A method and system for positioning an unmanned boat for an automatic refueling robot

Abstract

The present invention provides an unmanned ship positioning method and system for an automatic refueling robot, and relates to the technical field of data processing. The method comprises: obtaining global position information, local position information and environmental dynamic parameters of the unmanned ship, extracting static feature data and dynamic interference feature data, adaptively correcting basic positioning data, generating hull motion trend prediction information, calculating a docking preparation path of the unmanned ship according to a preset geometric model of a target docking point, generating real-time control instructions according to the docking preparation path, and when the unmanned ship arrives at the target docking point, collecting relative position information, water flow disturbance information and obstacle information of a refueling interface in real time, calculating parameters required for fine-tuning the posture of a refueling mechanical arm, and obtaining a refueling mechanical arm posture fine-tuning instruction, which is used to adjust the rotation angle, horizontal position and vertical height of the refueling mechanical arm. The present invention improves the intelligence and accuracy of refueling.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and system for positioning an unmanned boat for an automatic refueling robot. Background Art

[0002] At present, unmanned ships are widely used in water operations in the fields of environmental monitoring, logistics transportation and energy supply. For the application of automatic refueling robots, GPS and inertial navigation system (INS) are often used to achieve the positioning of unmanned ships. Through the global location data provided by GPS and the short-term high-precision positioning capability of INS, unmanned ships can plan routes and dock with automatic refueling robots. In order to further improve the positioning accuracy, high-precision maps and real-time signal calibration technologies will also be introduced in existing technologies. However, these methods still have technical limitations in certain scenarios.

[0003] In a dynamic water flow environment, such as in rivers or tidal waters, unmanned ships will be impacted by the water flow and displaced, and this displacement may not be captured by the inertial navigation system in a short period of time. Since INS usually relies on historical position data and fixed algorithms for prediction, water flow interference will cause the accumulation of predicted trajectory deviations, especially when the unmanned ship is close to the refueling station, it is difficult to achieve high-precision hull attitude adjustment. In this case, the automatic refueling robot may not be able to accurately dock with the refueling interface of the unmanned ship, resulting in operation failure or additional ship calibration time, affecting the smooth completion of the energy supply mission. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for positioning an unmanned boat for an automatic refueling robot, aiming to solve the problems mentioned in the background technology.

[0005] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0006] In a first aspect, a method for positioning an unmanned boat for an automatic refueling robot is provided, the method comprising:

[0007] Obtain the global position information, local position information and environmental dynamic parameters of the unmanned ship, and fuse them to generate basic positioning data;

[0008] Perform dynamic hierarchical analysis on the basic positioning data, extract static feature data and dynamic interference feature data, and generate multi-level interference correction parameters, where the static feature data includes the coordinate information of the fixed reference point in the water area, and the dynamic interference feature data includes the water flow speed, water flow direction, and obstacle location distribution;

[0009] According to the multi-level interference correction parameters, the basic positioning data is adaptively corrected to obtain real-time corrected positioning data;

[0010] Generate ship motion trend prediction information based on real-time corrected positioning data, including the expected position trajectory and attitude adjustment parameters of the unmanned ship;

[0011] Calculate the docking preparation path of the unmanned ship according to the motion trend prediction information and the preset geometric model of the target docking point, wherein the docking preparation path includes a multi-node dynamic path, and each node contains a posture adjustment instruction within a preset time window;

[0012] Generate real-time control instructions according to the docking preparation path, wherein the control instructions are used to adjust the movement and posture of the unmanned ship;

[0013] When the unmanned ship reaches the target docking point, it collects the relative position information of the refueling interface, water flow disturbance information and obstacle information in real time to generate fine-tuning analysis data;

[0014] According to the fine-tuning analysis data, the parameters that need to be fine-tuned for the posture of the refueling robot arm are calculated, and the refueling robot arm posture fine-tuning instructions are obtained, wherein the refueling robot arm posture fine-tuning instructions are used to dynamically adjust the rotation angle, horizontal position and vertical height of the refueling robot arm.

[0015] Preferably, the basic positioning data is dynamically layered and analyzed to extract static feature data and dynamic interference feature data, and generate multi-level interference correction parameters, wherein the static feature data includes coordinate information of fixed reference points in the water area, and the dynamic interference feature data includes water flow velocity, water flow direction, and obstacle position distribution, including:

[0016] According to the basic positioning data, the fixed reference point information is extracted to generate static feature data. The fixed reference point information includes the geographical coordinates of the fixed reference point and the water area limitation;

[0017] According to the basic positioning data, the water flow velocity, water flow direction and obstacle position distribution are extracted to generate dynamic interference feature data;

[0018] Generate multi-level interference correction parameters according to the static characteristic data and the dynamic interference characteristic data, the correction parameters including a static correction component and a dynamic correction component;

[0019] The calculation formula of the static correction component is: ,in, is the static correction component, is the number of fixed reference points, For the The weight coefficient of a fixed reference point, and Respectively The actual and theoretical positions of a fixed reference point, is the adjustment coefficient;

[0020] The calculation formula of the dynamic correction component is: ,in, is the dynamic correction component, is the water flow velocity, is the angle of water flow direction, is the distance between the obstacle and the unmanned ship, is the weight coefficient of the obstacle, , , and is the adjustment coefficient;

[0021] The calculation formula of the multi-level interference correction parameter is: ,in, is the multi-level interference correction parameter, and is the weight coefficient.

[0022] Preferably, the basic positioning data is adaptively corrected according to the multi-level interference correction parameters to obtain real-time corrected positioning data, including:

[0023] Obtaining static correction components and dynamic correction components in multi-level interference correction parameters;

[0024] Applying the static correction component to the basic positioning data, and generating preliminary corrected positioning data by correcting the position deviation between the fixed reference point and the unmanned ship;

[0025] Apply the dynamic correction component to the preliminary correction positioning data, generate dynamic correction positioning data by real-time compensation of the interference amount of water flow speed and direction, and adjust the hull posture according to the spatial position of the obstacle;

[0026] According to the dynamic correction positioning data, the global position and attitude information of the unmanned ship are updated in real time to obtain real-time correction positioning data. The real-time correction positioning data includes the position coordinates of the unmanned ship, attitude adjustment parameters and real-time dynamic adjustment information.

[0027] Preferably, the hull motion trend prediction information is generated according to the real-time corrected positioning data, and the hull motion trend prediction information includes the expected position trajectory and attitude adjustment parameters of the unmanned ship, including:

[0028] Obtain the current position coordinates and current motion state parameters of the unmanned ship in the real-time correction positioning data. The motion state parameters include the ship's speed, direction, and distance information from the target docking point;

[0029] According to the distance information between the current position of the unmanned ship and the target docking point, combined with the dynamic interference feature data, the short-term motion trajectory of the unmanned ship within the preset time window is predicted. The short-term motion trajectory consists of multiple continuous estimated position points.

[0030] According to the short-term motion trajectory, the attitude adjustment requirements of the unmanned ship are analyzed, including the real-time adjustment of the direction, angle and position of the unmanned ship, and the attitude adjustment parameters are generated;

[0031] The short-term motion trajectory and attitude adjustment parameters are combined to form the hull motion trend prediction information.

[0032] Preferably, the docking preparation path of the unmanned ship is calculated according to the motion trend prediction information and the preset geometric model of the target docking point, wherein the docking preparation path includes a multi-node dynamic path, each node includes a posture adjustment instruction within a preset time window, including:

[0033] Obtain short-term motion trajectory and posture adjustment parameters from motion trend prediction information;

[0034] Acquire preset geometric model information of the target docking point, the preset geometric model information including the spatial position of the target docking point, the docking direction and the attitude requirement for the unmanned ship;

[0035] According to the short-term motion trajectory and the preset geometric model information of the target docking point, the spatial relationship between the current position of the unmanned ship and the target docking point is analyzed, and multiple path nodes for the unmanned ship to reach the target docking point are determined;

[0036] Generate an attitude adjustment instruction within a time window for each path node, the attitude adjustment instruction including operating parameters of the unmanned ship's hull direction adjustment, speed adjustment, and position correction;

[0037] Multiple path nodes and their corresponding posture adjustment instructions are combined to form a docking preparation path.

[0038] Preferably, a real-time control instruction is generated according to the docking preparation path, wherein the control instruction is used to adjust the movement and posture of the unmanned ship, including:

[0039] Obtaining path nodes in the docking preparation path and their corresponding attitude adjustment instructions;

[0040] Analyze the deviation between the current path node and the current position of the unmanned ship, and calculate the movement adjustment amount of the unmanned ship based on the deviation. The movement adjustment parameters include the real-time correction values ​​of the movement direction, movement speed and movement angle;

[0041] Generate real-time control instructions based on the movement adjustment parameters and the posture adjustment instructions of the current path node.

[0042] Preferably, according to the fine-tuning analysis data, the parameters that need to be fine-tuned for the posture of the refueling robot arm are calculated to obtain a refueling robot arm posture fine-tuning instruction, wherein the refueling robot arm posture fine-tuning instruction is used to dynamically adjust the rotation angle, horizontal position and vertical height of the refueling robot arm, including:

[0043] Extract relative position information, water flow disturbance information and obstacle information based on fine-tuning analysis data;

[0044] According to the relative position information and the water flow disturbance information, the lateral position correction amount and the longitudinal position correction amount of the refueling robot arm are calculated to obtain the horizontal position adjustment parameters;

[0045] The calculation formula for the lateral position correction is: ,

[0046] The calculation formula for the longitudinal position correction is: ,

[0047] in, and are the lateral position correction and the longitudinal position correction respectively, and are the horizontal and vertical coordinates of the refueling interface, and are the horizontal and vertical coordinates of the end of the current refueling robot arm, is the change in water flow velocity, is the change in the angle of the water flow direction, , , and is the weight coefficient;

[0048] According to the relative position information and the obstacle information, the rotation angle adjustment amount of the refueling robot arm is calculated to obtain the rotation angle adjustment parameter;

[0049] ,in, is the rotation angle adjustment amount, and They are the direction angles of the refueling interface and the end of the refueling robot arm. is the rotational influence of the obstacle, and is the weight coefficient;

[0050] , and are the horizontal and vertical coordinates of the obstacle, respectively. is the speed of the obstacle, is the angular change of the obstacle’s moving direction, and is the weight coefficient, is the cosine function, is the inverse trigonometric function;

[0051] According to the relative position information and obstacle information, the height adjustment amount of the refueling robot arm is calculated to obtain the height adjustment parameter;

[0052] ,in, is the height adjustment amount, and They are the current heights of the refueling interface and the end of the refueling robot arm, is the dynamic height interference factor of the obstacle, , and is the adjustment coefficient;

[0053] , is the height of the obstacle, is the weight coefficient;

[0054] The horizontal position adjustment parameters, the rotation angle adjustment parameters and the height adjustment parameters are combined to generate the attitude fine-tuning instructions of the refueling robot arm, and the attitude fine-tuning instructions are used to control the motion path and attitude of the refueling robot arm.

[0055] In a second aspect, an unmanned boat positioning system for an automatic refueling robot is provided, the system comprising:

[0056] The data acquisition module is used to obtain the global position information, local position information and environmental dynamic parameters of the unmanned ship, and fuse them to generate basic positioning data;

[0057] The dynamic analysis module is used to perform dynamic hierarchical analysis on the basic positioning data, extract static feature data and dynamic interference feature data, and generate multi-level interference correction parameters, wherein the static feature data includes the coordinate information of the fixed reference point in the water area, and the dynamic interference feature data includes the water flow velocity, water flow direction, and obstacle location distribution;

[0058] A positioning correction module is used to adaptively correct the basic positioning data according to the multi-level interference correction parameters to obtain real-time corrected positioning data;

[0059] A motion prediction module is used to generate ship motion trend prediction information based on real-time correction positioning data, the ship motion trend prediction information including the expected position trajectory and attitude adjustment parameters of the unmanned ship;

[0060] A path generation module is used to calculate the docking preparation path of the unmanned ship according to the motion trend prediction information and the preset geometric model of the target docking point, wherein the docking preparation path includes a multi-node dynamic path, and each node contains a posture adjustment instruction within a preset time window;

[0061] An instruction generation module is used to generate real-time control instructions according to the docking preparation path, wherein the control instructions are used to adjust the movement and posture of the unmanned ship;

[0062] The fine-tuning analysis data acquisition module is used to collect the relative position of the refueling interface and the water flow disturbance parameters in real time when the unmanned ship arrives at the target docking point, and generate fine-tuning analysis data;

[0063] The hull fine-tuning module is used to calculate the parameters required for fine-tuning the attitude of the refueling robot arm based on the fine-tuning analysis data, and obtain the refueling robot arm attitude fine-tuning instruction, wherein the refueling robot arm attitude fine-tuning instruction is used to dynamically adjust the rotation angle, horizontal position and vertical height of the refueling robot arm.

[0064] The above solution of the present invention includes at least the following beneficial effects:

[0065] This method combines the fusion processing of global position information, local position information and environmental dynamic parameters, which can effectively make up for the problem of insufficient positioning accuracy of existing technologies in dynamic water flow environments. Through dynamic hierarchical analysis of basic positioning data, this method extracts static feature data and dynamic interference feature data, and performs targeted corrections and calibrations for stable environments and dynamic disturbances respectively. Static feature data uses the coordinate information of fixed reference points in the water area to provide an accurate global positioning benchmark, while dynamic interference feature data dynamically compensates for the impact of environmental interference on the trajectory of the unmanned ship by obtaining the water flow velocity, water flow direction and obstacle location distribution in real time.

[0066] In a dynamic water flow environment, the problem of short-term position offset caused by water flow impact, which is difficult for the inertial navigation system in the existing technology to capture, has been significantly improved through the multi-level interference correction parameters in this method. The static correction component accurately corrects the position deviation of the fixed reference point, and the dynamic correction component realizes real-time compensation and attitude adjustment of position offset by comprehensively analyzing the dynamic impact of water flow speed, direction and obstacle position on the unmanned ship. This technical setting can provide high-precision real-time correction positioning data, especially when the unmanned ship is close to the refueling station, to ensure the accurate docking of the refueling robot and the refueling interface of the unmanned ship.

[0067] In addition, this method calculates the docking preparation path of the unmanned ship by generating the prediction information of the hull motion trend and combining it with the preset geometric model of the target docking point. This setting enables the unmanned ship to optimize the motion trajectory in advance, efficiently avoid obstacles in complex water environments, and reduce the hull posture adjustment time before docking. At the same time, by collecting the relative position information, water flow disturbance information and obstacle information of the refueling interface in real time to generate fine-tuning analysis data, the dynamic adjustment capability of the refueling manipulator is further improved, ensuring the accuracy and reliability of the docking operation.

[0068] Compared with the existing technology, this method can significantly improve the positioning accuracy and attitude adjustment ability of the unmanned ship in a dynamic water flow environment, especially when close to the target docking point, it can show efficient and stable docking support functions. Through the comprehensive setting of dynamic adjustment and multi-level correction, this method solves the problem of docking failure caused by the accumulation of predicted trajectory deviations in the inertial navigation system, significantly improves the docking success rate of the unmanned ship, and reduces the additional ship calibration time, thereby ensuring the efficient completion of the energy supply mission. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 The present invention is a flowchart of a method for positioning an unmanned boat for an automatic refueling robot provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0070] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0071] like Figure 1 As shown, an embodiment of the present invention provides a method for positioning an unmanned boat for an automatic refueling robot, the method comprising:

[0072] S100, obtaining the global position information, local position information and environmental dynamic parameters of the unmanned ship, and fusing them to generate basic positioning data;

[0073] S200, dynamically analyzing the basic positioning data, extracting static feature data and dynamic interference feature data, and generating multi-level interference correction parameters, wherein the static feature data includes coordinate information of a fixed reference point in the water area, and the dynamic interference feature data includes water flow velocity, water flow direction, and obstacle position distribution;

[0074] S300, adaptively correcting the basic positioning data according to the multi-level interference correction parameters to obtain real-time corrected positioning data;

[0075] S400, generating ship motion trend prediction information according to the real-time corrected positioning data, the ship motion trend prediction information including the expected position trajectory and attitude adjustment parameters of the unmanned ship;

[0076] S500, calculating a docking preparation path of the unmanned ship according to the motion trend prediction information and the preset geometric model of the target docking point, wherein the docking preparation path includes a multi-node dynamic path, and each node includes a posture adjustment instruction within a preset time window;

[0077] S600, generating a real-time control instruction according to the docking preparation path, wherein the control instruction is used to adjust the movement and posture of the unmanned ship;

[0078] S700, when the unmanned ship arrives at the target docking point, collect the relative position information, water flow disturbance information and obstacle information of the refueling interface in real time, and generate fine-tuning analysis data;

[0079] S800. Calculate the parameters required for fine-tuning the posture of the refueling robot arm according to the fine-tuning analysis data, and obtain a refueling robot arm posture fine-tuning instruction, wherein the refueling robot arm posture fine-tuning instruction is used to dynamically adjust the rotation angle, horizontal position and vertical height of the refueling robot arm.

[0080] In an embodiment of the present invention, by fusing the global position information, local position information and environmental dynamic parameters of the unmanned ship, the deviation problem that may be caused by a single data source can be effectively reduced, especially in a complex water environment, to achieve high-precision basic positioning data generation. Through the dynamic hierarchical analysis method, static feature data and dynamic interference feature data are independently extracted to ensure that the effects of static environment and dynamic environment on the positioning of the unmanned ship are modeled and processed separately. The static feature data uses the coordinate information of a fixed reference point in the water area to provide a stable benchmark and avoid global positioning offsets caused by environmental changes. The dynamic interference feature data integrates real-time data on water velocity, water direction and obstacle location distribution, providing high dynamic adaptability for subsequent interference correction.

[0081] By introducing multi-level interference correction parameters, the static correction component accurately corrects the deviation between the unmanned ship and the fixed reference point, while the dynamic correction component compensates for the real-time impact of water flow and obstacles on positioning, thereby achieving comprehensive correction in dynamic and static environments. The real-time correction positioning data is further combined with the hull motion trend prediction information to provide the unmanned ship with a complete expected position trajectory and attitude adjustment parameters, enhancing its adaptability to future paths. In addition, the calculation of the docking preparation path ensures the accurate positioning of the unmanned ship when approaching the target docking point through a multi-node dynamic path, and the attitude adjustment instructions within the preset time window provide specific guidance for the execution of each step.

[0082] Finally, near the target docking point, the relative position information of the refueling interface, water disturbance information and obstacle information were collected in real time, and fine-tuning analysis data was generated, effectively solving the interference of complex environments on the refueling operation of the unmanned ship. Combined with the fine-tuning analysis data, the posture of the refueling manipulator was dynamically adjusted, including the rotation angle, horizontal position and vertical height, which significantly improved the accuracy and reliability of the refueling docking process.

[0083] More specifically, the global position information, local position information and environmental dynamic parameters of the unmanned ship are obtained and integrated to generate basic positioning data, including:

[0084] Global position information refers to the geographic location coordinates of the unmanned ship, which is usually obtained through a satellite positioning system, such as GPS or differential GPS. This information provides the absolute position reference point of the unmanned ship in the water. Local position information usually comes from an inertial navigation system (INS) or other high-precision sensors, such as ultrasonic sensors, lidar or visual sensors, which capture the relative position of the unmanned ship relative to nearby fixed objects (such as docks, landmarks). Environmental dynamic parameters include real-time water flow velocity, water flow direction, and the location distribution of obstacles, which are obtained through water flow sensors, cameras or sonar equipment.

[0085] Through data fusion technology, global location information is combined with local location information, and Kalman filtering or other fusion algorithms are used to remove noise and redundant information to generate high-precision basic positioning data. This fusion process can compensate for the shortcomings of a single data source. For example, GPS may jump in a multipath reflection environment, and local location information can provide a supplement; at the same time, the introduction of environmental dynamic parameters can lay the foundation for subsequent dynamic adjustments, especially in scenarios with severe water disturbances or obstacles.

[0086] More specifically, when the unmanned ship reaches the target docking point, the relative position information, water flow disturbance information and obstacle information of the refueling interface are collected in real time to generate fine-tuning analysis data, including:

[0087] When the unmanned ship approaches the target docking point, in order to ensure the accuracy of the refueling operation, it is necessary to collect data related to the target refueling interface in real time. The relative position information of the refueling interface refers to the spatial position relationship between the unmanned ship and the target interface, including the lateral, longitudinal and height distance deviations. This information can be obtained through visual sensors, lidar or ultrasonic ranging equipment.

[0088] Water flow disturbance information describes the dynamic changes in the speed and direction of the water flow in the current water area, which directly affects the stability and docking accuracy of the unmanned ship. Obstacle information includes the spatial position, size and dynamic characteristics of the obstacle, such as speed and direction, which are used to determine its potential impact on the docking operation of the unmanned ship. By integrating the above data and generating fine-tuning analysis data, the system can adjust the posture of the refueling robot arm in real time to ensure the accuracy of the docking operation.

[0089] In a preferred embodiment of the present invention, the basic positioning data is dynamically layered and analyzed to extract static feature data and dynamic interference feature data, and generate multi-level interference correction parameters, wherein the static feature data includes coordinate information of a fixed reference point in the water area, and the dynamic interference feature data includes water flow velocity, water flow direction, and obstacle position distribution, including:

[0090] According to the basic positioning data, the fixed reference point information is extracted to generate static feature data. The fixed reference point information includes the geographical coordinates of the fixed reference point and the water area limitation;

[0091] According to the basic positioning data, the water flow velocity, water flow direction and obstacle position distribution are extracted to generate dynamic interference feature data;

[0092] Generate multi-level interference correction parameters according to the static characteristic data and the dynamic interference characteristic data, the correction parameters including a static correction component and a dynamic correction component;

[0093] The calculation formula of the static correction component is: ,in, is the static correction component, is the number of fixed reference points, For the The weight coefficient of a fixed reference point, and Respectively The actual and theoretical positions of a fixed reference point, is the adjustment coefficient;

[0094] The calculation formula of the dynamic correction component is: ,in, is the dynamic correction component, is the water flow velocity, is the angle of water flow direction, is the distance between the obstacle and the unmanned ship, is the weight coefficient of the obstacle, , , and is the adjustment coefficient;

[0095] The calculation formula of the multi-level interference correction parameter is: ,in, is the multi-level interference correction parameter, and is the weight coefficient.

[0096] In the embodiment of the present invention, through the dynamic hierarchical analysis of the basic positioning data, the extraction of static feature data and dynamic interference feature data is clearer and more detailed. The static feature data is based on the geographic coordinates of the fixed reference point to ensure that the unmanned ship has accurate global positioning capabilities in a static environment. The dynamic interference feature data significantly improves the adaptability of the unmanned ship in dynamic waters through the integrated analysis of water flow speed, water flow direction and obstacle location distribution.

[0097] The generated multi-level interference correction parameters are divided into static correction components and dynamic correction components. The static correction component can accurately correct the positioning error between the fixed reference point and the unmanned ship, providing a reliable benchmark for the basic positioning data; the dynamic correction component compensates for the positioning interference caused by water flow and obstacles in real time, especially in complex dynamic environments, the unmanned ship can maintain a high positioning accuracy. The calculation of multi-level interference correction parameters not only enhances the real-time performance of the data, but also lays a solid foundation for subsequent adaptive correction.

[0098] In a preferred embodiment of the present invention, the basic positioning data is adaptively corrected according to the multi-level interference correction parameters to obtain real-time corrected positioning data, including:

[0099] Obtaining static correction components and dynamic correction components in multi-level interference correction parameters;

[0100] Applying the static correction component to the basic positioning data, and generating preliminary corrected positioning data by correcting the position deviation between the fixed reference point and the unmanned ship;

[0101] Apply the dynamic correction component to the preliminary correction positioning data, generate dynamic correction positioning data by real-time compensation of the interference amount of water flow speed and direction, and adjust the hull posture according to the spatial position of the obstacle;

[0102] According to the dynamic correction positioning data, the global position and attitude information of the unmanned ship are updated in real time to obtain real-time correction positioning data. The real-time correction positioning data includes the position coordinates of the unmanned ship, attitude adjustment parameters and real-time dynamic adjustment information.

[0103] In the embodiment of the present invention, the adaptive correction method effectively integrates multi-level interference correction parameters, and corrects the deviation of the fixed reference point through the static correction component to ensure the accuracy of the basic positioning data. At the same time, the dynamic correction component can compensate for the interference of water flow speed and direction in real time, and adjust the hull posture according to the spatial position of the obstacle to generate dynamic correction positioning data. The real-time correction positioning data contains the position coordinates, posture adjustment parameters and dynamic adjustment information of the unmanned ship, which provide high-precision input for subsequent motion trend prediction.

[0104] Through the real-time update of dynamic correction positioning data, the unmanned ship can maintain stable global position and attitude information in a dynamic environment, providing reliable support for subsequent path planning and attitude adjustment. This real-time correction method significantly improves the adaptability and stability of the unmanned ship in complex water environments.

[0105] More specifically, the static correction component is applied to the basic positioning data to generate preliminary corrected positioning data by correcting the position deviation between the fixed reference point and the unmanned ship, specifically including:

[0106] The static correction component is based on fixed reference points in the water, such as markers, buoys or preset reference points, whose positions are known and stable. The unmanned ship obtains the real-time coordinates of the reference points through global and local sensors, compares them with their theoretical positions, and calculates the deviation. For example. The static correction component further adjusts the deviation through weights and nonlinear functions to reduce the impact of errors on the global positioning of the unmanned ship.

[0107] Applying the static correction component to the basic positioning data can correct the systematic offset caused by signal interference or equipment errors in global positioning, generate more accurate preliminary corrected positioning data, and provide a stable benchmark for subsequent dynamic correction.

[0108] More specifically, the dynamic correction component is applied to the preliminary correction positioning data, and the dynamic correction positioning data is generated by real-time compensation of the interference amount of water flow speed and direction, and adjusting the hull posture according to the spatial position of the obstacle, which specifically includes:

[0109] The dynamic correction component monitors the speed and direction of the water flow in real time and calculates its impact on the position deviation of the unmanned boat. The spatial position adjustment of the obstacle is based on the relative distance and direction between the obstacle and the unmanned boat. The dynamic correction component adjusts the posture of the unmanned boat, such as the rotation angle and direction, to avoid the impact of the obstacle on the operation of the unmanned boat. This process combines water disturbance compensation with obstacle avoidance to generate dynamic correction positioning data to ensure that the unmanned boat can adapt to complex dynamic environments.

[0110] More specifically, according to the dynamic correction positioning data, the global position and attitude information of the unmanned ship is updated in real time to obtain real-time correction positioning data. The real-time correction positioning data includes the position coordinates of the unmanned ship, attitude adjustment parameters and real-time dynamic adjustment information, specifically including:

[0111] The dynamic correction positioning data not only contains the current position and attitude information of the unmanned ship, but also integrates the results of real-time dynamic adjustment. This information is generated by fusing the sensor data, static correction components and dynamic correction components of the unmanned ship to ensure that the global positioning and attitude of the unmanned ship remain highly consistent.

[0112] In the real-time correction positioning data, the position coordinates of the unmanned ship are used to describe its absolute position in the global environment; the attitude adjustment parameters describe the current direction, inclination and other states of the hull, which are used to guide the further actions of the unmanned ship; the real-time dynamic adjustment information includes compensation data for environmental factors such as water disturbances and obstacles, which provides support for subsequent motion planning and attitude adjustment. Through real-time updates, the unmanned ship can dynamically adjust its position and attitude according to environmental changes to ensure the stability and accuracy of the entire mission.

[0113] In a preferred embodiment of the present invention, the ship motion trend prediction information is generated according to the real-time corrected positioning data, and the ship motion trend prediction information includes the expected position trajectory and attitude adjustment parameters of the unmanned ship, including:

[0114] Obtain the current position coordinates and current motion state parameters of the unmanned ship in the real-time correction positioning data. The motion state parameters include the ship's speed, direction, and distance information from the target docking point;

[0115] According to the distance information between the current position of the unmanned ship and the target docking point, combined with the dynamic interference feature data, the short-term motion trajectory of the unmanned ship within the preset time window is predicted. The short-term motion trajectory consists of multiple continuous estimated position points.

[0116] According to the short-term motion trajectory, the attitude adjustment requirements of the unmanned ship are analyzed, including the real-time adjustment of the direction, angle and position of the unmanned ship, and the attitude adjustment parameters are generated;

[0117] The short-term motion trajectory and attitude adjustment parameters are combined to form the hull motion trend prediction information.

[0118] In the embodiment of the present invention, by using the real-time correction positioning data, the unmanned ship can dynamically obtain the current position coordinates and motion state parameters, where the motion state parameters include the ship speed, direction and distance information from the target docking point. Based on these data, the system can accurately analyze the path change trend of the unmanned ship and predict the short-term motion trajectory in combination with the dynamic interference feature data.

[0119] The prediction of short-term motion trajectory consists of multiple continuous estimated position points, which provides basic data support for the attitude adjustment needs of the unmanned ship. This method can dynamically adjust the direction, angle and position of the unmanned ship to ensure that the unmanned ship can stably maintain the target path in dynamic waters. By analyzing the short-term motion trajectory and attitude adjustment parameters, the generated hull motion trend prediction information has high real-time and reliability.

[0120] More specifically, based on the distance information between the current position of the unmanned ship and the target docking point, combined with the dynamic interference feature data, the short-term motion trajectory of the unmanned ship within the preset time window is predicted. The short-term motion trajectory consists of multiple continuous estimated position points, specifically including:

[0121] The distance information between the unmanned ship and the target docking point is calculated by the difference in the spatial position coordinates of the two, specifically the three-dimensional Euclidean distance; based on the distance change data at consecutive time points, the path change trend is calculated by the differential method; combined with dynamic interference feature data, such as water flow speed, water flow direction, and obstacle position, a short-term motion trajectory of the unmanned ship within the preset time window is formed, and the short-term motion trajectory consists of multiple continuous estimated position points.

[0122] The short-term motion trajectory can be calculated jointly by the kinematic equation of the unmanned ship and the environmental interference model:

[0123] In the kinematic equation of the unmanned ship, the future position of the unmanned ship is determined by its current speed, direction angle and time step, and the calculation formula is:

[0124] ,

[0125] ,in, and is the position coordinate of the unmanned ship at the current time t, and For unmanned ships in the next moment The predicted position coordinates of is the speed of the unmanned ship at the current moment, is the current direction angle of the unmanned ship and the end of the refueling robot arm, that is, the due north direction relative to the water area, is the time step.

[0126] In the environmental interference model, the impact of environmental dynamic characteristics, such as water flow speed and direction angle, on the unmanned ship is described by the following formula:

[0127] ,

[0128] ,in, and is the lateral and longitudinal displacement of the unmanned ship caused by the water disturbance, is the water disturbance weight coefficient, which is used to adjust the impact of water flow on the movement of the unmanned ship. is the water flow velocity, It is the water flow direction angle, that is, the north direction relative to the water area.

[0129] During obstacle avoidance adjustment, if the predicted trajectory overlaps with the obstacle path, adjustments are made using the path offset correction amount:

[0130] ,

[0131] ,in, and is the lateral and longitudinal position correction of the obstacle to the unmanned ship, is the obstacle avoidance weight coefficient, which is used to adjust the avoidance intensity. and are the coordinates of the obstacle, and is the current position coordinate of the unmanned ship.

[0132] In the generation of short-term motion trajectories, the above three parts are combined to predict future trajectory points:

[0133] ,

[0134] ,in, and It is the predicted position coordinates of the unmanned ship at the next moment, which integrates the results of kinematic calculation, water disturbance and obstacle avoidance.

[0135] More specifically, according to the short-term motion trajectory, the attitude adjustment requirements of the unmanned ship are analyzed, including the real-time adjustment of the direction, angle and position of the unmanned ship, and the attitude adjustment parameters are generated, including:

[0136] The attitude adjustment requirements of the unmanned ship are determined by the relative relationship between the target position point and the current position point in the short-term motion trajectory. The adjustment content includes direction adjustment, angle adjustment and position correction, specifically:

[0137] Direction adjustment: adjust the direction of the unmanned ship according to the deviation between the current heading and the target point direction;

[0138] Angle adjustment: adjust the attitude angle by calculating the roll, pitch and yaw angle differences of the unmanned ship;

[0139] Position correction: Use the difference between the estimated point and the actual point of the short-term motion trajectory to adjust the motion path of the unmanned ship in real time.

[0140] The attitude adjustment parameters are generated by integrating the above adjustment requirements, including angle change, speed adjustment and corresponding time window.

[0141] In a preferred embodiment of the present invention, a docking preparation path of the unmanned ship is calculated according to the motion trend prediction information and the preset geometric model of the target docking point, wherein the docking preparation path includes a multi-node dynamic path, each node includes a posture adjustment instruction within a preset time window, including:

[0142] Obtain short-term motion trajectory and posture adjustment parameters from motion trend prediction information;

[0143] Acquire preset geometric model information of the target docking point, the preset geometric model information including the spatial position of the target docking point, the docking direction and the attitude requirement for the unmanned ship;

[0144] According to the short-term motion trajectory and the preset geometric model information of the target docking point, the spatial relationship between the current position of the unmanned ship and the target docking point is analyzed, and multiple path nodes for the unmanned ship to reach the target docking point are determined;

[0145] Generate an attitude adjustment instruction within a time window for each path node, the attitude adjustment instruction including operating parameters of the unmanned ship's hull direction adjustment, speed adjustment, and position correction;

[0146] Multiple path nodes and their corresponding posture adjustment instructions are combined to form a docking preparation path.

[0147] In an embodiment of the present invention, the unmanned ship can accurately calculate the docking preparation path by combining the motion trend prediction information and the preset geometric model of the target docking point. The docking preparation path consists of a multi-node dynamic path, and each node contains a posture adjustment instruction within a preset time window. By analyzing the spatial relationship between the current position of the unmanned ship and the target docking point, the system can generate multiple path nodes to reach the target docking point, providing clear guidance for the dynamic adjustment of the unmanned ship.

[0148] The attitude adjustment instructions generated for each path node include operating parameters for direction adjustment, speed adjustment, and position correction, which enable the unmanned ship to gradually adjust to the target position in a dynamic environment. Finally, the docking preparation path formed by combining multiple path nodes and their corresponding attitude adjustment instructions provides technical support for the accurate docking of the unmanned ship in a complex environment.

[0149] More specifically, the preset geometric model information of the target docking point is obtained, and the preset geometric model information includes the spatial position of the target docking point, the docking direction and the attitude requirements for the unmanned ship, specifically including:

[0150] The preset geometric model information of the target docking point includes:

[0151] Spatial position: The three-dimensional coordinates of the target docking point, which is used to determine the distance and direction between the unmanned ship and the target docking point. Docking direction: Describes the direction of the target docking point, such as the direction vector of the interface.

[0152] Attitude requirements: specific docking attitude constraints for the unmanned ship, such as specific restrictions on the angle, roll, pitch, and yaw of the unmanned ship.

[0153] After obtaining this information, the system can clarify the geometric characteristics of the target docking point and provide basic data for path planning and posture adjustment.

[0154] More specifically, according to the short-term motion trajectory and the preset geometric model information of the target docking point, the spatial relationship between the current position of the unmanned ship and the target docking point is analyzed, and multiple path nodes for the unmanned ship to reach the target docking point are determined, including:

[0155] Spatial relationship analysis includes:

[0156] Position relationship: Calculate the spatial displacement vector from the current point to the target point, and divide the path into several path nodes according to the short-term motion trajectory. Each path node is the intermediate position that the unmanned ship needs to reach during the movement.

[0157] Attitude relationship: Analyze the angle difference between the current attitude and the target docking attitude, generate attitude adjustment parameters, and gradually adjust the unmanned ship to the target attitude.

[0158] The path nodes are determined by combining the short-term motion trajectory with the target geometric model information. Each node clarifies the position information and attitude adjustment requirements of the unmanned ship.

[0159] More specifically, an attitude adjustment instruction within a time window is generated for each path node. The attitude adjustment instruction includes operating parameters for the unmanned ship's hull direction adjustment, speed adjustment, and position correction, specifically including:

[0160] Direction adjustment: The heading adjustment of the unmanned ship to ensure that the movement direction of each node is consistent with the target direction.

[0161] Speed ​​adjustment: Dynamically adjust the movement speed of the unmanned ship according to the distance between nodes and the time required.

[0162] Position correction: The correction amount is calculated using the deviation between the current node and the target node to ensure that the unmanned ship passes through the path node accurately.

[0163] The attitude adjustment command integrates the above parameters to provide a node-by-node operation plan for the unmanned ship, ultimately enabling the unmanned ship to reach the target docking point according to the planned path and meet the attitude requirements for docking.

[0164] In a preferred embodiment of the present invention, a real-time control instruction is generated according to the docking preparation path, wherein the control instruction is used to adjust the movement and posture of the unmanned ship, including:

[0165] Obtaining path nodes in the docking preparation path and their corresponding attitude adjustment instructions;

[0166] Analyze the deviation between the current path node and the current position of the unmanned ship, and calculate the movement adjustment amount of the unmanned ship based on the deviation. The movement adjustment parameters include the real-time correction values ​​of the movement direction, movement speed and movement angle;

[0167] Generate real-time control instructions based on the movement adjustment parameters and the posture adjustment instructions of the current path node.

[0168] In an embodiment of the present invention, the generation of real-time control instructions is completed by analyzing the path nodes of the docking preparation path and their attitude adjustment instructions. The system can analyze the deviation between the current path node and the current position of the unmanned ship in real time, and calculate the movement adjustment parameters based on the deviation, including the real-time correction values ​​of the movement direction, movement speed and movement angle. Combining the movement adjustment parameters and the attitude adjustment instructions of the current path node, the generated real-time control instructions can dynamically adjust the movement and attitude of the unmanned ship.

[0169] This real-time control method ensures the unmanned ship's high-precision path following capability in a dynamic environment, while being able to promptly respond to deviations caused by environmental changes, ensuring the unmanned ship's stability and docking efficiency.

[0170] In a preferred embodiment of the present invention, the parameters that need to be fine-tuned for the posture of the refueling robot arm are calculated according to the fine-tuning analysis data, and a refueling robot arm posture fine-tuning instruction is obtained, wherein the refueling robot arm posture fine-tuning instruction is used to dynamically adjust the rotation angle, horizontal position and vertical height of the refueling robot arm, including:

[0171] Extract relative position information, water flow disturbance information and obstacle information based on fine-tuning analysis data;

[0172] According to the relative position information and the water flow disturbance information, the lateral position correction amount and the longitudinal position correction amount of the refueling robot arm are calculated to obtain the horizontal position adjustment parameters;

[0173] The calculation formula for the lateral position correction is: ,

[0174] The calculation formula for the longitudinal position correction is: ,

[0175] in, and are the lateral position correction and the longitudinal position correction respectively, and are the horizontal and vertical coordinates of the refueling interface, and are the horizontal and vertical coordinates of the end of the current refueling robot arm, is the change in water flow velocity, is the change in the angle of the water flow direction, , , and is the weight coefficient;

[0176] According to the relative position information and the obstacle information, the rotation angle adjustment amount of the refueling robot arm is calculated to obtain the rotation angle adjustment parameter;

[0177] ,in, is the rotation angle adjustment amount, and They are the direction angles of the refueling interface and the end of the refueling robot arm. is the rotational influence of the obstacle, and is the weight coefficient;

[0178] , and are the horizontal and vertical coordinates of the obstacle, respectively. is the speed of the obstacle, is the angular change of the obstacle’s moving direction, and is the weight coefficient, is the cosine function, is the inverse trigonometric function;

[0179] According to the relative position information and obstacle information, the height adjustment amount of the refueling robot arm is calculated to obtain the height adjustment parameter;

[0180] ,in, is the height adjustment amount, and They are the current heights of the refueling interface and the end of the refueling robot arm, is the dynamic height interference factor of the obstacle, , and is the adjustment coefficient;

[0181] , is the height of the obstacle, is the weight coefficient;

[0182] The horizontal position adjustment parameters, the rotation angle adjustment parameters and the height adjustment parameters are combined to generate the attitude fine-tuning instructions of the refueling robot arm, and the attitude fine-tuning instructions are used to control the motion path and attitude of the refueling robot arm.

[0183] In the embodiment of the present invention, by collecting the relative position information, water flow disturbance information and obstacle information of the refueling interface in real time, the system can generate fine-tuning analysis data to provide support for the posture adjustment of the refueling robot arm. Combined with the fine-tuning analysis data, the calculated lateral position correction amount and longitudinal position correction amount can accurately adjust the horizontal position of the refueling robot arm.

[0184] At the same time, based on the relative position information and obstacle information, the system can dynamically calculate the rotation angle adjustment to ensure that the direction of the refueling robot arm is always consistent with the refueling interface. Combining the height interference factor of the water flow disturbance information and the obstacle information, the height adjustment can dynamically adjust the vertical height of the refueling robot arm.

[0185] Finally, by combining the horizontal position adjustment parameters, rotation angle adjustment parameters and height adjustment parameters, the generated attitude fine-tuning instructions can control the motion path and attitude of the refueling robot arm in real time, significantly improving the accuracy and reliability of refueling docking.

[0186] In a preferred embodiment of the present invention, an unmanned boat positioning system for an automatic refueling robot comprises:

[0187] The data acquisition module is used to obtain the global position information, local position information and environmental dynamic parameters of the unmanned ship, and fuse them to generate basic positioning data;

[0188] The dynamic analysis module is used to perform dynamic hierarchical analysis on the basic positioning data, extract static feature data and dynamic interference feature data, and generate multi-level interference correction parameters, wherein the static feature data includes the coordinate information of the fixed reference point in the water area, and the dynamic interference feature data includes the water flow velocity, water flow direction, and obstacle location distribution;

[0189] A positioning correction module is used to adaptively correct the basic positioning data according to the multi-level interference correction parameters to obtain real-time corrected positioning data;

[0190] A motion prediction module is used to generate ship motion trend prediction information based on real-time correction positioning data, the ship motion trend prediction information including the expected position trajectory and attitude adjustment parameters of the unmanned ship;

[0191] A path generation module is used to calculate the docking preparation path of the unmanned ship according to the motion trend prediction information and the preset geometric model of the target docking point, wherein the docking preparation path includes a multi-node dynamic path, and each node contains a posture adjustment instruction within a preset time window;

[0192] An instruction generation module is used to generate real-time control instructions according to the docking preparation path, wherein the control instructions are used to adjust the movement and posture of the unmanned ship;

[0193] The fine-tuning analysis data acquisition module is used to collect the relative position of the refueling interface and the water flow disturbance parameters in real time when the unmanned ship arrives at the target docking point, and generate fine-tuning analysis data;

[0194] The hull fine-tuning module is used to calculate the parameters required for fine-tuning the attitude of the refueling robot arm based on the fine-tuning analysis data, and obtain the refueling robot arm attitude fine-tuning instruction, wherein the refueling robot arm attitude fine-tuning instruction is used to dynamically adjust the rotation angle, horizontal position and vertical height of the refueling robot arm.

[0195] It should be noted that the system is a system corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0196] The embodiment of the present invention further provides a computing device, including: a processor, a memory storing a computer program, and when the computer program is executed by the processor, the above method is executed. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0197] The embodiment of the present invention further provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the above method. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0198] The above are preferred embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for positioning an unmanned boat for an automatic refueling robot, characterized in that: The method comprises: Obtain the global position information, local position information and environmental dynamic parameters of the unmanned ship, and fuse them to generate basic positioning data; Perform dynamic hierarchical analysis on the basic positioning data, extract static feature data and dynamic interference feature data, and generate multi-level interference correction parameters, where the static feature data includes the coordinate information of the fixed reference point in the water area, and the dynamic interference feature data includes the water flow speed, water flow direction, and obstacle location distribution; According to the multi-level interference correction parameters, the basic positioning data is adaptively corrected to obtain real-time corrected positioning data; Generate ship motion trend prediction information based on real-time corrected positioning data, including the expected position trajectory and attitude adjustment parameters of the unmanned ship; Calculate the docking preparation path of the unmanned ship according to the motion trend prediction information and the preset geometric model of the target docking point, wherein the docking preparation path includes a multi-node dynamic path, and each node contains a posture adjustment instruction within a preset time window; Generate real-time control instructions according to the docking preparation path, wherein the control instructions are used to adjust the movement and posture of the unmanned ship; When the unmanned ship reaches the target docking point, it collects the relative position information of the refueling interface, water flow disturbance information and obstacle information in real time to generate fine-tuning analysis data; According to the fine-tuning analysis data, the parameters that need to be fine-tuned for the posture of the refueling robot arm are calculated to obtain a fine-tuning instruction for the posture of the refueling robot arm, wherein the fine-tuning instruction for the posture of the refueling robot arm is used to dynamically adjust the rotation angle, horizontal position and vertical height of the refueling robot arm; Perform dynamic hierarchical analysis on the basic positioning data, extract static feature data and dynamic interference feature data, and generate multi-level interference correction parameters. The static feature data includes the coordinate information of fixed reference points in the water area, and the dynamic interference feature data includes water flow velocity, water flow direction, and obstacle location distribution, including: According to the basic positioning data, the fixed reference point information is extracted to generate static feature data. The fixed reference point information includes the geographical coordinates of the fixed reference point and the water area limitation; According to the basic positioning data, the water flow velocity, water flow direction and obstacle position distribution are extracted to generate dynamic interference feature data; Generate multi-level interference correction parameters according to the static characteristic data and the dynamic interference characteristic data, the correction parameters including a static correction component and a dynamic correction component; The calculation formula of the static correction component is: ,in, is the static correction component, is the number of fixed reference points, For the The weight coefficient of a fixed reference point, and Respectively The actual and theoretical positions of a fixed reference point, is the adjustment coefficient; The calculation formula of the dynamic correction component is: ,in, is the dynamic correction component, is the water flow velocity, is the angle of water flow direction, is the distance between the obstacle and the unmanned ship, is the weight coefficient of the obstacle, , , and is the adjustment coefficient; The calculation formula of multi-level interference correction parameters is: ,in, is the multi-level interference correction parameter, and is the weight coefficient.

2. A method for positioning an unmanned boat for an automatic refueling robot according to claim 1, characterized in that: Adaptively correct the basic positioning data according to the multi-level interference correction parameters to obtain real-time corrected positioning data, including: Obtaining static correction components and dynamic correction components in multi-level interference correction parameters; Applying the static correction component to the basic positioning data, and generating preliminary corrected positioning data by correcting the position deviation between the fixed reference point and the unmanned ship; Apply the dynamic correction component to the preliminary correction positioning data, generate dynamic correction positioning data by real-time compensation of the interference amount of water flow speed and direction, and adjust the hull posture according to the spatial position of the obstacle; According to the dynamic correction positioning data, the global position and attitude information of the unmanned ship are updated in real time to obtain real-time correction positioning data. The real-time correction positioning data includes the position coordinates of the unmanned ship, attitude adjustment parameters and real-time dynamic adjustment information.

3. A method for positioning an unmanned boat for an automatic refueling robot according to claim 2, characterized in that: The ship motion trend prediction information is generated based on the real-time corrected positioning data. The ship motion trend prediction information includes the expected position trajectory and attitude adjustment parameters of the unmanned ship, including: Obtain the current position coordinates and current motion state parameters of the unmanned ship in the real-time correction positioning data. The motion state parameters include the ship's speed, direction, and distance information from the target docking point; According to the distance information between the current position of the unmanned ship and the target docking point, combined with the dynamic interference feature data, the short-term motion trajectory of the unmanned ship within the preset time window is predicted. The short-term motion trajectory consists of multiple continuous estimated position points. According to the short-term motion trajectory, the attitude adjustment requirements of the unmanned ship are analyzed, including the real-time adjustment of the direction, angle and position of the unmanned ship, and the attitude adjustment parameters are generated; The short-term motion trajectory and attitude adjustment parameters are combined to form the hull motion trend prediction information.

4. A method for positioning an unmanned boat for an automatic refueling robot according to claim 3, characterized in that: According to the motion trend prediction information and the preset geometric model of the target docking point, the docking preparation path of the unmanned ship is calculated, wherein the docking preparation path includes a multi-node dynamic path, each node contains a posture adjustment instruction within a preset time window, including: Obtain short-term motion trajectory and posture adjustment parameters from motion trend prediction information; Acquire preset geometric model information of the target docking point, the preset geometric model information including the spatial position of the target docking point, the docking direction and the attitude requirement for the unmanned ship; According to the short-term motion trajectory and the preset geometric model information of the target docking point, the spatial relationship between the current position of the unmanned ship and the target docking point is analyzed, and multiple path nodes for the unmanned ship to reach the target docking point are determined; Generate an attitude adjustment instruction within a time window for each path node, the attitude adjustment instruction including operating parameters of the unmanned ship's hull direction adjustment, speed adjustment, and position correction; Multiple path nodes and their corresponding posture adjustment instructions are combined to form a docking preparation path.

5. A method for positioning an unmanned boat for an automatic refueling robot according to claim 4, characterized in that: Generate real-time control instructions based on the docking preparation path, where the control instructions are used to adjust the movement and posture of the unmanned ship, including: Obtaining path nodes in the docking preparation path and their corresponding attitude adjustment instructions; Analyze the deviation between the current path node and the current position of the unmanned ship, and calculate the movement adjustment amount of the unmanned ship based on the deviation. The movement adjustment parameters include the real-time correction values ​​of the movement direction, movement speed and movement angle; Generate real-time control instructions based on the movement adjustment parameters and the posture adjustment instructions of the current path node.

6. An unmanned boat positioning system for an automatic refueling robot, characterized in that: Applied in the method according to any one of claims 1 to 5, the system comprises: The data acquisition module is used to obtain the global position information, local position information and environmental dynamic parameters of the unmanned ship, and fuse them to generate basic positioning data; The dynamic analysis module is used to perform dynamic hierarchical analysis on the basic positioning data, extract static feature data and dynamic interference feature data, and generate multi-level interference correction parameters, wherein the static feature data includes the coordinate information of the fixed reference point in the water area, and the dynamic interference feature data includes the water flow velocity, water flow direction, and obstacle location distribution; A positioning correction module is used to adaptively correct the basic positioning data according to the multi-level interference correction parameters to obtain real-time corrected positioning data; A motion prediction module is used to generate ship motion trend prediction information based on real-time correction positioning data, and the ship motion trend prediction information includes the expected position trajectory and attitude adjustment parameters of the unmanned ship; A path generation module is used to calculate the docking preparation path of the unmanned ship according to the motion trend prediction information and the preset geometric model of the target docking point, wherein the docking preparation path includes a multi-node dynamic path, and each node contains a posture adjustment instruction within a preset time window; An instruction generation module is used to generate real-time control instructions according to the docking preparation path, wherein the control instructions are used to adjust the movement and posture of the unmanned ship; The fine-tuning analysis data acquisition module is used to collect the relative position of the refueling interface and the water flow disturbance parameters in real time when the unmanned ship arrives at the target docking point, and generate fine-tuning analysis data; The hull fine-tuning module is used to calculate the parameters required for fine-tuning the attitude of the refueling robot arm based on the fine-tuning analysis data, and obtain the refueling robot arm attitude fine-tuning instruction, wherein the refueling robot arm attitude fine-tuning instruction is used to dynamically adjust the rotation angle, horizontal position and vertical height of the refueling robot arm.

7. A computing device, characterized in that include: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by one or more processors, the one or more processors implement the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: A program is stored in the computer-readable storage medium, and when the program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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