Underwater robot hydrodynamic identification method, system, equipment and medium

Abstract

The invention discloses an underwater robot hydrodynamic identification method, system and device and a medium, and the method comprises the steps: obtaining the control input quantity of an underwater robot, and enabling the control input quantity to be used for adjusting the motion of the underwater robot; state data are obtained, and the state data represent the motion state of the underwater robot in water; inputting the control input quantity into the dynamic model to obtain prediction state data; wherein the dynamic model is established based on the structural characteristics of the underwater robot and the motion characteristics of the underwater robot in the fluid; and determining the hydrodynamic parameters according to the state data and the predicted state data. According to the method, the hydrodynamic parameter identification of the underwater robot can be realized at low cost, and the method has a relatively strong application prospect.

Description

Technical Field

[0001] The present application relates to the field of underwater observation technology, and in particular to a method, system, equipment and medium for identifying hydrodynamics of an underwater robot. Background Art

[0002] As a new type of ocean observation platform, underwater robots have the advantages of autonomous control, energy saving, and strong mobility. They are widely used in various aspects of ocean research, marine engineering, and marine development. Underwater robots need to perform tasks in complex and changeable underwater environments. Precise motion control and efficient response speed are crucial to the success of their mission execution. However, in underwater environments, they are often affected by hydrodynamic forces, such as fluid resistance and additional mass, which will affect their motion state and control strategy.

[0003] Therefore, accurately identifying the hydrodynamic model parameters of an underwater robot moving in water, such as the drag coefficient, lift coefficient, and yaw force coefficient, is an important step to improve motion control accuracy and stability. However, the existing method for identifying hydrodynamic parameters relies on water tank tests, which has the problem of high test costs. Summary of the invention

[0004] The present application aims to propose a method, device, equipment and medium for underwater robot hydrodynamic identification, which can realize the identification of hydrodynamic parameters of underwater robot at low cost.

[0005] According to the first aspect of the present application, the underwater robot hydrodynamic identification method includes:

[0006] Acquiring a control input of the underwater robot, wherein the control input is used to adjust the movement of the underwater robot;

[0007] Acquiring state data, wherein the state data represents the motion state of the underwater robot in water;

[0008] Inputting the control input into a dynamic model to obtain predicted state data; wherein the dynamic model is established based on the structural characteristics of the underwater robot and the motion characteristics of the underwater robot in the fluid;

[0009] A hydrodynamic parameter is determined based on the state data and the predicted state data.

[0010] According to some embodiments of the present application, the kinetic model is constrained by the following mathematical expression:

[0011] η(t,θ)=C(η)∫vdt;

[0012]

[0013] Wherein, η(t,θ)=[η1,η2] is the predicted state data, which represents the predicted state of the underwater robot in the geodetic coordinate system, θ represents the hydrodynamic parameter, η1=[x,y,z] T is the predicted position state data, η2=[φ,θ,Ψ] T is the predicted posture state data; v represents the motion state of the underwater robot in the body coordinate system, represents the derivative of v; C(η) is the vector conversion matrix between the earth coordinate system and the body coordinate system; I m represents the mass matrix of the underwater robot, I c represents the Coriolis and centrifugal matrices, H(t,θ) represents the hydrodynamic model, τ(i) represents the external force and torque applied to the underwater robot, and i represents the control input of the underwater robot.

[0014] According to some embodiments of the present application, the hydrodynamic parameter θ=[θ f ,θ i ], the hydrodynamic model is constrained by the following mathematical expression:

[0015] H f (t,θ)=θ f v(t)|v(t)|;

[0016]

[0017] Among them, H f represents the fluid hydrodynamic force, H i represents the inertial hydrodynamic force, θ f represents the fluid dynamic coefficient, θ i represents the additional hydrodynamic coefficient.

[0018] According to some embodiments of the present application, determining a hydrodynamic parameter according to the state data and the predicted state data includes:

[0019] Determining an optimization target according to the state data and the predicted state data;

[0020] When the optimization target satisfies the optimization stop condition, the hydrodynamic parameters are determined.

[0021] According to some embodiments of the present application, the optimization objective is constrained by the following mathematical expression:

[0022]

[0023] Among them, J is the optimization target, T is the time period, is the state data, and η(t,θ) is the predicted state data.

[0024] According to some embodiments of the present application, the status data includes location status data and posture status data Wherein, the position state data is obtained according to the position information of the underwater robot, and the posture state data is obtained according to the posture information of the underwater robot;

[0025] The step of determining an optimization target according to the state data and the predicted state data includes:

[0026] According to the setting state data The posture state data The predicted position state data η1 = [x, y, z] T and the predicted posture state data η2 = [φ, θ, Ψ] T , determine the optimization goal.

[0027] According to some embodiments of the present application, after obtaining the status data, the method further includes:

[0028] Perform feature engineering processing on the state data, wherein the feature engineering processing includes data preprocessing, noise filtering and normalization.

[0029] According to the underwater robot hydrodynamic identification device of the second aspect of the present application, the device includes:

[0030] A first acquisition unit, used to acquire a control input amount of the underwater robot, wherein the control input amount is used to adjust the action of the underwater robot;

[0031] A second acquisition unit, configured to acquire state data, wherein the state data represents a motion state of the underwater robot in water;

[0032] A prediction unit, used for inputting the control input into a dynamic model to obtain predicted state data; wherein the dynamic model is established based on the structural characteristics of the underwater robot and the motion characteristics of the underwater robot in the fluid;

[0033] A determination unit is used to determine a hydrodynamic parameter according to the state data and the predicted state data.

[0034] According to an electronic device of an embodiment of the third aspect of the present application, the device includes a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the programs or instructions are executed by the processor, the steps of the underwater robot hydrodynamic identification method as described in any one of the embodiments of the first aspect are implemented.

[0035] According to the computer-readable storage medium of the fourth aspect embodiment of the present application, computer-executable instructions are stored, and the computer-executable instructions are used to execute the underwater robot hydrodynamic identification method as described in the first aspect embodiment above.

[0036] In the embodiment of the present application, the actual observed state data of the underwater robot is obtained, and the predicted state data obtained by the dynamic model is obtained, and the hydrodynamic parameters are obtained by combining the state data and the predicted state data for analysis, thereby completing the hydrodynamic identification of the underwater robot. The method of the embodiment of the present application does not require an actual water tank test, avoids the problems of complicated test process and high test cost, can realize the dynamic identification of the underwater robot in a complex environment, and has a wide range of application prospects.

[0037] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent from the description, or may be understood by practicing the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0039] Figure 1 This is a flow chart of a method for identifying hydrodynamic force of an underwater robot according to an embodiment of the present application;

[0040] Figure 2 It is a schematic diagram of constructing a reference coordinate system for the motion state of an underwater robot according to an embodiment of the present application;

[0041] Figure 3 This is a schematic diagram of the process of an underwater robot hydrodynamic identification method according to an embodiment of the present application;

[0042] Figure 4 It is a structural schematic diagram of an embodiment of the underwater robot hydrodynamic identification device of the present application;

[0043] Figure 5 It is a schematic diagram of the hardware structure of an embodiment of the electronic device of the present application. DETAILED DESCRIPTION

[0044] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be understood as limiting the present application.

[0045] In the description of this application, if there is a description of first, second, etc., it is only for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0046] In the description of the present application, it should be understood that the descriptions involving orientation, such as the orientation or positional relationship indicated as up, down, etc., are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0047] In the description of this application, it should be noted that, unless otherwise clearly defined, terms such as setting, installing, connecting, etc. should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meaning of the above terms in this application based on the specific content of the technical solution.

[0048] The technical solution of the present application will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described below are only part of the embodiments of the present application, not all of the embodiments.

[0049] Figure 1 This is a flow chart of a method for identifying hydrodynamic force of an underwater robot according to an embodiment of the present application; Figure 2 It is a schematic diagram of constructing a reference coordinate system for the motion state of an underwater robot according to an embodiment of the present application; Figure 3 This is a schematic diagram of the process of an underwater robot hydrodynamic identification method according to an embodiment of the present application; Figure 4 It is a structural schematic diagram of an embodiment of the underwater robot hydrodynamic identification device of the present application; Figure 5 It is a schematic diagram of the hardware structure of an embodiment of the electronic device of the present application.

[0050] See below Figure 1 , the embodiments of the present application are further described.

[0051] The present application embodiment proposes a method for identifying the hydrodynamic force of an underwater robot, the method comprising the following steps:

[0052] Step 101: Obtain a control input of the underwater robot, wherein the control input is used to adjust the motion of the underwater robot;

[0053] Step 102: Acquire state data, wherein the state data represents the motion state of the underwater robot in water;

[0054] Step 103: input the control input into the dynamic model to obtain predicted state data; wherein the dynamic model is established based on the structural characteristics of the underwater robot and the motion characteristics of the underwater robot in the fluid;

[0055] Step 104: Determine the hydrodynamic parameters according to the state data and the predicted state data.

[0056] In the embodiment of the present application, the actual observed state data of the underwater robot is obtained, and the predicted state data obtained by the dynamic model is obtained, and the hydrodynamic parameters are obtained by combining the state data and the predicted state data for analysis, thereby completing the hydrodynamic identification of the underwater robot. The method of the embodiment of the present application does not require an actual water tank test, avoids the problems of complicated test process and high test cost, can realize the dynamic identification of the underwater robot in a complex environment, and has a wide range of application prospects.

[0057] The above-mentioned state data may be collected according to sensors carried by the underwater robot, and may reflect the motion state of the underwater robot in water, and may include position information and posture information of the underwater robot.

[0058] The above-mentioned predicted state data may include predicted position information and predicted posture information of the underwater robot.

[0059] The control input can be used to adjust and control the action and motion state of the underwater robot, and the control input can be manually input.

[0060] For example, the control input quantity may be the rotation speed of the propeller of the underwater robot, and the corresponding predicted state data may be obtained by setting the rotation speed value and inputting it into the dynamic model.

[0061] The above-mentioned dynamic model can be established based on the mechanical structure characteristics of the underwater robot and the movement characteristics of the underwater robot in the fluid; the above-mentioned dynamic model can output corresponding predicted state data according to the input control input quantity, so as to predict the predicted motion state of the underwater robot under the adjustment control of the control input quantity and the influence of the underwater environment.

[0062] It should be noted that the above dynamic model is established based on the motion characteristics of the underwater robot in the fluid, and therefore contains hydrodynamic parameter information. When the predicted state data obtained by the dynamic model is consistent with the state data actually observed by the underwater robot to a certain extent, it can be shown that the dynamic model at this time is more accurate, and the hydrodynamic parameter information contained in it is more accurate and close to the actual value.

[0063] The above-mentioned determination of the hydrodynamic parameters based on the state data and the predicted state data may be to determine that the hydrodynamic parameter values ​​in the dynamic model are the hydrodynamic parameters of the underwater robot when the state data and the predicted state data are consistent to a certain extent.

[0064] In some embodiments, the kinetic model is constrained by the following mathematical expression:

[0065] η(t,θ)=C(η)∫νdt;

[0066]

[0067] Among them, η(t,θ)=[η1,η2] is the predicted state data, which represents the predicted state of the underwater robot in the geodetic coordinate system, θ represents the hydrodynamic parameters, η1=[x,y,z] T To predict the position state data, η2=[φ,γ,ψ] T is the predicted posture state data; v represents the motion state of the underwater robot in the body coordinate system, represents the derivative of v; C(η) is the vector transformation matrix between the earth coordinate system and the body coordinate system; I m represents the mass matrix of the underwater robot, I c represents the Coriolis and centrifugal matrices, H(t,θ) represents the hydrodynamic model, τ(i) represents the external force and torque acting on the underwater robot, and i represents the control input of the underwater robot.

[0068] In this embodiment, it can be understood that the first mathematical expression is a kinematic equation and the second mathematical expression is a dynamic equation. By inputting the control input of the underwater robot and based on other known parameters, the speed v representing the motion state of the underwater robot in the body coordinate system can be obtained, and then the displacement state of the underwater robot can be obtained by integrating v. By using the vector conversion matrix, the predicted state of the underwater robot in the earth coordinate system can be obtained.

[0069] The above-mentioned earth coordinate system and the above-mentioned body coordinate system can refer to Figure 2 As shown, for the underwater robot, there are two reference coordinate systems in its motion space, namely the earth coordinate system and the body coordinate system. The specific composition of the earth coordinate system E-E1E2E3 includes: the origin E, the horizontal axis EE1, the transverse axis EE2 are located at the sea level, the longitudinal axis EE3 points to the center of the earth, and each axis of the earth coordinate system obeys the right-hand theorem. The specific composition of the body coordinate system O-XYZ includes: the origin O is located at the buoyancy center of the underwater robot, the horizontal axis OX points to the bow of the underwater robot, the transverse axis OY points to the side of the underwater robot, and the longitudinal axis OZ points to the bottom of the underwater robot. Each axis of the body coordinate system obeys the right-hand theorem.

[0070] The above η1=[x,y,z] T To predict the position state data, the position of the underwater robot in the earth coordinate system is represented by η2 = [φ, θ, Ψ] T To predict the attitude state data, φ, θ, and Ψ are the roll angle, pitch angle, and yaw angle, respectively, which represent the attitude of the underwater robot. The superscript T represents transposition, and the vector is written in the form of a column vector.

[0071] The above represents the speed v = [v1,v2] of the underwater robot in the body coordinate system, where v1 = [u,v,w] T represents the speed of the underwater robot, ν2=[p,q,r] T Represents the angular velocity of the underwater robot; where u, v, and w represent the linear velocity components of the underwater robot in the directions of the three coordinate axes in the body coordinate system, and p, q, and r represent the angular velocity components of the underwater robot around the three coordinate axes. The combination of v1 and v2 can describe the motion state of the robot in three-dimensional space.

[0072] The above C(η) can be a vector conversion matrix from the earth coordinate system to the body coordinate system.

[0073] In some embodiments, the hydrodynamic parameter θ = [θ f ,θ i ], the hydrodynamic model is constrained by the following mathematical expression:

[0074] H f (t,θ)=θ f v(t)|v(t)|;

[0075]

[0076] Among them, H f represents the fluid hydrodynamic force, H i represents the inertial hydrodynamic force, θ f represents the fluid dynamic coefficient, θ i represents the additional hydrodynamic coefficient.

[0077] In this embodiment, since the motion characteristics of the hydrodynamic model underwater robot in the fluid are established, it is constrained by the hydrodynamic model, that is, the above formula. The hydrodynamic model includes a fluid dynamic model and an inertia model. The hydrodynamic model contains hydrodynamic parameters, specifically including fluid dynamic coefficients and additional hydrodynamic coefficients.

[0078] The above fluid dynamics model focuses on the fluid dynamics that an object is subject to when it moves in a fluid, and the above inertial model focuses on the inertial effect of an object when it moves in a fluid.

[0079] The fluid dynamics model includes a fluid hydrodynamic coefficient, which may be a parameter describing the relationship between the force and torque to which an object is subjected when the object moves in a fluid and the motion state of the object.

[0080] The above inertial model includes an additional hydrodynamic coefficient, which is a parameter related to the force generated by the inertial effect related to fluid motion, and is related to the inertial effect of an object when it accelerates in a fluid.

[0081] The above-mentioned two types of hydrodynamic parameters, fluid dynamic coefficient and additional hydrodynamic coefficient, are crucial for predicting and analyzing the dynamic response of underwater robots in fluids.

[0082] In some embodiments, determining a hydrodynamic parameter based on the state data and the predicted state data includes:

[0083] Determine the optimization target based on the status data and predicted status data;

[0084] When the optimization objective meets the optimization stop condition, the hydrodynamic parameters are determined.

[0085] In this embodiment, the optimization target can be the difference between the state data and the predicted state data. When the state data and the predicted state data are close to a certain extent, the optimization stop condition is set, which can indicate that the dynamic model at this time is more accurate, and the hydrodynamic parameter information contained therein is more accurate and close to the actual value.

[0086] In some embodiments, determining a hydrodynamic parameter based on the state data and the predicted state data includes:

[0087] Based on the evolutionary algorithm, the optimization target is determined according to the state data and the predicted state data; when the optimization target meets the optimization stop condition, the hydrodynamic parameters are determined.

[0088] The above-mentioned evolutionary algorithm may be a genetic algorithm, a particle swarm algorithm or a differential evolution algorithm, etc.

[0089] In some embodiments, the above-mentioned evolutionary algorithm adopts a particle swarm algorithm, based on the evolutionary algorithm, and determines the optimization target according to the state data and the predicted state data; when the optimization target meets the optimization stop condition, the hydrodynamic parameters are determined, including:

[0090] Initialize particle swarm;

[0091] Calculate the fitness value of each particle, that is, the value of the optimization target;

[0092] Update the global optimal position and the historical optimal position of each particle;

[0093] Update each particle according to the global optimal position and the historical optimal position;

[0094] When the optimization stop condition is met, the global optimal solution is output and the hydrodynamic parameters are determined;

[0095] If the optimization stop condition is not met, return to the step of calculating the fitness value of each particle.

[0096] In this embodiment, a particle swarm algorithm is used. The particle swarm algorithm is favored for its simplicity, robustness and global search ability. It can effectively handle nonlinear, multi-peak and high-dimensional optimization problems, and is easy to parallelize, has strong adaptability, fast convergence, and does not require gradient information, making it perform well in a variety of fields and complex problems. Compared with the problem of long simulation time in computational fluid dynamics simulation, this embodiment can improve the efficiency of hydrodynamic parameter identification and can effectively improve the accuracy of hydrodynamic parameter identification to a certain extent.

[0097] The above-mentioned initialized particle swarm can be understood as randomly generating a variety of different hydrodynamic parameter values ​​at the beginning, thereby forming a plurality of different dynamic models. The particle swarm algorithm is used to optimize the multiple dynamic models. When the optimization stop condition is met, the optimal dynamic model is finally obtained, that is, the dynamic model closest to the actual one, and the hydrodynamic parameter values ​​in the model are determined as the hydrodynamic parameters of the underwater robot.

[0098] In some embodiments, the optimization objective is constrained by the following mathematical expression:

[0099]

[0100] Among them, J is the optimization target, T is the time period, is the state data, and η(t,θ) is the predicted state data.

[0101] In this embodiment, argmin represents the variable value θ when the optimization target J takes the minimum value, that is, the hydrodynamic parameter value when the optimization target takes the minimum value. The error between the state data and the predicted state data is The hydrodynamic parameters are determined by calculating the mean square error within the time period T and minimizing the error, that is, when the state data is closest to the predicted state data.

[0102] like Figure 3As shown, in some embodiments, a control input is input to the underwater robot, and actual state data can be obtained according to the sensor. A control input is input to the dynamic model, and generated predicted state data can be obtained. The optimization target is set as the error between the state data and the predicted state data. The dynamic model is optimized based on the evolutionary algorithm so that the optimization target is continuously reduced until the optimization stop condition is reached. The optimization is stopped and the optimal dynamic model is obtained. The hydrodynamic parameters at this time are determined as the hydrodynamic parameters of the underwater robot, and the hydrodynamic identification is completed.

[0103] In some embodiments, the status data includes location status data and posture status data Wherein, the position state data is obtained according to the position information of the underwater robot, and the attitude state data is obtained according to the attitude information of the underwater robot;

[0104] Based on the status data and predicted status data, determine the optimization goals, including:

[0105] Based on location status data Posture state data Predicted position state data η1 = [x, y, z] T and predicted posture state data η2 = [φ,θ,Ψ] T , determine the optimization goal.

[0106] In this embodiment, since the state data includes position state data and posture state data, and the predicted state data includes predicted position state data and predicted posture state data, obtaining the deviation between the state data and the predicted state data can be achieved by obtaining the deviation between the position state data and the predicted position state data and the deviation between the posture state data and the predicted posture state data.

[0107] In some implementations, after obtaining the status data, the method further includes:

[0108] Perform feature engineering on the state data, where the feature engineering includes data preprocessing, noise filtering, and normalization.

[0109] In this implementation, feature engineering of state data is a key step in optimizing data analysis and machine learning model performance. This process may include data preprocessing, noise filtering, and normalization. Through these feature engineering steps, the quality of the data can be improved, making the features more suitable for model training, thereby improving the model's prediction accuracy and generalization ability.

[0110] The above data preprocessing can handle missing values ​​and outliers to ensure data quality.

[0111] The above noise filtering can reduce random fluctuations in the data and remove outliers, thereby improving the accuracy of the data.

[0112] The above normalization can improve the training effect and prediction accuracy of the model by scaling the data to a uniform range or standardizing it to make the features comparable.

[0113] The underwater robot hydrodynamic identification method provided in the embodiment of the present application can be executed by the underwater robot hydrodynamic identification device 200. In the embodiment of the present application, the underwater robot hydrodynamic identification device 200 executing the underwater robot hydrodynamic identification method is taken as an example to illustrate the underwater robot hydrodynamic identification device 200 provided in the embodiment of the present application.

[0114] See also Figure 4 , is a structural schematic diagram of an underwater robot hydrodynamic identification device 200 provided in an embodiment of the present application.

[0115] like Figure 4 As shown, the underwater robot hydrodynamic identification device 200 includes:

[0116] A first acquisition unit 201 is used to acquire a control input amount of the underwater robot, wherein the control input amount is used to adjust the action of the underwater robot;

[0117] A second acquisition unit 202 is used to acquire state data, wherein the state data represents the motion state of the underwater robot in water;

[0118] The prediction unit 203 is used to input the control input into the dynamic model to obtain the predicted state data; wherein the dynamic model is established based on the structural characteristics of the underwater robot and the motion characteristics of the underwater robot in the fluid;

[0119] The determination unit 204 is used to determine the hydrodynamic parameters according to the state data and the predicted state data.

[0120] In some embodiments, the kinetic model is constrained by the following mathematical expression:

[0121] η(t,θ)=C(η)∫vdt;

[0122]

[0123] Among them, η(t,θ)=[η1,η2] is the predicted state data, which represents the predicted state of the underwater robot in the geodetic coordinate system, θ represents the hydrodynamic parameters, η1=[x,y,z] T is the predicted position state data, η2=[φ,θ,Ψ] T is the predicted posture state data; v represents the motion state of the underwater robot in the body coordinate system, represents the derivative of v; C(η) is the vector transformation matrix between the earth coordinate system and the body coordinate system; I m represents the mass matrix of the underwater robot, I c represents the Coriolis and centrifugal matrices, H(t,θ) represents the hydrodynamic model, τ(i) represents the external force and torque acting on the underwater robot, and i represents the control input of the underwater robot.

[0124] In some embodiments, the hydrodynamic parameter θ = [θ f ,θ i ], the hydrodynamic model is constrained by the following mathematical expression:

[0125] H f (t,θ)=θ f v(t)|v(t)|;

[0126]

[0127] Among them, H f represents the fluid hydrodynamic force, H i represents the inertial hydrodynamic force, θ f represents the fluid dynamic coefficient, θ i represents the additional hydrodynamic coefficient.

[0128] In some implementations, the determining unit 204 may be configured to:

[0129] Determine the optimization target based on the status data and predicted status data;

[0130] When the optimization objective meets the optimization stop condition, the hydrodynamic parameters are determined.

[0131] In some embodiments, the optimization objective is constrained by the following mathematical expression:

[0132]

[0133] Among them, J is the optimization target, T is the time period, is the state data, and η(t,θ) is the predicted state data.

[0134] In some embodiments, the status data includes location status data and posture status data Wherein, the position state data is obtained according to the position information of the underwater robot, and the attitude state data is obtained according to the attitude information of the underwater robot;

[0135] The determining unit 204 may be configured to:

[0136] Based on location status data Posture state data Predicted position state data η1 = [x, y, z] T and predicted posture state data η2 = [φ,θ,Ψ] T , determine the optimization goal.

[0137] In some implementations, the first acquisition unit 201 may also be used to:

[0138] Perform feature engineering on the state data, where the feature engineering includes data preprocessing, noise filtering, and normalization.

[0139] Since the underwater robot hydrodynamic identification device 200 adopts all the technical solutions of the underwater robot hydrodynamic identification method of the above embodiment, it at least has all the beneficial effects brought by the technical solutions of the above embodiment, which will not be described in detail here.

[0140] Figure 5 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application.

[0141] The electronic device may include a processor 301 and a memory 302 storing computer program instructions.

[0142] Specifically, the processor 301 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0143] The memory 302 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 302 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the memory 302 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 302 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 302 is a non-volatile solid-state memory.

[0144] In some embodiments, the memory 302 may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage medium device, an optical storage medium device, a flash memory device, an electrical, optical or other physical / tangible memory storage device. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to an aspect of the present disclosure.

[0145] The processor 301 reads and executes the computer program instructions stored in the memory 302 to implement any one of the underwater robot hydrodynamic identification methods in the above embodiments.

[0146] In one example, the electronic device may further include a communication interface 303 and a bus 310. Figure 5 As shown, the processor 301, the memory 302, and the communication interface 303 are connected via a bus 310 and communicate with each other.

[0147] The communication interface 303 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0148] Bus 310 includes hardware, software or both, and the parts of online data flow billing equipment are coupled to each other. For example, but not limitation, bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front-end bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 310 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the present application considers any suitable bus or interconnection.

[0149] The electronic device can execute the underwater robot hydrodynamic identification method in the embodiment of the present application, thereby realizing the combination Figure 1 and Figure 4 The invention describes a method and device for identifying the hydrodynamic force of an underwater robot.

[0150] In addition, in combination with the underwater robot hydrodynamic identification method in the above embodiment, the present application embodiment can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by the processor, any one of the underwater robot hydrodynamic identification methods in the above embodiment is implemented.

[0151] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0152] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0153] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0154] Aspects of the present disclosure are described above with reference to the flowchart and / or block diagram of the method, device (system) and computer program product according to the embodiment of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It can also be understood that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs a specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0155] The above is only a specific implementation of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application.

Claims

1. A method for identifying hydrodynamic force of an underwater robot, characterized in that: include: Acquiring a control input of the underwater robot, wherein the control input is used to adjust the movement of the underwater robot; Acquiring state data, wherein the state data represents the motion state of the underwater robot in water; Inputting the control input into a dynamic model to obtain predicted state data; wherein the dynamic model is established based on the structural characteristics of the underwater robot and the motion characteristics of the underwater robot in the fluid; A hydrodynamic parameter is determined based on the state data and the predicted state data.

2. The underwater robot hydrodynamic identification method according to claim 1, characterized in that: The kinetic model is constrained by the following mathematical expression: η(t,θ)=C(η)∫vdt; Wherein, η(t,θ)=[η1,η2] is the predicted state data, which represents the predicted state of the underwater robot in the geodetic coordinate system, θ represents the hydrodynamic parameter, η1=[x,y,z] T is the predicted position state data, η2=[φ,θ,Ψ] T is the predicted posture state data; ν represents the motion state of the underwater robot in the body coordinate system, represents the derivative of ν; C(η) is the vector conversion matrix between the earth coordinate system and the body coordinate system; I m represents the mass matrix of the underwater robot, I c represents the Coriolis and centrifugal matrices, H(t,θ) represents the hydrodynamic model, τ(i) represents the external force and torque applied to the underwater robot, and i represents the control input of the underwater robot.

3. The underwater robot hydrodynamic identification method according to claim 2, characterized in that: The hydrodynamic parameter θ=[θ f ,θ i ], the hydrodynamic model is constrained by the following mathematical expression: H f (t,θ)=θ f v(t)|v(t)|; Among them, H f represents the fluid hydrodynamic force, H i represents the inertial hydrodynamic force, θ f represents the fluid dynamic coefficient, θ i represents the additional hydrodynamic coefficient.

4. The underwater robot hydrodynamic identification method according to claim 2, characterized in that: Determining the hydrodynamic parameters according to the state data and the predicted state data includes: Determining an optimization target according to the state data and the predicted state data; When the optimization target satisfies the optimization stop condition, the hydrodynamic parameters are determined.

5. The underwater robot hydrodynamic identification method according to claim 4, characterized in that: The optimization objective is constrained by the following mathematical expression: Among them, J is the optimization target, T is the time period, is the state data, and η(t,θ) is the predicted state data.

6. The underwater robot hydrodynamic identification method according to claim 5, characterized in that: The status data includes position status data and posture status data Wherein, the position state data is obtained according to the position information of the underwater robot, and the posture state data is obtained according to the posture information of the underwater robot; The step of determining an optimization target according to the state data and the predicted state data includes: According to the setting state data The posture state data The predicted position state data η1 = [x, y, z] T and the predicted posture state data η2 = [φ, θ, Ψ] T , determine the optimization goal.

7. The underwater robot hydrodynamic identification method according to claim 1, characterized in that: After obtaining the status data, the method further includes: Perform feature engineering processing on the state data, wherein the feature engineering processing includes data preprocessing, noise filtering and normalization.

8. A hydrodynamic identification device for an underwater robot, characterized in that: include: A first acquisition unit, used to acquire a control input amount of the underwater robot, wherein the control input amount is used to adjust the action of the underwater robot; A second acquisition unit, configured to acquire state data, wherein the state data represents a motion state of the underwater robot in water; A prediction unit, used for inputting the control input into a dynamic model to obtain predicted state data; wherein the dynamic model is established based on the structural characteristics of the underwater robot and the motion characteristics of the underwater robot in the fluid; A determination unit is used to determine a hydrodynamic parameter according to the state data and the predicted state data.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the programs or instructions are executed by the processor, the steps of the underwater robot hydrodynamic identification method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the underwater robot hydrodynamic identification method according to any one of claims 1 to 7.

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