Multi-sensor collaborative pipeline robot positioning method and system

Abstract

The invention provides a multi-sensor cooperative pipeline robot positioning method and system, and relates to the technical field of pipeline robot positioning systems.By setting different working modes, the method and system can be suitable for different working environments, and whether a to-be-detected pipeline is enough to support robot passing detection or not is judged through different logics; if the condition is not met, an alarm is sent to a user in time, the user can conveniently conduct subsequent adjustment, the robot is prevented from being damaged, if the pipeline meets the passing condition, the inner wall of the pipeline is detected, the risk areas existing in the inner wall of the pipeline are recognized, the working condition of the pipeline is judged according to the number of the risk areas of the pipeline, and the safety of the pipeline is improved. And a user can conveniently carry out subsequent maintenance and replacement according to a judgment result.

Description

Technical Field

[0001] The present invention relates to the technical field of pipeline robot positioning systems, and in particular to a multi-sensor coordinated pipeline robot positioning method and system. Background Art

[0002] With the development of technology, robot technology has gradually become popular in small pipelines during pipeline maintenance work, and pipeline robots are used to inspect the inside of pipelines.

[0003] However, in the existing technology, there are still many problems in the technology of using pipeline robots to inspect the inside of small pipelines. First, since some pipelines are installed in locations that are difficult for maintenance personnel to reach, it is difficult to determine whether the robot can enter the pipeline to perform operations. Although the pipeline size in some areas can be obtained through construction information, in areas with earlier construction time, construction information may be omitted or missing, which will increase the difficulty of robot judgment, not only reducing the speed of maintenance work, but also may cause damage to the robot, making the robot less applicable and making it more difficult to locate the problem points inside the pipeline.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0005] The object of the present invention is to provide a multi-sensor coordinated pipeline robot positioning method and system to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A multi-sensor collaborative pipeline robot positioning method, the specific steps include:

[0008] S1: Collect the real-time position of the robot, and search whether there is corresponding pipeline information according to the real-time position of the robot. If there is corresponding pipeline information, execute step S2; if there is no corresponding pipeline information, execute step S3;

[0009] S2: judging whether the robot can enter the pipeline based on the pipeline information and the preset robot size parameters; if the robot can enter the pipeline, executing step S4; if the robot cannot enter the pipeline, generating a first alarm signal and sounding an alarm;

[0010] S3: setting a plurality of sampling points on the outer wall of the pipeline, calculating the theoretical outer diameter of the pipeline according to the positions of the sampling points, and judging whether the robot can enter the pipeline according to the calculated theoretical outer diameter of the pipeline and the preset robot size parameters. If the robot can enter, executing step S4; if the robot cannot enter, generating a first alarm signal and sounding an alarm;

[0011] S4: several groups of sampling points are set on the inner wall of the pipeline to collect the robot's motion information, laser signals, and pipeline images in the pipeline, and a risk index is jointly generated based on the laser signals and pipeline images. Positions with higher risk indexes are marked as risk areas, and the position information of each risk area in the pipeline is generated based on the motion information;

[0012] S5: Divide the pipeline condition according to the number of risk areas, generate a second alarm signal according to the division result, and issue an alarm.

[0013] Preferably, the pipeline information is the actual inner diameter of the pipeline, the robot size includes the maximum height and the maximum width, and the motion information includes the movement speed and the movement time of the robot in the pipeline.

[0014] Preferably, in step S2, the logic for determining whether the robot can enter the pipeline is:

[0015] According to the maximum height H of the robot max and the maximum width D max Calculate the minimum entry diameter R that the robot can enter min , calculated as:

[0016]

[0017] The minimum allowable inner diameter R min Compare with the actual pipe inner diameter Rs, if it satisfies It is considered that the robot can enter the pipeline, where Indicates the preset error margin.

[0018] Preferably, in step S3, the logic for determining whether the robot can enter the pipeline is:

[0019] Take the vertex of the outer wall of the pipeline as the first sampling point, take the first sampling point as the reference, move to the left at a constant speed V1 for T1 time, take this point as the second sampling point, collect the deflection angle θ between the second sampling point and the first sampling point, and move along the outer wall of the pipeline for a distance d1 after the collection is completed;

[0020] Calculate the outer diameter Rl of the sampling pipeline according to the speed V1, time T1 and deflection angle θ i , calculated as:

[0021]

[0022] In the formula, the subscript i represents the number of the outer diameter of the sampling pipe;

[0023] Repeat the above steps to generate several groups of sampling pipeline outer diameters Rl i , and according to the outer diameter Rl of multiple sets of sampling pipes i Calculate the theoretical pipe outer diameter Rl as follows:

[0024]

[0025] Where N represents the number of sampling points;

[0026] The theoretical pipeline outer diameter Rl and the minimum allowable inner diameter R min Compare, if satisfied It is considered that the robot can enter the pipeline, where d' represents the preset pipeline thickness.

[0027] Preferably, the logic for generating the risk index is:

[0028] According to the value of the theoretical pipeline outer diameter Rl, the center of the pipeline is used as the emission point of the laser signal, and the interval distance D between the robot and the inner wall of the pipeline is calculated as follows:

[0029]

[0030] Where c represents the speed of light, Tj represents the receiving time of the laser signal, and Tf represents the sending time of the laser signal;

[0031] Adjust the laser signal emission angle at the same interval, collect n groups of interval distances and calibrate them as D j , the subscript j represents the number of the interval distance, and the average value of multiple groups of interval distances is calculated And the mean square error σ1 is calculated as:

[0032]

[0033]

[0034] According to the interval distance D j and the average of the interval distance The mean square error σ1 jointly generates the first evaluation coefficient The calculation method is:

[0035]

[0036] Wherein k1 represents the preset first fluctuation coefficient;

[0037] Taking the sampling point of the laser signal as the center point, the pipeline image of the inner wall of the pipeline is collected and Gaussian filtering is performed on it. The gray value HD of the pipeline image after collection and processing (x,y) , the subscript (x, y) represents the coordinates of the pixel point, and the pipeline image is evenly divided into p*q regions in the horizontal and vertical directions, and the horizontal and vertical lengths of each region are X and Y pixels respectively;

[0038] Calculate the regional mean for each region and the overall mean of the pipeline images And the image mean square error σ2 is calculated as:

[0039]

[0040]

[0041]

[0042] According to the regional mean Overall mean of pipeline images And the image mean square error σ2 together generate the second evaluation coefficient The calculation method is:

[0043]

[0044] Wherein k2 represents the preset second fluctuation coefficient;

[0045] Generate a risk index based on the first evaluation coefficient and the second evaluation coefficient The calculation formula is:

[0046]

[0047] Where a1 and a2 are the preset first weight coefficient and second weight coefficient respectively, a1 and a2 are both greater than or equal to 0, and a1+a2=1. When , the corresponding area is divided into risk areas. is the preset risk threshold.

[0048] Preferably, the logic for generating the risk area location information is:

[0049] According to the motion speed V2 and motion time T2 in the motion information, the distance L between a group of sampling points inside the pipeline and the pipeline mouth is calculated as follows:

[0050] L=V2T2

[0051] According to the emission angle of the laser signal corresponding to the risk area, the position of the image sampling area on the inner wall of the pipeline is obtained;

[0052] According to the coordinates of the central pixel point of the risk area, the center distance d and the deflection angle ω between the central pixel point of the risk area and the center point of the pipeline image are calculated. The calculation formula is:

[0053]

[0054]

[0055] Where x1 and y1 represent the horizontal and vertical coordinates of the central pixel of the risk area, respectively.

[0056] Preferably, when the number of risk areas M satisfies M∈[0,M1], the pipeline condition is considered to be a general risk, and the generated second alarm coefficient is a low level;

[0057] When the number of risk areas M satisfies M∈(M1,M2], the pipeline condition is considered to be medium risk, and the second alarm coefficient generated is at the medium level;

[0058] When the number of risk areas M satisfies M∈(M1,+∞), the pipeline condition is considered to be high risk, and the generated second alarm coefficient is a high level, where M1 and M2 are two preset sets of quantity thresholds.

[0059] A multi-sensor coordinated pipeline robot positioning system, the positioning system is constructed using the above positioning method, including a robot body and a control module, wherein:

[0060] The robot body comprises:

[0061] A sensor unit, wherein the sensor module is electrically connected to the data processing unit and is used to collect the real-time position, motion information, laser signal, and pipeline image of the robot body and send them to the data processing unit for processing;

[0062] A data processing unit, the data processing unit is electrically connected to the alarm unit and the data storage unit, and is used to identify the risk area in the pipeline, generate location information of the risk area, and send the location information to the alarm unit and the data storage unit for judgment and storage;

[0063] An alarm unit, the alarm unit being electrically connected to the first data transceiver module, and configured to generate and send different first alarm signals and second alarm signals according to different situations;

[0064] A driving unit, the driving unit being electrically connected to the first data transceiver unit and configured to control the movement of the robot body or adjust the position of the sensor unit according to the control signal;

[0065] A first data transceiver unit, the first data transceiver unit is used to send the location information of the risk area, the first alarm signal and the second alarm signal;

[0066] A data storage unit, the data storage unit is used to store the location information of the risk area;

[0067] The control module comprises:

[0068] a second data transceiver unit, the second data transceiver unit being communicatively connected to the first data transceiver unit and electrically connected to the user panel, and being used for receiving the location information of the risk area, the first alarm signal and the second alarm signal, and sending a control instruction;

[0069] The user panel is used to classify and visualize the location information of the risk area, the first alarm signal and the second alarm signal, and issue control instructions according to user needs.

[0070] Preferably, the sensing unit includes a laser ranging unit, an image acquisition unit, a GPS positioning unit, a motion sensing unit, and a gyroscope, and the driving unit includes a motion unit and a lifting unit.

[0071] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention can be applied to different working environments by setting different working modes, and can use different logics to determine whether the pipeline to be inspected is sufficient to support the robot's passage detection. If the conditions are not met, an alarm is promptly issued to the user, which is convenient for the user to make subsequent adjustments and avoids damage to the robot itself. If the pipeline meets the passage conditions, the inner wall of the pipeline is inspected to identify the risk areas on the inner wall of the pipeline, and the working condition of the pipeline is judged according to the number of risk areas in the pipeline, which is convenient for the user to perform subsequent maintenance and replacement according to the judgment results. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 It is a schematic diagram of the overall method flow of the present invention;

[0073] Figure 2 It is a schematic diagram of the module structure of the present invention. DETAILED DESCRIPTION

[0074] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0075] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0076] Example:

[0077] See also Figure 1 , the present invention provides a technical solution:

[0078] A multi-sensor collaborative pipeline robot positioning method, the specific steps include:

[0079] S1: Collect the real-time position of the robot, and search whether there is corresponding pipeline information based on the real-time position of the robot. The pipeline information is the actual inner diameter of the pipeline. If there is corresponding pipeline information, execute step S2. If there is no corresponding pipeline information, execute step S3. The pipeline information can be retrieved by connecting to an external network. This method can improve the applicability of the pipeline robot and make it suitable for different places.

[0080] S2: Determine whether the robot can enter the pipeline based on the pipeline information and the preset robot size parameters. The robot size includes the maximum height and maximum width. This parameter can be obtained from the product manual provided by the pipeline robot manufacturer, or measured by the user. If it can enter, execute step S4. If it cannot enter, generate a first alarm signal and issue an alarm. In other words, the first alarm signal reflects the adaptability between the pipeline robot and the pipeline. If the first alarm signal appears, it means that the pipeline robot is too large and is not suitable for operation in the pipeline, which is convenient for the user to make subsequent adjustments.

[0081] S3: several groups of sampling points are set on the outer wall of the pipeline, and the theoretical outer diameter of the pipeline is calculated according to the positions of the sampling points. It is determined whether the robot can enter the pipeline based on the calculated theoretical outer diameter of the pipeline and the preset robot size parameters. If it can enter, step S4 is executed. If it cannot enter, a first alarm signal is generated and an alarm is issued. That is to say, in places such as pipeline installation spaces that are difficult for personnel to enter, a relatively small pipeline robot can be placed on the outer wall of the pipeline to determine whether it is suitable for operation in the pipeline. Even in areas where construction was carried out at an earlier stage and no pipeline information was left or where pipeline information was missing, operations can be carried out in a timely manner, thereby improving maintenance efficiency.

[0082] S4: Several groups of sampling points are set on the inner wall of the pipeline to collect the movement information, laser signals, and pipeline images of the robot in the pipeline. The risk index is jointly generated based on the laser signals and pipeline images. The positions with higher risk indexes are marked as risk areas, and the position information of each risk area in the pipeline is generated based on the movement information. The movement information includes the movement speed and movement time of the robot in the pipeline.

[0083] S5: The pipeline condition is divided according to the number of risk areas, and a second alarm signal is generated and an alarm is issued according to the division result. When the number of risk areas M satisfies M∈[0,M1], the pipeline condition is considered to be of general risk, and the second alarm coefficient generated is of low gear. When the number of risk areas M satisfies M∈(M1,M2], the pipeline condition is considered to be of medium risk, and the second alarm coefficient generated is of medium gear. When the number of risk areas M satisfies M∈(M1,+∞), the pipeline condition is considered to be of high risk, and the second alarm coefficient generated is of high gear. M1 and M2 are two preset sets of quantity thresholds. That is to say, the gear of the second alarm signal reflects the working condition of the pipeline. The higher the gear of the second alarm signal, the higher the risk area of ​​the pipeline, the worse the working condition of the pipeline, and the more it needs to be repaired and replaced.

[0084] In step S2, the logic for determining whether the robot can enter the pipeline is:

[0085] According to the maximum height H of the robot max and the maximum width D max The maximum height and maximum width refer to the maximum height and maximum width that the robot equipment can reach in various working states. Calculate the minimum entry diameter R that the robot can enter min , calculated as:

[0086]

[0087] The minimum allowable inner diameter R min Compare with the actual pipe inner diameter Rs, if it satisfies It is assumed that the robot can enter the pipeline in any normal working state and will not hit the inner wall of the pipeline during work. Indicates the preset error margin.

[0088] In step S3, the logic for determining whether the robot can enter the pipeline is:

[0089] Take the vertex of the outer wall of the pipeline as the first sampling point, take the first sampling point as the reference, move to the left at a constant speed of V1 for T1 time, take this point as the second sampling point, collect the deflection angle θ between the second sampling point and the first sampling point, and move along the outer wall of the pipeline after the collection. The deflection angle θ of the pipeline robot should not be too large to avoid falling from the outer wall of the pipeline. Since the robot is translated along the outer wall of the circular pipeline, the moving distance is the arc length of a certain arc on the outer wall of the pipeline. Therefore, the outer diameter Rl of the sampling pipeline can be calculated based on the speed V1, time T1, and deflection angle θ. i , calculated as:

[0090]

[0091] In the formula, the subscript i represents the number of the outer diameter of the sampling pipe;

[0092] After the collection is completed, the robot moves a distance along the pipeline and repeats the above steps to calculate the outer diameter of the sampling pipeline at that location. Finally, several groups of sampling pipeline outer diameters Rl are generated. i , and according to the outer diameter Rl of multiple sets of sampling pipes i Calculate the theoretical pipe outer diameter Rl as follows:

[0093]

[0094] Where N represents the number of sampling points;

[0095] The theoretical pipeline outer diameter Rl and the minimum allowable inner diameter R min Compare, if satisfied It is considered that the robot can enter the pipeline, where d' represents the preset pipeline thickness, which can be determined through G / B files or user experience.

[0096] The generation logic of the risk index is:

[0097] The distance D between the robot and the inner wall of the pipe is calculated based on the laser signal. The calculation method is:

[0098]

[0099] Where c represents the speed of light, Tj represents the receiving time of the laser signal, and Tf represents the sending time of the laser signal;

[0100] Adjust the laser signal emission angle at the same interval. The emission angle refers to the angle between the laser signal and the horizontal direction. Collect n groups of interval distances and calibrate them as D j The robot always moves along the axis of the pipeline, the body is parallel to the pipeline, and the laser signal is always perpendicular to the inner wall of the pipeline. The subscript j represents the number of the interval distance, and the average value of multiple groups of interval distances is calculated. And the mean square error σ1 is calculated as:

[0101]

[0102]

[0103] According to the interval distance D j and the average of the interval distance The mean square error σ1 jointly generates the first evaluation coefficient The calculation method is:

[0104]

[0105] Where k1 represents the preset first fluctuation coefficient. It can be seen that under normal circumstances, the thickness of the inner wall of the pipeline should be uniform. Therefore, when emitting laser signals from the center of the pipeline, the interval between the emission point and the inner wall of the pipeline should also be equal or close. If there are sampling points with a large interval, the pipeline at that location is likely to have unexpected conditions such as inner wall damage and impurity accumulation. In other words, the first evaluation coefficient It reflects the change of pipeline thickness at the sampling point, thus indirectly reflecting the working condition of the pipeline.

[0106] Taking the sampling point of the laser signal as the center point, the pipeline image of the inner wall of the pipeline is collected and Gaussian filtering is performed on it. The gray value HD of the pipeline image after collection and processing (x,y) , the subscript (x, y) represents the coordinates of the pixel point. The pipeline image is evenly divided into p*q regions in the horizontal and vertical directions. The horizontal and vertical lengths of each region are X and T pixels respectively. The regional mean of each region is calculated. and the overall mean of the pipeline images And the image mean square error σ2 is calculated as:

[0107]

[0108]

[0109]

[0110] According to the regional mean Overall mean of pipeline images And the image mean square error σ2 together generate the second evaluation coefficient The calculation method is:

[0111]

[0112] In the formula, k2 represents the preset second fluctuation coefficient. The sampling point of the laser signal is used as the center point to collect the pipeline image of the inner wall of the pipeline. This method is equivalent to changing from point detection to surface detection, which plays a role in expanding the detection range. Similarly, the change of the gray value of the inner wall of the pipeline is also likely to be caused by unexpected conditions such as inner wall damage and impurity accumulation. The greater the gray value fluctuation area, the higher the possibility of unexpected conditions. In other words, the second evaluation coefficient It reflects the change of pipeline thickness in an area near the sampling point, thus indirectly reflecting the working condition of the pipeline;

[0113] Generate a risk index based on the first evaluation coefficient and the second evaluation coefficient The calculation formula is:

[0114]

[0115] Where a1 and a2 are the preset first weight coefficient and second weight coefficient respectively, a1 and a2 are both greater than or equal to 0, and a1+a2=1. When , the corresponding area is divided into risk areas. is the preset risk threshold. From the above description, it can be understood that the values ​​of the first evaluation coefficient and the second evaluation coefficient are proportional to the degree of risk. Therefore, the risk index after weighted processing is It is also proportional to the degree of risk.

[0116] The generation logic of the risk area location information is:

[0117] According to the motion speed V2 and motion time T2 in the motion information, the distance L between a group of sampling points inside the pipeline and the pipeline mouth is calculated as follows:

[0118] L=V2T2

[0119] According to the emission angle of the laser signal corresponding to the risk area, the position of the image sampling area on the inner wall of the pipeline is obtained;

[0120] According to the coordinates of the central pixel point of the risk area, the center distance d and the axis angle ω between the central pixel point of the risk area and the center point of the pipeline image are calculated. The calculation formula is:

[0121]

[0122]

[0123] Where x1 and y1 represent the horizontal and vertical coordinates of the central pixel point of the risk area respectively, and the axis angle refers to the angle between the line connecting the central pixel point of the risk area and the center point of the pipeline image and the pipeline axis. In this way, we can know the distance between the risk area and the pipeline mouth, the angle between the image sampling center point and the horizontal direction, and the center distance and deflection angle between the center point of the risk area and the image sampling center point. Therefore, we can quickly find the location of the risk area by following the steps, realize the rapid positioning of the risk area, and improve the maintenance efficiency.

[0124] See also Figure 2 The present invention also provides a multi-sensor coordinated pipeline robot positioning system, the positioning system is constructed using the above positioning method, including a robot body and a control module, wherein:

[0125] The robot body comprises:

[0126] The sensing unit includes a laser ranging unit, an image acquisition unit, a GPS positioning unit, a motion sensing unit, and a gyroscope, which are all electrically connected to the data processing unit and are used to collect the real-time position, motion information, laser signal, and pipeline image of the robot body, and send them to the data processing unit for processing. The gyroscope is used to detect the tilt angle of the robot, so as to determine whether the robot is at the top of the outer wall of the pipeline or the lowest point of the inner wall of the pipeline, and can also detect the deflection angle θ of the robot during sampling;

[0127] A data processing unit, the data processing unit is electrically connected to the alarm unit and the data storage unit, and is used to identify the risk area in the pipeline, generate location information of the risk area, and send the location information to the alarm unit and the data storage unit for judgment and storage;

[0128] An alarm unit, the alarm unit being electrically connected to the first data transceiver module, and configured to generate and send different first alarm signals and second alarm signals according to different situations;

[0129] A driving unit, the driving unit being electrically connected to the first data transceiver unit and configured to control the movement of the robot body according to the control signal;

[0130] A first data transceiver unit, the first data transceiver unit is used to send the location information of the risk area, the first alarm signal and the second alarm signal;

[0131] A data storage unit, the data storage unit is used to store the location information of the risk area;

[0132] The control module comprises:

[0133] a second data transceiver unit, the second data transceiver unit being communicatively connected to the first data transceiver unit and electrically connected to the user panel, and being used for receiving the location information of the risk area, the first alarm signal and the second alarm signal, and sending a control instruction;

[0134] The user panel is used to classify and visualize the location information of the risk area, the first alarm signal and the second alarm signal, and to issue control instructions according to user needs. The user panel adopts an integrated industrial control panel with a screen, which has a built-in operating system for issuing control instructions and a network interface, and can retrieve historical construction information.

[0135] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0136] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by computer software, electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software depends on the specific application and design constraints of the technical solution.

[0137] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0138] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A multi-sensor collaborative pipeline robot positioning method, characterized in that: The specific steps include: S1: Collect the real-time position of the robot, and search whether there is corresponding pipeline information according to the real-time position of the robot. If there is corresponding pipeline information, execute step S2; if there is no corresponding pipeline information, execute step S3; S2: judging whether the robot can enter the pipeline based on the pipeline information and the preset robot size parameters; if the robot can enter the pipeline, executing step S4; if the robot cannot enter the pipeline, generating a first alarm signal and sounding an alarm; S3: setting a plurality of sampling points on the outer wall of the pipeline, calculating the theoretical outer diameter of the pipeline according to the positions of the sampling points, and judging whether the robot can enter the pipeline according to the calculated theoretical outer diameter of the pipeline and the preset robot size parameters. If the robot can enter, executing step S4; if the robot cannot enter, generating a first alarm signal and sounding an alarm; S4: several groups of sampling points are set on the inner wall of the pipeline to collect the robot's motion information, laser signals, and pipeline images in the pipeline, and a risk index is jointly generated based on the laser signals and pipeline images. Positions with higher risk indexes are marked as risk areas, and the position information of each risk area in the pipeline is generated based on the motion information; S5: Divide the pipeline condition according to the number of risk areas, generate a second alarm signal according to the division result, and issue an alarm.

2. A multi-sensor coordinated pipeline robot positioning method according to claim 1, characterized in that: The pipeline information is the actual inner diameter of the pipeline, the robot size includes the maximum height and the maximum width, and the motion information includes the motion speed and motion time of the robot in the pipeline.

3. A multi-sensor coordinated pipeline robot positioning method according to claim 2, characterized in that: In step S2, the logic for determining whether the robot can enter the pipeline is: According to the maximum height H of the robot max and the maximum width D max Calculate the minimum entry diameter R that the robot can enter min , calculated as: The minimum allowable inner diameter R min Compare with the actual pipe inner diameter Rs, if it satisfies It is considered that the robot can enter the pipeline, where Indicates the preset error margin.

4. The multi-sensor coordinated pipeline robot positioning method according to claim 3, characterized in that: In step S3, the logic for determining whether the robot can enter the pipeline is: Take the vertex of the outer wall of the pipeline as the first sampling point, take the first sampling point as the reference, move to the left at a constant speed V1 for T1 time, take this point as the second sampling point, collect the deflection angle θ between the second sampling point and the first sampling point, and move along the outer wall of the pipeline for a distance d1 after the collection is completed; Calculate the outer diameter Rl of the sampling pipeline according to the speed V1, time T1 and deflection angle θ i , calculated as: In the formula, the subscript i represents the number of the outer diameter of the sampling pipe; Repeat the above steps to generate several groups of sampling pipeline outer diameters Rl i , and according to the outer diameter Rl of multiple sets of sampling pipes i Calculate the theoretical pipe outer diameter Rl as follows: Where N represents the number of sampling points; The theoretical pipeline outer diameter Rl and the minimum allowable inner diameter R min Compare, if satisfied It is considered that the robot can enter the pipeline, where d' represents the preset pipeline thickness.

5. The multi-sensor coordinated pipeline robot positioning method according to claim 4, characterized in that: The logic for generating the risk index is: According to the value of the theoretical pipeline outer diameter Rl, the center of the pipeline is used as the emission point of the laser signal, and the interval distance D between the robot and the inner wall of the pipeline is calculated as follows: Where c represents the speed of light, Tj represents the receiving time of the laser signal, and Tf represents the sending time of the laser signal; Adjust the laser signal emission angle at the same interval, collect n groups of interval distances and calibrate them as D j , the subscript j represents the number of the interval distance, and the average value of multiple groups of interval distances is calculated And the mean square error σ1 is calculated as: According to the interval distance D j and the average of the interval distance The mean square error σ1 jointly generates the first evaluation coefficient The calculation method is: Wherein k1 represents the preset first fluctuation coefficient; Taking the sampling point of the laser signal as the center point, the pipeline image of the inner wall of the pipeline is collected and Gaussian filtering is performed on it. The gray value HD of the pipeline image after collection and processing (x,y) , the subscript (x, y) represents the coordinates of the pixel point, and the pipeline image is evenly divided into p*q regions in the horizontal and vertical directions, and the horizontal and vertical lengths of each region are X and Y pixels respectively; Calculate the regional mean for each region and the overall mean of the pipeline images And the image mean square error σ2 is calculated as: According to the regional mean Overall mean of pipeline images And the image mean square error σ2 together generate the second evaluation coefficient The calculation method is: Wherein k2 represents the preset second fluctuation coefficient; Generate a risk index based on the first evaluation coefficient and the second evaluation coefficient The calculation formula is: Where a1 and a2 are the preset first weight coefficient and second weight coefficient respectively, a1 and a2 are both greater than or equal to 0, and a1+a2=1. When , the corresponding area is divided into risk areas. is the preset risk threshold.

6. A multi-sensor coordinated pipeline robot positioning method according to claim 5, characterized in that: The generation logic of the risk area location information is: According to the motion speed V2 and motion time T2 in the motion information, the distance L between a group of sampling points inside the pipeline and the pipeline mouth is calculated as follows: L=V2T2 According to the emission angle of the laser signal corresponding to the risk area, the position of the image sampling area on the inner wall of the pipeline is obtained; According to the coordinates of the central pixel point of the risk area, the center distance d and the deflection angle ω between the central pixel point of the risk area and the center point of the pipeline image are calculated. The calculation formula is: Where x1 and y1 represent the horizontal and vertical coordinates of the central pixel of the risk area, respectively.

7. The multi-sensor coordinated pipeline robot positioning method according to claim 6, characterized in that: When the number of risk areas M satisfies M∈[0,M1], the pipeline condition is considered to be a general risk, and the second alarm coefficient generated is a low level; When the number of risk areas M satisfies M∈(M1,M2], the pipeline condition is considered to be medium risk, and the second alarm coefficient generated is at the medium level; When the number of risk areas M satisfies M∈(M1,+∞), the pipeline condition is considered to be high risk, and the generated second alarm coefficient is a high level, where M1 and M2 are two preset sets of quantity thresholds.

8. A multi-sensor coordinated pipeline robot positioning system, characterized in that: The positioning system is constituted by the positioning method according to any one of claims 1 to 8, and comprises a robot body and a control module, wherein: The robot body comprises: A sensor unit, wherein the sensor module is electrically connected to the data processing unit and is used to collect the real-time position, motion information, laser signal, and pipeline image of the robot body and send them to the data processing unit for processing; A data processing unit, the data processing unit is electrically connected to the alarm unit and the data storage unit, and is used to identify the risk area in the pipeline, generate location information of the risk area, and send the location information to the alarm unit and the data storage unit for judgment and storage; An alarm unit, the alarm unit being electrically connected to the first data transceiver module, and configured to generate and send different first alarm signals and second alarm signals according to different situations; A driving unit, the driving unit being electrically connected to the first data transceiver unit and configured to control the movement of the robot body or adjust the position of the sensor unit according to the control signal; A first data transceiver unit, the first data transceiver unit is used to send the location information of the risk area, the first alarm signal and the second alarm signal; A data storage unit, the data storage unit is used to store the location information of the risk area; The control module comprises: a second data transceiver unit, the second data transceiver unit being communicatively connected to the first data transceiver unit and electrically connected to the user panel, and being used for receiving the location information of the risk area, the first alarm signal and the second alarm signal, and sending a control instruction; The user panel is used to classify and visualize the location information of the risk area, the first alarm signal and the second alarm signal, and issue control instructions according to user needs.

9. The multi-sensor coordinated pipeline robot positioning system according to claim 8, characterized in that: The sensing unit comprises a laser distance measuring unit, an image acquisition unit, a GPS positioning unit, a motion sensing unit, and a gyroscope, and the driving unit comprises a motion unit and a lifting unit.

Images