A multi-sensor cooperative pipeline robot positioning method and system

Abstract

The application provides a kind of multi-sensor cooperative pipeline robot positioning method and system, it is related to pipeline robot positioning system technical field, different working modes are set, it can be suitable for different working environment, and the logic of different is used to determine whether the pipeline to be detected is enough to support robot passage detection, if it does not meet the condition, timely alarm is sent to the user, it is convenient for the user to carry out subsequent adjustment, also avoid the damage of robot itself, if the pipeline meets the passage condition, the inner wall of the pipeline is detected, the risk area existing in the inner wall of the pipeline is identified, and the working condition of the pipeline is judged according to the number of pipeline risk area, it is convenient for the user to carry out subsequent maintenance and replacement according to the 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 their interior.

[0003] However, the existing technology for using pipeline robots to inspect the interiors of small pipelines still faces many problems. First, because 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 pipeline dimensions in some areas can be obtained through construction information, in areas with earlier construction, construction information may be missing or incomplete, making it more difficult for the robot to determine the location. This not only slows down maintenance work but also may cause damage to the robot, making it less applicable and making it more difficult to locate 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 form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0005] The purpose of the present invention is to provide a multi-sensor collaborative 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, specifically comprising the following steps:

[0008] S1: Collect the real-time position of the robot, and search for corresponding pipeline information based on the real-time position of the robot. If the corresponding pipeline information exists, execute step S2; if not, execute step S3;

[0009] S2: Determine whether the robot can enter the pipeline based on the pipeline information and preset robot size parameters. If so, execute step S4. If not, generate a first alarm signal and sound an alarm.

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

[0011] S4: Several groups of sampling points are set up on the inner wall of the pipeline to collect the robot's motion information, laser signals, and pipeline images. A risk index is generated based on the laser signals and pipeline images. Locations with higher risk indices are marked as risk areas, and the location 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 maximum width, and the movement information includes the movement speed and 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 believed 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 pipe as the first sampling point, and use the first sampling point as the reference to move to the left at a constant speed of V1 for T1 time. Use this point as the second sampling point, and collect the deflection angle θ between the second sampling point and the first sampling point. After the collection is completed, move along the outer wall of the pipe for a distance d1;

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

[0021]

[0022] Wherein 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 pipe 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 pipe 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 risk index generation logic is:

[0028] According to the theoretical pipe outer diameter Rl, the center of the pipe is used as the emission point of the laser signal, and the distance D between the robot and the inner wall of the pipe 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 , 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 spacing distance D j and the average of the interval distances The mean square error σ1 jointly generates the first evaluation coefficient The calculation method is:

[0035]

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

[0037] 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, and Gaussian filter 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. The horizontal and vertical lengths of each region are X, Y pixels respectively;

[0038] Calculate the regional mean for each region and the overall mean of the pipeline image 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] Where 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 and second weight coefficients respectively, a1 and a2 are both greater than or equal to 0, and a1+a2=1. When the risk index 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 movement speed V2 and movement time T2 in the movement information, the distance L between a group of sampling points inside the pipeline and the pipeline outlet 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 point in 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 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 collaborative pipeline robot positioning system, which is constructed using the above-mentioned positioning method and includes a robot body and a control module, wherein:

[0060] The robot body comprises:

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

[0062] A data processing unit, the data processing unit being electrically connected to the alarm unit and the data storage unit, and configured to identify risk areas in the pipeline, generate location information of the risk areas, and send the location information to the alarm unit and the data storage unit for determination and storage, respectively;

[0063] An alarm unit, the alarm unit being electrically connected to the first data transceiver module, and configured to generate and send different first 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 sensing unit according to the control signal;

[0065] a first data transceiver unit, configured to transmit location information of the risk zone, a first alarm signal, and a second alarm signal;

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

[0067] The control module includes:

[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 configured to receive location information of the risk zone, the first alarm signal and the second alarm signal, and send 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 to 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: by setting different working modes, the present invention can be applied to different working environments, and different logics are used to judge 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 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 with reference to specific embodiments.

[0075] It should be noted that, unless otherwise defined, the technical or scientific terms used in the present invention should have the usual meanings understood by people with ordinary skills in the field to which the present invention belongs. The "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 position relationships. When the absolute position of the object being described changes, the relative position 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, specifically comprising the following steps:

[0079] S1: Collect the real-time position of the robot. According to the real-time position of the robot, search whether there is corresponding pipeline information. 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 facilitates the user to make subsequent adjustments.

[0081] S3: Set up several groups of sampling points on the outer wall of the pipeline, calculate the theoretical outer diameter of the pipeline according to the position of the sampling points, and determine 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, execute step S4. If it cannot enter, generate a first alarm signal and issue an alarm. 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 the pipeline information was missing, operations can be carried out in a timely manner, thereby improving maintenance efficiency.

[0082] S4: Set up several groups of sampling points on the inner wall of the pipeline to collect the robot's motion information, laser signals, and pipeline images in the pipeline. Generate a risk index based on the laser signals and pipeline images. Mark locations with higher risk indices as risk areas, and generate the position information of each risk area in the pipeline based on the motion information. The motion information includes the robot's motion speed and motion time 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 based on 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 generated second alarm coefficient is a 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 generated second alarm coefficient is a medium gear. When the number of risk areas M satisfies M∈(M1,+∞), the pipeline condition is considered to be of high risk, and the generated second alarm coefficient is a 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 greater the need for maintenance and replacement.

[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 pipe as the first sampling point, and use the first sampling point as the reference to move to the left at a constant speed of V1 for T1 time. Use this point as the second sampling point and collect the deflection angle θ between the second sampling point and the first sampling point. After the collection is completed, move along the outer wall of the pipe by a distance d1. The deflection angle θ of the pipe robot should not be too large to avoid falling from the outer wall of the pipe. Since the robot is translated along the outer wall of the circular pipe, the distance moved is also the arc length of a certain arc on the outer wall of the pipe. Therefore, the outer diameter Rl of the sampling pipe can be calculated based on the speed V1, time T1, and deflection angle θ. i , calculated as:

[0090]

[0091] Wherein 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, and finally generates 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:

[0093]

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

[0095] The theoretical pipe 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 logic for generating 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 pipe, the body is parallel to the pipe, and the laser signal is always perpendicular to the inner wall of the pipe. 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 spacing distance D j and the average of the interval distances 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 distance 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 in pipe thickness at the sampling point, thereby indirectly reflecting the working condition of the pipeline.

[0106] 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, and Gaussian filter 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, T pixels respectively, and the regional mean of each region is calculated and the overall mean of the pipeline image 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] Where 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 pipe thickness in an area near the sampling point, and thus indirectly reflects 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 and second weight coefficients respectively, a1 and a2 are both greater than or equal to 0, and a1+a2=1. When the risk index When , the corresponding area is divided into risk areas. is the preset risk threshold. From the above description, we can understand that the value of the first evaluation coefficient and the second evaluation coefficient is proportional to the level 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 movement speed V2 and movement time T2 in the movement information, the distance L between a group of sampling points inside the pipeline and the pipeline outlet 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. 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, which is constructed using the above positioning method and includes 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 signals, and pipeline images 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 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. It can also detect the deflection angle θ of the robot during sampling;

[0127] A data processing unit, the data processing unit being electrically connected to the alarm unit and the data storage unit, and configured to identify risk areas in the pipeline, generate location information of the risk areas, and send the location information to the alarm unit and the data storage unit for determination and storage, respectively;

[0128] An alarm unit, the alarm unit being electrically connected to the first data transceiver module, and configured to generate and send different first 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, configured to transmit location information of the risk zone, a first alarm signal, and a second alarm signal;

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

[0132] The control module includes:

[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 configured to receive location information of the risk zone, the first alarm signal and the second alarm signal, and send 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 formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0136] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art will appreciate that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can 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 separate, and the components shown as units may or may not be physical units, and may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment as needed.

[0138] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection 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 for corresponding pipeline information based on the real-time position of the robot. If the corresponding pipeline information exists, execute step S2; if not, execute step S3; S2: Determine whether the robot can enter the pipeline based on the pipeline information and preset robot size parameters. If so, execute step S4. If not, generate a first alarm signal and sound an alarm. S3: Setting a plurality of sampling points on the outer wall of the pipeline, calculating a theoretical pipeline outer diameter based on the positions of the sampling points, and determining whether the robot can enter the pipeline based on the calculated theoretical pipeline outer diameter and preset robot size parameters. If the robot can enter the pipeline, executing step S4; if not, generating a first alarm signal and sounding an alarm; The logic for determining whether the robot can enter the pipeline is: Take the vertex of the outer wall of the pipe as the first sampling point, take the first sampling point as the reference, and move left at a speed of uniform motion Time, take this point as the second sampling point, and collect the deflection angle between the second sampling point and the first sampling point After the collection is completed, move along the outer wall of the pipe ; According to speed ,time , deflection angle Calculate the outer diameter of the sampling pipe , calculated as: ； Subscript A number indicating the outer diameter of the sampling pipe; Repeat the above steps to generate several groups of sampling pipe outer diameters , and according to the outer diameters of multiple sets of sampling pipes Calculate theoretical pipe outer diameter , calculated as: ； In the formula Indicates the number of groups of sampling points; The theoretical pipe outer diameter Minimum entry diameter Compare, if satisfied , then it is considered that the robot can enter the pipeline, where Indicates the preset pipe thickness; S4: Several groups of sampling points are set up on the inner wall of the pipeline to collect the robot's motion information, laser signals, and pipeline images. A risk index is generated based on the laser signals and pipeline images. Locations with higher risk indices are marked as risk areas, and the location information of each risk area in the pipeline is generated based on the motion information. The logic for generating the risk index is: According to the theoretical pipe outer diameter The center of the pipe is used as the emission point of the laser signal to calculate the distance between the robot and the inner wall of the pipe. , the calculation method is: ； In the formula represents the speed of light, Indicates the receiving time of the laser signal, Indicates the sending time of the laser signal; Adjust the laser signal emission angle at the same interval to collect The group interval distance is calibrated as , subscript Indicates the number of interval distances, and calculates the average value of multiple groups of interval distances and mean square error , the calculation method is: ； According to the interval distance and the average of the interval distances , mean square error Jointly generate the first evaluation coefficient , the calculation method is: ； In the formula Indicates 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 grayscale value of the processed pipeline image is collected. , subscript Represents the coordinates of the pixel points, and divides the pipeline image into areas, and the horizontal and vertical lengths of each area are pixels; Calculate the regional mean for each region and the overall mean of the pipeline image and image mean square error , the calculation method is: ； According to the regional mean , the overall mean of pipeline images and image mean square error Jointly generate the second evaluation coefficient , the calculation method is: ； In the formula Indicates the preset second fluctuation coefficient; Generate a risk index based on the first evaluation coefficient and the second evaluation coefficient , the calculation formula is: ； In the formula , are respectively the preset first weight coefficient and the second weight coefficient, , are both greater than or equal to 0, and , when the risk index When , the corresponding area is divided into risk areas. is the preset risk threshold; 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. The multi-sensor collaborative 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 maximum width, and the movement information includes the movement speed and movement time of the robot in the pipeline.

3. The 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 of the robot and maximum width Calculate the minimum entry diameter that the robot can enter , the calculation method is: ； Minimum entry diameter The actual pipe inner diameter Compare, if satisfied , then 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: The generation logic of the risk area location information is: According to the movement speed in the movement information , exercise time Calculate the distance between a set of sampling points inside the pipeline and the pipeline mouth , calculated as: ； 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, calculate the center distance between the central pixel point of the risk area and the center point of the pipeline image and deflection angle , the calculation formula is: ； In the formula and They represent the horizontal and vertical coordinates of the central pixel point in the risk area respectively.

5. The multi-sensor coordinated pipeline robot positioning method according to claim 4, characterized in that: When the number of risk areas satisfy When , 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 satisfy When , the pipeline condition is considered to be medium risk, and the second alarm coefficient generated is the medium level; When the number of risk areas satisfy When the pipeline condition is considered to be high risk, the second alarm coefficient generated is high level, where , There are two sets of preset quantity thresholds.

6. A multi-sensor collaborative pipeline robot positioning system, characterized by: The positioning system is constructed using the positioning method according to any one of claims 1 to 5, and includes a robot body and a control module, wherein: The robot body comprises: A sensing unit, electrically connected to the data processing unit, for collecting the real-time position, motion information, laser signals, and pipeline images of the robot body and sending them to the data processing unit for processing; A data processing unit, the data processing unit being electrically connected to the alarm unit and the data storage unit, and configured to identify risk areas in the pipeline, generate location information of the risk areas, and send the location information to the alarm unit and the data storage unit for determination and storage, respectively; An alarm unit, the alarm unit being electrically connected to the first data transceiver module, and configured to generate and send different first 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 sensing unit according to the control signal; a first data transceiver unit, configured to transmit location information of the risk zone, a first alarm signal, and a second alarm signal; A data storage unit, the data storage unit is used to store location information of the risk area; The control module includes: 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 configured to receive location information of the risk zone, the first alarm signal and the second alarm signal, and send 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 to issue control instructions according to user needs.

7. The multi-sensor coordinated pipeline robot positioning system according to claim 6, characterized in that: The sensing unit includes a laser ranging unit, an image acquisition unit, a GPS positioning unit, a motion sensing unit, and a gyroscope; the driving unit includes a motion unit and a lifting unit.

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