Autonomous underwater vehicle stable navigation control method, system, device and medium

Abstract

This application relates to the field of autonomous underwater vehicle (AUV) navigation control technology, and discloses a stable navigation control method, system, device, and medium for an AUV. Through a hardware system comprised of a depth gauge, altimeter, Doppler velocimeter, single-beam forward-looking sonar, attitude measurement instrument, and central controller mounted on the AUV, the system achieves stable navigation control of the AUV while maintaining it within an optimal altitude range through the coordinated work of modules such as information perception, parameter calculation, slope estimation, altitude prediction, and attitude control. By employing adaptive sampling frequency adjustment, dynamic sliding window slope estimation, and attitude control based on terrain slope compensation, the system solves problems such as weak maneuverability of underactuated AUVs in complex undulating terrain, difficulty in terrain tracking due to high sensor measurement noise, and unstable bottom height. Furthermore, it features low hardware cost, high control accuracy, and strong robustness, and can be widely applied to various underwater surveying operations.

Description

Technical Field

[0001] This application relates to the field of autonomous underwater robot navigation control technology, specifically to a stable navigation control method, system, device, and medium for an autonomous underwater robot. Background Technology

[0002] During underwater near-bottom mapping operations, autonomous underwater vehicles (AUVs) typically carry various detection devices such as acoustic and optical payloads to conduct precise near-bottom mapping and data collection of targets such as seabed topography, mineral resources, and historical shipwrecks. Based on the operational requirements of the payloads, AUVs must maintain a relatively fixed alignment with the seabed during operations to ensure that seabed targets remain within the effective detection range of the payloads, thus guaranteeing the accuracy and completeness of the detection data.

[0003] However, near-bottom operating areas for autonomous underwater vehicles (AUVs) often face complex seabed topography. Most seabeds are not flat but exhibit significant undulations. Furthermore, limited by platform design and propulsion system characteristics, underactuated AUVs have relatively weak maneuverability and struggle to respond quickly to changes in terrain. Therefore, controlling AUVs to accurately track complex, undulating terrain and maintain a stable seabed elevation has become a significant technical challenge in the field of near-bottom mapping operations for AUVs.

[0004] Current research trends in terrain tracking control for autonomous underwater robots are shifting from static tracking modes relying on precise prior maps to adaptive and predictive control modes based on real-time environmental perception. However, existing technologies still have many shortcomings: some control methods have high hardware requirements, relying on high-precision, high-cost sensor combinations, which is not conducive to the widespread application of the technology; some algorithms have insufficient slope estimation accuracy in scenarios with drastic changes in terrain slope, resulting in lag in altitude prediction and attitude control; and some control strategies do not fully consider the adaptability of sampling frequency to speed and terrain slope, which can easily lead to untimely or redundant data sampling under complex terrain conditions, affecting the control effect.

[0005] Therefore, there is an urgent need for a stable navigation control method for autonomous underwater robots that features low hardware cost, accurate slope estimation, timely altitude prediction, stable attitude control, and the ability to adapt to complex and undulating terrain changes, in order to meet the needs of actual near-bottom mapping operations and promote the further application of autonomous underwater robots in the field of marine exploration. Summary of the Invention

[0006] To address the aforementioned issues, this application provides a method, system, device, and medium for stable navigation control of an autonomous underwater vehicle (AUV). By optimizing the control process, the AUV's bottom-level altitude is maintained within the optimal range for payload operation during navigation, while ensuring stable attitude.

[0007] The embodiments of this application adopt the following technical solutions:

[0008] In a first aspect, this application provides a method for stable navigation control of an autonomous underwater robot, including:

[0009] Acquire depth data, ground clearance data, speed data, attitude angle data, and forward sight distance data for each sampling point;

[0010] Based on depth data, ground clearance data, speed data, attitude angle data, and forward sight distance data, determine the terrain undulation height and cumulative forward travel distance of each sampling point, as well as the terrain undulation height and cumulative forward travel distance of the forward detection point;

[0011] Based on the terrain undulation height and cumulative forward travel distance of each sampling point, as well as the terrain undulation height and cumulative forward travel distance of the forward detection point, estimate the terrain slope of the sampling point at the current moment.

[0012] Based on the terrain slope, ground clearance, speed, and attitude angle of the current sampling point, predict the ground clearance for a preset time in the future.

[0013] If the predicted height above the bottom exceeds the optimal height range of the autonomous underwater vehicle, the pitch control angle is determined based on the target height above the bottom within the optimal height range, the height above the bottom data at the current sampling point, the preset scaling factor, and the terrain slope result at the current sampling point. The autonomous underwater vehicle continues to navigate according to the pitch control angle.

[0014] If the predicted height above the bottom does not exceed the optimal height range of the autonomous underwater vehicle, the autonomous underwater vehicle will continue to navigate according to the attitude angle data of the sampling point at the current moment.

[0015] Secondly, this application also provides a stable navigation control system for an autonomous underwater robot, comprising:

[0016] The information sensing module is used to acquire depth data, ground clearance data, speed data, attitude angle data, and forward-looking distance data for each sampling point;

[0017] The parameter calculation module is used to determine the terrain undulation height and cumulative forward travel distance of each sampling point, as well as the terrain undulation height and cumulative forward travel distance of the forward detection point, based on depth data, ground clearance data, speed data, attitude angle data, and forward look-ahead distance data.

[0018] The slope estimation module is used to estimate the slope of the sampling points at the current moment based on the terrain undulation height and cumulative forward travel distance of each sampling point, as well as the terrain undulation height and cumulative forward travel distance of the forward detection point.

[0019] The altitude prediction module is used to predict the predicted altitude to the ground for a preset time based on the terrain slope, the altitude above the ground, the speed, and the attitude angle of the current sampling point.

[0020] The attitude control module is used to determine the pitch control angle based on the target height above the bottom within the optimal height range, the height above the bottom data of the current sampling point, the preset scaling factor, and the terrain slope result of the current sampling point if the predicted height above the bottom exceeds the optimal height range of the autonomous underwater robot. The autonomous underwater robot continues to navigate according to the pitch control angle.

[0021] The attitude control module is also used to continue controlling the autonomous underwater vehicle to navigate according to the attitude angle data of the sampling point at the current moment if the predicted height above the bottom does not exceed the optimal height range of the autonomous underwater vehicle.

[0022] Thirdly, this application also provides an electronic 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 steps of the above-described stable navigation control method for an autonomous underwater robot.

[0023] Fourthly, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method for stable navigation control of an autonomous underwater robot.

[0024] The above-described technical solutions adopted in the embodiments of this application can achieve the following beneficial effects:

[0025] This application utilizes the collaborative operation of multiple sensors, including depth gauges, altimeters, Doppler velocimeters, and single-beam forward-looking sonar, to achieve comprehensive perception of the robot's navigation and terrain conditions, providing rich and comprehensive basic data for control decisions. Depth gauges, altimeters, Doppler velocimeters, and single-beam forward-looking sonar are all commonly used low-cost sensors for autonomous underwater vehicles, eliminating the need for high-precision, high-cost sensor combinations and reducing system hardware costs.

[0026] The sampling frequency of this application can be adaptively adjusted based on airspeed data and terrain slope results. In areas with high airspeed and complex terrain, the sampling frequency is increased to ensure data timeliness and completeness; in areas with low airspeed and gentle terrain, the sampling frequency is reduced to decrease data redundancy and system energy consumption, thus balancing the accuracy of data acquisition with the efficiency of system operation.

[0027] This application employs a combined strategy of dynamic sliding window data selection, interquartile range outlier removal, and random sample consensus algorithm fitting, effectively addressing the impact of sensor noise and environmental interference on slope estimation. The linkage adjustment between flight speed data and terrain slope results on the number of data samples within the dynamic sliding window ensures the stability of slope estimation on gentle terrain while improving the response speed of slope estimation on steep terrain.

[0028] This application predicts the estimated height above the ground for a predetermined time in advance, enabling proactive anticipation of changes in the ground clearance. The attitude control strategy combines proportional control with terrain slope compensation, correcting current height deviations while adapting to changes in terrain slope in advance. This effectively avoids control lag issues, ensuring the robot can accurately track complex undulating terrain and always maintain its ground clearance within the optimal range for carrying the payload.

[0029] The control process described in this application is a continuously iterative loop, ensuring that the autonomous underwater vehicle (AUV) can adapt to terrain changes in real time and maintain a stable navigation state. This iteratively optimized control mode endows the AUV with extremely strong dynamic adaptability, enabling it to cope with complex and ever-changing seabed terrain environments and effectively solving the problem of terrain tracking difficulties caused by the weak maneuverability of underactuated AUVs. Attached Figure Description

[0030] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0031] Figure 1 A flowchart illustrating a stable navigation control method for an autonomous underwater robot according to an embodiment of this application is shown.

[0032] Figure 2 A schematic diagram illustrating the principle of parameter calculation according to an embodiment of this application is shown;

[0033] Figure 3 A schematic diagram illustrating the principle of height prediction according to an embodiment of this application is shown;

[0034] Figure 4A schematic diagram of the structure of a stable navigation control system for an autonomous underwater robot according to an embodiment of this application is shown.

[0035] Figure 5 A schematic diagram of the structure of an electronic device according to an embodiment of this application is shown. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0037] First, a brief introduction to the autonomous underwater robot proposed in this application.

[0038] The autonomous underwater robot proposed in this application is equipped with at least the following sensors: depth gauge, altimeter, Doppler velocimeter, single-beam forward-looking sonar, attitude measurement instrument, and central controller, forming a hardware system.

[0039] The autonomous underwater vehicle (AUV) proposed in this application is an underactuated AUV, equipped with various detection devices such as acoustic and optical payloads (payloads). Based on the operational characteristics of the payloads, the AUV must maintain an optimal altitude relative to the seabed during operation.

[0040] Secondly, a brief introduction to each point involved in this application will be given.

[0041] The current sampling point represents the sampling time node of the current round and is the reference point for the analysis of the current round, denoted by the symbol. express.

[0042] The sampling point at the previous moment and the sampling point at the next moment are corresponding; the sampling point at the previous moment and the sampling point at the next moment represent two adjacent sampling time nodes.

[0043] The previous and next sampling points are corresponding, used to represent the previous and next sampling time nodes of the current sampling point. The previous sampling point is represented by the symbol... This indicates that the sampling point at the next time step is represented by a symbol. express.

[0044] Forward detection points are used to indicate terrain features located ahead of the autonomous underwater vehicle's trajectory within the detection range of a single-beam forward-looking sonar.

[0045] Figure 1A schematic flowchart of a stable navigation control method for an autonomous underwater robot according to an embodiment of this application is shown. (Refer to...) Figure 1 As shown, this embodiment includes steps S110 to S160. It should be noted that the stable navigation control method for an autonomous underwater vehicle is a continuously iterative cyclic process, and steps S110 to S160 shown in this embodiment represent one round of this iterative process.

[0046] Step S110: Obtain depth data, ground clearance data, speed data, attitude angle data, and forward-looking distance data for each sampling point.

[0047] The system acquires depth data, height above the bottom, speed data, attitude angle data, and forward-looking distance data from various sampling points collected by the sensors mounted on the autonomous underwater vehicle.

[0048] Depth data is used to represent the vertical distance of an autonomous underwater vehicle from the water surface.

[0049] Bottom clearance data is used to represent the vertical distance between an autonomous underwater vehicle and the seabed.

[0050] Speed ​​data is used to represent the speed at which an autonomous underwater vehicle (AUV) travels.

[0051] Attitude and bearing angle data are used to represent the longitudinal tilt angle of an autonomous underwater vehicle.

[0052] Forward-looking distance data is used to represent the straight-line distance between terrain features ahead of the autonomous underwater vehicle's (AUV) trajectory detected by single-beam forward-looking sonar and the AUV's position.

[0053] In some optional implementations, step S110, acquiring depth data, height above the bottom data, speed data, attitude angle data, and forward-looking distance data for each sampling point, includes: acquiring depth data continuously collected at a sampling frequency by a depth gauge mounted on an autonomous underwater vehicle. ; Acquire bottom elevation data continuously collected at a sampling frequency using an altimeter mounted on an autonomous underwater vehicle. ; Acquire speed data continuously collected at a sampling frequency using a Doppler velocimeter mounted on an autonomous underwater vehicle. ; Acquire attitude angle data continuously collected at a sampling frequency by the attitude measurement instrument mounted on the autonomous underwater vehicle. Acquire forward-looking distance data continuously collected at sampling frequency using a single-beam forward-looking sonar mounted on an autonomous underwater vehicle. ;in, Indicates the sampling point at the current time. This indicates the previous sampling time node of the current sampling point. Indicates the number of sampling points. This represents the depth data of the sampling point at the current moment. This represents the current height of the sampling point above the ground. This represents the airspeed data at the sampling point at the current moment. This represents the attitude angle data of the sampling point at the current moment. This represents the forward distance data of the sampling point at the current moment. The sampling frequency is adaptively adjusted based on the speed data of the autonomous underwater vehicle and the terrain slope results.

[0054] For subsequent analysis, depth data, altitude above ground data, speed data, attitude angle data, and forward-looking distance data can be obtained for each sampling point with a fixed number of sampling points. For example, the number of sampling points can be 1000 ( If the current sampling point is used, the depth data, ground clearance data, speed data, attitude angle data, and forward sight distance data of the previous 999 sampling points will be obtained.

[0055] Depth data is collected by a depth gauge mounted on an autonomous underwater vehicle and is represented as follows: .

[0056] Bottom clearance data is collected based on an altimeter mounted on an autonomous underwater vehicle and is represented as follows: .

[0057] Speed ​​data is collected based on a Doppler velocimeter aboard an autonomous underwater vehicle and is represented as follows: .

[0058] The attitude angle data is collected based on the attitude measurement instrument carried by the autonomous underwater vehicle, and is represented as follows: .

[0059] Forward-looking range data is acquired based on a single-beam forward-looking sonar mounted on an autonomous underwater vehicle, and is represented as follows: .

[0060] For each of the above sampling points, the sampling frequency is not fixed, but is adaptively adjusted based on the speed data of the autonomous underwater vehicle and the terrain slope results.

[0061] In some optional implementations, the sampling frequency is adaptively adjusted based on the speed data of the autonomous underwater vehicle and the terrain slope results, including: calculating the initial sampling time interval between the sampling point at the next moment and the sampling point at the previous moment based on the speed data of the sampling point at the previous moment and the preset correlation coefficient, according to the following formula; ,in, Indicates the sampling point index and , This indicates the preset correlation coefficient. Indicates the first Speed ​​data at each sampling point Indicates the first +1 sampling point sampling point and the first The initial sampling time interval for each sampling point; if the absolute value of the terrain slope result of the sampling point at the previous moment does not exceed the preset slope threshold, the initial sampling time interval is used as the sampling time interval, and the sampling point at the next moment is determined; if the absolute value of the terrain slope result of the sampling point at the previous moment exceeds the preset slope threshold, the updated sampling time interval between the sampling point at the next moment and the sampling point at the previous moment is calculated based on the following formula. ,in, Indicates the preset multiplier and , Indicates the first The sampling point and the sampling point of the first sampling point The sampling time interval for updating the sampling points is used as the sampling time interval, and the sampling points for the next time step are determined.

[0062] For two adjacent sampling points, the sampling point at the next time step is determined based on the relevant information of the sampling point at the previous time step.

[0063] Taking the current round as an example, we will explain how to use the current sampling point as the previous sampling point and adaptively adjust the sampling frequency to determine the sampling point at the next moment (i.e., the sampling point at the next moment).

[0064] The current speed data collected at the sampling point is Then, the initial sampling time interval between the sampling point at the next time step and the sampling point at the current time step is calculated based on the following formula (1):

[0065] , formula (1);

[0066] in, This indicates a preset correlation coefficient, with the unit being meters. This represents the initial sampling time interval between the sampling point at the next time step and the sampling point at the current time step.

[0067] As can be seen from formula (1), there is a negative correlation between the initial sampling time interval and the speed data. That is, when the speed of the autonomous underwater vehicle is relatively fast, the initial sampling time interval is small and the sampling frequency is increased to ensure timely capture of terrain change information; when the speed of the autonomous underwater vehicle is relatively slow, the initial sampling time interval is large and the sampling frequency is reduced to avoid data redundancy.

[0068] If, after analysis, the absolute value of the terrain slope result at the current sampling point does not exceed the preset slope threshold, then the initial sampling time interval is used as the sampling time interval, and the sampling point for the next time moment is determined. In other words, in areas with gentle terrain, maintaining the sampling frequency adjusted based on flight speed ensures both data validity and control over computational load.

[0069] That is, if ,but , ;

[0070] in, This represents the terrain slope result at the current sampling point, expressed in degrees. This indicates the preset slope threshold. This represents the sampling time interval between the sampling point at the next time step and the sampling point at the current time step. Indicates the sampling point at the next time step. This indicates the sampling point at the current moment.

[0071] If, after analysis, the absolute value of the terrain slope result at the current sampling point exceeds the preset slope threshold, then the update sampling time interval between the sampling point at the next time step and the sampling point at the current time step is calculated based on the following formula (2):

[0072] , formula (2);

[0073] in, Indicates the preset multiplier and , This represents the time interval between updating the sampling point at the next time step and the sampling point at the current time step.

[0074] The updated sampling time interval is used as the sampling time interval to determine the sampling point for the next moment. In other words, when the terrain changes drastically, by further reducing the initial sampling time interval and increasing the sampling frequency, data can be acquired more densely, ensuring the accuracy and timeliness of the analysis.

[0075] That is, if ,but , .

[0076] Step S120: Based on depth data, ground clearance data, speed data, attitude angle data, and forward sight distance data, determine the terrain undulation height and cumulative forward travel distance of each sampling point, as well as the terrain undulation height and cumulative forward travel distance of the forward detection point.

[0077] The acquired information is transformed into terrain feature parameters. For each sampling point, a preset spurious full ocean depth is introduced as a unified reference benchmark to obtain the corresponding terrain undulation height, achieving unified quantification of terrain undulation in different areas. The cumulative forward navigation distance is calculated based on the speed and each sampling time node, and attitude angle data compensation is introduced to eliminate the influence of the autonomous underwater vehicle's pitch attitude on the horizontal navigation distance calculation, ensuring the accuracy of the cumulative forward navigation distance. For forward detection points, the feature information of the terrain ahead is obtained in advance by combining the preset fixed installation angle of the single-beam forward-looking sonar and the attitude angle data of the sampling point at the current moment.

[0078] Figure 2 A schematic diagram illustrating the principle of parameter calculation is shown. (Refer to...) Figure 2 As shown, in some optional implementations, step S120, determining the terrain undulation height and cumulative forward travel distance of each sampling point and the terrain undulation height and cumulative forward travel distance of the forward detection point based on depth data, bottom height data, speed data, attitude angle data and forward look-ahead distance data, includes: calculating the terrain undulation height of each sampling point based on the following formula according to the depth data, bottom height data and preset false full ocean depth; ,in, Indicates the preset false full ocean depth and , Indicates the first The terrain undulation height of each sampling point Indicates the first Depth data of each sampling point Indicates the first The height of the sampling point above the bottom is calculated based on the following formula, using the current sampling point's depth data, current sampling point's attitude angle data, current sampling point's forward-looking distance data, preset false full ocean depth, and preset fixed installation angle of the single-beam forward-looking sonar; ,in, This indicates the preset fixed installation angle for a single-beam forward-looking sonar. The elevation of the terrain undulations at the forward detection point is indicated; based on the speed data, sampling time interval, and attitude angle data, the cumulative forward travel distance at each sampling point is calculated using the following formula; ,in, Indicates the first Speed ​​data at each sampling point Indicates the first The sampling point and the first The sampling time interval for each sampling point Indicates the first Attitude and bearing angle data at each sampling point Indicates the first The cumulative forward travel distance of each sampling point Indicates the first The cumulative forward travel distance of each sampling point and Based on the cumulative forward travel distance of the sampling point at the current moment, the forward-looking distance data of the sampling point at the current moment, the attitude angle data of the sampling point at the current moment, and the preset fixed installation angle of the single-beam forward-looking sonar, the cumulative forward travel distance of the forward detection point is calculated based on the following formula. ,in, This indicates the cumulative forward travel distance of the forward detection point.

[0079] Taking the current round as an example, the depth data of the sampling point in the previous time step is: The height of the sampling point from the bottom at the previous moment was The speed data of the sampling point at the previous moment was The attitude angle data of the sampling point at the previous moment was The forward distance data of the sampling point at the previous moment is The depth data of the sampling point at the current moment is: The current sampling point's height above the bottom is The current speed data of the sampling point is The attitude angle data of the sampling point at the current moment is The forward distance data of the sampling point at the current moment is .

[0080] Based on the depth data of the sampling point at the current moment, the height above the bottom data of the sampling point at the current moment, and the preset false full ocean depth, the terrain undulation height of the sampling point at the current moment is calculated based on the following formula (3):

[0081] , formula (3);

[0082] in, Indicates the preset false full ocean depth and The false full ocean depth is a fixed virtual reference depth. By subtracting the depth data and bottom clearance data of the autonomous underwater vehicle from the virtual reference depth, sampling points at different locations can be unified under the same reference benchmark, accurately reflecting the seabed topographic relief corresponding to each sampling point and eliminating the influence of different regions on the judgment of topographic relief.

[0083] The calculation method for the other sampling points is the same, and will not be repeated here.

[0084] Based on the current sampling point's depth data, current sampling point's attitude angle data, current sampling point's forward-looking distance data, preset false full ocean depth, and preset fixed installation angle of the single-beam forward-looking sonar, the terrain undulation height of the forward detection point is calculated using the following formula (4):

[0085] , formula (4);

[0086] in, This indicates the preset fixed installation angle for a single-beam forward-looking sonar. This indicates the elevation of the terrain undulations at the forward detection point.

[0087] Since the detection point in front is not the sampling point in the next moment, a subscript is used. This indicates that, by fully considering the preset fixed installation angle of the single-beam forward-looking sonar and the attitude angle data of the current sampling point of the autonomous underwater vehicle, the forward-looking distance data of the current sampling point is converted into the terrain undulation height of the forward detection point, thus realizing the early prediction of changes in the terrain ahead.

[0088] Based on the current speed data of the sampling point, the sampling time interval between the current sampling point and the previous sampling point, and the attitude angle data of the current sampling point, the cumulative forward travel distance of the current sampling point is calculated based on the following formula (5);

[0089] , formula (5);

[0090] in, This represents the cumulative forward travel distance of the sampling points at the previous moment. This represents the cumulative forward travel distance of the sampling point at the current moment, and .

[0091] The calculation method for the other sampling points is the same, and will not be repeated here.

[0092] Based on the cumulative forward travel distance of the sampling point at the current moment, the forward sight distance data of the sampling point at the current moment, the attitude angle data of the sampling point at the current moment, and the preset fixed installation angle of the single-beam forward-looking sonar, the cumulative forward travel distance of the forward detection point is calculated based on the following formula (6):

[0093] , formula (6);

[0094] in, This indicates the cumulative forward travel distance of the forward detection point.

[0095] Step S130: Estimate the terrain slope of the sampling point at the current moment based on the terrain undulation height and cumulative forward travel distance of each sampling point and the terrain undulation height and cumulative forward travel distance of the forward detection point.

[0096] Slope estimation can employ a combination of strategies including dynamic sliding window, outlier removal, and data fitting. Data samples are selected based on a dynamic sliding window, where the number of samples adaptively adjusts according to the current speed data of the autonomous underwater vehicle (AUV) at the sampling point and the terrain slope result from the previous sampling point. A higher current speed results in a larger sample size, ensuring sufficient terrain information coverage; a lower current speed results in a smaller sample size, improving estimation efficiency. A smaller sample size is found when the absolute value of the terrain slope result from the previous sampling point exceeds a preset slope threshold, and a larger sample size is found when the absolute value does not exceed the preset slope threshold. An interquartile range (IQR) jump point removal method is used to eliminate outliers and purify the data samples. The cumulative forward travel distance is used as the independent variable, and the terrain undulation height as the dependent variable. The Random Sample Consensus (RANSAC) algorithm is used for fitting. RANSAC is a mature and effective method for estimating mathematical model parameters from a sample set containing outliers, proposed by Fischler and Bolles in 1981. This random sample consensus algorithm has strong anti-interference ability and can accurately fit the rate of change of terrain undulation height with the cumulative forward travel distance, i.e., the terrain slope result, even in the presence of residual noise.

[0097] In some optional implementations, step S130, estimating the terrain slope result of the current sampling point based on the terrain undulation height and cumulative forward travel distance of each sampling point and the terrain undulation height and cumulative forward travel distance of the forward detection point, includes: selecting data samples from the terrain undulation height and cumulative forward travel distance of each sampling point and the forward detection point based on a dynamic sliding window; wherein, the number of data samples in the dynamic sliding window is determined based on the speed data of the current sampling point of the autonomous underwater vehicle and the terrain slope result of the sampling point at the previous moment, and the data samples include historical sampling points, the current sampling point, and the forward detection point; using the interquartile range jump point elimination method to remove outliers from the terrain undulation height in the data samples, the retained terrain undulation height and the retained cumulative forward travel distance form valid data samples; using the cumulative forward travel distance of the valid data samples as the independent variable and the terrain undulation height of the valid data samples as the dependent variable, the random sample consensus algorithm is used to fit the rate of change to obtain the terrain slope result of the current sampling point.

[0098] The data samples within the dynamic sliding window are determined based on the speed data of the autonomous underwater vehicle at the current sampling point and the terrain slope results of the previous sampling point.

[0099] For example, by establishing a mapping relationship between preset airspeed data and the number of data samples, the number of preliminary data samples can be determined based on the airspeed data of the sampling point at the current moment.

[0100] If the absolute value of the terrain slope result at the sampling point in the previous moment exceeds the preset slope threshold, that is... This reduces the number of initial data samples. times ( ).

[0101] If the absolute value of the terrain slope result at the sampling point in the previous moment does not exceed the preset slope threshold, that is... Then the initial number of data samples is maintained.

[0102] A data sample, including the terrain elevation and cumulative forward travel distance at the corresponding sampling point, can be represented as: ,in, Indicates the index of the data sample. Indicates the first One data sample.

[0103] Regardless of the number of data samples (i.e., regardless of the dynamic sliding window), the data samples include historical sampling points, current sampling points, and forward detection points.

[0104] Outliers in the terrain relief height data samples were removed using the interquartile range (ICM) jump point removal method. ICM jump point removal is a robust outlier detection method that calculates the quartile range of the data to determine a reasonable data distribution interval. Outliers exceeding this interval are then removed, effectively eliminating the impact of sensor measurement noise, environmental interference, and other factors on slope estimation, thus improving the validity and reliability of the data samples. The retained data samples form the valid data samples.

[0105] Using the cumulative forward travel distance of the valid data samples as the independent variable and the terrain undulation height of the valid data samples as the dependent variable, the rate of change is fitted using the random sample consensus algorithm to obtain the terrain slope result of the sampling point at the current time. .

[0106] Step S140: Based on the terrain slope results, the ground clearance data, the speed data, and the attitude angle data of the sampling point at the current time, predict the ground clearance for a future preset time period.

[0107] Based on the information from the sampling points at the current moment, the future height above the bottom of the autonomous underwater vehicle is predicted to avoid the height exceeding the optimal range for the payload due to sudden changes in terrain.

[0108] Figure 3 A schematic diagram illustrating the principle of height prediction is shown. (Refer to...) Figure 3 As shown, in some optional implementations, step S140, predicting the predicted bottom height for a future preset time based on the terrain slope result of the sampling point at the current time, the bottom height data of the sampling point at the current time, the speed data of the sampling point at the current time, and the attitude angle data of the sampling point at the current time, includes: calculating the theoretical horizontal distance of the autonomous underwater robot based on the following formula according to the speed data of the sampling point at the current time, the attitude angle data of the sampling point at the current time, and the future preset time. ,in, Indicates the preset duration in the future. The theoretical horizontal distance represents the future preset time; based on the speed data of the sampling point at the current time, the attitude angle data of the sampling point at the current time, and the future preset time, the theoretical vertical distance of the autonomous underwater robot is calculated based on the following formula; ,in, The theoretical vertical flight distance represents the future preset time; based on the theoretical horizontal flight distance and the terrain slope results of the current sampling point, the change in terrain height over the future preset time is calculated using the following formula; ,in, This indicates the terrain slope result at the current sampling point. This represents the change in terrain elevation over a future preset time period. Based on the current sampling point's ground elevation data, theoretical vertical flight distance, and terrain elevation change, the predicted ground elevation over the future preset time period is calculated using the following formula. ,in, This indicates the predicted height from the bottom over a predetermined time period.

[0109] Based on the current speed data of the sampling point, the current attitude angle data of the sampling point, and the future preset duration, the theoretical horizontal range of the autonomous underwater robot is calculated based on the following formula (7);

[0110] , formula (7);

[0111] in, Indicates the preset duration in the future. This represents the theoretical horizontal flight distance with a predetermined future duration.

[0112] By using the speed and attitude angle data of the autonomous underwater vehicle (AUV) at the current sampling point, the theoretical horizontal distance of the AUV in the future preset time can be accurately calculated, providing a basis for calculating the change in terrain height.

[0113] Based on the current speed data of the sampling point, the current attitude angle data of the sampling point, and the future preset duration, the theoretical vertical range of the autonomous underwater robot is calculated based on the following formula (8);

[0114] , formula (8);

[0115] in, This represents the theoretical vertical flight distance over a predetermined future duration.

[0116] The theoretical vertical range reflects the change in vertical displacement of an autonomous underwater vehicle over a predetermined time period based on the attitude angle data of the sampling point at the current moment. It is an important factor affecting the predicted height above the bottom.

[0117] Based on the theoretical horizontal flight distance and the terrain slope results of the current sampling point, the terrain height change over the future preset time is calculated using the following formula (9);

[0118] , formula (9);

[0119] in, This indicates the terrain slope result at the current sampling point. This indicates the amount of terrain elevation change over a predetermined future time period.

[0120] Based on the relationship between the topographic slope results of the current sampling point and the theoretical horizontal distance, the vertical height change of the seabed topography over a future preset time period can be accurately calculated.

[0121] Based on the current sampling point's ground clearance data, theoretical vertical flight distance, and terrain height change, the predicted ground clearance for the future preset duration is calculated using the following formula (10);

[0122] , formula (10);

[0123] in, This indicates the predicted height from the bottom over a predetermined time period.

[0124] By comprehensively considering the current seabed height data of the autonomous underwater vehicle (AUV), the vertical displacement changes over a preset time period, and the vertical height changes of the seabed topography, the AUV's seabed height status over a preset time period can be accurately predicted.

[0125] Step S150: If the predicted height above the bottom exceeds the optimal height range of the autonomous underwater vehicle, then determine the pitch control angle based on the target height above the bottom within the optimal height range, the height data above the bottom at the current sampling point, the preset scaling factor, and the terrain slope result at the current sampling point, and continue to control the autonomous underwater vehicle to navigate according to the pitch control angle.

[0126] If the predicted altitude above the bottom exceeds the optimal altitude range for the autonomous underwater vehicle (AUV) to carry its payload, the attitude angle data of the AUV's current sampling point needs to be changed to a pitch control angle so that the AUV can stably navigate within the optimal altitude range for a predetermined period of time.

[0127] In some optional implementations, step S150, determining the pitch control angle based on the target height above the ground within the optimal height range, the height above the ground data of the sampling point at the current moment, the preset scaling factor, and the terrain slope result of the sampling point at the current moment, includes: determining the target height above the ground based on the optimal height range; and calculating the height difference based on the following formula using the target height above the ground and the height above the ground data of the sampling point at the current moment. ,in, Indicates the height of the target above the ground. The elevation difference is represented; based on the elevation difference, the preset scaling factor, and the terrain slope result of the sampling point at the current moment, the pitch control angle is calculated using the following formula; ,in, Indicates the preset scaling factor and , This indicates the pitch control angle.

[0128] When calculating the pitch control angle, first determine the target's height above the bottom based on the optimal height range. For example, if the optimal height range is 1-3 meters above the bottom for the autonomous underwater vehicle, then the target's height above the bottom can be chosen as the intermediate value of 2 meters.

[0129] The height difference is calculated based on the target's height above the ground and the height of the sampling point above the ground at the current moment, using the following formula (11):

[0130] , formula (11);

[0131] in, Indicates the height of the target above the ground. Indicates the height difference.

[0132] For example: if the target is 2 meters above the ground and the current sampling point is 1.5 meters above the ground, then the height difference is 0.5 meters.

[0133] Based on the height difference, the preset scaling factor, and the terrain slope of the sampling point at the current moment, the pitch control angle is calculated using the following formula (12):

[0134] , formula (12);

[0135] in, Indicates the preset scaling factor and , This indicates the pitch control angle.

[0136] In other words, the baseline target attitude for altitude control is calculated using a preset proportional coefficient based on the altitude difference. The greater the altitude difference, the greater the adjustment range of the baseline target attitude. Then, the terrain slope result from the current sampling point is compensated for in the baseline target attitude to obtain the final pitch control angle.

[0137] Step S160: If the predicted height above the bottom does not exceed the optimal height range of the autonomous underwater vehicle, then the autonomous underwater vehicle continues to navigate according to the attitude angle data of the sampling point at the current moment.

[0138] If the predicted height above the bottom does not exceed the optimal height range for the payload carried by the autonomous underwater vehicle (AUV), there is no need to adjust the attitude angle data of the AUV at the current sampling point. The AUV will continue to be controlled based on the attitude angle data of the current sampling point.

[0139] Steps S110 to S160 constitute a continuous cyclical process. The autonomous underwater vehicle continuously collects new data, updates the slope, predicts altitude, and adjusts its attitude to ensure that its altitude above the seabed remains within the optimal range for carrying the payload, thus achieving stable navigation in complex and undulating terrain.

[0140] Figure 4 A stable navigation control system for an autonomous underwater robot according to one embodiment of this application is shown. (Refer to...) Figure 4 As shown, the stable navigation control system 400 of the autonomous underwater robot includes:

[0141] The information sensing module 410 is used to acquire depth data, ground clearance data, speed data, attitude angle data, and forward-looking distance data for each sampling point;

[0142] The parameter calculation module 420 is used to determine the terrain undulation height and cumulative forward travel distance of each sampling point, as well as the terrain undulation height and cumulative forward travel distance of the forward detection point, based on depth data, ground clearance data, speed data, attitude angle data, and forward look-ahead distance data.

[0143] The slope estimation module 430 is used to estimate the slope of the sampling point at the current moment based on the terrain undulation height and cumulative forward travel distance of each sampling point and the terrain undulation height and cumulative forward travel distance of the forward detection point.

[0144] The altitude prediction module 440 is used to predict the predicted altitude to the ground for a preset time based on the terrain slope result of the sampling point at the current time, the altitude above the ground data of the sampling point at the current time, the speed data of the sampling point at the current time, and the attitude angle data of the sampling point at the current time.

[0145] The attitude control module 450 is used to determine the pitch control angle based on the target height above the bottom within the optimal height range, the height above the bottom data of the sampling point at the current moment, the preset scaling factor, and the terrain slope result of the sampling point at the current moment if the predicted height above the bottom exceeds the optimal height range of the autonomous underwater robot. The autonomous underwater robot continues to navigate according to the pitch control angle.

[0146] The attitude control module 450 is also used to continue controlling the autonomous underwater vehicle to navigate according to the attitude angle data of the sampling point at the current moment if the predicted height above the bottom does not exceed the optimal height range of the autonomous underwater vehicle.

[0147] In some alternative implementations, in the above system, the information sensing module 410 is used to: acquire depth data continuously collected at a sampling frequency by a depth gauge mounted on an autonomous underwater vehicle. ; Acquire bottom elevation data continuously collected at a sampling frequency using an altimeter mounted on an autonomous underwater vehicle. ; Acquire speed data continuously collected at a sampling frequency using a Doppler velocimeter mounted on an autonomous underwater vehicle. ; Acquire attitude angle data continuously collected at a sampling frequency by the attitude measurement instrument mounted on the autonomous underwater vehicle. Acquire forward-looking distance data continuously collected at sampling frequency using a single-beam forward-looking sonar mounted on an autonomous underwater vehicle. ;in, Indicates the sampling point at the current time. This indicates the previous sampling time node of the current sampling point. Indicates the number of sampling points. This represents the depth data of the sampling point at the current moment. This represents the current height of the sampling point above the ground. This represents the airspeed data at the sampling point at the current moment. This represents the attitude angle data of the sampling point at the current moment. This represents the forward distance data of the sampling point at the current moment. The sampling frequency is adaptively adjusted based on the speed data of the autonomous underwater vehicle and the terrain slope results.

[0148] In some optional implementations, in the above system, the information sensing module 410 is further configured to: calculate the initial sampling time interval between the next sampling point and the previous sampling point based on the airspeed data of the sampling point at the previous moment and the preset correlation coefficient, according to the following formula. ,in, Indicates the sampling point index and , This indicates the preset correlation coefficient. Indicates the first Speed ​​data at each sampling point Indicates the first +1 sampling point and the first The initial sampling time interval for each sampling point; if the absolute value of the terrain slope result of the sampling point at the previous moment does not exceed the preset slope threshold, the initial sampling time interval is used as the sampling time interval, and the sampling point at the next moment is determined; if the absolute value of the terrain slope result of the sampling point at the previous moment exceeds the preset slope threshold, the updated sampling time interval between the sampling point at the next moment and the sampling point at the previous moment is calculated based on the following formula. ,in, Indicates the preset multiplier and , Indicates the first The sampling point and the first The sampling time interval is updated for each sampling point; the updated sampling time interval is used as the sampling time interval, and the sampling point at the next time moment is determined.

[0149] In some alternative implementations, in the above system, the parameter calculation module 420 is used to: calculate the terrain undulation height of each sampling point based on the following formula, according to the depth data, the height above the bottom data, and the preset false full ocean depth; ,in, Indicates the preset false full ocean depth and , Indicates the first The terrain undulation height of each sampling point Indicates the first Depth data of each sampling point Indicates the first The height of the sampling point above the bottom is calculated based on the following formula, using the current sampling point's depth data, current sampling point's attitude angle data, current sampling point's forward-looking distance data, preset false full ocean depth, and preset fixed installation angle of the single-beam forward-looking sonar; ,in, This indicates the preset fixed installation angle for a single-beam forward-looking sonar. The elevation of the terrain undulations at the forward detection point is indicated; based on the speed data, sampling time interval, and attitude angle data, the cumulative forward travel distance at each sampling point is calculated using the following formula; ,in, Indicates the first Speed ​​data at each sampling point Indicates the first The sampling point and the first The sampling time interval for each sampling point Indicates the first Attitude and bearing angle data at each sampling point Indicates the first The cumulative forward travel distance of each sampling point Indicates the first The cumulative forward travel distance of each sampling point and Based on the cumulative forward travel distance of the sampling point at the current moment, the forward-looking distance data of the sampling point at the current moment, the attitude angle data of the sampling point at the current moment, and the preset fixed installation angle of the single-beam forward-looking sonar, the cumulative forward travel distance of the forward detection point is calculated based on the following formula. ,in, This indicates the cumulative forward travel distance of the forward detection point.

[0150] In some optional implementations, in the above system, the slope estimation module 430 is further configured to: select data samples from the terrain undulation height and cumulative forward travel distance of each sampling point and the forward detection point based on a dynamic sliding window; wherein, the number of data samples in the dynamic sliding window is determined according to the speed data of the sampling point at the current moment of the autonomous underwater vehicle and the terrain slope result of the sampling point at the previous moment, and the data samples include historical sampling points, the current sampling point, and the forward detection point; use the interquartile range jump point elimination method to remove outliers from the terrain undulation height in the data samples, and the retained terrain undulation height and the retained cumulative forward travel distance form valid data samples; use the cumulative forward travel distance of the valid data samples as the independent variable and the terrain undulation height of the valid data samples as the dependent variable, and use the random sample consensus algorithm to fit the rate of change to obtain the terrain slope result of the sampling point at the current moment.

[0151] In some alternative implementations, in the above system, the altitude prediction module 440 is used to: calculate the theoretical horizontal range of the autonomous underwater robot based on the following formula, according to the speed data of the sampling point at the current moment, the attitude angle data of the sampling point at the current moment, and the preset future duration; ,in, Indicates the preset duration in the future. The theoretical horizontal distance represents the future preset time; based on the speed data of the sampling point at the current time, the attitude angle data of the sampling point at the current time, and the future preset time, the theoretical vertical distance of the autonomous underwater robot is calculated based on the following formula; ,in, The theoretical vertical flight distance represents the future preset time; based on the theoretical horizontal flight distance and the terrain slope results of the current sampling point, the change in terrain height over the future preset time is calculated using the following formula; ,in, This indicates the terrain slope result at the current sampling point. This represents the change in terrain elevation over a future preset time period. Based on the current sampling point's ground elevation data, theoretical vertical flight distance, and terrain elevation change, the predicted ground elevation over the future preset time period is calculated using the following formula. ,in, This indicates the predicted height from the bottom over a predetermined time period.

[0152] In some alternative implementations, in the above system, the attitude control module 450 is used to: determine the target's altitude above the ground based on an optimal altitude range; and calculate the altitude difference based on the target's altitude above the ground and the altitude above the ground data of the sampling point at the current moment, using the following formula. ,in, Indicates the height of the target above the ground. The elevation difference is represented; based on the elevation difference, the preset scaling factor, and the terrain slope result of the sampling point at the current moment, the pitch control angle is calculated using the following formula; ,in, Indicates the preset scaling factor and , This indicates the pitch control angle.

[0153] It should be noted that the aforementioned stable navigation control system 400 of the autonomous underwater robot can implement the aforementioned stable navigation control methods of the autonomous underwater robot, which will not be elaborated further.

[0154] Figure 5 This invention illustrates a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 5 As shown, the electronic device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used for communication with external devices via a network connection. When executed by the processor, the computer program implements the functions or steps of a stable navigation control method for an autonomous underwater robot.

[0155] In one embodiment, the electronic device provided in this application includes a memory and a processor. The memory stores a database and a computer program that can run on the processor. When the processor executes the computer program, it implements the steps of a stable navigation control method for an autonomous underwater robot.

[0156] The above is as stated in this application. Figure 4The method for executing the stable navigation control system of an autonomous underwater robot disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. During implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The steps of the method disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0157] In one embodiment, a computer-readable storage medium is also provided, on which a computer program is stored, the computer program being executed by a processor to implement the steps of a stable navigation control method for an autonomous underwater robot.

[0158] It should be noted that the functions or steps that the above-mentioned electronic devices or computer-readable storage media can achieve can be referred to the relevant descriptions in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0159] 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. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0160] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0161] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for stable navigation control of an autonomous underwater robot, characterized in that, include: Acquire depth data, ground clearance data, speed data, attitude angle data, and forward sight distance data for each sampling point; Based on depth data, ground clearance data, speed data, attitude angle data, and forward sight distance data, determine the terrain undulation height and cumulative forward travel distance of each sampling point, as well as the terrain undulation height and cumulative forward travel distance of the forward detection point; Based on the terrain undulation height and cumulative forward travel distance of each sampling point, as well as the terrain undulation height and cumulative forward travel distance of the forward detection point, estimate the terrain slope of the sampling point at the current moment. Based on the terrain slope, ground clearance, speed, and attitude angle of the current sampling point, predict the ground clearance for a preset time in the future. If the predicted height above the bottom exceeds the optimal height range of the autonomous underwater vehicle, the pitch control angle is determined based on the target height above the bottom within the optimal height range, the height above the bottom data at the current sampling point, the preset scaling factor, and the terrain slope result at the current sampling point. The autonomous underwater vehicle continues to navigate according to the pitch control angle. If the predicted height above the bottom does not exceed the optimal height range of the autonomous underwater vehicle, the autonomous underwater vehicle will continue to navigate according to the attitude angle data of the sampling point at the current moment.

2. The stable navigation control method for an autonomous underwater robot according to claim 1, characterized in that, The acquisition of depth data, ground clearance data, speed data, attitude angle data, and forward-looking distance data for each sampling point includes: Acquire depth data continuously collected at a sampling frequency using a depth gauge mounted on an autonomous underwater vehicle. ; Obtain bottom elevation data continuously collected at a sampling frequency using an altimeter mounted on an autonomous underwater vehicle. ; Acquire speed data continuously collected at a sampling frequency using a Doppler velocimeter mounted on an autonomous underwater vehicle. ; Acquire attitude angle data continuously collected at a sampling frequency by an attitude measurement system mounted on an autonomous underwater vehicle. ; Acquire forward-looking distance data continuously collected at sampling frequency using a single-beam forward-looking sonar mounted on an autonomous underwater vehicle. ; in, Indicates the sampling point at the current time. This indicates the previous sampling time node of the current sampling point. Indicates the number of sampling points. This represents the depth data of the sampling point at the current moment. This represents the current height of the sampling point above the ground. This represents the airspeed data at the sampling point at the current moment. This represents the attitude angle data of the sampling point at the current moment. This represents the forward distance data of the sampling point at the current moment. The sampling frequency is adaptively adjusted based on the speed data of the autonomous underwater vehicle and the terrain slope results.

3. The method for stable navigation control of an autonomous underwater robot according to claim 2, characterized in that, The sampling frequency is adaptively adjusted based on the autonomous underwater vehicle's speed data and terrain slope results, including: Based on the airspeed data of the previous sampling point and the preset correlation coefficient, the initial sampling time interval between the next sampling point and the previous sampling point is calculated using the following formula; ， in, Indicates the sampling point index and , This indicates the preset correlation coefficient. Indicates the first Speed ​​data at each sampling point Indicates the first The sampling point and the first The initial sampling time interval for each sampling point; If the absolute value of the terrain slope result of the sampling point at the previous moment does not exceed the preset slope threshold, the initial sampling time interval is used as the sampling time interval, and the sampling point at the next moment is determined. If the absolute value of the terrain slope result of the sampling point at the previous moment exceeds the preset slope threshold, the update sampling time interval between the sampling point at the next moment and the sampling point at the previous moment is calculated based on the following formula. ， in, Indicates the preset multiplier and , Indicates the first The sampling point and the first The update sampling time interval for each sampling point; The updated sampling time interval is used as the sampling time interval, and the sampling point at the next moment is determined.

4. The method for stable navigation control of an autonomous underwater robot according to claim 2, characterized in that, The process of determining the terrain undulation height and cumulative forward travel distance of each sampling point, as well as the terrain undulation height and cumulative forward travel distance of the forward detection point, based on depth data, ground clearance data, speed data, attitude angle data, and forward look-ahead distance data, includes: Based on depth data, bottom elevation data, and a preset false full ocean depth, the terrain undulation height at each sampling point is calculated using the following formula; ， in, Indicates the preset false full ocean depth and , Indicates the first The terrain undulation height of each sampling point Indicates the first Depth data of each sampling point Indicates the first Data on the height above the bottom of each sampling point; Based on the current sampling point's depth data, current sampling point's attitude angle data, current sampling point's forward-looking distance data, preset false full ocean depth, and preset fixed installation angle of the single-beam forward-looking sonar, the terrain undulation height of the forward detection point is calculated using the following formula; ， in, This indicates the preset fixed installation angle for a single-beam forward-looking sonar. Indicates the elevation of the terrain undulations at the forward detection point; Based on air speed data, sampling time interval, and attitude angle data, the cumulative forward travel distance at each sampling point is calculated using the following formula; ， in, Indicates the first Speed ​​data at each sampling point Indicates the first The sampling point and the first The sampling time interval for each sampling point Indicates the first Attitude and bearing angle data at each sampling point Indicates the first The cumulative forward travel distance of each sampling point Indicates the first The cumulative forward travel distance of each sampling point and ; Based on the cumulative forward travel distance of the sampling point at the current moment, the forward-looking distance data of the sampling point at the current moment, the attitude angle data of the sampling point at the current moment, and the preset fixed installation angle of the single-beam forward-looking sonar, the cumulative forward travel distance of the forward detection point is calculated based on the following formula; ， in, This indicates the cumulative forward travel distance of the forward detection point.

5. The method for stable navigation control of an autonomous underwater robot according to claim 1, characterized in that, The estimation of the terrain slope of the sampling point at the current moment based on the terrain undulation height of each sampling point and the cumulative forward travel distance, includes: Data samples are selected from the terrain undulation height and cumulative forward travel distance of each sampling point and the forward detection point based on a dynamic sliding window. The number of data samples in the dynamic sliding window is determined based on the speed data of the sampling point at the current moment and the terrain slope result of the sampling point at the previous moment. The data samples include historical sampling points, current sampling points and forward detection points. Outliers in the terrain relief height of the data sample are removed using the interquartile range jump point removal method. The retained terrain relief height and the retained cumulative forward travel distance correspond to form the valid data sample. Using the cumulative forward travel distance of the valid data samples as the independent variable and the terrain undulation height of the valid data samples as the dependent variable, the terrain slope result of the sampling point at the current time is obtained by fitting the rate of change using the random sample consensus algorithm.

6. The method for stable navigation control of an autonomous underwater robot according to claim 2, characterized in that, The step of predicting the predicted altitude above the ground for a preset duration based on the terrain slope, altitude above ground, speed, and attitude angle data of the current sampling point includes: Based on the current speed data of the sampling point, the current attitude angle data of the sampling point, and the preset future duration, the theoretical horizontal range of the autonomous underwater robot is calculated using the following formula; ， in, Indicates the preset duration in the future. This represents the theoretical horizontal flight distance over a predetermined future duration. Based on the current speed data of the sampling point, the current attitude angle data of the sampling point, and the preset future duration, the theoretical vertical range of the autonomous underwater robot is calculated using the following formula; ， in, This represents the theoretical vertical flight distance for a predetermined future duration. Based on the theoretical horizontal flight distance and the terrain slope results of the current sampling point, the change in terrain height over the future preset time period is calculated using the following formula; ， in, This indicates the terrain slope result at the current sampling point. This indicates the amount of terrain elevation change over a predetermined time period. Based on the current sampling point's ground clearance data, theoretical vertical flight distance, and terrain elevation change, the predicted ground clearance for a future preset time period is calculated using the following formula; ， in, This indicates the predicted height from the bottom over a predetermined time period.

7. The method for stable navigation control of an autonomous underwater robot according to claim 6, characterized in that, The process of determining the pitch control angle based on the target height above the ground within the optimal height range, the height above the ground data at the current sampling point, a preset scaling factor, and the terrain slope result at the current sampling point includes: Determine the target's height above the ground based on the optimal height range; The height difference is calculated based on the target's height above the ground and the current sampling point's height above the ground, using the following formula. ， in, Indicates the height of the target above the ground. Indicates the height difference; Based on the height difference, the preset scaling factor, and the terrain slope results of the sampling point at the current moment, the pitch control angle is calculated using the following formula; ， in, Indicates the preset scaling factor and , This indicates the pitch control angle.

8. A stable navigation control system for an autonomous underwater robot, characterized in that, include: The information sensing module is used to acquire depth data, ground clearance data, speed data, attitude angle data, and forward-looking distance data for each sampling point; The parameter calculation module is used to determine the terrain undulation height and cumulative forward travel distance of each sampling point, as well as the terrain undulation height and cumulative forward travel distance of the forward detection point, based on depth data, ground clearance data, speed data, attitude angle data, and forward look-ahead distance data. The slope estimation module is used to estimate the slope of the sampling points at the current moment based on the terrain undulation height and cumulative forward travel distance of each sampling point, as well as the terrain undulation height and cumulative forward travel distance of the forward detection point. The altitude prediction module is used to predict the predicted altitude to the ground for a preset time based on the terrain slope, the altitude above the ground, the speed, and the attitude angle of the current sampling point. The attitude control module is used to determine the pitch control angle based on the target height above the bottom within the optimal height range, the height above the bottom data of the current sampling point, the preset scaling factor, and the terrain slope result of the current sampling point if the predicted height above the bottom exceeds the optimal height range of the autonomous underwater robot. The autonomous underwater robot continues to navigate according to the pitch control angle. The attitude control module is also used to continue controlling the autonomous underwater vehicle to navigate according to the attitude angle data of the sampling point at the current moment if the predicted height above the bottom does not exceed the optimal height range of the autonomous underwater vehicle.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the stable navigation control method for an autonomous underwater robot as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the stable navigation control method for an autonomous underwater robot as described in any one of claims 1 to 7.

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