Method and system for planning navigation speed of AUV (Autonomous Underwater Vehicle) based on vertical depth

Abstract

The invention provides an AUV navigation speed planning method and system based on vertical depth, and the method comprises the steps: building an AUV general propulsion model according to a preset battery internal resistance model, a DC motor model and a propeller model of an AUV, and obtaining the expected speed of the to-be-planned AUV according to the AUV general propulsion model; obtaining battery SOC values of the to-be-planned AUV at different depths through a Bayesian Monte Carlo method; and according to the battery SOC value and the expected speed, obtaining the final navigation speed of the to-be-planned AUV. According to the technical scheme, the influence of the vertical direction of the AUV on the speed is considered during speed planning, so that the AUV can obtain a more accurate navigation speed.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence, and more particularly to a planning method and system for AUV navigation speed based on vertical depth. Background Art

[0002] Autonomous underwater vehicles (AUVs) are unmanned vehicles used to perform underwater missions. They possess a high degree of autonomy and are widely used in ocean exploration, resource exploration, military reconnaissance, environmental monitoring, and seafloor topography mapping. With the growing demand for ocean development, AUVs are playing an indispensable role in deep-sea exploration, polar expeditions, and shipwreck archaeology.

[0003] The core mission of an AUV is to achieve efficient, stable, and precise underwater navigation and operations, and speed planning is one of the key links in achieving mission objectives. Reasonable speed planning can not only significantly improve mission efficiency, but also optimize energy consumption and enhance the overall reliability of the system.

[0004] Existing speed planning only considers the AUV's horizontal modeling, which is insufficient for complex missions requiring high AUV performance. Furthermore, AUVs generally rely on lithium-ion batteries for energy, which have limited power and are difficult to recharge during missions. They also face complex ocean environments, including dynamic changes in currents, temperature, and pressure.

[0005] While current research is progressing towards optimizing AUV speed planning, further innovation and improvement are still needed for the application of underwater vehicles in complex environments. Future research should focus on how to improve the stability of speed planning in AUV energy management systems in complex marine environments and how to improve the accuracy of AUV lithium-ion battery SOC estimation. Summary of the Invention

[0006] The main purpose of the embodiments of the present invention is to provide a method and system for planning the navigation speed of an AUV based on vertical depth, so that the planning of the navigation speed of the AUV is more accurate.

[0007] In a first aspect, a method for planning the navigation speed of an AUV based on vertical depth is provided, the planning method comprising:

[0008] Establishing an AUV general propulsion model based on a preset battery internal resistance model, a DC motor model, and a propeller model of the autonomous underwater vehicle AUV, and obtaining an expected speed of the AUV to be planned based on the AUV general propulsion model;

[0009] Obtaining the battery SOC values ​​of the AUV to be planned at different depths by using the Bayesian Monte Carlo method;

[0010] The final navigation speed of the AUV to be planned is obtained according to the battery SOC value and the expected speed.

[0011] In one possible implementation, the battery internal resistance model is:

[0012] Among them, V oc-bat represents the open circuit voltage, R bat Indicates the total internal resistance of the battery pack, V bat Indicates the battery output voltage, I bat represents the battery current, SOC(t0) represents the initial SOC of the battery, C bat is the capacity of the battery, η bat Represents the coulombic efficiency of the battery.

[0013] In another possible implementation, the DC motor model is:

[0014] Among them, I a Indicates the motor input current, V m is the armature voltage, R a Indicates the armature resistance, L af Represents the mutual inductance between the magnetic field and the armature, L a Indicates the armature inductance, L f Represents the magnetic field inductance, ω, B m , J represent the motor speed, the motor maximum magnetic flux potential and the rotor moment of inertia, Q f represents the Coulomb friction torque, Q prop Indicates load torque.

[0015] In another possible implementation, the propeller model is:

[0016] T prop =ρ·D 4 ·K T (J0)·n·|n|

[0017] Q prop =ρ·D 5 ·K Q (J0)·n·|n|;

[0018] Where D is the propeller diameter, ρ is the water density, K T and K Q are the thrust and torque coefficients, n is the propeller speed, and u is the horizontal speed of the vehicle.

[0019] Among them, K T and K Q It can be expressed as a polynomial: Among them, k t1 ,k t2 ,k t3 and k q1 ,k q2 ,k q3 is a coefficient suitable for propeller design, then the propeller model is expressed as:

[0020] In another possible implementation, the AUV to be planned obtains the final navigation speed using PI control.

[0021] In another possible implementation, the representation model of the PI control is:

[0022] e(t)=u * (t)-u(t)

[0023]

[0024] Among them, k p 、k i and k d are the proportional, integral and derivative parameters respectively.

[0025] In a second aspect, a planning system for an AUV navigation speed based on vertical depth is provided, the planning system comprising:

[0026] The expected speed acquisition module is used to establish an AUV general propulsion model based on the preset battery internal resistance model, DC motor model, and propeller model of the autonomous underwater robot AUV, and obtain the expected speed of the AUV to be planned based on the AUV general propulsion model;

[0027] A battery SOC value acquisition module is used to obtain the battery SOC values ​​of the AUV to be planned at different depths through the Bayesian Monte Carlo method;

[0028] The final navigation speed acquisition module is used to obtain the final navigation speed of the AUV to be planned according to the battery SOC value and the expected speed.

[0029] In a possible implementation, the AUV to be planned obtains the final navigation speed using PI control.

[0030] In a third aspect, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method for planning the AUV navigation speed based on vertical depth as provided in the first aspect is implemented.

[0031] In a fourth aspect, a non-transitory computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method for planning the AUV navigation speed based on vertical depth as provided in the first aspect is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for describing the embodiments of the present application.

[0033] Figure 1 A flowchart of a method for planning AUV navigation speed based on vertical depth provided by one embodiment of the present invention;

[0034] Figure 2 A structural diagram of a system for planning AUV navigation speed based on vertical depth provided by one embodiment of the present invention;

[0035] Figure 3 This is a schematic diagram of the physical structure of an electronic device provided by the present invention.

[0036] Specific implementation method

[0037] The following describes embodiments of the present application in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar modules or modules 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 are not to be construed as limiting the present invention.

[0038] It will be understood by those skilled in the art that, unless expressly stated otherwise, the singular forms "a", "an", "said" and "the" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the specification of this application refers to the presence of the features, integers, steps, operations, modules and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, modules, components and / or groups thereof. It should be understood that when we refer to a module as being "connected" or "coupled" to another module, it may be directly connected or coupled to the other module, or there may be an intermediate module. In addition, "connected" or "coupled" as used herein may include wireless connection or wireless coupling. The term "and / or" used herein includes all or any modules and all combinations of one or more associated listed items.

[0039] In order to make the objectives, technical solutions and advantages of this application clearer, the implementation of this application will be further described in detail below with reference to the accompanying drawings.

[0040] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0041] like Figure 1 FIG. 1 is a flow chart of a method for planning an AUV navigation speed based on vertical depth according to an embodiment of the present invention. The method includes:

[0042] Step S101: establishing a universal propulsion model for the autonomous underwater vehicle (AUV) based on a preset battery internal resistance model, a DC motor model, and a propeller model, and obtaining a desired speed of the AUV to be planned based on the universal propulsion model;

[0043] Step S102, obtaining the battery SOC values ​​of the AUV to be planned at different depths by using the Bayesian Monte Carlo method;

[0044] Step S103: obtaining the final navigation speed of the AUV to be planned according to the battery SOC value and the expected speed.

[0045] In step S101, the battery internal resistance model is:

[0046] Among them, V oc-bat represents the open circuit voltage, R bat Indicates the total internal resistance of the battery pack, V bat Indicates the battery output voltage, I bat represents the battery current, SOC(t0) represents the initial SOC of the battery, C bat is the capacity of the battery, η bat Represents the coulombic efficiency of the battery.

[0047] The DC motor model is:

[0048] Among them, I a Indicates the motor input current, V m is the armature voltage, R a Indicates the armature resistance, L af Represents the mutual inductance between the magnetic field and the armature, L a Indicates the armature inductance, L f Represents the magnetic field inductance, ω, B m , J represent the motor speed, the motor maximum magnetic flux potential and the rotor moment of inertia, Q f represents the Coulomb friction torque, Q prop Indicates load torque.

[0049] The propeller model is:

[0050] T prop =ρ·D 4 ·K T (J0)·n·|n|

[0051] Q prop =ρ·D 5 ·K Q (J0)·n·|n|;

[0052] Where D is the propeller diameter, ρ is the water density, K T and K Q are the thrust and torque coefficients, n is the propeller speed, and u is the horizontal speed of the vehicle.

[0053] Among them, K T and K Q It can be expressed as a polynomial: Among them, k t1 ,k t2 ,k t3 and k q1 ,k q2 ,k q3 is a coefficient suitable for propeller design, then the propeller model is expressed as:

[0054] The expected speed of the AUV to be planned is obtained according to the AUV general propulsion model, specifically:

[0055] Assuming that the center of gravity of the AUV is located at the origin of the fixed reference system of the AUV, the AUV is standardly centrosymmetric, and the swing, roll, and yaw motions are ignored when moving in the vertical plane. The dynamic equation established in the vertical plane is as follows:

[0056]

[0057] Where m is the mass of the spacecraft, and is the hydrodynamic additional mass, ω represents the vertical velocity of the vehicle, q represents the pitch angular velocity, X prop represents the propeller thrust, δ s is the deflection of the aircraft elevator, X d and Z d It is the disturbance of the marine environment, Z uuδs is the elevator force coefficient, X u|u| ,X ωq ,X qq ,Z ω|ω| ,Z q|q| ,Z uq ,Z uωis the hydrodynamic coefficient of the AUV.

[0058] In the formula, the mechanical equations in both the horizontal and vertical directions have coupling forces. If the vehicle under study is a large AUV, performs uniform linear motion, has low requirements for motion accuracy, has weak nonlinearity, and has a small angle of attack α when moving forward, the coupling effect can be ignored. If the ocean environment is simple, the effect of the environment on the vehicle can be ignored. And X u|u| and Z ω|ω| It can be expressed as:

[0059]

[0060] Among them C d is the hydrodynamic coefficient, A f is the frontal area of ​​the aircraft, represents the projection of the front area on the x-axis. Represents the projection of the front area in the z-axis direction.

[0061] When the spacecraft moves in a straight line at a uniform speed, the traction power P in the x-axis direction is tra,x and the traction power P in the z-axis direction tra,z They are:

[0062]

[0063] Total traction power:

[0064] P tra =P tra,x +P tra,z

[0065] The power P required by the AUV dem is the ratio of traction power to total conversion efficiency:

[0066]

[0067] According to the battery model, the power required by the AUV is the output power of the lithium-ion battery:

[0068] P dem =P bat =V bat I bat

[0069] According to the above formula, the battery current is expressed as:

[0070]

[0071] The consumption of battery energy can be expressed as:

[0072]

[0073] Assuming the battery current changes slowly over time:

[0074]

[0075] The distance traveled by the aircraft can be expressed as:

[0076]

[0077] Where U is the total speed of the vehicle.

[0078] Assuming that the rate of change of the vehicle's velocity when moving in the vertical plane is close to 0 within a very short time, the above formula can be expressed as:

[0079]

[0080] The expected speed can be expressed by the above formula:

[0081]

[0082] Considering the relationship between the AUV's speed and horizontal speed, vertical speed, and angle of attack, the expected speed can be expressed as:

[0083]

[0084] In step S102, when the AUV is sailing in the ocean, the depth of the AUV will change according to the actual needs of the navigation. At different depths, the external temperature will also change accordingly, and different temperatures will produce corresponding changes in the SOC value of the AUV. Therefore, it is necessary to obtain the SOC value at different temperatures, specifically:

[0085] The Bayesian Monte Carlo method is used to estimate the battery SOC, which approximates the probability density function through a set of weighted random samples, which is expressed as follows:

[0086]

[0087] in represents the state variable, represents the state variable of random particles, N s represents the number of random particles, represents the weight associated with each particle, and the weight update can be expressed as:

[0088]

[0089] Where V b,k is the observed value of the battery output voltage at time k, is the predicted value of the battery output voltage considering the temperature at time k:

[0090]

[0091] The total weight of all particles can be normalized as:

[0092]

[0093] The seawater temperature decreases as the depth decreases. The expression for the change of temperature with depth is:

[0094]

[0095] Where h represents the depth of the seawater, T(h) represents the seawater temperature that changes with depth, β represents the ocean temperature gradient, T0 represents the sea surface temperature, which is assumed to be 25°C, and T ∞ Indicates the temperature of the deep sea, assuming it is 2°C. The capacity of lithium-ion batteries changes with temperature, as shown below:

[0096] C bat (T)=C nom ·f(T)

[0097] f(T)=1-α(T ref -T) 2

[0098] Among them, C nom is the nominal capacity of the lithium-ion battery, f(T) is the temperature coefficient function, α is 0.002, T ref The reference temperature is 25°C. The particle sampling after introducing the temperature is:

[0099]

[0100] Among them, η is a tuning parameter used to adjust the intensity of the noise's influence on the particle state transition, and N(0,Q) refers to Gaussian noise with a mean of 0 and a variance of Q. Finally, the estimated value of the SOC at time k can be obtained:

[0101]

[0102] As an optional embodiment of the present invention, the AUV to be planned obtains the final navigation speed using a PI control method.

[0103] The PI control can be represented by the following model:

[0104] e(t)=u * (t)-u(t)

[0105]

[0106] Among them, k p 、k i and kd are the proportional, integral and derivative parameters respectively.

[0107] In this embodiment of the present invention, a universal propulsion model for an autonomous underwater vehicle (AUV) is established based on a preset battery internal resistance model, DC motor model, and propeller model. The desired speed of the AUV to be planned is then determined based on this universal propulsion model. The battery SOC values ​​of the AUV to be planned at different depths are then determined using a Bayesian Monte Carlo method. The final navigation speed of the AUV to be planned is then determined based on these battery SOC values ​​and the desired speed. This technical solution considers the influence of the AUV's vertical direction on its speed during speed planning, enabling the AUV to obtain a more accurate navigation speed.

[0108] like Figure 2 FIG. 1 is a structural diagram of a planning system for AUV navigation speed based on vertical depth according to an embodiment of the present invention. The planning system includes:

[0109] The expected speed acquisition module 201 is used to establish an AUV general propulsion model based on a preset battery internal resistance model, DC motor model, and propeller model of the autonomous underwater vehicle (AUV), and to acquire an expected speed of the AUV to be planned based on the AUV general propulsion model;

[0110] A battery SOC value acquisition module 202 is configured to acquire the battery SOC values ​​of the AUV to be planned at different depths using a Bayesian Monte Carlo method;

[0111] The final navigation speed acquisition module 203 is used to acquire the final navigation speed of the AUV to be planned according to the battery SOC value and the expected speed.

[0112] The battery internal resistance model is:

[0113] Among them, V oc-bat represents the open circuit voltage, R bat Indicates the total internal resistance of the battery pack, V bat Indicates the battery output voltage, I bat represents the battery current, SOC(t0) represents the initial SOC of the battery, C bat is the capacity of the battery, η bat Represents the coulombic efficiency of the battery.

[0114] The DC motor model is:

[0115] Among them, I a Indicates the motor input current, V m is the armature voltage, R a Indicates the armature resistance, L af Represents the mutual inductance between the magnetic field and the armature, La Indicates the armature inductance, L f Represents the magnetic field inductance, ω, B m , J represent the motor speed, the motor maximum magnetic flux potential and the rotor moment of inertia, Q f represents the Coulomb friction torque, Q prop Indicates load torque.

[0116] The propeller model is:

[0117] T prop =ρ·D 4 ·K T (J0)·n·|n|

[0118] Q prop =ρ·D 5 ·K Q (J0)·n·|n|;

[0119] Where D is the propeller diameter, ρ is the water density, K T and K Q are the thrust and torque coefficients, n is the propeller speed, and u is the horizontal speed of the vehicle.

[0120] Among them, K T and K Q It can be expressed as a polynomial: Among them, k t1 ,k t2 ,k t3 and k q1 ,k q2 ,k q3 is a coefficient suitable for propeller design, then the propeller model is expressed as:

[0121] The expected speed of the AUV to be planned is obtained according to the AUV general propulsion model, specifically:

[0122] Assuming that the center of gravity of the AUV is located at the origin of the fixed reference system of the AUV, the AUV is standardly centrosymmetric, and the swing, roll, and yaw motions are ignored when moving in the vertical plane. The dynamic equation established in the vertical plane is as follows:

[0123]

[0124] Where m is the mass of the spacecraft, and is the hydrodynamic additional mass, ω represents the vertical velocity of the vehicle, q represents the pitch angular velocity, X prop represents the propeller thrust, δ s is the deflection of the aircraft elevator, X dand Z d It is the disturbance of the marine environment. is the elevator force coefficient, X u|u| ,X ωq ,X qq ,Z ω|ω| ,Z q|q| ,Z uq ,Z uω is the hydrodynamic coefficient of the AUV.

[0125] In the formula, the mechanical equations in both the horizontal and vertical directions have coupling forces. If the vehicle under study is a large AUV, performs uniform linear motion, has low requirements for motion accuracy, has weak nonlinearity, and has a small angle of attack α when moving forward, the coupling effect can be ignored. If the ocean environment is simple, the effect of the environment on the vehicle can be ignored. And X u|u| and Z ω|ω| It can be expressed as:

[0126]

[0127] Among them C d is the hydrodynamic coefficient, A f is the frontal area of ​​the aircraft, represents the projection of the front area on the x-axis. Represents the projection of the front area in the z-axis direction.

[0128] When the spacecraft moves in a straight line at a uniform speed, the traction power P in the x-axis direction is tra,x and the traction power P in the z-axis direction tra,z They are:

[0129]

[0130] Total traction power:

[0131] P tra =P tra,x +P tra,z

[0132] The power P required by the AUV dem is the ratio of traction power to total conversion efficiency:

[0133]

[0134] According to the battery model, the power required by the AUV is the output power of the lithium-ion battery:

[0135] P dem =P bat =V bat I bat

[0136] According to the above formula, the battery current is expressed as:

[0137]

[0138] The consumption of battery energy can be expressed as:

[0139]

[0140] Assuming the battery current changes slowly over time:

[0141]

[0142] The distance traveled by the aircraft can be expressed as:

[0143]

[0144] Where U is the total speed of the vehicle.

[0145] Assuming that the rate of change of the vehicle's velocity when moving in the vertical plane is close to 0 within a very short time, the above formula can be expressed as:

[0146]

[0147] The expected speed can be expressed by the above formula:

[0148]

[0149] Considering the relationship between the AUV's speed and horizontal speed, vertical speed, and angle of attack, the expected speed can be expressed as:

[0150]

[0151] When an AUV is sailing in the ocean, its depth will change according to the actual needs of navigation. At different depths, its external temperature will also change accordingly, and different temperatures will produce corresponding changes in the SOC value of the AUV. Therefore, it is necessary to obtain the SOC value at different temperatures, specifically:

[0152] The Bayesian Monte Carlo method is used to estimate the battery SOC, which approximates the probability density function through a set of weighted random samples, which is expressed as follows:

[0153]

[0154] in represents the state variable, represents the state variable of random particles, N s represents the number of random particles, represents the weight associated with each particle, and the weight update can be expressed as:

[0155]

[0156] Where V b,k is the observed value of the battery output voltage at time k, is the predicted value of the battery output voltage considering the temperature at time k:

[0157]

[0158] The total weight of all particles can be normalized as:

[0159]

[0160] The seawater temperature decreases as the depth decreases. The expression for the change of temperature with depth is:

[0161]

[0162] Where h represents the depth of the seawater, T(h) represents the seawater temperature that changes with depth, β represents the ocean temperature gradient, T0 represents the sea surface temperature, which is assumed to be 25°C, and T ∞ Indicates the temperature of the deep sea, assuming it is 2°C. The capacity of lithium-ion batteries changes with temperature, as shown below:

[0163] C bat (T)=C nom ·f(T)

[0164] f(T)=1-α(T ref -T) 2

[0165] Among them, C nom is the nominal capacity of the lithium-ion battery, f(T) is the temperature coefficient function, α is 0.002, T ref The reference temperature is 25°C. The particle sampling after introducing the temperature is:

[0166]

[0167] Among them, η is a tuning parameter used to adjust the intensity of the noise's influence on the particle state transition, and N(0,Q) refers to Gaussian noise with a mean of 0 and a variance of Q. Finally, the estimated value of the SOC at time k can be obtained:

[0168]

[0169] Figure 3 An example of a physical structure diagram of an electronic device is shown below. Figure 3As shown, the electronic device may include: a processor (processor) 301, a communication interface (CommunicationsInterface) 302, a memory (memory) 303 and a communication bus 304, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus. The processor can call the logic instructions in the memory to execute a planning method for the AUV navigation speed based on the vertical depth. The planning method includes: establishing an AUV general propulsion model based on a preset battery internal resistance model, a DC motor model, and a propeller model of an autonomous underwater robot (AUV); obtaining the expected speed of the AUV to be planned based on the AUV general propulsion model; obtaining the battery SOC value of the AUV to be planned at different depths through the Bayesian Monte Carlo method; and obtaining the final navigation speed of the AUV to be planned based on the battery SOC value and the expected speed.

[0170] In addition, the logical instructions in the above-mentioned memory can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program code.

[0171] On the other hand, an embodiment of the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the AUV navigation speed planning method based on vertical depth provided by the above-mentioned method embodiments. The planning method includes: establishing an AUV general propulsion model based on a preset battery internal resistance model, DC motor model, and propeller model of an autonomous underwater robot AUV, and obtaining the expected speed of the AUV to be planned based on the AUV general propulsion model; obtaining the battery SOC value of the AUV to be planned at different depths through the Bayesian Monte Carlo method; and obtaining the final navigation speed of the AUV to be planned based on the battery SOC value and the expected speed.

[0172] On the other hand, an embodiment of the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the AUV navigation speed planning method based on vertical depth provided in the above embodiments, the planning method comprising: establishing an AUV general propulsion model based on a preset battery internal resistance model, a DC motor model, and a propeller model of an autonomous underwater robot AUV, and obtaining the expected speed of the AUV to be planned based on the AUV general propulsion model; obtaining the battery SOC value of the AUV to be planned at different depths through a Bayesian Monte Carlo method; and obtaining the final navigation speed of the AUV to be planned based on the battery SOC value and the expected speed.

[0173] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the flowcharts of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0174] The above is only a partial implementation of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for planning AUV navigation speed based on vertical depth, characterized in that: The planning method includes: Establishing an AUV general propulsion model based on a preset battery internal resistance model, a DC motor model, and a propeller model of the autonomous underwater vehicle AUV, and obtaining an expected speed of the AUV to be planned based on the AUV general propulsion model; Obtaining the battery SOC values ​​of the AUV to be planned at different depths by using the Bayesian Monte Carlo method; The final navigation speed of the AUV to be planned is obtained according to the battery SOC value and the expected speed.

2. The method according to claim 1, wherein The battery internal resistance model is: Among them, V oc-bat represents the open circuit voltage, R bat Indicates the total internal resistance of the battery pack, V bat Indicates the battery output voltage, I bat represents the battery current, SOC(t0) represents the initial SOC of the battery, C bat is the capacity of the battery, η bat Represents the coulombic efficiency of the battery.

3. The method according to claim 1, wherein The DC motor model is: Among them, I a Indicates the motor input current, V m is the armature voltage, R a Indicates the armature resistance, L af Represents the mutual inductance between the magnetic field and the armature, L a Indicates the armature inductance, L f Represents the magnetic field inductance, ω, B m , J represent the motor speed, the motor maximum magnetic flux potential and the rotor moment of inertia, Q f represents the Coulomb friction torque, Q prop Indicates load torque.

4. The method according to claim 1, wherein The propeller model is: T prop =ρ·D 4 ·K T (J0)·n·|n| Q prop =ρ·D 5 ·K Q (J0)·n·|n|; Where D is the propeller diameter, ρ is the water density, K T and K Q are the thrust and torque coefficients, n is the propeller speed, and u is the horizontal speed of the vehicle; Among them, K T and K Q It can be expressed as a polynomial: Among them, k t1 ,k t2 ,k t3 and k q1 ,k q2 ,k q3 is a coefficient suitable for propeller design, then the propeller model is expressed as:

5. The method according to claim 1, wherein The AUV to be planned obtains the final navigation speed using a PI control method.

6. The method according to claim 5, wherein The expression model of the PI control is: e(t)=u * (t)-u(t) Among them, k p 、k i and k d are the proportional, integral and derivative parameters respectively.

7. A planning system for AUV navigation speed based on vertical depth, characterized in that: The planning system comprises: The expected speed acquisition module is used to establish an AUV general propulsion model based on the preset battery internal resistance model, DC motor model, and propeller model of the autonomous underwater robot AUV, and obtain the expected speed of the AUV to be planned based on the AUV general propulsion model; A battery SOC value acquisition module is used to obtain the battery SOC values ​​of the AUV to be planned at different depths through the Bayesian Monte Carlo method; The final navigation speed acquisition module is used to obtain the final navigation speed of the AUV to be planned according to the battery SOC value and the expected speed.

8. The system according to claim 7, wherein: The AUV to be planned uses PI control to obtain the final navigation speed.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for planning the AUV navigation speed based on vertical depth according to any one of claims 1 to 6 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for planning the AUV navigation speed based on vertical depth according to any one of claims 1 to 6 is implemented.

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