Long-distance cable posture detection method for underwater robots

Abstract

The present invention relates to the field of cable attitude detection technology, and in particular to a long-distance cable attitude detection method for underwater robots. Acceleration time series data of all monitoring positions on the long-distance cable of the underwater robot are obtained, and cable attitude changes are analyzed. Considering the acceleration differences and position distribution of different monitoring positions on the cable, the position attitude change degree of each monitoring position is determined. When performing a specific task, the attitude changes of some monitoring positions on the cable are consistent, and the monitoring positions are divided based on the attitude change degree. The more consistent the attitude changes of the monitoring positions close to the underwater robot, the greater the impact of the motion and the higher the probability of abnormality, so the degree of influence value of each monitoring position is calculated. Since the torsional change of the cable is a process of gradual accumulation of stress, the degree of influence value of each monitoring position at all times is comprehensively analyzed to obtain the attitude distortion degree value, and finally the abnormal position of the attitude is determined.

Description

Technical Field

[0001] The present invention relates to the technical field of cable posture detection, and in particular to a long-distance cable posture detection method for an underwater robot. Background Art

[0002] Underwater robots (AUVs) play a vital role in deep-sea exploration, underwater operations, and marine science research. They are connected to ground control stations via long-distance cables for data transmission, energy supply, and command control. However, in complex and changing underwater environments, especially under adverse conditions such as strong deep-sea currents and undulating seabed topography, these cables are prone to distortion, entanglement, and even breakage. This not only affects the AUV's operation but can also damage it, leading to mission failure. Therefore, posture detection of the AUV's long-distance cables is often required.

[0003] The posture of long-distance cables underwater is usually affected by the movement of underwater robots and the influence of buoyancy. Under the combined action of multiple forces, the cables at different positions on the long-distance cables will be twisted or entangled to varying degrees, resulting in changes in the movement speed. When performing posture detection on long-distance cables, the existing technology usually screens out the positions where the movement speed on the long-distance cables is abnormal and regards them as positions with posture distortion. However, when a position on the cable moves under the action of external force, the stress generated will affect the surrounding monitoring positions. Therefore, only analyzing the movement speed of each position on the long-distance cable separately will lead to a decrease in the accuracy of screening the positions with abnormal posture. Summary of the Invention

[0004] In order to solve the technical problem that when a position on a cable moves under the action of external force, the stress generated will affect the surrounding monitoring positions. Therefore, only analyzing the movement speed of each position on a long-distance cable separately will lead to a decrease in the accuracy of screening the final abnormal posture position. The purpose of the present invention is to provide a long-distance cable posture detection method for an underwater robot. The technical solution adopted is as follows:

[0005] Obtain acceleration time series data at all monitoring positions on the long-distance cable of the underwater robot;

[0006] At each moment, the position and attitude change degree of each monitoring position is determined based on the difference between the acceleration values ​​at different monitoring positions on the long-distance cable and the distribution of the monitoring positions;

[0007] At each moment, all monitoring locations on the long-distance cable are divided into a set based on their position and attitude change. Based on the distribution of the monitoring locations in the set and their position distribution on the long-distance cable, the correlation between the position and attitude change of the monitoring locations is analyzed to determine the impact level of each monitoring location.

[0008] For any monitoring position, the changes in the affected degree value at the monitoring position at different times are analyzed to obtain the posture distortion degree value of the monitoring position; based on the posture distortion degree value, the posture distortion conditions of all monitoring positions on the long-distance cable are judged to determine the abnormal posture position.

[0009] Furthermore, the method for obtaining the position and attitude change degree includes:

[0010] On the long-distance cable, all monitoring locations are sorted from near to far according to their distance from the underwater robot to obtain a position sorting sequence;

[0011] At each moment, for any monitoring position, the normalized value of the difference between the acceleration value of the monitoring position and the acceleration value of the next adjacent monitoring position in the position sorting sequence is used as the motion change indicator of the monitoring position;

[0012] The ratio of the motion change index of the monitoring position to the sequence number of the monitoring position in the position sorting sequence is used as the position attitude change degree of the monitoring position at each moment.

[0013] Furthermore, the method for obtaining the partition set includes:

[0014] At each moment, in the position sorting sequence, the first monitoring position is taken as the position to be measured and the traversal begins. The position to be measured corresponds to a position set. It is determined whether the similarity value of the position attitude change between the position to be measured and the next adjacent monitoring position meets the preset conditions.

[0015] If the condition is satisfied, the next adjacent monitoring position is incorporated into the position set corresponding to the position to be measured, and the similarity value of the position attitude change between the last monitoring position in the position set and the next adjacent monitoring position is judged to be satisfied. If not, the next adjacent monitoring position is taken as the new position to be measured for traversal.

[0016] After the traversal is completed, all position sets are obtained as partition sets;

[0017] The preset condition is that the similarity value is greater than or equal to a preset similarity threshold.

[0018] Furthermore, the method for obtaining the similarity value includes:

[0019] The absolute value of the difference in the position and posture change between the two monitoring positions is negatively correlated and normalized, and is used as the similarity value between the two monitoring positions.

[0020] Furthermore, the method for obtaining the impact degree value includes:

[0021] At each moment, for any monitoring position on the long-distance cable, calculate the average of the position and attitude change degrees of all monitoring positions in the partition set to which the monitoring position belongs, as the first average;

[0022] Determine a comparison set corresponding to the monitoring position according to the position distribution of the monitoring position on the long-distance cable, and use the average of the position attitude change degrees of all monitoring positions in the comparison set as the second average value;

[0023] The absolute value of the difference between the first mean and the second mean corresponding to the monitoring position is negatively correlated and normalized, and the value is used as the impact degree value at the monitoring position.

[0024] Furthermore, the method for obtaining the comparison set includes:

[0025] At each moment, for any monitoring position on the long-distance cable, the monitoring position and all monitoring positions preceding the monitoring position in the position sorting sequence constitute a comparison set for the monitoring position.

[0026] Furthermore, the method for obtaining the posture distortion value includes:

[0027] For any monitoring location, in the time series, calculate the absolute value of the difference between the impact degree value of the monitoring location at each moment and the impact degree value at the next adjacent moment as the change factor;

[0028] The normalized value of the mean value of all the change factors corresponding to the monitoring position is used as the posture distortion degree value of the monitoring position.

[0029] Furthermore, the method for determining the abnormal posture position includes:

[0030] On long-distance cables, the monitoring position with a value greater than the preset abnormality threshold is regarded as the posture abnormality position.

[0031] Furthermore, the value range of the abnormal threshold is [0.6, 1).

[0032] Furthermore, the monitoring locations are evenly distributed at fixed intervals on the long-distance cable.

[0033] The present invention has the following beneficial effects:

[0034] The underwater environment is complex and ever-changing, and cables may be affected by a variety of factors, causing their attitude to change. Acceleration time-series data can directly reflect the cable's motion state at different moments, including changes in speed and magnitude, and is the basis for analyzing cable attitude changes. Therefore, we first obtain acceleration time-series data at all monitoring locations on the underwater robot's long-distance cable as attitude data. State changes during the underwater robot's operation drive cable motion, and this influence gradually decreases as the distance between the cable location and the underwater robot increases. In this method, the acceleration differences at different monitoring locations on the long-distance cable at each moment, as well as the distribution of monitoring locations, are considered to determine the position attitude change at each monitoring location and quantify the cable attitude change at each monitoring location. When the underwater robot performs a specific task, some monitoring locations on the long-distance cable often exhibit relatively consistent attitude changes. Therefore, at each moment, the monitoring locations can be divided based on their attitude change degrees to obtain divided sets. In this case, the monitoring locations in each divided set exhibit relatively consistent attitude changes. When the underwater robot moves, it will cause the posture changes of different monitoring points on the long-distance cable. Therefore, if the posture change at a certain position is more consistent with the posture change at a position close to the underwater robot, it means that the cable at that position is more affected by the movement of the underwater robot, and the probability of an abnormality is higher. Therefore, based on the above logic, the degree of influence value at each monitoring position is calculated. Furthermore, since the torsional change of the cable is a process of gradual accumulation of stress in the long-distance cable, that is, as time goes by, the torsional stress of the cable may continue to accumulate. Therefore, for any monitoring position, the degree of influence value of the monitoring position at all times is comprehensively analyzed to obtain the posture distortion value of the monitoring position, and finally the abnormal posture position is determined among all monitoring positions based on the posture distortion value. In summary, the present invention comprehensively analyzes the posture changes at different positions on the long-distance cable and the gradual accumulation process of torsional changes, and more accurately judges the posture distortion of each monitoring position, thereby improving the screening accuracy of abnormal posture positions. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0036] Figure 1A flow chart of a method for monitoring the attitude of a long-distance cable used in an underwater robot according to an embodiment of the present invention;

[0037] Figure 2 A flow chart of a method for obtaining a hand influence degree value provided by one embodiment of the present invention;

[0038] Figure 3 A system block diagram of a long-distance cable posture detection system for an underwater robot provided by one embodiment of the present invention;

[0039] Figure 4 A schematic diagram of the system structure of a long-distance cable posture detection system for an underwater robot provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0040] To further illustrate the technical means and effectiveness of the present invention in achieving its intended objectives, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effectiveness of a long-distance cable posture detection method for an underwater robot according to the present invention. In the following description, references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0041] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0042] The following describes in detail a specific solution of a long-distance cable posture detection method for an underwater robot provided by the present invention with reference to the accompanying drawings.

[0043] See also Figure 1 , which shows a method flow chart of a long-distance cable posture detection method for an underwater robot provided by one embodiment of the present invention, the method comprising the following steps:

[0044] Step S1: Acquire acceleration time series data at all monitoring positions on the long-distance cable of the underwater robot.

[0045] Cable power is a common power supply method for underwater robots (especially remotely operated vehicles (ROVs)). Surface power supplies provide continuous power via cables, enabling the robot to operate for extended periods without needing to return to the surface for recharging. These cables, typically composite cables containing power lines and optical fibers, can simultaneously transmit both power and high-speed data, facilitating real-time data transmission and control instructions. They are suitable for high-power tasks requiring continuous power, such as subsea construction, maintenance, and inspection. Long-distance cable posture detection is crucial, helping to monitor and manage the cable's shape and position, preventing entanglement, excessive bending, or damage, thereby ensuring smooth mission progress and reliable robot operation.

[0046] For cable posture analysis, simulation objects such as cables are usually discretized using particles. That is, the cable is represented as a chain structure composed of a series of discrete particles (or nodes). These particles are distributed along the length of the cable. When a particle moves under the action of an external force, the stress generated acts on other adjacent particles, thereby transmitting the force to the surrounding area and driving the adjacent particles to move. Therefore, the deformation of the cable is caused by the movement of the particles.

[0047] In this embodiment of the present invention, the mass points on the long-distance cable are evenly spaced at a fixed interval of 10. These evenly spaced mass points on the long-distance cable serve as monitoring locations, and an inertial measurement unit (IMU) is installed at each monitoring location to capture key motion patterns. During installation, ensure the sensor is oriented correctly and establish a stable data transmission path via wired or wireless means.

[0048] Data is collected simultaneously for all monitoring positions on the long-distance cable to obtain the acceleration values ​​at all monitoring positions at the same time (IMU can obtain acceleration values ​​in three directions. In this embodiment of the present invention, the intensity of movement is mainly considered, so the acceleration values ​​in the three directions at each monitoring position are synthesized to obtain a total acceleration value as the acceleration value at each monitoring position). The collection time interval is set to 1s, and the collection time period length is set to 1 minute, so as to obtain the acceleration time series data at all monitoring positions on the long-distance cable.

[0049] It should be noted that the distribution interval of the monitoring positions on the long-distance cable can be adjusted according to the actual length of the long-distance cable, and the collection time interval and time period length can also be adjusted according to the implementation scenario, which is not limited here.

[0050] Step S2: At each moment, according to the difference between the acceleration values ​​at different monitoring positions on the long-distance cable and the distribution of the monitoring positions, determine the position attitude change degree at each monitoring position.

[0051] The changed state of the underwater robot during operation will drive the cable to move, and the movement resistance of the cable tends to maintain its posture stability. In addition, the dynamic stress caused by the movement of the underwater robot will have a greater impact on the part of the long-distance cable close to the robot end. Therefore, the cable may be affected by the combined action of multiple external forces in the complex underwater environment, causing its posture to change. Therefore, by monitoring the acceleration values ​​at different positions on the cable and considering the differences between these acceleration values ​​and the distribution of the monitoring positions, the position and posture change degree at each monitoring position at each moment can be determined, and the posture change can be quantified.

[0052] Preferably, in one embodiment of the present invention, the method for obtaining the position and attitude change degree includes:

[0053] For long-distance cables, the closer they are to the underwater robot, the more their posture is affected by the underwater robot's movement. Therefore, on long-distance cables, all monitoring positions are first sorted from near to far according to their distance from the underwater robot to obtain a position sorting sequence. The position sorting sequence establishes the relative position relationship between the monitoring position and the underwater robot, which helps in the subsequent analysis process.

[0054] At each moment, for any monitoring position, the difference in acceleration change between the monitoring position and the adjacent monitoring position is evaluated to reflect the motion state of the cable at the monitoring position: in the position sorting sequence, the absolute value of the difference between the acceleration value of the monitoring position and the acceleration value of the next adjacent monitoring position is calculated. The larger the absolute value of the difference, the more prominent the influence of the force on the monitoring position. The value after normalizing the absolute value of the difference is used as the motion change indicator of the monitoring position. Normalization is a technical means well known to those skilled in the art. The normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0055] The closer the distance to the robot, the greater the impact of the robot on the cable's posture. Therefore, the ratio of the motion change index of the monitoring position to the serial number value of the monitoring position in the position sorting sequence is used as the position and posture change degree of the monitoring position at each moment. The smaller the serial number value, the closer the distance between the monitoring position and the robot, and the greater the impact. Therefore, the greater the position and posture change degree obtained by combining the serial number value and the motion change index, the greater the degree of position and posture change of the monitoring position.

[0056] It should be noted that in this embodiment of the present invention, the serial number of the position sorting sequence starts from 1. In other embodiments, it can also start with other positive numbers; the motion change index at the last monitoring position is set to be consistent with the motion change index at the adjacent previous monitoring position.

[0057] Step S3: At each moment, all monitoring positions are divided based on the position attitude change degrees of all monitoring positions on the long-distance cable to obtain a divided set; based on the position distribution of the monitoring positions in the divided set and the position distribution on the long-distance cable, the correlation between the position attitude change degrees of the monitoring positions is analyzed to determine the degree of influence value at each monitoring position.

[0058] Since the posture changes on long-distance cables tend to be distributed in stages, their staged distribution is affected by the robot's movement and actual operating range. That is, when the underwater robot performs specific tasks, such as inspecting pipelines, performing repairs, and collecting samples, often only the conditions at some monitoring positions are more consistent. That is, this part of the cable will move within a certain spatial range and the monitoring data at each monitoring position will tend to be consistent. Therefore, by dividing the monitoring positions, a set of divisions with different degrees of posture change can be obtained, which helps to understand the posture changes at different positions on the cable in more detail.

[0059] When the underwater robot moves, it will cause posture changes at different monitoring points on the long-distance cable, such as twisting or entanglement. Therefore, the more consistent the posture change at a certain position is with the posture change at a position close to the underwater robot, the greater the influence of the underwater robot's movement on the cable at that position, and the higher the probability of an abnormality. Therefore, the correlation between the posture changes at different monitoring positions affected by the robot's operation is closely related to the monitoring position and the position of the underwater robot. Therefore, based on the aforementioned partitioned sets, the correlation between the position posture change degree at the monitoring position can be analyzed based on the position distribution of the monitoring position in the partitioned set and the position distribution on the long-distance cable, thereby determining the degree of influence at each monitoring position.

[0060] Preferably, in one embodiment of the present invention, the method for obtaining a partition set includes:

[0061] At each moment, in the position sorting sequence, the first monitoring position is taken as the position to be measured to start traversal, and the position to be measured corresponds to a position set, and the similarity value of the position attitude change between the position to be measured and the adjacent next monitoring position is judged to meet the preset conditions, wherein the similarity value calculation method includes: calculating the absolute value of the difference in the position attitude change between the two monitoring positions, the smaller the absolute value of the difference, the more similar the position attitude change between the two adjacent monitoring positions is, and then the absolute value of the difference is negatively correlated and normalized to achieve logical relationship correction, thereby obtaining the similarity value between the two monitoring positions, at this time, the larger the similarity value, the higher the similarity of the position attitude change between the two monitoring positions, and they should be divided into a partition set, conversely, if the similarity value is smaller, it indicates that the similarity of the position attitude change between the two monitoring positions is low, and they should not be divided into a partition set. Normalization is a technical means well known to those skilled in the art, and the normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0062] Therefore, if the preset conditions are met, the next adjacent monitoring position will be incorporated into the position set corresponding to the position to be measured, and the similarity value of the position posture change between the last monitoring position in the position set and the next adjacent monitoring position will be judged to see whether it meets the preset conditions. If the preset conditions are not met, the next adjacent monitoring position will be used as the new position to be measured and traversed.

[0063] After the traversal is completed, all position sets can be obtained, and each position set can be used as a partition set.

[0064] The preset condition is that the similarity value is greater than or equal to a preset similarity threshold. It should be noted that the preset similarity threshold is set to 0.6, and the specific value can be adjusted according to the implementation scenario and is not limited here.

[0065] An example is given to illustrate the process of obtaining the above partitioning set: for example, the total number of monitoring locations is 5. In the position sorting sequence, they are recorded in order as monitoring location 1-monitoring location 5. First, monitoring location 1 is taken as the location to be measured, and monitoring location 1 corresponds to a location set. Initially, the location set only contains monitoring location 1. The similarity value between monitoring location 1 and monitoring location 2 is calculated. If the similarity value is greater than or equal to the preset similarity threshold, monitoring location 2 is incorporated into the location set corresponding to monitoring location 1. Then, the similarity value between monitoring location 2 and monitoring location 3 is calculated. If the similarity value is less than the preset similarity threshold, monitoring location 3 is used as a new location to be measured and corresponds to a location set (this time only containing monitoring location 3), and the first partitioning set a (consisting of monitoring location 1 and monitoring location 2) is obtained. Next, the similarity between monitoring location 3 and monitoring location 4 is calculated. If the similarity is greater than or equal to the preset similarity threshold, monitoring location 4 is incorporated into the location set corresponding to monitoring location 3. The similarity between monitoring location 4 and monitoring location 5 is then calculated. If the similarity is still greater than or equal to the preset similarity threshold, monitoring location 5 is also incorporated into the location set corresponding to monitoring location 3. This results in the second partition set b (including monitoring locations 3, 4, and 5). The traversal is now complete, and all partition sets are obtained.

[0066] Based on the above steps, all monitoring locations can be divided into different partition sets, and the monitoring locations in each partition set have relatively consistent posture changes. When the underwater robot moves, it will cause posture changes at different monitoring points on the long-distance cable, such as twisting or entanglement. The closer the position is to the robot, the higher the probability of posture changes. Therefore, the more consistent the posture changes at a certain position are with the posture changes at the position close to the underwater robot, the greater the impact of the underwater robot's movement on the cable at that position, and the higher the probability of an abnormality. Therefore, after obtaining the partition set, based on the position distribution of the monitoring locations in the partition set and the position distribution on the long-distance cable, the correlation between the position posture change degree at the monitoring location is analyzed to determine the impact level value at each monitoring location.

[0067] Preferably, in one embodiment of the present invention, the method for obtaining the impact degree value includes:

[0068] See also Figure 2 , which shows a flow chart of a method for obtaining an impact degree value in one embodiment of the present invention, the method comprising the following steps:

[0069] Step S301: At each moment, in the partition set to which any monitoring position on the long-distance cable belongs, determine the local average change status of the posture.

[0070] At each moment, for any monitoring position on a long-distance cable, the mean of the position attitude change of all monitoring positions in the divided set to which the monitoring position belongs is calculated as the first mean, which reflects the average level of the position attitude change of all monitoring positions in the divided set.

[0071] Step S302: Based on the position distribution of any monitoring position on the long-distance cable, a corresponding comparison set is determined.

[0072] Given the need to analyze changes in position attitude change values ​​based on the distribution of monitoring positions on the cable, the comparison set corresponding to the monitoring position is determined based on the position distribution of the monitoring position on the long-distance cable: at each moment, for any monitoring position on the long-distance cable, the monitoring position and all monitoring positions before the monitoring position in the position sorting sequence constitute the comparison set of the monitoring position. The monitoring positions in the comparison set can be regarded as monitoring positions that are relatively close to the underwater robot.

[0073] Step S303: At each moment, in a comparison set corresponding to any monitoring position on the long-distance cable, determine the overall average change of the posture.

[0074] Similarly, the mean of the position and attitude change degrees of all monitoring positions in the comparison set is calculated as the second mean, which reflects the average level of attitude change of all monitoring positions in the comparison set.

[0075] Step S304: at each moment, based on the correlation between the local average change condition and the overall average change condition of the posture corresponding to any monitoring position, determine the impact degree value at the monitoring position.

[0076] By comparing the first mean with the second mean, the correlation between the position and posture changes in the partition set to which the monitoring position belongs and the position and posture changes in the corresponding comparison set can be evaluated to characterize the influence of the underwater robot on the monitoring position: calculate the absolute value of the difference between the first mean and the second mean corresponding to the monitoring position. The larger the absolute value of the difference, the lower the similarity between the position and posture changes in the partition set to which the monitoring position belongs and the position and posture changes in the corresponding comparison set, and the weaker the influence of the underwater robot on it. Conversely, the smaller the absolute value of the difference, the higher the similarity between the position and posture changes in the partition set to which the monitoring position belongs and the position and posture changes in the corresponding comparison set, and the greater the influence of the underwater robot on it. Therefore, the absolute value of the difference can be negatively correlated and normalized to achieve logical relationship correction and obtain the degree of influence value at the monitoring position. At this time, the larger the degree of influence value, the higher the degree of influence of the underwater robot on the monitoring position. It should be noted that the negative correlation mapping and normalization processing here can be adopted function, where It represents the exponential function with the natural constant e as the base, and x represents the independent variable.

[0077] Step S4: For any monitoring position, analyze the changes in the affected degree value at the monitoring position at different times to obtain the posture distortion degree value of the monitoring position; based on the posture distortion degree value, judge the posture distortion conditions of all monitoring positions on the long-distance cable to determine the abnormal posture position.

[0078] As the underwater robot moves or changes direction, the cable rotates along its axis, increasing internal torsional stress. As the torsion angle increases, the cable's outer material experiences greater shear stress, causing it to twist. This torsional change is a gradual accumulation process within the long cable. This means that as the underwater robot operates, the torsional stress on the long cable continues to accumulate, and the cable's posture exhibits a distinct torsional tendency. Therefore, we can analyze the temporal changes in the impact level at each monitoring location to obtain a posture distortion value for each monitoring location. Finally, based on this posture distortion value, we can determine the posture distortion at all monitoring locations on the long cable and identify locations with abnormal postures. This means we can capture the sudden changes in posture at each location within the acquisition phase.

[0079] Preferably, in one embodiment of the present invention, the method for obtaining the posture distortion value includes:

[0080] For any monitoring position, in the time series, the absolute value of the difference between the affected degree value of the monitoring position at each moment and the affected degree value at the next adjacent moment is calculated as the variation factor. The variation factor can capture the dynamic changes of the affected degree value of the monitoring position at different moments. The larger the variation factor, the more significant the change in the posture of the monitoring position, which is considered to be more likely to be distorted.

[0081] Finally, the mean of all variation factors corresponding to the monitoring location is normalized to obtain the value of the posture distortion degree at that monitoring location. A larger posture distortion degree indicates a higher likelihood of posture distortion at that monitoring location. Normalization is a well-known technique for those skilled in the art. The normalization function can be linear normalization or standard normalization, and the specific normalization method is not limited here.

[0082] Based on the above method, the posture distortion degree values ​​at all monitoring positions on the long-distance cable of the underwater robot can be obtained, and then the positions with abnormal posture can be screened out based on the posture distortion degree values.

[0083] Preferably, in one embodiment of the present invention, the method for obtaining the abnormal posture position includes:

[0084] Because the larger the posture distortion value is, the more likely the cable at the corresponding monitoring position may be twisted or entangled, etc., so on long-distance cables, the monitoring position greater than the preset abnormal threshold is regarded as the posture abnormal position, where the abnormal threshold value is set to [0.6, 1). In this embodiment of the present invention, it is set to 0.7. The specific value can be adjusted according to the implementation scenario and is not limited here.

[0085] In summary, the underwater environment is complex and changeable, and cables may be affected by a variety of factors, causing their attitude to change. Acceleration time series data can directly reflect the cable's motion state at different moments, including changes in speed and magnitude, and is the basis for analyzing cable attitude changes. Therefore, we first obtain acceleration time series data at all monitoring locations on the underwater robot's long-distance cable as attitude data. State changes during underwater robot operation drive cable motion, and this influence gradually decreases as the distance between the cable location and the underwater robot increases. In an embodiment of the present invention, the position attitude change degree at each monitoring location is determined, and the cable attitude change at each monitoring location is quantified, considering the differences in acceleration at different monitoring points on the long-distance cable at each moment and the distribution of monitoring locations. When the underwater robot performs a specific task, some monitoring locations on the long-distance cable often exhibit relatively consistent attitude changes. Therefore, at each moment, the monitoring locations can be divided based on their attitude change degrees to obtain divided sets. In this case, the monitoring locations in each divided set exhibit relatively consistent attitude changes. When the underwater robot moves, it will cause the posture changes of different monitoring points on the long-distance cable. Therefore, if the posture change at a certain position is more consistent with the posture change at a position close to the underwater robot, it means that the cable at that position is more affected by the movement of the underwater robot, and the probability of an abnormality is higher. Therefore, based on the above logic, the degree of influence value at each monitoring position is calculated. Furthermore, since the torsional change of the cable is a process of gradual accumulation of stress in the long-distance cable, that is, as time goes by, the torsional stress of the cable may continue to accumulate. Therefore, for any monitoring position, the degree of influence value of the monitoring position at all times is comprehensively analyzed to obtain the posture distortion value of the monitoring position, and finally the abnormal posture position is determined in all monitoring positions based on the posture distortion value. In summary, the embodiment of the present invention comprehensively analyzes the posture changes at different positions on the long-distance cable and the gradual accumulation process of torsional changes, and more accurately judges the posture distortion of each monitoring position, thereby improving the screening accuracy of abnormal posture positions.

[0086] The present invention also provides a long distance cable posture detection system for underwater robots. Figure 3 , which shows a system block diagram, including a data acquisition module 401, used to implement step S1 in the above method; a posture analysis module 402, used to implement step S2 in the above method; an impact degree analysis module 403, used to implement step S3 in the above method; and an abnormal position screening module 404, used to implement step S4 in the above method.

[0087] It should be noted that the system provided in the above embodiment is merely an example of the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. In addition, the long-distance cable posture detection system for underwater robots and the long-distance cable posture detection method for underwater robots provided in the above embodiment are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0088] See also Figure 4 , which shows a system structure diagram of a long-distance cable posture detection system for an underwater robot provided by an embodiment of the present invention, including a processor 500, a memory 501, a bus 502 and a communication interface 503, wherein the processor 500, the communication interface 503 and the memory 501 are connected via the bus 502; wherein the memory 501 may include a high-speed random access memory, the bus 502 may be an ISA bus, a PCI bus or an EISA bus, etc., and the processor 500 may be an integrated circuit chip with signal processing capabilities; the memory 501 stores at least one instruction, at least one program, a code set or an instruction set, and when the at least one instruction, at least one program, a code set or an instruction set is loaded and executed by the processor, the steps in a long-distance cable posture detection method for an underwater robot are implemented.

[0089] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0090] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A long-distance cable posture detection method for an underwater robot, characterized in that: The method comprises: Obtain acceleration time series data at all monitoring positions on the long-distance cable of the underwater robot; At each moment, the position and attitude change degree of each monitoring position is determined based on the difference between the acceleration values ​​at different monitoring positions on the long-distance cable and the distribution of the monitoring positions; At each moment, all monitoring positions are divided based on the position attitude change degrees of all monitoring positions on the long-distance cable to obtain a divided set; based on the position distribution of the monitoring positions in the divided set and the position distribution on the long-distance cable, the correlation between the position attitude change degrees of the monitoring positions is analyzed to determine the impact degree value of each monitoring position; the impact degree value is a value obtained by negatively correlating and normalizing the absolute value of the difference between the first mean and the second mean, the first mean is the mean of the position attitude change degrees of all monitoring positions in the divided set to which the monitoring position belongs, and the second mean is the mean of the position attitude change degrees of all monitoring positions in the comparison set, and the comparison set is determined based on the position distribution of the monitoring positions on the long-distance cable; For any monitoring position, the changes in the affected degree value at the monitoring position at different times are analyzed to obtain the posture distortion degree value of the monitoring position; based on the posture distortion degree value, the posture distortion conditions of all monitoring positions on the long-distance cable are judged to determine the abnormal posture position.

2. A long-distance cable posture detection method for an underwater robot according to claim 1, characterized in that: The method for obtaining the position and attitude change degree includes: On the long-distance cable, all monitoring locations are sorted from near to far according to their distance from the underwater robot to obtain a position sorting sequence; At each moment, for any monitoring position, the normalized value of the difference between the acceleration value of the monitoring position and the acceleration value of the next adjacent monitoring position in the position sorting sequence is used as the motion change indicator of the monitoring position; The ratio of the motion change index of the monitoring position to the sequence number of the monitoring position in the position sorting sequence is used as the position attitude change degree of the monitoring position at each moment.

3. A method for detecting the attitude of a long-distance cable for an underwater robot according to claim 2, characterized in that: The method for obtaining the partition set includes: At each moment, in the position sorting sequence, the first monitoring position is taken as the position to be measured and the traversal begins. The position to be measured corresponds to a position set. It is determined whether the similarity value of the position attitude change between the position to be measured and the next adjacent monitoring position meets the preset conditions. If the condition is satisfied, the next adjacent monitoring position is incorporated into the position set corresponding to the position to be measured, and the similarity value of the position attitude change between the last monitoring position in the position set and the next adjacent monitoring position is judged to be satisfied. If not, the next adjacent monitoring position is taken as the new position to be measured for traversal. After the traversal is completed, all position sets are obtained as partition sets; The preset condition is that the similarity value is greater than or equal to a preset similarity threshold.

4. A long-distance cable posture detection method for an underwater robot according to claim 3, characterized in that: The method for obtaining the similarity value includes: The absolute value of the difference in the position and posture change between the two monitoring positions is negatively correlated and normalized, and is used as the similarity value between the two monitoring positions.

5. The method for detecting the attitude of a long-distance cable for an underwater robot according to claim 1, wherein: The method for obtaining the comparison set includes: At each moment, for any monitoring position on the long-distance cable, the monitoring position and all monitoring positions preceding the monitoring position in the position sorting sequence constitute a comparison set for the monitoring position.

6. The method for detecting the attitude of a long-distance cable used in an underwater robot according to claim 1, wherein: The method for obtaining the posture distortion value includes: For any monitoring location, in the time series, calculate the absolute value of the difference between the impact degree value of the monitoring location at each moment and the impact degree value at the next adjacent moment as the change factor; The normalized value of the mean value of all the change factors corresponding to the monitoring position is used as the posture distortion degree value of the monitoring position.

7. The method for detecting the attitude of a long-distance cable for an underwater robot according to claim 1, wherein: The method for determining the abnormal posture position includes: On long-distance cables, the monitoring position with a value greater than the preset abnormality threshold is regarded as the posture abnormality position.

8. A method for detecting the attitude of a long-distance cable for an underwater robot according to claim 7, characterized in that: The value range of the abnormal threshold is [0.6, 1).

9. The method for detecting the attitude of a long-distance cable used in an underwater robot according to claim 1, wherein: The monitoring locations are evenly distributed at fixed intervals on long-distance cables.

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