement, then α' is "positive maximum"; if the vertical acceleration of the load platform is greater than the preset acceleration, then α' is "positive maximum"; The modal-dominant rule model includes: if the roll mode energy proportion is "high" and the relative velocity of the load platform vertical velocity and the electromagnetic damping mechanism piston are both "positive and large", then α' is "positive maximum"; if the roll mode energy proportion is "high" and the relative velocity of the load platform vertical velocity and the electromagnetic damping mechanism piston are both "negative and large", then α' is "negative maximum"; if the pitch mode energy proportion is "high", and the relative velocity of the load platform vertical velocity and the electromagnetic damping mechanism piston are in the same direction and the amplitude is greater than a preset amplitude, then α' is "positive maximum" or "negative maximum"; if the roll mode energy proportion is "high" and the relative displacement of the electromagnetic damping mechanism is "near limit positive" or "near limit negative", then α' is "positive maximum" or "negative maximum" respectively. The ceiling-floor cooperative rule model includes:

1. If the vertical velocity of the load platform and the relative velocity of the electromagnetic damping mechanism piston are in the same direction and both are "positive large" or "negative large", then α' is "positive large" or "negative large"; if the vertical velocity of the load platform and the relative velocity of the electromagnetic damping mechanism piston are in opposite directions, then α' is "negative small" or "positive small"; if the velocity of the load platform is close to zero and the velocity of the electromagnetic damping mechanism piston is greater than the second preset velocity, then α' is "positive medium" or "negative medium". The compensation and integrated rule model includes: if the initial expected force is "positive" and the platform's vertical velocity is "positive", then α' is "positive"; if all key state variables are in the central region, then α' is "zero"; if the proportions of roll and pitch energy are both "medium" and the relative velocity is close to zero, then α' is "zero". The default rule model includes: if none of the previously prioritized rule models are matched, then α' is "zero"; Where α' is the fuzzy value of the damping adjustment coefficient.

7. The control method for a wave-adaptive catamaran unmanned surface vessel for marine mapping according to any one of claims 1 to 6, characterized in that, The method further includes: Determine whether obstacle avoidance is triggered, and generate a local path when obstacle avoidance is triggered; control the catamaran to move forward according to the local path, and after successfully bypassing the obstacle and meeting the preset conditions, control the catamaran to return to the initial path.

8. A wave-adaptive catamaran unmanned surface vessel for marine mapping, characterized in that, The control method for wave-adaptive catamaran unmanned surface vessel for marine mapping as described in any one of claims 1 to 7 is applied. The catamaran unmanned vessel includes: A payload platform used to carry surveying equipment; Two pontoons are symmetrically distributed along the roll axis of the catamaran unmanned vessel and are respectively connected to the load platform through two electromagnetic damping mechanisms; Two thrusters are respectively installed at the stern of the two pontoons; A frameless motor is mounted at the bottom of the load platform. A multibeam sonar is attached to a mounting frame, and the mounting frame is connected to a connecting rod; the frameless motor is driven by the connecting rod to drive the multibeam sonar to rotate. A binocular camera and a lidar are mounted on the payload platform; Both of the pontoons and the load platform are equipped with IMUs.

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