A method, device, medium, and product for controlling the underwater depth of a robotic fish.

Abstract

This application discloses a method, device, medium, and product for underwater depth control of a robotic fish, relating to the field of underwater robot control. The method comprises obtaining first depth data, second depth data, and a pitch angle of a robotic fish to be controlled at its current position; preprocessing the first depth data and the second depth data to obtain processed depth data; low-pass filtering the pitch angle to obtain a processed pitch angle; determining a superimposed control signal using a parallel-stage PID controller based on the processed depth data and the processed pitch angle; and controlling the robotic fish to be controlled to move at a desired depth and pitch angle based on the superimposed control signal. This application enables the robotic fish to quickly respond to changes in the external environment, avoid overshoot and oscillation, and maintain stable operation at the desired depth.

Description

Technical Field

[0001] This application relates to the field of underwater robot control, and in particular to a method, device, medium and product for underwater depth control of a robotic fish. Background Technology

[0002] In the field of underwater robotics, especially in the research and development of biomimetic robotic fish, achieving precise depth control is a core requirement to ensure the effective execution of underwater tasks. However, due to the complexity of the underwater environment, including pressure changes, fluid disturbances, and sensor measurement noise, existing technologies still have many shortcomings in achieving high-precision and high-stability depth control.

[0003] Currently, depth control technology for biomimetic robotic fish mainly relies on two methods: thrust adjustment based on the up-and-down flapping of the tail fin and pitch adjustment based on the pectoral fin. The former adjusts thrust by changing the frequency and amplitude of the tail fin's up-and-down flapping motion, enabling the robotic fish to rise or dive. However, due to the significant lag in the tail fin's power output, rapid depth adjustment is difficult, and it is easily affected by environmental disturbances, resulting in poor stability in depth control. The latter adjusts the pitch angle by regulating the angle of the pectoral fins, utilizing the force of water flow on the pectoral fins to adjust the pitch angle, thereby controlling depth. While this method has a faster response speed, it requires high precision in controlling the pectoral fin angle, and is prone to overshoot or steady-state oscillations, especially under conditions of multivariate disturbances.

[0004] From a sensor measurement perspective, existing technologies typically employ a single sensor (such as a pressure sensor or ultrasonic rangefinder) to acquire water depth data. However, single sensors are susceptible to external environmental interference, potentially leading to significant deviations in their measurements. For example, while traditional pressure sensors provide high-precision measurement data, they are easily affected by temperature variations and dynamic noise in practical applications. Furthermore, stray signals and random disturbances in the underwater environment can cause data fluctuations, impacting the accuracy and stability of depth control. In recent years, multi-sensor fusion technology has gained increasing attention, as combining data from multiple sensors can improve the reliability and accuracy of measurements. However, existing multi-sensor fusion algorithms still have room for improvement in weight allocation and computational efficiency, particularly the inability to adapt to real-time environmental changes, which limits their practical application effectiveness.

[0005] In terms of depth control strategies, while traditional PID controllers have the advantages of simple design and high computational efficiency, single PID control is difficult to simultaneously address the multi-objective control requirements of depth and pitch angle. Some studies have proposed improved control strategies, such as fuzzy control, adaptive control, and neural network control. However, these methods often suffer from insufficient robustness in practical engineering applications due to high computational complexity or strong environmental dependence. Therefore, there is an urgent need for a computationally lightweight control strategy that is easy to implement in engineering. Summary of the Invention

[0006] The purpose of this application is to provide a method, device, medium, and product for underwater depth control of robotic fish, so as to improve the efficiency and stability of depth control.

[0007] To achieve the above objectives, this application provides the following solution:

[0008] In a first aspect, this application provides a method for controlling the underwater depth of a robotic fish, comprising:

[0009] Acquire the first depth data, second depth data, and pitch angle of the robotic fish to be controlled at its current position;

[0010] The first depth data and the second depth data are preprocessed to obtain processed depth data; the preprocessing includes Kalman filtering and adaptive weighted fusion filtering.

[0011] The pitch angle is low-pass filtered to obtain the processed pitch angle;

[0012] Based on the processed depth data and the processed pitch angle, a superimposed control signal is determined using a parallel PID controller; the parallel PID controller includes a depth PID controller and a pitch angle PID controller connected in parallel; the superimposed control signal is obtained by superimposing the depth control signal and the pitch angle control signal; the depth control signal is determined using the depth PID controller; the pitch angle control signal is determined using the pitch angle PID controller.

[0013] Based on the superimposed control signal, the robotic fish to be controlled is controlled to move at a desired depth and desired pitch angle.

[0014] Optionally, the first depth data and the second depth data are preprocessed to obtain processed depth data, specifically including:

[0015] Kalman filtering is performed on the first depth data and the second depth data respectively to obtain filtered first depth data and filtered second depth data;

[0016] Based on the filtered first depth data and the filtered second depth data, an adaptive weighted fusion filtering algorithm is used to determine the processed depth data.

[0017] Optionally, based on the processed depth data and the processed pitch angle, a superimposed control signal is determined using a parallel PID controller, specifically including:

[0018] Determine the first error between the processed depth data and the desired depth;

[0019] Based on the first error, the depth control signal is determined using the depth PID controller;

[0020] Determine the second error between the processed pitch angle and the desired pitch angle;

[0021] Based on the second error, the pitch angle control signal is determined using a pitch angle PID controller.

[0022] Optionally, the first depth data is acquired by a first barometric pressure sensor; the second depth data is acquired by a second barometric pressure sensor.

[0023] Optionally, the first pressure sensor and the second pressure sensor are symmetrically arranged on both sides of the head of the robotic fish to be controlled.

[0024] Optionally, both the first and second pressure sensors are WF5803F digital pressure sensors.

[0025] In a second aspect, 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 underwater depth control method for the robotic fish described above.

[0026] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the underwater depth control method for the robotic fish described above.

[0027] Fourthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the underwater depth control method for the robotic fish described above.

[0028] According to the specific embodiments provided in this application, this application has the following technical effects:

[0029] This application provides a method, device, medium, and product for underwater depth control of a robotic fish. The method involves obtaining the current depth and pitch angle data of the robotic fish through sensors. After processing through multi-stage fusion filtering and a low-pass filter, the data is subtracted from a given value. The depth error and pitch angle are input to a depth PID controller and a pitch angle PID controller, respectively. The control values ​​from these two controllers are superimposed and output to the pectoral fin servo motor, thereby adjusting the depth and pitch angle of the robotic fish. This application enables the robotic fish to respond quickly to changes in the external environment, avoid overshoot and oscillation, and maintain stable operation at the desired depth. Attached Figure Description

[0030] 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.

[0031] Figure 1 A flowchart illustrating an underwater depth control method for a robotic fish, provided as an embodiment of this application;

[0032] Figure 2 This is a block diagram of a parallel double closed-loop control system;

[0033] Figure 3 It is a graph of depth data from a constant depth control experiment;

[0034] Figure 4 This is a graph showing the depth error data from a constant depth control experiment.

[0035] Figure 5 This is a graph showing the pitch angle error data from a depth control experiment.

[0036] Figure 6 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0037] 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 skilled in the art without creative effort are within the scope of protection of this application.

[0038] To make the above-mentioned 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.

[0039] This application provides a method for controlling the underwater depth of a robotic fish. This method can accurately respond to changes in the external environment, adjust the operating status of the robotic fish in real time, and ensure that it can maintain stable performance under different water depth conditions.

[0040] This application utilizes the pectoral fins of a robotic fish to control its swimming depth. When the robotic fish reaches a certain speed, the water flow acting on the angled pectoral fins exerts upward or downward pressure, causing the robotic fish's head to pitch up or down, thus enabling it to rise or dive. Precisely controlling the tilt angle of the pectoral fins provides accurate vertical force to the robotic fish, thereby accurately adjusting its swimming depth during movement. Therefore, controlling the pectoral fin angle is crucial.

[0041] In one exemplary embodiment, such as Figure 1 As shown, a method for controlling the underwater depth of a robotic fish is provided, including the following steps:

[0042] S1: Obtain the first depth data, second depth data, and pitch angle of the robotic fish to be controlled at its current position.

[0043] As an optional implementation, the first depth data is acquired by a first barometric pressure sensor; the second depth data is acquired by a second barometric pressure sensor.

[0044] As an alternative implementation, the first pressure sensor and the second pressure sensor are symmetrically arranged on both sides of the head of the robotic fish to be controlled.

[0045] As an optional implementation, both the first and second pressure sensors are WF5803F digital pressure sensors.

[0046] In this embodiment, the depth control of a biomimetic robotic fish with an active body and compliant tail drive structure is taken as an example. The robotic fish to be controlled consists of three parts: a head, an active body, and a compliant tail. The head is equipped with a pair of pectoral fins and carries a control board, a battery, two servo motors (one to drive the active tail and one to drive the pectoral fins) and a wireless communication module. The active body and the compliant tail constitute an Active and Compliant Propulsion Mechanism (ACPM), which is driven by only one motor.

[0047] The control algorithm and data communication in this embodiment are implemented based on the ROS (Robot Operating System) framework. The modular design of the system functions ensures the efficient execution of the deep control strategy.

[0048] In practical applications, this application uses the WF5803F digital barometric pressure sensor to accurately measure the real-time depth of the robotic fish underwater. Two barometric pressure sensors are symmetrically arranged on either side of the robotic fish's head, simultaneously acquiring pressure values ​​at the same horizontal plane, overcoming the limitations of a single sensor. This sensor is based on a piezoresistive MEMS pressure sensor, featuring high accuracy, low power consumption, and small size. Measuring underwater depth using the barometric pressure sensor can be achieved by establishing a model relating atmospheric pressure to underwater depth. This model is based on the principle of hydrostatic pressure, that is, the pressure exerted on an underwater object is proportional to the weight of the water column above it. The model is described by the following equation:

[0049] P = P0 + ρgh (1)

[0050] Where: P is the absolute pressure at a point underwater (unit: Pascal, Pa); P0 is the atmospheric pressure above the water surface (unit: Pascal, Pa), which can be measured by a barometer; ρ is the density of water (unit: kilograms per cubic meter, kg / m³). 3 For pure water, the density at 4°C is approximately 1000 kg / m³. 3 g is the acceleration due to gravity (unit: meters per second squared, m / s²). 2 The local gravitational acceleration is approximately 9.80 m / s². 2 h is the underwater depth (unit: meter, m), which is the value to be measured.

[0051] To solve for the underwater depth h, the formula can be rearranged as follows:

[0052]

[0053] In this embodiment, the ROS system acquires data from key sensors (the first and second pressure sensors) in real time via a protocol interface, including pitch angle data from the IMU attitude sensor and water pressure measurement data from the WF5803F digital barometer. The water pressure data is then processed using a formula... The data is processed and converted into the corresponding raw underwater depth data (i.e., first depth data and second depth data).

[0054] S2: Preprocess the first depth data and the second depth data to obtain processed depth data; the preprocessing includes Kalman filtering and adaptive weighted fusion filtering.

[0055] As an optional implementation, S2 specifically includes:

[0056] Kalman filtering is performed on the first depth data and the second depth data respectively to obtain the filtered first depth data and the filtered second depth data.

[0057] Based on the filtered first depth data and the filtered second depth data, an adaptive weighted fusion filtering algorithm is used to determine the processed depth data.

[0058] In practical applications, raw underwater depth data is processed by a multi-stage fusion filtering algorithm within the ROS system. This application employs a multi-stage fusion filtering algorithm to process raw underwater depth data from multiple barometric sensors, significantly reducing external environmental noise interference and sensor-specific measurement errors, thereby improving the accuracy of underwater depth measurements. The algorithm consists of two stages:

[0059] First, in the single-sensor data processing stage, the Kalman filter algorithm is used to filter the depth data of each sensor. Kalman filtering achieves dynamic optimization of sensor data by combining prior estimates and posterior observations, based on an iterative mechanism of state prediction and measurement updates. This process effectively suppresses random noise interference and corrects inherent sensor errors, improving the reliability and accuracy of single-sensor data.

[0060] Secondly, in the multi-sensor data fusion stage, an adaptive weighted fusion filtering algorithm is further introduced to comprehensively process the data from multiple sensors after Kalman filtering. This algorithm dynamically adjusts the weight allocation based on the variance characteristics of the measurement data from each sensor, ensuring that effective data from different sensor outputs are reasonably fused. By comprehensively utilizing multi-source information, the adaptive weighted fusion filtering algorithm significantly enhances the robustness and accuracy of the measurement results.

[0061] The Kalman filter algorithm mainly consists of two stages: prediction and update. In the prediction stage, the state transition equation is used, and based on the previous time-instance state estimate and the system's dynamic model, the current time-instance state estimate and covariance matrix are predicted, i.e.:

[0062]

[0063] In the formula: This is the predicted estimate of the state at time t, obtained based on the state at time t-1. For the updated state estimate at time t-1; A t B is the state transition matrix; t For control input matrix; U t P is the control input at time t; t|t-1 P is the system covariance matrix at time t predicted from time t-1; t-1|t-1 Q is the system covariance matrix at time t-1; t Let be the system process noise covariance matrix.

[0064] During the update phase, the predicted estimates and covariance matrix are corrected based on the actual measurement data and the measurement equation. That is:

[0065]

[0066] P t|t =(IK G H t )P t|t-1 (7)

[0067] In the formula: K G Kalman gain; Z is the measurement matrix; t For measurement data; R t The covariance matrix of the measurement noise; P is the corrected estimate of the state at time t; t|t This is the corrected covariance matrix.

[0068] After performing a first-stage Kalman filter on the barometric pressure sensor data, an adaptive weighted fusion algorithm is used to fuse the Kalman-filtered data to better reflect the true values. The derivation of the adaptive weighted average fusion algorithm is as follows:

[0069] Assume that n identical sensors are used (in this embodiment, n = 2), and the measured values ​​of the n sensors are X1, X2, ..., X... n Furthermore, the data they measure are independent of each other, and the true value to be estimated is... The measurement variances of each sensor are as follows: The weights of the sensor data's influence on the true value are W1, W2, ..., W. n ,and

[0070] The total variance of the data from n sensors is shown in the following formula:

[0071]

[0072] Where, σ 2 Let be the total variance of the data from n sensors.

[0073] The data from each sensor are independent, hence the equation:

[0074] E[(XX i (XX) j )]=0(i≠j)(9)

[0075]

[0076] As shown in the above equation, the total variance is a multivariate quadratic function of the weighting coefficients, and therefore has a minimum value. According to the extremum theory of multivariate functions, the weighting coefficients corresponding to the minimum total variance are:

[0077]

[0078] The minimum total variance is then:

[0079]

[0080] Therefore, the fused value of the multiple sensors is:

[0081]

[0082] In the formula: The average of k historical data measurements for each sensor.

[0083] S3: Perform low-pass filtering on the pitch angle to obtain the processed pitch angle.

[0084] In practical applications, pitch angle data is passed through a low-pass filter. Processing to remove high-frequency noise interference, where x θ ,y θ τ represents the filter input, output, and time constant, respectively. In the experiment, τ = 0.5.

[0085] S4: Based on the processed depth data and the processed pitch angle, a superimposed control signal is determined using a parallel PID controller; the parallel PID controller includes a depth PID controller and a pitch angle PID controller connected in parallel; the superimposed control signal is obtained by superimposing the depth control signal and the pitch angle control signal; the depth control signal is determined using the depth PID controller; the pitch angle control signal is determined using the pitch angle PID controller.

[0086] As an optional implementation, S4 specifically includes:

[0087] Determine the first error between the processed depth data and the desired depth.

[0088] Based on the first error, the depth control signal is determined using the depth PID controller.

[0089] Determine the second error between the processed pitch angle and the desired pitch angle.

[0090] Based on the second error, the pitch angle control signal is determined using a pitch angle PID controller.

[0091] In practical applications, the mathematical model of a cascaded PID controller can be expressed as the sum of the outputs of multiple PID controllers. If there are m PID controllers (m = 2 in this embodiment), then the total control input of the system can be expressed as:

[0092]

[0093] The strategy employed in this application for depth control using a cascaded PID controller involves superimposing the outputs of two independent control loops—depth control and pitch angle control—each equipped with an independent PID controller. This design allows for independent adjustment of each control requirement, while ultimately amplifying the control outputs and sending them to the same actuator.

[0094] (1) Depth PID controller: The main working principle is to compare the current depth value measured by the barometric pressure sensor with the set desired depth, determine the error, input it into the depth PID controller, and generate the first closed-loop output control signal. This closed loop is responsible for adjusting the vertical position of the robotic fish relative to the water surface, ensuring that the robotic fish can swim stably at the specified depth.

[0095] (2) Pitch Angle PID Controller: The main working principle is to compare the pitch angle attitude data of the robotic fish obtained by the airborne IMU with the desired pitch angle, determine the error, input it into the pitch angle PID controller, and generate a second closed-loop output control signal u. PID2 (t) enables the pitch angle of the robotic fish to quickly converge to the equilibrium position as it approaches the desired depth, thus avoiding overshoot and oscillation.

[0096] Each PID controller adjusts based on real-time sensor data feedback. Through a closed-loop control structure, it continuously monitors and adjusts to ensure that the robotic fish reaches and maintains the desired depth and posture.

[0097] The parallel PID control scheme adopted in this application is as follows: Figure 2 As shown. d0 and θ0 are the desired depth and desired pitch angle set in depth control, respectively. d and θ are the depth and pitch angle data obtained by the sensor, respectively. After being processed by filters F1 (multi-stage fusion filter) and F2 (low-pass filter) in the feedback loop, the difference between these values ​​and the given values ​​is calculated and input to the depth PID controller G. 1_PID and pitch angle PID controller G 2_PID The combined control values ​​from both are then output to the pectoral fin servo motor, which in turn adjusts the swimming state of the robotic fish.

[0098] S5: Based on the superimposed control signal, control the robotic fish to move at the desired depth and desired pitch angle.

[0099] The processed depth and pitch angle data are published to the closed-loop control node via topic communication. After receiving the sensor data, the closed-loop control node calculates the control input based on the designed parallel PID control strategy. The calculation results are transmitted to the STM32F103 microcontroller via the ROS serial communication module. After receiving the data, the driver board parses it and controls the pectoral or caudal fin servos to execute the corresponding actions, ultimately achieving real-time depth adjustment of the robotic fish and forming closed-loop control.

[0100] With the desired depth set at 30cm, the desired pitch angle at 0rad, and the control frequency at 10Hz, the experimental data curve is as follows: Figure 3-Figure 5 As shown.

[0101] The depth data curves show that the steady-state error of the robotic fish can be maintained within 2cm without overshoot, with a response time of 3.2s and a diving speed of 9.4cm / s, demonstrating stable overall control. The error curve trend during depth control is consistent with the depth curve. When the robotic fish is stable, the error fluctuates around 0cm and gradually converges. In the pitch angle curve during depth control, a positive angle represents upward movement, and a negative angle represents downward movement. Initially, the robotic fish shows an upward trend, followed by a downward movement as it dives. As the robotic fish approaches the desired depth, the pitch angle first decreases and then increases, eventually stabilizing around 0 (rad) when the fish reaches the desired depth, indicating that it is moving almost horizontally and the depth no longer changes.

[0102] This application provides a method for controlling the underwater depth of a robotic fish, which is particularly suitable for a type of biomimetic robotic fish that is driven by a tail swing and whose pectoral fins do not participate in the propulsion but only serve to change the pitch angle.

[0103] This application provides a method for controlling the underwater depth of a robotic fish. By precisely adjusting the angle of the pectoral fin, the robotic fish can be made to rise and dive. The WF5803F digital barometer and Kalman filter algorithm are used to improve the accuracy and stability of depth measurement.

[0104] This application employs a multi-level fusion filtering algorithm, which combines the Kalman filtering of a single sensor with the adaptive weighted fusion filtering algorithm of multiple sensors. This effectively reduces underwater noise interference and measurement errors, improves the accuracy and real-time performance of depth data, and provides a reliable data foundation for subsequent control strategies, thereby more realistically reflecting underwater depth.

[0105] In addition, this application also adopts a parallel PID control strategy, which divides the depth control and pitch angle control into two independent closed-loop control loops to independently control the depth and pitch angle of the robotic fish, thereby improving the convergence of the vertical position and attitude error of the robotic fish. By superimposing the control quantities of the two loops, it ensures that the robotic fish can respond quickly to changes in the external environment, avoid overshoot and oscillation, and ensure that the robotic fish can maintain stable performance under different water depth conditions.

[0106] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described underwater depth control method for robotic fish.

[0107] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the above-described underwater depth control method for robotic fish.

[0108] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the above-described underwater depth control method for robotic fish.

[0109] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 6 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media to run. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for underwater depth control of a robotic fish.

[0110] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0111] 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.

[0112] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. 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 of the above methods. 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).

[0113] 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.

[0114] 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.

[0115] 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 method for controlling the underwater depth of a robotic fish, characterized in that, include: Acquire the first depth data, second depth data, and pitch angle of the robotic fish to be controlled at its current position; The first depth data and the second depth data are preprocessed to obtain processed depth data; the preprocessing includes Kalman filtering and adaptive weighted fusion filtering. The pitch angle is low-pass filtered to obtain the processed pitch angle; Based on the processed depth data and the processed pitch angle, a superimposed control signal is determined using a parallel PID controller; the parallel PID controller includes a depth PID controller and a pitch angle PID controller connected in parallel; the superimposed control signal is obtained by superimposing the depth control signal and the pitch angle control signal; the depth control signal is determined using the depth PID controller; the pitch angle control signal is determined using the pitch angle PID controller. Based on the superimposed control signal, the robotic fish to be controlled is controlled to move at a desired depth and desired pitch angle.

2. The underwater depth control method for robotic fish according to claim 1, characterized in that, The first depth data and the second depth data are preprocessed to obtain processed depth data, specifically including: Kalman filtering is performed on the first depth data and the second depth data respectively to obtain filtered first depth data and filtered second depth data; Based on the filtered first depth data and the filtered second depth data, an adaptive weighted fusion filtering algorithm is used to determine the processed depth data.

3. The underwater depth control method for robotic fish according to claim 1, characterized in that, Based on the processed depth data and the processed pitch angle, a superimposed control signal is determined using a parallel PID controller, specifically including: Determine the first error between the processed depth data and the desired depth; Based on the first error, the depth control signal is determined using the depth PID controller; Determine the second error between the processed pitch angle and the desired pitch angle; Based on the second error, the pitch angle control signal is determined using a pitch angle PID controller.

4. The underwater depth control method for robotic fish according to claim 1, characterized in that, The first depth data is acquired by a first barometric pressure sensor; the second depth data is acquired by a second barometric pressure sensor.

5. The underwater depth control method for robotic fish according to claim 4, characterized in that, The first pressure sensor and the second pressure sensor are symmetrically arranged on both sides of the head of the robotic fish to be controlled.

6. The underwater depth control method for robotic fish according to claim 4, characterized in that, Both the first and second pressure sensors are WF5803F digital pressure sensors.

7. 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 underwater depth control method for a robotic fish according to any one of claims 1-6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the underwater depth control method for the robotic fish as described in any one of claims 1-6.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the underwater depth control method for the robotic fish as described in any one of claims 1-6.

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