A deep water co-operation platform dynamic relative positioning system and method

Abstract

The application discloses a deep water cooperative operation platform dynamic relative positioning system and method, and relates to the field of deep sea engineering.The system comprises: a first operation platform, which is provided with a first beacon and a second beacon symmetrically along a central axis; a second operation platform, which is provided with a third beacon and a fourth beacon symmetrically along a central axis; a dynamic positioning network, which is formed by continuously performing bidirectional ranging between the beacons; an edge computing unit arranged on the second operation platform, which comprises a data acquisition interface for receiving original distance observation data, attitude data and depth data of the beacons and a data processing mechanism; and the data processing mechanism unifies all beacon coordinates to a horizontal coordinate system by fusing ranging data, depth data and attitude data, and calculates a three-dimensional relative position and a relative yaw angle between the two platforms.The system and method utilize multi-source data fusion and edge computing, can overcome the problem of deep water transmission delay, and can realize high-precision real-time feedback of the dynamic relative position and pose between the cooperative operation platforms.

Description

Technical Field

[0001] This specification relates to the field of deep-sea engineering technology, and in particular to a dynamic relative positioning system and method for a deep-sea collaborative operation platform. Background Technology

[0002] With the development of deep-sea exploration and development, operations such as deep-sea shipwreck salvage and residual oil recovery are increasing. In deep-water environments, working-class ROVs (Remotely Operated Vehicles) and dedicated pumping and storage shuttle platforms are typically used in conjunction with each other. Due to the complex operating environment, the unstable structure of the shipwreck, and the potential for fluid interference between platforms, ensuring a safe distance between the operating platforms is crucial.

[0003] In existing technologies, underwater positioning largely relies on ultra-short baseline (USBL) or long baseline (LBL) systems, using a mother ship to achieve absolute positioning of underwater targets. However, in deep-sea environments, the transmission of acoustic signals from the surface to the underwater platform and back incurs significant delays, failing to meet the requirements for real-time, high-rate relative position monitoring. Furthermore, small errors in absolute position are amplified when calculating relative position, resulting in inaccurate determination of the relative bearing and distance between platforms. Current technologies lack an effective means to directly establish stable, real-time, and high-precision relative positional relationships between deep-sea operating platforms. Summary of the Invention

[0004] In view of the shortcomings of the prior art, one objective of this specification is to provide a dynamic relative positioning system and method for deep-water collaborative operation platforms, which can overcome the problem of deep-water transmission delay and provide real-time and accurate feedback of the dynamic relative position information between collaborative operation platforms.

[0005] To achieve the above objectives, this specification provides a dynamic relative positioning system for a deep-water collaborative operation platform, comprising:

[0006] A first working platform is provided, on which a first beacon and a second beacon are provided; the first beacon and the second beacon are symmetrically arranged about the central axis of the first working platform;

[0007] The second working platform is equipped with a third beacon and a fourth beacon. The third beacon and the fourth beacon are symmetrically arranged about the central axis of the second working platform. The first beacon, the second beacon, the third beacon and the fourth beacon are all intelligent beacons with built-in attitude sensors and pressure sensors, which can measure their own attitude data and depth data in real time.

[0008] A dynamic positioning network is used to generate raw distance observation data; the dynamic positioning network consists of continuous bidirectional ranging between the first beacon, the second beacon, the third beacon, and the fourth beacon;

[0009] The edge computing unit, located on the second operating platform, includes a data acquisition interface and a data processing mechanism. The data acquisition interface is used to locally receive the raw distance observation data, attitude data, and depth data of all beacons in the dynamic positioning network. The data processing mechanism includes a data preprocessing module, a fusion calculation module, and an adjustment optimization module. The data preprocessing module is used to preprocess the raw distance observation data using a one-dimensional data filtering algorithm to obtain preprocessed ranging data. The fusion calculation module is used to calculate the three-dimensional relative position and relative yaw angle between the first and second operating platforms based on the preprocessed ranging data, the attitude data, and the depth data of each beacon. The adjustment optimization module is used to optimize the calculation results using the least squares method.

[0010] In a preferred embodiment, the built-in attitude sensor includes an inertial measurement unit; the attitude data includes roll angle φ, pitch angle θ, and yaw angle. The three-dimensional relative position is represented as (ΔX, ΔY, ΔZ), and the relative yaw angle is... .

[0011] In a preferred embodiment, the first working platform is a liquid extraction and storage shuttle platform; the second working platform is a deep-water ROV platform; and an independent right-handed coordinate system is established for each of the first and second working platforms.

[0012] In a preferred embodiment, for the coordinate system of the first working platform, the center of the first working platform is taken as the origin, the direction from the centerline of the first working platform pointing bow is taken as the X-axis, the direction perpendicular to the deck upwards is taken as the Z-axis, and the Y-axis is determined by the right-hand rule; the coordinates of the first beacon and the second beacon in the coordinate system of the first working platform are respectively (d P ,0,0) T and (-d) P ,0,0) T , where d P The distance from the first or second beacon to the central axis of the first working platform;

[0013] For the second work platform coordinate system, the center of the second work platform is taken as the origin, the direction from the centerline of the second work platform pointing bow is taken as the X-axis, the direction perpendicular to the deck upwards is taken as the Z-axis, and the Y-axis is determined by the right-hand rule; the coordinates of the third and fourth beacons in the second work platform coordinate system are respectively (d R ,0,0) T and (-d) R ,0,0) T , where d RThis refers to the distance from the third or fourth beacon to the central axis of the second working platform.

[0014] In a preferred embodiment, the fusion calculation module is used to calculate the three-dimensional coordinates of each beacon in a unified horizontal coordinate system based on the preprocessed ranging data, depth data, and attitude data of each beacon, and then calculate the three-dimensional relative position and relative yaw angle between the first operating platform and the second operating platform; the calculation of the three-dimensional coordinates of each beacon in the unified horizontal coordinate system includes:

[0015] Step 1: Based on the attitude data of each beacon, calculate the platform attitude of the platform where it is located; for two beacons on the same platform, take the arithmetic mean of the roll angle and pitch angle in their attitude data respectively, and use them as the roll angle φ and pitch angle θ of the platform; process the heading angle in their attitude data through geometric relationship or averaging, and use it as the heading angle ψ of the platform.

[0016] Step 2: Construct the rotation matrix from each platform coordinate system to the horizontal coordinate system; for any platform, its rotation matrix R is calculated by the following formula:

[0017]

[0018] in, , , These are the basic rotation matrices about the X, Y, and Z axes, respectively;

[0019] Step 3: Establish the relationship between the beacon coordinates and the platform center coordinates in the horizontal coordinate system; let the coordinates of the platform center in the horizontal coordinate system be... Then the coordinates on the platform are The coordinates of the beacon in the horizontal coordinate system for:

[0020]

[0021] Step 4: Establish a depth observation equation by combining beacon depth data; the depth value H measured by the beacon's pressure sensor (with sea level as a reference, downward as positive) and the Z coordinate in the horizontal coordinate system. satisfy:

[0022]

[0023] Step 5: Establish the distance observation equation based on the distance measurement data; for any two beacons i and j, their coordinates in the horizontal coordinate system... and satisfy:

[0024]

[0025] in This is the preprocessed ranging data;

[0026] Step 6: Solve the depth observation equation from Step 4 and the distance observation equation from Step 5 simultaneously to find the coordinates T of each platform center in the horizontal coordinate system. Then, obtain the three-dimensional coordinates of all beacons in the horizontal coordinate system through Step 3.

[0027] In a preferred embodiment, calculating the three-dimensional relative position and relative yaw angle between the first working platform and the second working platform includes:

[0028] Let the coordinates of the center of the first working platform in the horizontal coordinate system be... The coordinates of the center of the second working platform in the horizontal coordinate system are: Then the three-dimensional relative positions are:

[0029]

[0030] Let the rotation matrix from the first working platform coordinate system to the horizontal coordinate system be... The rotation matrix from the second working platform coordinate system to the horizontal coordinate system is: Then the relative rotation matrix from the second working platform coordinate system to the first working platform coordinate system is... for:

[0031]

[0032] from Extracting the rotation angle around the Z-axis gives the relative yaw angle. Calculated using the following formula:

[0033]

[0034] Where atan2 is the arctangent function in the fourth quadrant. Representation matrix The element in the i-th row and j-th column.

[0035] As a preferred embodiment, the one-dimensional data filtering algorithm includes a moving average filtering algorithm or a Kalman filtering algorithm.

[0036] As a preferred embodiment, it also includes an output and alarm unit for displaying the real-time relative distance and orientation information optimized by the adjustment and optimization module, and issuing an alarm when the relative distance is less than a safety threshold.

[0037] This application also provides a dynamic relative positioning method for a deep-water collaborative operation platform. The method uses the system described in any of the above embodiments for positioning, and the method includes the following steps:

[0038] Step S10: Operation preparation; Perform multi-beam precision measurement on the operation area to establish the operation background field, and deploy the first and second operation platforms to the operation area;

[0039] Step S20: Construct the dynamic positioning network in the work area;

[0040] Step S30: Data acquisition; The edge computing unit locally receives all raw distance observation data, depth data and attitude data of each beacon generated in the dynamic positioning network;

[0041] Step S40: Data preprocessing; Perform one-dimensional data filtering on the original distance observation data to obtain preprocessed distance measurement data;

[0042] Step S50: Relative pose fusion calculation; Based on the preprocessed ranging data, depth data and attitude data, calculate the three-dimensional coordinates of each beacon in the horizontal coordinate system, and then obtain the three-dimensional relative position and relative yaw angle between the first working platform and the second working platform;

[0043] Step S60: Geometric adjustment optimization; The solution results are optimized using the least squares method to obtain the optimal relative position and orientation estimates;

[0044] Step S70: Output and monitor the optimized relative pose information.

[0045] Beneficial effects:

[0046] The dynamic relative positioning system for deep-water collaborative operation platforms provided in this embodiment integrates intelligent beacons and edge computing capabilities. By deploying intelligent beacons with built-in attitude sensors and pressure sensors on the first and second collaborative operation platforms, a local dynamic positioning network is constructed. The edge computing unit is used to perform localized, real-time multi-source data fusion processing and nonlinear optimal estimation to achieve high-frequency, real-time, and accurate calculation of the relative position and orientation between platforms.

[0047] The core advantage of this invention lies in its ability to effectively overcome the problems of excessive acoustic positioning error, transmission delay, and difficulty in solving complex geometric configurations caused by long distances in deep water through deep fusion of ranging, depth, and attitude data, and by adopting nonlinear least squares optimization and overall adjustment algorithms. It directly provides high-update-rate and high-precision three-dimensional relative position and relative heading information, and can provide real-time and accurate feedback on the dynamic relative pose between collaborative operation platforms, significantly improving the safety, autonomy, and operational efficiency of deep-water collaborative operations.

[0048] This invention achieves high-precision, high-update-rate relative positioning between deep-water collaborative operation platforms by organically combining intelligent beacon multi-source sensing, edge computing, nonlinear fusion calculation, and overall adjustment optimization. The system configuration is optimized, the algorithm is complete, and it has good engineering application prospects. Specifically, it has the following advantages:

[0049] 1. High accuracy and completeness: Through the deep fusion of ranging, depth, and full attitude data, each operating platform can calculate the complete three-dimensional relative position in a unified horizontal coordinate system using only two beacons. and relative yaw angle The method further optimizes the geometric consistency among multiple observations by combining overall least squares adjustment, and its accuracy is significantly better than that of traditional methods.

[0050] 2. Extremely high real-time performance: By utilizing edge computing units to process all raw data locally at the work site, the long latency caused by uploading data to the surface vessel is completely avoided, enabling high-frequency, real-time updates and calculations of relative pose.

[0051] 3. High reliability: Built-in one-dimensional data filtering, robust nonlinear iterative solution and overall adjustment algorithm can effectively suppress interference, noise and observation jumps in complex underwater acoustic environments, ensuring the stability, reliability and continuity of output data.

[0052] 4. Configuration and cost optimization: Using only four smart beacons and one edge computing unit, without relying on a surface mother ship for complex calculations, the system architecture is simple, reducing system complexity and deployment costs, and making it more conducive to engineering applications and promotion.

[0053] Specific embodiments of the present invention are disclosed in detail with reference to the following description and accompanying drawings, indicating how the principles of the invention can be employed. It should be understood that the embodiments of the present invention are not limited in scope as a result.

[0054] Features described and / or illustrated for one embodiment may be used in the same or similar manner in one or more other embodiments, combined with features in other embodiments, or substituted for features in other embodiments.

[0055] It should be emphasized that the term "including / comprises" as used herein refers to the presence of a feature, whole, step, or component, but does not exclude the presence or addition of one or more other features, wholes, steps, or components. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 This is a simplified structural diagram of a dynamic relative positioning system for a deep-water collaborative operation platform provided in this embodiment;

[0058] Figure 2 This is a schematic diagram of the system in this invention in the scenario of deep-water shipwreck residual oil recovery;

[0059] Figure 3 This is a distance measurement link diagram consisting of two operating platforms and their beacons in this invention;

[0060] Figure 4 This is a flowchart illustrating the steps of a dynamic relative positioning method for a deep-water collaborative operation platform provided in this embodiment.

[0061] Explanation of reference numerals in the attached figures:

[0062] 1. Shipwreck; 2. Seabed; 3. Mother ship; 4. First working platform; 5. Second working platform; A1. First beacon; A2. Second beacon; B1. Third beacon; B2. Fourth beacon; 6. Edge computing unit; 7. Data processing mechanism; 71. Data preprocessing module; 72. Fusion calculation module; 73. Adjustment optimization module; 8. Dynamic positioning network; 9. Output and alarm unit; 10. Residual oil. Detailed Implementation

[0063] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this invention.

[0064] It should be noted that when an element is referred to as being "set on" another element, it can be directly on the other element or may be interposed with another element. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or may be interposed with another element. The terms "vertical," "horizontal," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only possible implementations.

[0065] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0066] Please see Figures 1 to 3 This application provides a dynamic relative positioning system for a deep-water collaborative operation platform, comprising: a first operation platform 4, a second operation platform 5, a dynamic positioning network 8, and an edge computing unit 6.

[0067] The first working platform 4 is symmetrically equipped with a first beacon A1 and a second beacon A2 along its central axis. The second working platform 5 is symmetrically equipped with a third beacon B1 and a fourth beacon B2 along its central axis. The first beacon A1, second beacon A2, third beacon B1, and fourth beacon B2 are all intelligent beacons with built-in attitude and pressure sensors, possessing long baseline (LBL) ranging and communication capabilities, and capable of measuring their own attitude and depth data in real time. A dynamic positioning network 8 is used to generate raw distance observation data. The dynamic positioning network 8 consists of continuous bidirectional ranging between the first beacon A1, second beacon A2, third beacon B1, and fourth beacon B2.

[0068] Edge computing unit 6 is mounted on the second operating platform 5. Edge computing unit 6 includes a data acquisition interface (e.g., an LBL transducer deployment and retraction mechanism) and a data processing mechanism 7. The data acquisition interface is used to locally receive raw distance observation data of all beacons in the dynamic positioning network 8, as well as attitude and depth data of each beacon. The data processing mechanism 7 includes a data preprocessing module 71, a fusion calculation module 72, and an adjustment optimization module 73.

[0069] The data preprocessing module 71 preprocesses the raw distance observation data using a one-dimensional data filtering algorithm to remove signal jumps and noise interference, resulting in high-quality preprocessed ranging data. The fusion calculation module 72, the core of the system, calculates the three-dimensional relative position and relative yaw angle between the first working platform 4 and the second working platform 5 based on the preprocessed ranging data, the attitude data of each beacon, and the depth data, through spatial geometric fusion. The adjustment optimization module 73 further optimizes the fusion calculation results using the overall least squares adjustment method, comprehensively utilizing all observation information, eliminating error inconsistencies, and obtaining the statistically optimal relative pose estimate (relative position and azimuth estimate), ensuring that multiple sets of data results satisfy geometric relationships.

[0070] The dynamic relative positioning system for deep-water collaborative operations provided in this embodiment is innovative in that it combines multi-source sensing, local edge computing, and advanced nonlinear estimation theory. By deploying intelligent beacons on the collaborative operations platform to construct a self-organizing network, and executing a complete processing chain locally at the ROV end—from data fusion and nonlinear iterative calculation to overall adjustment—it achieves high-precision, high-real-time, and high-reliability independent calculation of the relative pose between platforms. This completely overcomes the technical bottlenecks of traditional methods, such as reliance on surface mother ships, large transmission delays, and the inability to directly obtain accurate relative headings, providing crucial technical support for precise collaborative deep-water operations.

[0071] This invention achieves high-precision, high-update-rate relative positioning between deep-water collaborative operation platforms by organically combining intelligent beacon multi-source sensing, edge computing, nonlinear fusion calculation, and overall adjustment optimization. This effectively improves operational safety, autonomy, and efficiency. The system configuration is optimized, the algorithm is complete, and it has promising engineering application prospects. Specifically, it has the following advantages:

[0072] 1. High accuracy and completeness: Through the deep fusion of ranging, depth, and full attitude data, each operating platform can calculate the complete three-dimensional relative position in a unified horizontal coordinate system using only two beacons. and relative yaw angle The method further optimizes the geometric consistency among multiple observations by combining overall least squares adjustment, and its accuracy is significantly better than that of traditional methods.

[0073] 2. Extremely high real-time performance: By utilizing the edge computing unit 6 to process all raw data locally at the work site, the long latency caused by uploading data to the surface vessel is completely avoided, and high-frequency, real-time updates and calculations of relative pose are achieved.

[0074] 3. High reliability: Built-in one-dimensional data filtering, robust nonlinear iterative solution and overall adjustment algorithm can effectively suppress interference, noise and observation jumps in complex underwater acoustic environments, ensuring the stability, reliability and continuity of output data.

[0075] 4. Configuration and cost optimization: Using only four smart beacons and one edge computing unit, it does not rely on a surface mother ship for complex calculations. The system architecture is simple, reducing system complexity and deployment costs, and is more conducive to engineering applications and promotion.

[0076] Specifically, the first operating platform 4 is a liquid extraction and storage shuttle platform, and the second operating platform 5 is a deep-water ROV platform. For example... Figure 2 The diagram illustrates the operational scenario of this invention. A sunken ship 1 is situated on the seabed 2 in deep water, containing residual oil 10. A mother ship 3 is suspended above the water surface. A first operating platform 4 and a second operating platform 5 work collaboratively near the sunken ship 1. The mother ship 3 and the underwater system are connected via an acoustic communication link.

[0077] like Figure 3 As shown, multiple spatial triangles can be constructed by measuring the distances between each pair of beacons. Given the relative positions of the first beacon A1 and the second beacon A2, the relative positions of the third beacon B1 and the fourth beacon B2, and the attitude and depth data provided by each smart beacon, the relative position vector and relative yaw angle between the first operating platform 4 and the second operating platform 5 can be accurately calculated through fusion calculation.

[0078] In this embodiment, the built-in attitude sensor includes an inertial measurement unit (IMU). The attitude data includes roll angle φ, pitch angle θ, and yaw angle. For two beacons on the same operating platform, fusing their attitude data yields an accurate and robust overall attitude representation of the platform. This lays the foundation for subsequent calculation of three-dimensional relative positions in a unified horizontal coordinate system. ) and relative yaw angle Provides the foundation.

[0079] Specifically, independent right-handed coordinate systems are established for the first working platform 4 and the second working platform 5 respectively:

[0080] For the coordinate system P of the first working platform, the origin is the geometric center of the first working platform 4. The direction from the central axis of the first working platform 4 to the bow (i.e., the forward direction of the first working platform 4) is taken as... The axis is perpendicular to the upward direction of the deck. The axis is determined by the right-hand rule. axis( The coordinates of the first beacon A1 and the second beacon A2 in the coordinate system P of the first working platform are respectively (d... P ,0,0) T and (-d) P ,0,0) T Among them, d P The distance from the first beacon A1 or the second beacon A2 to the central axis of the first working platform 4.

[0081] For the coordinate system R of the second working platform, the origin is the geometric center of the second working platform 5. The direction from the central axis of the second working platform 5 to the bow (i.e., the forward direction of the second working platform 5) is taken as... The axis is perpendicular to the upward direction of the deck. The axis is determined by the right-hand rule. axis( The coordinates of the third beacon B1 and the fourth beacon B2 in the coordinate system of the second working platform 5 are respectively (d R,0,0) T and (-d) R ,0,0) T , where d R This is the distance from the third beacon B1 or the fourth beacon B2 to the central axis of the second working platform 5.

[0082] First, the raw distance observation data is preprocessed. One-dimensional data filtering algorithms, including moving average filtering or Kalman filtering, are used to smooth the continuous distance measurements for each beacon pair.

[0083] For the moving average filtering algorithm, the following formula is used:

[0084] ;

[0085] in, It is time The filtered output value; It is time Historical measurements; It is a positive integer representing the size of the moving window, which is typically chosen based on the data update rate and noise level.

[0086] The system model for the Kalman filter algorithm is as follows:

[0087] State variables: Indicates time The true distance estimate.

[0088] Measured values: Indicates time The distance observation value.

[0089] Process noise covariance: This indicates the uncertainty of the system model.

[0090] Measurement noise variance: The variance represents the measurement error.

[0091] The Kalman filter algorithm includes an initialization step, a prediction step, and an update step.

[0092] The initialization steps include:

[0093] .

[0094] in, It is a state estimate. These are measured values. It is the estimated value of the error covariance.

[0095] The prediction steps (time update) include:

[0096]

[0097]

[0098] in, It is a state prediction value. It is the predicted value of the error covariance, and Q is the process noise covariance.

[0099] The update steps (measurement update) include:

[0100]

[0101]

[0102]

[0103] Where ζ is the measurement noise covariance; This is the Kalman gain, used to balance the reliability of predicted and measured values. Kalman filtering adaptively adjusts its filtering effect, providing optimal estimates in noisy environments with minimal delay.

[0104] In this embodiment, pose fusion calculation is performed after preprocessing. Specifically, the fusion calculation module 72, based on the preprocessed ranging data, depth data, and attitude data of each beacon, calculates the three-dimensional coordinates of each beacon in a unified horizontal coordinate system using multi-source data fusion and a nonlinear least squares optimization algorithm. This allows for the accurate calculation of the three-dimensional relative position between the first working platform 4 and the second working platform 5. and relative yaw angle .

[0105] (1) Definition of coordinate systems P, R, W

[0106] In addition to the first working platform coordinate system P and the second working platform coordinate system R mentioned above, this application also establishes a horizontal coordinate system W. The horizontal coordinate system adopts the "Northeast-Upper Heaven" (ENU) right-handed rectangular coordinate system, with the origin at... Fixed at a point on the sea surface of the operating area, among which The axis points due east. The axis points due north. The axis is perpendicular to the horizontal plane and pointing upwards.

[0107] (2) Beacon installation parameters

[0108] The installation coordinates of the first beacon A1 and the second beacon A2 on the first working platform 4 in the P coordinate system are as follows:

[0109]

[0110]

[0111] The installation coordinates of the third beacon B1 and the fourth beacon B2 on the second working platform 5 in the R coordinate system are as follows:

[0112]

[0113]

[0114] (3) Platform posture and deep integration

[0115] Each smart beacon i provides raw observation data: distance observations Depth observations Attitude observations ( ).

[0116] Platform attitude fusion: Attitude observations of two beacons on the same platform are fused into the overall attitude of the platform.

[0117] , ,

[0118]

[0119] , ,

[0120]

[0121] Where atan2(y,x) is the arctangent function in the four quadrants; , , These are the roll angle, pitch angle, and yaw angle of the first working platform, respectively. , , These are the roll angle, pitch angle, and yaw angle of the second working platform, respectively. , , These are the roll angle, pitch angle, and heading angle of the first beacon, respectively. , , These are the roll angle, pitch angle, and heading angle of the second beacon, respectively. , , These are the roll angle, pitch angle, and heading angle of the third beacon, respectively. , , These are the roll angle, pitch angle, and heading angle of the fourth beacon, respectively.

[0122] Platform depth fusion: The depth of the platform center is obtained by fusing the depth observations of the two beacons on it.

[0123]

[0124]

[0125] The Z-coordinate of the platform center in the W coordinate system is:

[0126] , .

[0127] in, This represents the depth value of the first working platform. This represents the depth value of the second working platform. This is the depth value of the first beacon. This is the depth value of the second beacon. This is the depth value of the third beacon. This is the depth value of the fourth beacon; Let Z be the Z-coordinate of the platform center of the first working platform in the W coordinate system. Let Z be the Z coordinate of the platform center of the second working platform in the W coordinate system.

[0128] (4) Construction of rotation matrix

[0129] Using the fused platform attitude angles, construct the rotation matrix R (ZYX order) from the platform coordinate system to the horizontal coordinate system W:

[0130]

[0131] The basic rotation matrix is:

[0132] ;

[0133] ;

[0134]

[0135] Therefore, the rotation matrices of the first platform and the second platform are respectively and .

[0136] (5) Coordinate expression of the beacon in the W coordinate system

[0137] Coordinates of any beacon in the W coordinate system It can be determined from its platform center coordinates Platform rotation matrix and its installation position in the platform coordinate system Find:

[0138]

[0139] The specific expressions for the four beacons are as follows:

[0140] The coordinates of the first beacon A1 and the second beacon A2 on the first working platform 4 (oil pumping platform) in the W coordinate system are as follows:

[0141]

[0142]

[0143] The coordinates of the third beacon B1 and the fourth beacon B2 on the second operating platform 5 (ROV platform) in the W coordinate system are as follows:

[0144]

[0145]

[0146] in, and It is the rotation matrix calculated in step 2; and These are the coordinates of the center of the first working platform 4 and the center of the second working platform 5 in the W coordinate system. Preliminary findings have been made based on deep-sea observations. ); and Install the arm length for the known beacon.

[0147] (6) Complete establishment of observation equations and parameter solution

[0148] Substitute the coordinate expressions of the four beacons from step (5) into the distance observation equation. This yields a system containing six independent nonlinear equations, which are used to solve for the unknown parameters.

[0149] Example of range observation equation expansion (taking the first beacon A1 and the third beacon B1 as an example):

[0150]

[0151] in This represents the distance measurement between the first beacon A1 and the third beacon B1 after preprocessing. Similarly, the distance observation equations for the first beacon A1 and the fourth beacon B2, the second beacon A2 and the third beacon B1, the second beacon A2 and the fourth beacon B2, the first beacon A1 and the second beacon A2, and the third beacon B1 and the fourth beacon B2 can be written.

[0152] (7) Solution process

[0153] Since the equation is nonlinear with respect to the parameter vector X, an iterative optimization algorithm is used to solve it. The objective function F(X) is defined as the sum of squares of the differences between all distance observations and the values ​​calculated based on the geometric model (i.e., the residual sum of squares):

[0154]

[0155] here, This represents the preprocessed distance observations for the k-th beacon pair. Using nonlinear least squares algorithms such as the Gauss-Newton method or the Levenberg-Marquardt method, the parameter solution Xopt that minimizes the objective function F(X) is sought. This iterative process runs rapidly in the fusion solution module of the edge computing unit, and upon convergence, the horizontal positions of the two platform centers can be obtained. )and( The optimal estimate of ).

[0156] (8) Final calculation of relative pose

[0157] Obtain the optimal parameter solution Then, combined with the data directly determined from the depth data and This allows us to determine the complete platform center coordinates. and .

[0158] Three-dimensional relative position:

[0159]

[0160] Relative rotation matrix and yaw angle:

[0161] Rotation matrix from platform coordinate system to world coordinate system and The attitude has been determined from the fused attitude data. The relative rotation matrix from the second operating platform (ROV) coordinate system {R} to the first operating platform coordinate system {P} is:

[0162]

[0163] This matrix provides a complete three-dimensional description of the relative attitude between the two platforms. Among these, the relative yaw angle is particularly crucial for collaborative operations. By extracting the relative rotation matrix The component of rotation about the Z-axis is obtained as follows:

[0164]

[0165] In the formula, atan2(y,x) is the arctangent function in the fourth quadrant. Representation matrix The element in the i-th row and j-th column.

[0166] (9) Overall Adjustment Optimization

[0167] To achieve the highest accuracy and robustness, the above step-by-step solution process can be integrated into a rigorous least-squares global adjustment model, which performs a one-time global optimization on all observation data.

[0168] The parameter vector to be estimated: all unknowns to be solved are placed in a vector, including the three-dimensional center coordinates of the two platforms. and attitude angle vector ,

[0169] Let the total vector of parameters to be estimated be .

[0170] Observation vector: Contains all raw and fused valid observation data: depth observations from four beacons. Acoustic distance observations between the six beacon pairs and the original attitude angle observations of each beacon used to constrain the platform's attitude. , , The entire vector is denoted as the observation vector L.

[0171] Adjustment model: Establishing a nonlinear functional relationship between the observation vector L and the parameter vector U. ,in This is the observed noise vector.

[0172] According to the least squares criterion (Where P is a weight matrix defined based on the observation accuracy of each sensor), the optimal estimate of the parameters is obtained through an iterative algorithm. .

[0173] Advantages: This method can uniformly process all observation information, automatically weigh the contributions of different sensors based on accuracy, and strictly satisfy the system's geometric constraints. Theoretically, it can obtain the statistically optimal relative pose solution, significantly improving the accuracy and reliability of the system in complex underwater acoustic environments.

[0174] In summary, a complete closed-loop mathematical solution was achieved, from raw multi-sensor data to the precise three-dimensional relative position and relative yaw angle between the two operating platforms. This algorithm is fully integrated into the edge computing unit 6 on the second operating platform 5 (ROV), enabling localized real-time data processing and completely avoiding the acoustic transmission delay issues caused by transmitting raw data to the surface mother ship 3 for processing.

[0175] In this embodiment, the dynamic relative positioning system of the deep-water collaborative operation platform further includes an output and alarm unit 9, which displays the real-time relative distance and bearing information optimized by the adjustment and optimization module 73, and issues an alarm when the relative distance is less than a safety threshold. By setting the output and alarm unit 9, the optimized real-time relative distance and bearing information can be dynamically displayed on the operation interface and continuously compared with the safety threshold. Once the threshold is exceeded, an audible and visual alarm is immediately triggered. The output and alarm unit 9 can be installed on the mother ship 3.

[0176] This system establishes a local dynamic positioning network by installing two long-baseline positioning beacons on the collaborative pumping and storage shuttle platform and the deep-sea ROV platform, respectively. The positioning beacons are controlled by an LBL transducer located on the deep-sea ROV platform. By locally receiving distance measurement data between all beacons on the deep-sea ROV platform and processing it using data filtering and angle adjustment algorithms, the precise relative distance and orientation between the two platforms can be calculated in real time, guiding collaborative underwater construction operations.

[0177] See Figure 4 This application also provides a dynamic relative positioning method for a deep-water collaborative operation platform. The method uses the system described in any of the above embodiments for positioning, and the method includes the following steps:

[0178] Step S10: Job preparation.

[0179] Perform multi-beam precision measurement on the work area to establish the work background field, and then deploy the first work platform 4 and the second work platform 5 to the work area.

[0180] Step S20: Construct a dynamic positioning network 8 in the work area.

[0181] Specifically, the smart beacons of all built-in attitude and pressure sensors are activated to continuously measure distances between each other and synchronously collect their respective attitude data to form a dynamic positioning network.

[0182] Step S30: Data acquisition.

[0183] The edge computing unit 6 locally receives all raw distance observation data, depth data and attitude data of each beacon generated in the dynamic positioning network 8.

[0184] Step S40: Data preprocessing.

[0185] One-dimensional data filtering is performed on the raw distance observation data to remove measurement jumps caused by underwater multipath effects, noise, etc., to obtain preprocessed distance measurement data.

[0186] Step S50: Relative pose fusion calculation.

[0187] Based on the preprocessed ranging data, depth data, and attitude data, the three-dimensional coordinates of each beacon in the horizontal coordinate system are calculated, thereby obtaining the three-dimensional relative position and relative yaw angle between the first working platform 4 and the second working platform 5.

[0188] Step S60: Geometric adjustment optimization.

[0189] The least squares method is used to optimize the solution results, and the optimal relative position and orientation estimates are obtained.

[0190] Step S70: Output and monitor the optimized relative pose information.

[0191] The optimized real-time relative distance and orientation information will be dynamically displayed, and safety monitoring and alarms will be provided.

[0192] It should be noted that the method in this embodiment uses the system described in any of the above embodiments. For detailed descriptions of the relevant content, please refer to the system section above, which will not be repeated here. The system and method provided in this application are particularly suitable for collaborative operations between multiple underwater work platforms (deep-water ROV platforms) and liquid pumping and storage shuttle platforms in deep-water environments.

[0193] In this embodiment, the method implementation corresponds to the system implementation and can solve the technical problems solved by the system implementation, thereby achieving the technical effects of the system implementation. The specific details will not be elaborated here.

[0194] It should be noted that in the description of this specification, the terms "first," "second," etc., are used only for descriptive purposes and to distinguish similar objects; there is no order between them, nor should they be construed as indicating or implying relative importance. Furthermore, in the description of this specification, unless otherwise stated, "a plurality of" means two or more.

[0195] Any numerical values ​​cited herein include all values ​​ranging from a lower limit to an upper limit, increasing by one unit, with at least two units between any lower and any higher value. For example, if the quantity of a component or the value of a process variable (e.g., temperature, pressure, time, etc.) is described as being from 1 to 90, preferably from 20 to 80, more preferably from 30 to 70, the purpose is to illustrate that values ​​such as 15 to 85, 22 to 68, 43 to 51, 30 to 32 are also explicitly listed in this specification. For values ​​less than 1, a unit is appropriately considered to be 0.0001, 0.001, 0.01, 0.1, etc. These are merely examples intended for explicit expression, and it can be assumed that all possible combinations of values ​​listed between the minimum and maximum values ​​are explicitly described in this specification in a similar manner.

[0196] Unless otherwise stated, all ranges include the endpoints and all numbers between them. The terms "approximately" or "about" used with ranges apply to both endpoints of the range. Thus, "approximately 20 to 30" is intended to cover "approximately 20 to approximately 30," including at least the specified endpoints.

[0197] All articles and references disclosed herein, including patent applications and publications, are incorporated herein by reference for various purposes. The term “substantially constitutes…” used to describe a combination should include the identified elements, components, parts, or steps, as well as other elements, components, parts, or steps that do not substantially affect the essential novelty of the combination. The use of the terms “comprising” or “including” to describe combinations of elements, components, parts, or steps herein also contemplates embodiments substantially constituted by such elements, components, parts, or steps. The use of the term “may” herein is intended to indicate that any described attribute included by “may” is optional.

[0198] Multiple elements, components, parts, or steps can be provided by a single integrated element, component, part, or step. Alternatively, a single integrated element, component, part, or step can be divided into multiple separate elements, components, parts, or steps. The use of "a" or "an" to describe an element, component, part, or step does not imply the exclusion of other elements, components, parts, or steps.

[0199] It should be understood that the above description is for illustrative purposes and not for limitation. Many embodiments and applications beyond the provided examples will be apparent to those skilled in the art upon reading the above description. Therefore, the scope of this teaching should not be determined by reference to the above description, but rather by reference to the appended claims and the full scope of their equivalents. For purposes of completeness, all articles and references, including patent applications and publications, are incorporated herein by reference. The omission of any aspect of the subject matter disclosed herein in the preceding claims is not intended as a waiver of that subject matter, nor should it be construed as an indication that the inventors have not considered that subject matter as part of the disclosed inventive subject matter.

Claims

1. A dynamic relative positioning system for a deep-water collaborative operation platform, characterized in that, include: A first working platform is provided, on which a first beacon and a second beacon are provided; the first beacon and the second beacon are symmetrically arranged about the central axis of the first working platform; The second working platform is equipped with a third beacon and a fourth beacon. The third beacon and the fourth beacon are symmetrically arranged about the central axis of the second working platform. The first beacon, the second beacon, the third beacon and the fourth beacon are all intelligent beacons with built-in attitude sensors and pressure sensors, which can measure their own attitude data and depth data in real time. A dynamic positioning network is used to generate raw distance observation data; The dynamic positioning network consists of continuous bidirectional ranging between the first beacon, the second beacon, the third beacon, and the fourth beacon; The edge computing unit, located on the second operating platform, includes a data acquisition interface and a data processing mechanism. The data acquisition interface is used to locally receive the raw distance observation data, attitude data, and depth data of all beacons in the dynamic positioning network. The data processing mechanism includes a data preprocessing module, a fusion calculation module, and an adjustment optimization module. The data preprocessing module is used to preprocess the original distance observation data using a one-dimensional data filtering algorithm to obtain preprocessed distance measurement data. The fusion calculation module is used to calculate the three-dimensional relative position and relative yaw angle between the first and second operating platforms based on the preprocessed ranging data, attitude data and depth data of each beacon; the adjustment optimization module is used to optimize the calculation results using the least squares method.

2. The dynamic relative positioning system for deep-water collaborative operation platform according to claim 1, characterized in that, The built-in attitude sensor includes an inertial measurement unit; the attitude data includes roll angle φ, pitch angle θ, and yaw angle. The three-dimensional relative position is represented as (ΔX, ΔY, ΔZ), and the relative yaw angle is... .

3. The dynamic relative positioning system for deep-water collaborative operation platform according to claim 2, characterized in that, The first working platform is a liquid extraction and storage shuttle platform; the second working platform is a deep-water ROV platform; an independent right-handed coordinate system is established for the first working platform and the second working platform.

4. The dynamic relative positioning system for deep-water collaborative operation platform according to claim 3, characterized in that, For the coordinate system of the first working platform, the center of the first working platform is taken as the origin, the direction from the centerline of the first working platform pointing bow is taken as the X-axis, the direction perpendicular to the deck upwards is taken as the Z-axis, and the Y-axis is determined by the right-hand rule; the coordinates of the first beacon and the second beacon in the coordinate system of the first working platform are respectively (d P ,0,0) T and (-d) P ,0,0) T , where d P The distance from the first or second beacon to the central axis of the first working platform; For the second work platform coordinate system, the center of the second work platform is taken as the origin, the direction from the centerline of the second work platform pointing bow is taken as the X-axis, the direction perpendicular to the deck upwards is taken as the Z-axis, and the Y-axis is determined by the right-hand rule; the coordinates of the third and fourth beacons in the second work platform coordinate system are respectively (d R ,0,0) T and (-d) R ,0,0) T , where d R This refers to the distance from the third or fourth beacon to the central axis of the second work platform.

5. The dynamic relative positioning system for deep-water collaborative operation platform according to claim 4, characterized in that, The fusion calculation module is used to calculate the three-dimensional coordinates of each beacon in a unified horizontal coordinate system based on the preprocessed ranging data, depth data and attitude data of each beacon, and then calculate the three-dimensional relative position and relative yaw angle between the first operating platform and the second operating platform. The calculated three-dimensional coordinates of each beacon in a unified horizontal coordinate system include: Step 1: Based on the attitude data of each beacon, calculate the platform attitude of the platform where it is located; for two beacons on the same platform, take the arithmetic mean of the roll angle and pitch angle in their attitude data respectively, and use them as the roll angle φ and pitch angle θ of the platform; process the heading angle in their attitude data through geometric relationship or averaging, and use it as the heading angle ψ of the platform. Step 2: Construct the rotation matrix from each platform coordinate system to the horizontal coordinate system; for any platform, its rotation matrix R is calculated by the following formula: ； in, , , These are the basic rotation matrices about the X, Y, and Z axes, respectively; Step 3: Establish the relationship between the beacon coordinates and the platform center coordinates in the horizontal coordinate system; let the coordinates of the platform center in the horizontal coordinate system be... Then the coordinates on the platform are The coordinates of the beacon in the horizontal coordinate system for: ； Step 4: Establish a depth observation equation by combining beacon depth data; the depth value H measured by the beacon's pressure sensor and the Z coordinate in the horizontal coordinate system. satisfy: ； Step 5: Establish the distance observation equation based on the distance measurement data; for any two beacons i and j, their coordinates in the horizontal coordinate system... and satisfy: ； in This is the preprocessed ranging data; Step 6: Solve the depth observation equation from Step 4 and the distance observation equation from Step 5 simultaneously to find the coordinates T of each platform center in the horizontal coordinate system. Then, obtain the three-dimensional coordinates of all beacons in the horizontal coordinate system through Step 3.

6. The dynamic relative positioning system for deep-water collaborative operation platform according to claim 5, characterized in that, The calculation of the three-dimensional relative position and relative yaw angle between the first and second work platforms includes: Let the coordinates of the center of the first working platform in the horizontal coordinate system be... The coordinates of the center of the second working platform in the horizontal coordinate system are: Then the three-dimensional relative positions are: ； Let the rotation matrix from the first working platform coordinate system to the horizontal coordinate system be... The rotation matrix from the second working platform coordinate system to the horizontal coordinate system is: Then the relative rotation matrix from the second working platform coordinate system to the first working platform coordinate system is... for: ； from Extracting the rotation angle around the Z-axis gives the relative yaw angle. Calculated using the following formula: ； Where atan2 is the arctangent function in the fourth quadrant. Representation matrix The element in the i-th row and j-th column.

7. The dynamic relative positioning system for deep-water collaborative operation platform according to claim 1, characterized in that, The one-dimensional data filtering algorithm includes a moving average filtering algorithm or a Kalman filtering algorithm.

8. The dynamic relative positioning system for deep-water collaborative operation platform according to claim 1, characterized in that, It also includes an output and alarm unit, which displays the real-time relative distance and orientation information optimized by the adjustment and optimization module, and issues an alarm when the relative distance is less than a safety threshold.

9. A dynamic relative positioning method for a deep-water collaborative operation platform, characterized in that, The method uses the system described in any one of claims 1-8 for positioning, and the method includes the following steps: Step S10: Operation preparation; Perform multi-beam precision measurement on the operation area to establish the operation background field, and deploy the first and second operation platforms to the operation area; Step S20: Construct the dynamic positioning network in the work area; Step S30: Data acquisition; The edge computing unit locally receives all raw distance observation data, depth data and attitude data of each beacon generated in the dynamic positioning network; Step S40: Data preprocessing; Perform one-dimensional data filtering on the original distance observation data to obtain preprocessed distance measurement data; Step S50: Relative pose fusion calculation; Based on the preprocessed ranging data, depth data and attitude data, calculate the three-dimensional coordinates of each beacon in the horizontal coordinate system, and then obtain the three-dimensional relative position and relative yaw angle between the first working platform and the second working platform; Step S60: Geometric adjustment optimization; The least squares method is used to optimize the solution results to obtain the optimal relative position and orientation estimates; Step S70: Output and monitor the optimized relative pose information.

Images