Autonomous underwater vehicle, HOV and AUV cluster collaborative operation navigation positioning method, equipment and medium
By sending underwater acoustic pulse signals and location information from manned submersibles, and combining this with a multi-platform information fusion model based on inertial navigation data, the problem of insufficient navigation accuracy and autonomous decision-making capability in collaborative operations between manned submersibles and AUV clusters was solved, achieving high-precision navigation and positioning and autonomous decision-making.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HARBIN ENG UNIV
- Filing Date
- 2026-03-31
- Publication Date
- 2026-05-12
AI Technical Summary
In existing collaborative operations between manned submersibles and AUV swarms, the utilization rate of the mother ship is low, and the autonomous decision-making ability of the AUV swarm in complex underwater environments is limited, making it difficult to cope with emergencies. It requires supervision and intervention from surface vessels or manned submersibles.
By sending underwater acoustic pulse signals and location information from a manned submersible, an autonomous underwater vehicle receives and combines them with inertial navigation data. The system then uses a multi-platform information fusion collaborative positioning model for joint optimization to correct inertial navigation errors and achieve high-precision navigation and positioning.
It improves the navigation accuracy and autonomous decision-making ability of AUV clusters in complex underwater environments, enhances the utilization rate of manned submersibles' ship time, and reduces operating costs.
Smart Images
Figure CN122015869A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of underwater precision positioning, and in particular to a navigation and positioning method, device and medium for autonomous underwater vehicles, HOV and AUV swarm collaborative operation. Background Technology
[0002] Currently, the collaborative operation of manned submersibles (HOVs) and autonomous underwater vehicle (AUV) swarms primarily relies on acoustic navigation and positioning technology for precise underwater positioning. A typical collaborative system consists of a HOV support vessel, the HOVs, and the AUV swarm, forming a hierarchical operational structure. The support vessel, as a surface support platform, is typically equipped with an Ultra Short Baseline (USBL) underwater acoustic positioning system and an onboard underwater acoustic communication system, responsible for real-time positioning and communication support for the HOVs. The AUV swarm, on the other hand, mainly relies on pre-programmed path planning combined with an Inertial Navigation System (INS) and a Doppler Velocity Log (DVL) for autonomous navigation. In specific operational modes, it can also perform position calibration using a Long Baseline (LBL) or USBL acoustic positioning system.
[0003] In practical operations, acoustic navigation and positioning technology is crucial for connecting various units. For manned submersibles, the mother ship continuously tracks their position via the USBL system and transmits the positioning information to the submersible through underwater acoustic communication, assisting the pilot. For AUV swarms, when the terrain in the operating area is relatively flat and the operation frequency is high, a long baseline positioning system may be deployed on the seabed to provide high-precision positioning for the AUVs using acoustic beacons at multiple known locations. Furthermore, cooperative navigation methods developed in recent years, such as master-slave cooperative positioning, utilize a lead AUV (or manned submersible) equipped with high-precision navigation equipment to provide position references for follower AUVs equipped with lower-precision navigation equipment. Acoustic ranging and information fusion algorithms (such as Kalman filtering) are used to improve the positioning accuracy of the entire swarm.
[0004] However, this traditional operating model has significant limitations. When a manned submersible is conducting a dive, the support vessel can only provide support and assurance to the submersible, ensuring its underwater safety. The support vessel can only be used for other survey missions when the submersible is not diving. This means that despite the high daily operating costs of the support vessel, the utilization rate of ship time during manned submersible operations is not high. Furthermore, AUV swarms have limited autonomous decision-making capabilities in complex underwater environments, making it difficult to cope with emergencies or complex tasks, and still requiring supervision and intervention from surface vessels or manned submersibles. Summary of the Invention
[0005] The purpose of this application is to provide a navigation and positioning method, equipment and medium for autonomous underwater vehicles (AUVs), HOVs and AUVs working together, so as to achieve high-precision navigation and positioning of AUV clusters under the condition of manned submersible as a single anchor point.
[0006] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a navigation and positioning method for collaborative operation of HOV and AUV clusters, including: Control the manned submersible to send underwater acoustic pulse signals, its own position information, and the timestamp of the transmission time to the surrounding waters. The autonomous underwater vehicle receives the underwater acoustic pulse signal and determines the measurement equation for the autonomous underwater vehicle's position estimation information based on the signal propagation time and the speed of sound in water. Acquire the inertial navigation data of the autonomous underwater vehicle; the inertial navigation data includes attitude, position, and velocity information; Based on the measurement equation and the inertial navigation data, a multi-platform information fusion collaborative positioning model is used for joint optimization to correct the inertial navigation error of the autonomous underwater vehicle. Based on the corrected inertial navigation error, the corrected navigation information is output to achieve collaborative navigation and positioning between manned submersibles and autonomous underwater vehicle clusters.
[0007] Secondly, this application provides an autonomous underwater vehicle (AUV) that performs the above-mentioned navigation and positioning method for collaborative operation of HOV and AUV clusters. The AUV includes: a communication module, an inertial navigation module, and a processor. The communication module is used to receive underwater acoustic pulse signals, its own position information, and the timestamp of the transmission time sent by the manned submersible. The inertial navigation module is used to acquire the inertial navigation data of the autonomous underwater vehicle; The processor is used to determine the measurement equation for the position estimation information of the autonomous underwater vehicle based on the signal propagation time and the speed of sound in water, and to perform joint optimization using a multi-platform information fusion collaborative positioning model based on the measurement equation and the inertial navigation data, correct the inertial navigation error, and output the corrected navigation information.
[0008] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described navigation and positioning method for collaborative operation of HOV and AUV clusters.
[0009] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described navigation and positioning method for collaborative operation of HOV and AUV clusters.
[0010] According to the specific embodiments provided in this application, this application has the following technical effects: This application controls a manned submersible to send underwater acoustic pulse signals, its own position information, and a timestamp of the transmission time to the surrounding waters; controls an autonomous underwater vehicle (AUV) to receive the underwater acoustic pulse signals, and determines a measurement equation for the AUV's position estimation information based on the signal propagation time and the speed of sound in water; acquires the AUV's inertial navigation data; based on the measurement equation and the inertial navigation data, performs joint optimization using a multi-platform information fusion collaborative positioning model to correct the AUV's inertial navigation error; and outputs corrected navigation information based on the corrected inertial navigation error, ensuring high-precision navigation and positioning of an AUV cluster under conditions where the manned submersible acts as a single anchor point in a collaborative operation scenario. Attached Figure Description
[0011] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0012] Figure 1 A schematic flowchart illustrating a navigation and positioning method for collaborative operation of HOV and AUV clusters, provided as an embodiment of this application; Figure 2 A schematic diagram of a navigation and positioning method for collaborative operation of HOV and AUV clusters provided in an embodiment of this application; Figure 3 This is a schematic diagram illustrating the entire process of joint optimization of inertial information provided in an embodiment of this application. Detailed Implementation
[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.
[0014] To make the objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0015] like Figures 1-2 As shown in the figure, this application provides a navigation and positioning method for collaborative operation of HOV and AUV clusters, including: S1: Controls the manned submersible to send underwater acoustic pulse signals, its own position information, and the timestamp of the transmission time to the surrounding waters.
[0016] S2: Control the autonomous underwater vehicle to receive the underwater acoustic pulse signal, and determine the measurement equation for the autonomous underwater vehicle's position estimation information based on the signal propagation time and the speed of sound in water.
[0017] S3: Acquire the inertial navigation data of the autonomous underwater vehicle; the inertial navigation data includes attitude, position and velocity information.
[0018] S4: Based on the measurement equation and the inertial navigation data, a multi-platform information fusion collaborative positioning model is used for joint optimization to correct the inertial navigation error of the autonomous underwater vehicle.
[0019] S5: Based on the corrected inertial navigation error, output the corrected navigation information to achieve collaborative navigation and positioning between manned submersibles and autonomous underwater vehicle clusters.
[0020] In practical applications, in deep-sea areas, since HOVs and AUVs operate at similar depths and are approximately on the same plane, the positional relationship between the manned submersible and the AUV on a two-dimensional plane can be modeled based on the principle of cooperative navigation. The specific steps are as follows: In an exemplary embodiment, S2 specifically includes: S21: When the autonomous underwater vehicle receives a message from the manned submersible at a specific time... After the signal is transmitted, the relative distance between the manned submersible and the autonomous underwater vehicle is calculated by multiplying the signal propagation time by the speed of sound in water. ,but The autonomous underwater vehicle is always centered on the position of the manned submersible, with... On a circle with radius .
[0021] In practical applications, manned submersibles... The AUV sequentially transmits underwater acoustic pulse signals as markers, its own position information, and a timestamp of the transmission time to the manned submersible. After the marker is sent out, the relative distance between the manned submersible and the AUV is calculated by multiplying the signal's underwater acoustic propagation time by the speed of sound in water. ,but AUV at all times With the center as the center, On a circle with radius , the equation of the circumference is: In the formula: They are respectively The position coordinates of manned submersibles and AUVs in the navigation coordinate system at all times.
[0022] S22: When the autonomous underwater vehicle receives a message from the manned submersible at a specific time... After the marker is sent, the relative distance between the autonomous underwater vehicle and the manned submersible is calculated. ,but The autonomous underwater vehicle is always centered on the position of the manned submersible, with... On a circle with radius .
[0023] In practical applications, manned submersibles... The AUV sequentially sends out flag pulse signals, position information, and a timestamp of the transmission time. The AUV then receives the data from the manned submersible. After the marker is sent out at any time, the relative distance between the AUV and the manned submersible is calculated. ,but AUV at all times With the center as On a circle with radius , the equation of the circumference is: In the formula: They are respectively The position coordinates of the manned submersible and AUV in the navigation coordinate system at all times.
[0024] S23: Calculate the time using integrated navigation. At the time The motion vector of the autonomous underwater vehicle during this period.
[0025] In practical applications, AUVs utilize integrated navigation calculations to perform calculations. Time's up The AUV's own motion vector during the time period Then AUV Time and The equation relating time and position is: S24: Based on time At the time The distance of movement and the trajectory of its own movement will be at any time. Geometric position translation of autonomous underwater vehicle to time Find the intersection point of the two circles to obtain the time interval. Measurement equations for position estimation information of autonomous underwater vehicles.
[0026] In practical applications, AUVs, based on their own... Time's up The distance traveled at any given moment will The AUV and its geometric position are translated at time [time]. At time t, finding the intersection point of the two circles will yield the system of equations. The formula contains Position estimation information of the AUV at any given time.
[0027] This leads to the measurement equation, which is the position information output by the HOV-AUV collaborative navigation and positioning model based on distance measurement. This equation can provide position observations for the multi-platform information fusion collaborative positioning model and is used for multi-platform information fusion.
[0028] The measurement equation is: In the formula: To measure the noise, we assume it to be mutually independent Gaussian white noise with zero mean.
[0029] noise variance matrix R It can be represented as: in, To measure noise, To measure the noise transpose, for variance for The variance.
[0030] The above measurement equations are used as important observables in the HOV-AUV multi-platform inertial information fusion and joint optimization method. For single anchor point ranging, although some studies have pointed out that the position state of the platform is unobservable when the moving platform moves along the extension line of the anchor point, the inertial navigation solution ensures that the position has a solution at each inertial measurement moment. In the positioning system composed of HOV-AUV clusters, the measurement equation results can be corrected by filters to correct the accumulated error of the inertial navigation solution, thereby improving the positioning accuracy.
[0031] Because HOVs move slower and have a limited range of activity compared to AUVs, their inertial navigation systems accumulate errors at a lower rate. Furthermore, HOVs communicate frequently with the mother ship during missions to obtain high-precision position information. Therefore, the underwater positioning accuracy of HOVs is significantly better than that of AUV swarms. In cooperative positioning systems, HOVs can be regarded as a precisely positioned dynamic anchor point to provide a state reference for AUV swarms and improve the observability and positioning stability of the system in the case of sparse ranging.
[0032] In an exemplary embodiment, the entire process of joint optimization of inertial information is as follows: Figure 3 As shown in the flowchart, this process details the steps from single-anchor underwater acoustic ranging and optimal estimation of inertial information to state calculation. It corrects inertial offsets using ranging information from multiple platforms, achieving joint estimation and collaborative feedback of inertial navigation information across platforms. This effectively improves the system's positioning accuracy. The specific steps are as follows: S4 specifically includes: S41: Based on the measurement equation and the inertial navigation data, the set of actual inertial measurement values and the set of distance measurement values are fed into the optimizer.
[0033] S42: At any given moment, if there is a distance measurement value, the inertial information is optimized and corrected to obtain the optimized variable, which is then output to the filter, and the inertial information and distance measurement information are unified into a time series.
[0034] S43: If there is no distance measurement value at any time, the inertial navigation measurement information is directly input into the filter stage as the inertial navigation calculation result; the inertial navigation measurement information includes attitude, position and velocity.
[0035] S44: In the filter, the inertial measurement information output by the range fusion optimizer, calculated based on the inertial navigation calculation results, is compared with the range observations to obtain the update process, and the corrected attitude, velocity, and position information are output.
[0036] S45: The distance measurement and inertial information are continuously optimized and updated according to a unified time series to correct the inertial navigation error of the autonomous underwater vehicle.
[0037] In practical applications, inertial navigation data usually refers to the processed navigation results, such as position, velocity, and attitude angles, which are the output information of the Inertial Navigation System (INS).
[0038] Inertial information: The semantics are broad and may include raw measurements (such as acceleration and angular velocity), intermediate state quantities (such as attitude matrix), or the final navigation solution. The specific meaning depends on the context.
[0039] Inertial navigation measurement information: Specifically refers to the raw physical quantities directly output by the Inertial Measurement Unit (IMU), namely specific force and angular velocity, which are the input data of INS.
[0040] In practical applications, inertial measurement information (attitude, position, velocity, etc.) has a dense time series, while distance measurement values have a sparse time series. First, the actual inertial measurement set and the distance measurement set enter the optimizer. In the optimizer, if there is a distance measurement value at a certain moment, the inertial information is optimized and corrected to obtain the optimized variable, which is then output to the filter.
[0041] If there is no distance measurement value at a certain moment, the inertial navigation measurement information is directly used as the inertial navigation calculation result and fed into the filter stage.
[0042] In the filter, the distance is calculated based on the inertial navigation calculation results. The attitude, velocity, and position information output by the fusion optimizer are compared with the range observations. The update process is derived, and the corrected attitude, velocity, and position information are output; among them, In S2, the distance observation between the manned submersible and the AUV is obtained through acoustic ranging.
[0043] The inertial information and distance measurement information are combined into a time series. The distance measurement / inertial information is continuously updated and iterated according to the time series to obtain navigation information (attitude, velocity and position).
[0044] In an exemplary embodiment, the joint optimization in S4 is implemented based on Bayesian theory and a sensor error model, specifically including: When the actual inertial measurement value and distance measurement value are known, construct the conditional probability of the estimated value of the inertial information.
[0045] When the conditional probability reaches its maximum value, the corresponding estimated value is taken as the optimal estimate of the inertial information at the current moment; the optimal estimate of the inertial information is the value of the optimization variable at the extreme point.
[0046] Based on Bayes' theorem, the optimal estimate is written in the form of prior information, while the sensor error model is used to model and correct the uncertainty of the observed data.
[0047] In practical applications, the specific update method for joint model optimization is as follows: Based on Bayesian theory and sensor error models, in actual inertial measurement values... and Distance measurement value At that time, the estimated value of inertial information is and The conditional probability can be expressed as: in, : Estimated value (measured value) of angular velocity vector; : Estimated value (measured value) of the specific force vector; : The angular velocity vector of the carrier in the carrier coordinate system; : The specific force vector (acceleration) of the carrier in the carrier coordinate system.
[0048] Step 11. When this conditional probability reaches its maximum value, the corresponding estimated value can be considered the optimal estimate of the inertial information at that moment, that is: Step 12. According to Bayes' theorem, the optimal estimate can be written in the form of prior information, that is: Since inertial information follows a Gaussian distribution and the measurements from each sensor are independent, the optimal estimation of platform inertial information under single-anchor ranging can be represented by an optimization problem based on the Gaussian probability density function. The optimal estimate of inertial information is the value of the optimization variable at the extreme point of the function.
[0049] In an exemplary embodiment, the optimal estimate is written in the form of prior information according to Bayes' theorem, while the uncertainty of the observed data is modeled and corrected using a sensor error model, and then further includes joint optimization of multi-platform inertial information.
[0050] The joint optimization of multi-platform inertial information specifically includes: The single-platform inertial information optimization model is extended to a multi-platform inertial information joint optimization model, and multi-platform error coupling modeling is introduced.
[0051] The inertial measurement values of multiple platforms are combined into a joint state variable, and all platforms are optimized simultaneously to construct an objective function. The objective function is based on the set of inertial measurement information of multiple platforms and the set of distance measurement information of multiple platforms. When updated at each time step, the inertial information of all platforms is optimized and substituted into the navigation solution to correct the drift error.
[0052] In practical applications, the single-platform inertial information optimization model described above is extended to a multi-platform inertial information joint optimization model. Multi-platform error coupling modeling is introduced, combining the inertial measurements from multiple platforms into a joint state variable. Optimization is then performed simultaneously on all platforms, and the objective function can be constructed as follows: For the information set of angular velocity vectors of multiple platforms, The information set of force vectors of multiple platforms is used to optimize the inertial information of all platforms at each time step during the model update, and substitute it into the navigation solution to effectively correct drift error and improve the overall stability and collaborative observability of the multi-platform system.
[0053] In an exemplary embodiment, S4, further includes: using the relative positional relationships between platforms and prior information on geometric configurations, measuring the relative positional changes of the autonomous underwater vehicle cluster by means of area constraints.
[0054] In one exemplary embodiment, the manned submersible serves as a precisely positioned dynamic anchor point to provide a state reference for an autonomous underwater vehicle cluster. In the case of sparse ranging, inertial offsets are corrected by ranging information between multiple platforms, thereby achieving joint estimation and collaborative feedback of inertial navigation information between platforms.
[0055] First, a HOV-AUV cooperative navigation and positioning model based on distance measurement is established, and then a sensor error model is introduced. Second, a state update mechanism based on the sensor error dynamic model is designed to effectively integrate cluster unidirectional ranging and multi-source inertial navigation data, and to handle the error propagation of sensors from different platforms. Then, through maximum likelihood estimation, the state estimate is updated based on the latest data after each observation, while the sensor error model is used to model and correct the uncertainty of the observation data. Finally, area constraints of geometric configuration are further introduced. Using the relative positional relationship between platforms and prior information on geometric configuration, the relative positional changes of the cluster are measured through area constraints to achieve system stability and observability.
[0056] This application provides an autonomous underwater vehicle, including: a communication module, an inertial navigation module, and a processor.
[0057] The communication module is used to receive underwater acoustic pulse signals, its own position information, and the timestamp of the transmission time sent by the manned submersible.
[0058] The inertial navigation module is used to acquire the inertial navigation data of the autonomous underwater vehicle.
[0059] The processor is used to determine the measurement equation for the position estimation information of the autonomous underwater vehicle based on the signal propagation time and the speed of sound in water, and to perform joint optimization using a multi-platform information fusion collaborative positioning model based on the measurement equation and the inertial navigation data, correct the inertial navigation error, and output the corrected navigation information.
[0060] This application addresses the urgent need for precise navigation and positioning in collaborative operations between manned submersibles (HUVs) and AUV swarms. It focuses on the collaborative navigation and positioning problems of manned and unmanned submersibles in different application scenarios, aiming to study key technologies such as precise navigation of the submersible itself, collaborative navigation between manned and unmanned submersibles, and AUV swarm navigation with and without positioning support. It covers all scenarios of collaborative operations between manned and unmanned submersibles in the deep sea, establishing a collaborative navigation and positioning system for manned submersibles and AUV swarms. Addressing the practical difficulties encountered in collaborative operations between manned and unmanned submersibles in the deep sea, this application provides a navigation and positioning method for collaborative operations between HUVs and AUV swarms based on joint optimization of distance measurement and inertial information. This ensures high-precision navigation and positioning of AUV swarms in collaborative operations with manned submersibles as single anchor points. The expected results will provide theoretical support and technical assurance for collaborative operations between manned and unmanned submersibles in the deep sea, promoting the development of collaborative navigation and positioning technology for heterogeneous deep-sea submersibles in complex environments.
[0061] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments. The computer device can be a server or a terminal. The computer device includes a processor, a memory, an input / output interface (I / O), and a communication interface. The processor, memory, and I / O interface are connected via a system bus, and the communication interface is connected to the system bus via the I / O interface. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device stores data to be processed. The I / O interface of the computer device is used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communicating with an external terminal via a network connection. When the computer program is executed by the processor, it implements the above-described methods.
[0062] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.
[0063] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.
[0064] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.
[0065] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by hardware related to computer program instructions. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).
[0066] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.
[0067] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0068] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. A navigation and positioning method for collaborative operation of HOV and AUV clusters, characterized in that, include: Control the manned submersible to send underwater acoustic pulse signals, its own position information, and the timestamp of the transmission time to the surrounding waters. The autonomous underwater vehicle receives the underwater acoustic pulse signal and determines the measurement equation for the autonomous underwater vehicle's position estimation information based on the signal propagation time and the speed of sound in water. Acquire the inertial navigation data of the autonomous underwater vehicle; the inertial navigation data includes attitude, position, and velocity information; Based on the measurement equation and the inertial navigation data, a multi-platform information fusion collaborative positioning model is used for joint optimization to correct the inertial navigation error of the autonomous underwater vehicle. Based on the corrected inertial navigation error, the corrected navigation information is output to achieve collaborative navigation and positioning between manned submersibles and autonomous underwater vehicle clusters.
2. The navigation and positioning method for collaborative operation of HOV and AUV clusters according to claim 1, characterized in that, The autonomous underwater vehicle (AUV) receives the underwater acoustic pulse signal, and determines the measurement equation for the AUV's position estimation information based on the signal propagation time and the speed of sound in water. Specifically, this includes: When the autonomous underwater vehicle receives a message from the manned submersible at a certain time After the signal is transmitted, the relative distance between the manned submersible and the autonomous underwater vehicle is calculated by multiplying the signal propagation time by the speed of sound in water. ,but The autonomous underwater vehicle is always centered on the position of the manned submersible, with... On a circle with radius [radius]; When the autonomous underwater vehicle receives a message from the manned submersible at a certain time After the marker is sent, the relative distance between the autonomous underwater vehicle and the manned submersible is calculated. ,but The autonomous underwater vehicle is always centered on the position of the manned submersible, with... On a circle with radius [radius]; Calculate the time using integrated navigation. At the time The motion vector of the autonomous underwater vehicle during this period; According to time At the time The distance of movement and the trajectory of its own movement will be at any time. Geometric position translation of autonomous underwater vehicle to time Find the intersection point of the two circles to obtain the time interval. Measurement equations for position estimation information of autonomous underwater vehicles.
3. The navigation and positioning method for collaborative operation of HOV and AUV clusters according to claim 2, characterized in that, Based on the measurement equations and the inertial navigation data, a multi-platform information fusion collaborative positioning model is used for joint optimization to correct the inertial navigation error of the autonomous underwater vehicle, specifically including: Based on the measurement equation and the inertial navigation data, the set of actual inertial measurement values and the set of distance measurement values are fed into the optimizer; At any given moment, if a distance measurement is available, the inertial information is optimized and corrected to obtain the optimized variable, which is then output to the filter, and the inertial information and distance measurement information are unified into a time series. If no distance measurement is available at any given moment, the inertial navigation measurement information is directly input into the filter stage as the inertial navigation calculation result; the inertial navigation measurement information includes attitude, position, and velocity. In the filter, the inertial measurement information output by the range fusion optimizer, calculated based on the inertial navigation calculation results, is compared with the range observations to obtain the update process, and the corrected attitude, velocity, and position information are output. By continuously updating and iterating the joint optimization of distance measurement and inertial information according to a unified time series, the inertial navigation error of the autonomous underwater vehicle is corrected.
4. The navigation and positioning method for collaborative operation of HOV and AUV clusters according to claim 3, characterized in that, The joint optimization is based on Bayesian theory and a sensor error model, and specifically includes: When the actual inertial measurement value and the distance measurement value are known, construct the conditional probability of the estimated value of the inertial information; When the conditional probability reaches its maximum value, the corresponding estimated value is taken as the optimal estimate of the inertial information at the current moment; the optimal estimate of the inertial information is the value of the optimization variable at the extreme point. Based on Bayes' theorem, the optimal estimate is written in the form of prior information, while the sensor error model is used to model and correct the uncertainty of the observed data.
5. The navigation and positioning method for collaborative operation of HOV and AUV clusters according to claim 4, characterized in that, Based on Bayes' theorem, the optimal estimate is written in the form of prior information. At the same time, the uncertainty of the observed data is modeled and corrected using the sensor error model. This also includes joint optimization of inertial information from multiple platforms. The joint optimization of multi-platform inertial information specifically includes: The single-platform inertial information optimization model is extended to a multi-platform inertial information joint optimization model, and multi-platform error coupling modeling is introduced. The inertial measurement values of multiple platforms are combined into a joint state variable, and all platforms are optimized simultaneously to construct an objective function. The objective function is based on the set of inertial measurement information of multiple platforms and the set of distance measurement information of multiple platforms. When updated at each time step, the inertial information of all platforms is optimized and substituted into the navigation solution to correct the drift error.
6. The navigation and positioning method for HOV and AUV cluster cooperative operation according to any one of claims 1 to 5, characterized in that, Based on the measurement equation and the inertial navigation data, a multi-platform information fusion collaborative positioning model is used for joint optimization to correct the inertial navigation error of the autonomous underwater vehicle. The method further includes: using the relative positional relationship between platforms and prior information on geometric configuration, the relative positional changes of the autonomous underwater vehicle cluster are measured through area constraints.
7. The navigation and positioning method for collaborative operation of HOV and AUV clusters according to claim 1, characterized in that, The manned submersible serves as a precisely positioned dynamic anchor point, providing a state reference for the autonomous underwater vehicle cluster. In the case of sparse ranging, the inertial offset is corrected by ranging information between multiple platforms, thereby realizing joint estimation and collaborative feedback of inertial navigation information between platforms.
8. An autonomous underwater vehicle, characterized in that, The autonomous underwater vehicle performs the navigation and positioning method for collaborative operation of HOV and AUV clusters as described in any one of claims 1-7, and the autonomous underwater vehicle includes: a communication module, an inertial navigation module, and a processor; The communication module is used to receive underwater acoustic pulse signals, its own position information, and the timestamp of the transmission time sent by the manned submersible. The inertial navigation module is used to acquire the inertial navigation data of the autonomous underwater vehicle; The processor is used to determine the measurement equation for the position estimation information of the autonomous underwater vehicle based on the signal propagation time and the speed of sound in water, and to perform joint optimization using a multi-platform information fusion collaborative positioning model based on the measurement equation and the inertial navigation data, correct the inertial navigation error, and output the corrected navigation information.
9. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the navigation and positioning method for HOV and AUV cluster cooperative operation as described in any one of claims 1-7.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the navigation and positioning method for collaborative operation of HOV and AUV clusters as described in any one of claims 1-7.