A multi-sensor-based pipeline robot motion control method and related device

Abstract

This application discloses a multi-sensor-based motion control method and related equipment for pipeline robots, relating to the field of robot control technology. The method includes: determining the position of the pipeline robot within the pipeline based on target ranging values ​​where the detection signal is 1 obtained from various laser sensors; calibrating the target angular velocity of the roll angle acquired by the IMU unit to obtain a calibrated angular velocity; constructing a state vector using the calibrated angular velocity and its time-varying drift; performing filtering iterations based on the state vector to output a fused attitude angle; planning a movement path based on the position and the fused attitude angle; and controlling the movement of the pipeline robot according to the movement path. Multi-sensor detection technology has become a key solution for improving the autonomy, adaptability, and reliability of pipeline robots. This application, through multi-sensor fusion perception, intelligent algorithm decision-making, and dynamic control adjustment, can significantly improve the operational capability and robustness of pipeline robots in complex pipeline environments.

Description

Technical Field

[0001] This application relates to the field of robot control technology, and in particular to a motion control method and related equipment for a pipeline robot based on multiple sensors. Background Technology

[0002] Pipelines, as a crucial means of material transportation, are widely used in natural gas, petroleum, chemical, nuclear facilities, and specialized equipment. Pipeline operations involve multiple tasks such as inspection, cleaning, and maintenance. Manual inspection requires digging into or entering pipelines, which is time-consuming, labor-intensive, and difficult to cover long distances or complex pipeline structures. Therefore, pipeline robots have emerged. Pipeline robots can move autonomously or remotely within pipelines, equipped with various sensors to inspect and maintain them. However, the interior space of pipelines is confined and complex, containing changes in diameter, bends, tees, etc. Without rapid and accurate motion control, robots struggle to navigate these complex environments. Furthermore, the limited lighting and high repetitiveness of pipeline spaces mean that a single sensor cannot meet the demands for high precision and robustness. Summary of the Invention

[0003] The main objective of this application is to propose a multi-sensor-based motion control method and related equipment for pipeline robots, so as to improve the robustness of pipeline robots.

[0004] To achieve the above objectives, one aspect of this application proposes a multi-sensor-based motion control method for a pipeline robot. The pipeline robot has one laser sensor A positioned directly in front of it, and four laser sensors a, denoted as a1, a2, a3, and a4, evenly spaced around its front end. When the distance between any of the laser sensors and the pipe wall is within the measurement range, the corresponding detection signal is 1; when the distance between any of the laser sensors and the pipe wall is outside the measurement range, the corresponding detection signal is 0. The pipeline robot is equipped with an IMU unit. The method includes the following steps: The position of the pipeline robot inside the pipeline is determined based on the target ranging value where the detection signal is 1 as measured by each of the laser sensors. The target angular velocity of the roll angle acquired by the IMU unit is calibrated to obtain the calibrated angular velocity; A state vector is constructed using the calibrated angular velocity and its time-varying drift. The state vector is filtered and iterated to output the fused attitude angle. The movement path is planned based on the position and the fused attitude angle; The pipeline robot is controlled to move according to the stated movement path.

[0005] In some embodiments, the target ranging value is obtained through the following steps: Determine the theoretical ranging values ​​measured by the four laser sensors a; For each laser sensor a, the most recent N theoretical ranging values ​​with a detection signal of 1 are selected and filtered to obtain N filtered ranging values; wherein, the filtered ranging values ​​are used as the target ranging values.

[0006] In some embodiments, the method further includes the following steps: Establish a global reference system with the center of the pipe cross-section as the origin; The coordinates of the center of the pipeline robot relative to the global reference system are calculated using N target ranging values ​​and used as the center coordinates; The initial attitude angle of the pipeline robot is calculated based on the center coordinates; Calculate the difference between the fused attitude angle and the initial attitude angle; If the difference reaches a preset threshold, the process returns to calibrating the target angular velocity of the roll angle acquired by the IMU unit to obtain the calibrated angular velocity, and then recalculates the fused attitude angle; 2N target ranging values ​​are obtained by filtering and screening, and the center coordinates are recalculated using the 2N target ranging values, and then the initial attitude angle is recalculated using the recalculated center coordinates until the difference is less than the preset threshold.

[0007] In some embodiments, calculating the initial attitude angle of the pipeline robot based on the center coordinates includes the following steps: Establish the coordinate system of the pipeline robot, and determine the installation orientation angles of the four laser sensors a in the coordinate system of the pipeline robot as θ=[0°,90°,180°,270°]; Construct a design matrix A and an observation vector b; where b includes bi, and bi = R d i,filter R is the pipe radius. d i,filter The target ranging value is denoted by ; i is the serial number of the laser sensor a; Solving the linear least squares problem ,get ; Calculate the initial attitude angle .

[0008] In some embodiments, calibrating the target angular velocity of the roll angle acquired by the IMU unit to obtain the calibrated angular velocity includes the following steps: The original angular velocity of the roll angle is filtered using a low-pass filter to obtain the filtered angular velocity; The average value of the filter angular velocities of the set number is used as the zero bias value; The target angular velocity is obtained by subtracting the zero bias value from the filtered angular velocity.

[0009] In some embodiments, constructing the state vector using the calibrated angular velocity and the time-varying drift of the calibrated angular velocity includes the following steps: Constructing state vectors X k : ;in, φ k for k The calibrated angular velocity at time [time], b k The time-varying drift of the calibrated angular velocity; The state transition equation is constructed as follows: Among them, the state transition matrix , T s It is the synchronous sampling time interval between the laser sensor and the IMU. T s Used to construct the transition relationship between states at adjacent time points, process noise. ; Construction process noise covariance matrix Q for ; To calibrate angular velocity k The noise variance is used to quantify the random error in angular velocity measurements; For time-varying drift b k The noise variance is used to quantify the uncertainty of IMU drift; Constructing observation equations Z k for: , ls,k The original attitude angle output by the IMU at time k; ω x,cal,k The angular velocity after calibration at time k; V k For observation noise; where, the observation matrix The observation matrix is ​​based on It is derived that; Construct observation noise as ; Construct the observation noise covariance matrix as follows ; This represents the noise variance of the observations, used to quantify the errors in the observation data from the lidar and IMU. The noise variance of the raw IMU data is used to quantify the random fluctuations in the IMU's own measurements; The step of filtering and iterating based on the state vector to output the fused attitude angle includes the following steps: The prediction construction steps are as follows: ;in, Here, P represents the state estimate, and P is the state estimate covariance matrix. The Kalman gain is constructed as follows: ; The build and update steps are as follows: ; Output the fused attitude angle for: .

[0010] In some embodiments, determining the position of the pipeline robot within the pipeline based on the target ranging value where the detection signal is 1 measured by each of the laser sensors includes the following steps: If the detection signal corresponding to laser sensor A is 0, and the detection signal corresponding to the target ranging value collected by laser sensors a1, a2, a3, and a4 is 1, then it is determined that the pipeline robot is in a position without diameter change or tee within the pipeline. If the detection signal corresponding to laser sensor A is 0, the detection signal corresponding to the target ranging value collected by one of laser sensors a1, a2, a3, and a4 is 0, and the detection signals of the other three are 1, then it is determined that the pipeline robot is in the tee position of the main pipeline inside the pipeline. If the detection signal corresponding to laser sensor A is 0, and the detection signals corresponding to the target ranging values ​​collected by two of laser sensors a1, a2, a3, and a4 are 0, while the detection signals of the other two are 1, then it is determined that the pipeline robot is in the tee position of the branch pipeline inside the pipeline.

[0011] To achieve the above objectives, another aspect of this application proposes a multi-sensor-based motion control device for a pipeline robot. One laser sensor A is arranged at the front of the pipeline robot, and four laser sensors a, denoted as a1, a2, a3, and a4, are evenly spaced around the front end of the pipeline robot. When the distance between any of the laser sensors and the pipe wall is within the measurement range, the corresponding detection signal is 1; when the distance between any of the laser sensors and the pipe wall is outside the measurement range, the corresponding detection signal is 0. The pipeline robot is equipped with an IMU unit. The device includes: The position determination unit is used to determine the position of the pipeline robot in the pipeline based on the target ranging value where the detection signal is 1 measured by each of the laser sensors. An angular velocity calibration unit is used to calibrate the target angular velocity of the roll angle acquired by the IMU unit to obtain a calibrated angular velocity. A state variable construction unit is used to construct a state vector using the calibrated angular velocity and the time-varying drift of the calibrated angular velocity; The attitude angle fusion unit is used to perform filtering iteration based on the state vector, and then output the fused attitude angle; A path planning unit is used to plan a movement path based on the position and the fused attitude angle; A robot control unit is used to control the movement of the pipeline robot according to the movement path.

[0012] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method.

[0013] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0014] To achieve the above objectives, another aspect of this application provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0015] The embodiments of this application include at least the following beneficial effects: This application provides a multi-sensor-based motion control method and related equipment for pipeline robots. The scheme determines the position of the pipeline robot within the pipeline based on the target ranging value where the detection signal is 1 obtained from each laser sensor; calibrates the target angular velocity of the roll angle collected by the IMU unit to obtain a calibrated angular velocity; constructs a state vector using the calibrated angular velocity and its time-varying drift; performs filtering iterations based on the state vector to output a fused attitude angle; plans a movement path based on the position and the fused attitude angle; and controls the movement of the pipeline robot according to the movement path. Multi-sensor detection technology has become a key solution for improving the autonomy, adaptability, and reliability of pipeline robots. This application effectively solves problems such as motion direction recognition, attitude stability, obstacle avoidance and path planning, communication and positioning reliability, and energy management through multi-sensor fusion perception, intelligent algorithm decision-making, and dynamic control adjustment, significantly improving the operational capability and robustness of pipeline robots in complex pipeline environments. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A flowchart illustrating a multi-sensor-based motion control method for a pipeline robot, provided in an embodiment of this application; Figure 2 An example diagram showing the pipeline robot provided in this application in a position within a pipeline without diameter changes or tees; Figure 3 An example diagram showing a pipeline robot positioned at a tee on the main pipeline, as provided in an embodiment of this application; Figure 4 An example diagram showing another pipeline robot positioned at the tee of the main pipeline, provided in an embodiment of this application; Figure 5 An example diagram showing a pipeline robot positioned at a tee in a branch pipe, as provided in an embodiment of this application; Figure 6 An example diagram showing another pipeline robot positioned at the tee of a branch pipe within a pipeline, as provided in an embodiment of this application; Figure 7 A schematic diagram of a multi-sensor-based motion control device for a pipeline robot provided in an embodiment of this application; Figure 8 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0019] Unless otherwise defined, 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 application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0020] Reference Figure 1 This application provides a multi-sensor-based motion control method for a pipeline robot. One laser sensor A is positioned at the front of the pipeline robot, and four laser sensors a, denoted as a1, a2, a3, and a4, are evenly spaced around the front of the robot. When the distance between any of the laser sensors and the pipe wall is within the measurement range, the corresponding detection signal is 1; when the distance between any of the laser sensors and the pipe wall is outside the measurement range, the corresponding detection signal is 0. The pipeline robot is equipped with an IMU unit. This method may include, but is not limited to, S100 to S150, as detailed below: S100: Determine the position of the pipeline robot inside the pipeline based on the target ranging value where the detection signal is 1 as measured by each of the laser sensors; S110: The target angular velocity of the roll angle acquired by the IMU unit is calibrated to obtain the calibrated angular velocity; S120: Construct a state vector using the calibrated angular velocity and the time-varying drift of the calibrated angular velocity; S130: Perform filtering iterations based on the state vector, and then output the fused attitude angle; S140: A movement path is planned based on the position and the fused attitude angle; S150: Control the movement of the pipeline robot according to the movement path.

[0021] Optionally, the target ranging value is obtained through the following steps: Determine the theoretical ranging values ​​measured by the four laser sensors a; For each laser sensor a, the most recent N theoretical ranging values ​​with a detection signal of 1 are selected and filtered to obtain N filtered ranging values; wherein, the filtered ranging values ​​are used as the target ranging values.

[0022] Optionally, the method further includes the following steps: Establish a global reference system with the center of the pipe cross-section as the origin; The coordinates of the center of the pipeline robot relative to the global reference system are calculated using N target ranging values ​​and used as the center coordinates; The initial attitude angle of the pipeline robot is calculated based on the center coordinates; Calculate the difference between the fused attitude angle and the initial attitude angle; If the difference reaches a preset threshold, the process returns to calibrating the target angular velocity of the roll angle acquired by the IMU unit to obtain the calibrated angular velocity, and then recalculates the fused attitude angle; 2N target ranging values ​​are obtained by filtering and screening, and the center coordinates are recalculated using the 2N target ranging values, and then the initial attitude angle is recalculated using the recalculated center coordinates until the difference is less than the preset threshold.

[0023] Optionally, calculating the initial attitude angle of the pipeline robot based on the center coordinates includes the following steps: Establish the coordinate system of the pipeline robot, and determine the installation orientation angles of the four laser sensors a in the coordinate system of the pipeline robot as θ=[0°,90°,180°,270°]; Construct a design matrix A and an observation vector b; where b includes bi, and bi = R d i,filter R is the pipe radius. d i,filter The target ranging value is denoted by ; i is the serial number of the laser sensor a; Solving the linear least squares problem ,get ; Calculate the initial attitude angle .

[0024] Optionally, calibrating the target angular velocity of the roll angle acquired by the IMU unit to obtain the calibrated angular velocity includes the following steps: The original angular velocity of the roll angle is filtered using a low-pass filter to obtain the filtered angular velocity; The average value of the filter angular velocities of the set number is used as the zero bias value; The target angular velocity is obtained by subtracting the zero bias value from the filtered angular velocity.

[0025] Optionally, constructing the state vector using the calibrated angular velocity and its time-varying drift includes the following steps: Constructing state vectors X k : ;in, φ k for k The calibrated angular velocity at time [time], b k The time-varying drift of the calibrated angular velocity; The state transition equation is constructed as follows: Among them, the state transition matrix ,T s It is the synchronous sampling time interval between the laser sensor and the IMU. T s Used to construct the transition relationship between states at adjacent time points, process noise. ; Construction process noise covariance matrix Q for ; To calibrate angular velocity k The noise variance is used to quantify the random error in angular velocity measurements; For time-varying drift b k The noise variance is used to quantify the uncertainty of IMU drift; Constructing observation equations Z k for: , ls,k The original attitude angle output by the IMU at time k; ω x,cal,k The angular velocity after calibration at time k; V k For observation noise; where, the observation matrix The observation matrix is ​​based on It is derived that; Construct observation noise as ; Construct the observation noise covariance matrix as follows ; This represents the noise variance of the observations, used to quantify the errors in the observation data from the lidar and IMU. The noise variance of the raw IMU data is used to quantify the random fluctuations in the IMU's own measurements; The step of filtering and iterating based on the state vector to output the fused attitude angle includes the following steps: The prediction construction steps are as follows: ;in, Here, P represents the state estimate, and P is the state estimate covariance matrix. The Kalman gain is constructed as follows: ; The build and update steps are as follows: ; Output the fused attitude angle for: .

[0026] Optionally, determining the position of the pipeline robot within the pipeline based on the target ranging value where the detection signal is 1 measured by each of the laser sensors includes the following steps: If the detection signal corresponding to laser sensor A is 0, and the detection signal corresponding to the target ranging value collected by laser sensors a1, a2, a3, and a4 is 1, then it is determined that the pipeline robot is in a position without diameter change or tee within the pipeline. If the detection signal corresponding to laser sensor A is 0, the detection signal corresponding to the target ranging value collected by one of laser sensors a1, a2, a3, and a4 is 0, and the detection signals of the other three are 1, then it is determined that the pipeline robot is in the tee position of the main pipeline inside the pipeline. If the detection signal corresponding to laser sensor A is 0, and the detection signals corresponding to the target ranging values ​​collected by two of laser sensors a1, a2, a3, and a4 are 0, while the detection signals of the other two are 1, then it is determined that the pipeline robot is in the tee position of the branch pipeline inside the pipeline.

[0027] The following sections will provide a detailed description and explanation of some optional embodiments of this application, using specific application examples.

[0028] The pipeline robot in this embodiment includes a motion mechanism and a steering mechanism. The motion mechanism enables the robot to move inside the pipeline, and the steering mechanism enables the robot to turn at bends and tees.

[0029] One laser sensor A is positioned directly in front of the pipeline robot, while four laser sensors a (a1, a2, a3, a4) are evenly distributed around its front perimeter. The laser sensors detect the pipe wall and the distance to it. If there is a pipe wall in front of the laser sensor, the sensor signal is 1, and the corresponding detected distance is D, d1, d2, d3, d4. If there is no pipe wall to detect, meaning the detection distance is outside the laser sensor's measurement range, the signal is lost, and the laser sensor signal is 0.

[0030] In addition, an inertial measurement unit (IMU) is installed on the pipeline robot to collect the robot's three-axis angular velocities (ω) in real time, synchronized with the laser sensor sampling period (T_s=0.01s). x , ω (ωz) and triaxial acceleration (a x , a (, az), focusing on extracting the angular velocity ω related to the roll angle. x and acceleration a Data used for attitude angle correction.

[0031] Laser sensor A is used to detect whether there are obstructions or pipe walls in front of the robot, thus controlling the robot to decelerate or stop. When the laser sensor A detects a signal of 0, it means there are no obstructions in front of the robot inside the pipe, and the robot can operate normally. When the laser sensor A detects a signal of 1, it means there are obstructions or pipe walls in front. Based on the measured distance data D, the robot is controlled to decelerate or stop: a threshold L1 / L2 is set. When the distance D reaches L1, the robot decelerates; when the distance D reaches L2, the robot stops to avoid collision. L2 is a safety protection mechanism. In normal operation mode, when the robot enters the distance L1 range, the robot's position and posture are determined by the laser sensors a1, a2, a3, and a4 evenly distributed around it, and the robot's movement in the pipe is planned.

[0032] Four laser sensors (a1, a2, a3, a4) are evenly distributed at the front end of the pipeline robot, adjacent to each other at 90° angles. When installed, the laser sensors emit laser light perpendicular to the robot's central axis and pointing towards the pipe wall. Based on the applicable pipe diameter range of the pipeline robot, laser sensors with appropriate measuring ranges are selected. Within the measuring range, the distances detected by the laser sensors are d1, d2, d3, and d4, respectively. If the detected distance exceeds the measurement range, the laser sensor loses signal, and the laser sensor signal is recorded as 0.

[0033] Scenario 1: The laser sensor signal remains almost unchanged, signal A is 0, and the measured distances d1, d2, d3, and d4 of a1, a2, a3, and a4 are all within a reasonable range and the values ​​are basically stable.

[0034] Reference Figure 2 In scenario 1, the pipeline robot is determined to be in a normal pipeline with no diameter change or tee.

[0035] Scenario 2: The signal of one laser sensor changes. Signal A is 0, and the signal of one of the laser sensors a1, a2, a3, and a4 becomes 0, while the readings of the other three laser sensors are normal.

[0036] Reference Figure 3 , Figure 4 In scenario 2, the pipeline robot is determined to be at the tee position of the main pipeline. The direction of the tee opening can be determined based on the changing laser sensor number.

[0037] Scenario 3: Signal changes of 3 laser sensors. The signal of laser sensor A changes from 0 to distance D, and the signals of 2 of a1, a2, a3, and a4 become 0.

[0038] Reference Figure 5 , Figure 6In scenario 3, the system determines that the pipeline robot is at the tee position of a branch pipe. Based on the changing sequence number of laser sensor a, it determines the direction of the tee opening. Furthermore, when approaching the main pipe, according to the previously mentioned method, when the distance D detected by laser sensor A reaches L1, the robot decelerates until the signal of laser sensor a changes. The system then determines the robot's pose based on the changes of all laser sensors to plan the robot's movement.

[0039] This embodiment requires detailed fitting of the robot's position and attitude within the pipe based on signals collected by the laser sensor. The robot's cross-section within the pipe is considered as a point mass. The goal is to determine the offset (x, y) of this point mass relative to the pipe's center and the robot's yaw attitude (roll angle).

[0040] A hardware synchronization triggering mechanism is adopted, and the laser sensor and IMU start sampling through the same clock signal to ensure that the timestamps of each set of laser distance data and IMU attitude data are completely aligned (error ≤ 1ms).

[0041] The data storage format is "timestamp + laser sensor data (D, d1-d4, signal identifier) ​​+ IMU data (ω)". x ,ω ,ω_z,a x ,a Structured storage ("a_z") with reserved data check bits (based on CRC32 algorithm) to avoid data loss or corruption during transmission or storage.

[0042] Specifically, this embodiment may include the following technical solutions: 1. Establish a mathematical model.

[0043] Pipeline coordinate system (O-XY): The origin is O, which is the center of the pipe cross-section. This is the global reference system, with the pipe center set as the origin O(0,0).

[0044] Robot coordinate system (o-xy): with the robot's center (i.e., point mass) o as the origin. This is the objective to be solved.

[0045] The mounting orientation angle of the i-th laser sensor is θ_i (in the robot coordinate system). For four evenly distributed laser sensors, θ = [0°, 90°, 180°, 270°].

[0046] Each laser sensor measures the vertical distance from the robot body to the pipe wall, denoted as d_i_measured.

[0047] Let the robot's center coordinates be (x, y) and the pipe radius be R.

[0048] For the i-th laser sensor, the unit vector of its laser beam direction in the pipe coordinate system is (cosθ_i, sinθ_i).

[0049] The projection of the vector from the center (x, y) of the pipeline robot to the center (0, 0) of the pipeline onto the direction of the laser sensor is: proj_i = x cosθ_i + y sinθ_i; Therefore, the theoretical distance measurement value of the i-th laser sensor should be the pipe radius minus this projection (because the projection is the component of the vector from the robot center to the center of the circle in the direction of the laser sensor, while the distance is the radius from the center of the circle to the pipe wall minus this component): d_i_theoretical = R - (x cosθ_i + y sinθ_i).

[0050] Laser data validity screening: Invalid data is filtered based on signal indicators. When the laser sensor signal is 0 (detection distance exceeds the range), the distance data set is marked as invalid and will not be included in subsequent calculations. When the signal is 1, the distance data is retained and random noise is suppressed by a moving average filter (window size = 5). The filtering formula is as follows: .

[0051] in d i,filter This is the filter distance value. d i,measured ( t kTs The values ​​are the original measurements from the last 5 sampling periods.

[0052] IMU data calibration: Eliminate IMU static drift through zero-bias calibration, and collect 100 sets of ω data while the robot is stationary. x The data is used to calculate the average value as the zero bias. b ω0 The angular velocity after calibration is: ; At the same time, a low-pass filter (cutoff frequency = 10Hz) is used to filter high-frequency vibration noise in the IMU data.

[0053] 2. Least squares attitude fitting.

[0054] Calculate the robot's center offset (x, y) and initial attitude angle φ_ls using only valid laser distance data: Establish the pipeline coordinate system (O-XY) and the robot coordinate system (o-xy), and determine the laser sensor installation orientation angle θ=[0°,90°,180°,270°]; Construct the design matrix A and the observation vector b (bi=R) di, filter, R is the pipe radius); Solving the linear least squares problem ; Calculate the initial attitude angles: ; 3. Fusion correction of IMU and laser fitting results (Kalman filtering).

[0055] (1) System modeling.

[0056] State vector: ,in φ k for k Real posture angle at all times b k For the time-varying drift of the IMU angular velocity (assuming it changes slowly); State transition equation: ; Where the state transition matrix , T s This is the synchronization sampling time interval between the laser sensor and the IMU (usually 0.01 s). T s Used to construct the transition relationship between adjacent time states (reflecting the integral change of attitude angle with angular velocity), process noise. ; Process noise covariance matrix ;in, To calibrate angular velocity The noise variance of k is used to quantify the random error in angular velocity measurement; The noise variance of the time-varying drift bk is used to quantify the uncertainty of the IMU drift. Observation equation: ;, ls,k The raw attitude angle (uncalibrated) output by the IMU at time k. ω x,cal,k The angular velocity calibrated at time k (obtained by data fusion from the lidar and IMU); V k To observe noise; Where the observation matrix ; Observation noise ; Observation noise covariance matrix , ); The noise variance of the observations (such as attitude angles) is used to quantify the error in the observation data of the lidar and IMU. This represents the noise variance of the raw IMU data, used to quantify the random fluctuations in the IMU's own measurements.

[0057] (2) Filtering iteration process.

[0058] Prediction Step: ( (where P is the state estimate and P is the state estimate covariance matrix). Kalman gain: ; Update steps: ; Output fused attitude angles: (The first element of the state vector).

[0059] 4. Special data processing in the T-junction position.

[0060] When the laser sensor signal exhibits either scenario 2 (one laser sensor signal is 0) or scenario 3 (two laser sensor signals are 0) and the root mean square error (RMSE) suddenly increases, it is determined that the area has entered the three-way region. Filter valid laser distance data (laser sensor data with a signal of 1), and re-perform the least squares method to fit the initial attitude angle. ; Keeping the Kalman filter parameters unchanged, the system continues to fuse effective laser data with IMU calibration data, and outputs... The direction of the tee opening is determined by combining the sequence number of the laser sensor signal change, providing attitude basis for path planning.

[0061] 5. Result verification.

[0062] Calculate the residuals of the attitude angles after fusion. ,when At that time, the fusion result is deemed reliable; like This triggers IMU zero-bias recalibration, while simultaneously increasing the laser data moving average window to 10 to improve data stability before re-fusion.

[0063] In summary, pipeline tees (T-shaped, Y-shaped, etc.) have branching paths, and traditional robots are prone to getting lost or entering dead ends due to insufficient environmental perception. Selecting the correct path (e.g., main branch, target branch) at tees is a crucial task for pipeline robots. A single sensor may lead to incorrect decisions due to incomplete information; therefore, this embodiment employs a multi-sensor fusion strategy. This embodiment's motion control method for pipeline robots at tees, based on multi-sensor fusion perception, intelligent algorithm decision-making, and dynamic control adjustment, effectively solves problems related to motion direction recognition, posture stability, obstacle avoidance and path planning, communication and positioning reliability, and energy management, significantly improving the autonomous operation capability of pipeline robots in complex pipeline environments. This embodiment is of great significance for pipeline inspection, maintenance, and repair in the fields of oil, natural gas, and municipal water supply.

[0064] This embodiment details a method for a pipeline robot to detect and determine the structure (tees or bends) of a pipeline. The identification of the pipeline terrain is the basis for the robot's movement in the pipeline. Therefore, this embodiment can improve the working performance and robustness of the pipeline robot.

[0065] Reference Figure 7 This application also provides a multi-sensor-based pipeline robot motion control device, which can realize the above-mentioned multi-sensor-based pipeline robot motion control device method. One laser sensor A is arranged in front of the pipeline robot, and four laser sensors a, denoted as a1, a2, a3, and a4, are evenly spaced around the front end of the pipeline robot. When the distance between any of the laser sensors and the pipe wall is within the measurement range, the corresponding detection signal is 1; when the distance between any of the laser sensors and the pipe wall is outside the measurement range, the corresponding detection signal is 0. The pipeline robot is equipped with an IMU unit. The device includes: The position determination unit is used to determine the position of the pipeline robot in the pipeline based on the target ranging value where the detection signal is 1 measured by each of the laser sensors. An angular velocity calibration unit is used to calibrate the target angular velocity of the roll angle acquired by the IMU unit to obtain a calibrated angular velocity. A state variable construction unit is used to construct a state vector using the calibrated angular velocity and the time-varying drift of the calibrated angular velocity; The attitude angle fusion unit is used to perform filtering iteration based on the state vector, and then output the fused attitude angle; A path planning unit is used to plan a movement path based on the position and the fused attitude angle; A robot control unit is used to control the movement of the pipeline robot according to the movement path.

[0066] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0067] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method of this application. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0068] It is understood that the content of the above method embodiments is applicable to the device embodiments. The specific functions implemented by the device embodiments are the same as those of the methods of this application, and the beneficial effects achieved are the same as those achieved by the methods of this application.

[0069] Figure 8 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 101 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 102 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 102 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 102 and is called and executed by the processor 101. Input / output interface 103 is used to implement information input and output; The communication interface 104 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 105 transmits information between various components of the device (e.g., processor 101, memory 102, input / output interface 103, and communication interface 104); The processor 101, memory 102, input / output interface 103 and communication interface 104 are connected to each other within the device via bus 105.

[0070] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method of this application.

[0071] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0072] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0073] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0074] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0075] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0076] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0077] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0078] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0079] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0080] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0081] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0082] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0083] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0084] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A motion control method for a pipeline robot based on multiple sensors, characterized in that, The pipeline robot has one laser sensor A positioned directly in front of it, and four laser sensors a, denoted as a1, a2, a3, and a4, evenly spaced around its front end. When any of the laser sensors is within the measurement range from the pipe wall, the corresponding detection signal is 1; when any of the laser sensors is outside the measurement range, the corresponding detection signal is 0. The pipeline robot is equipped with an IMU unit. The method includes the following steps: The position of the pipeline robot inside the pipeline is determined based on the target ranging value where the detection signal is 1 as measured by each of the laser sensors. The target angular velocity of the roll angle acquired by the IMU unit is calibrated to obtain the calibrated angular velocity; A state vector is constructed using the calibrated angular velocity and its time-varying drift. The state vector is filtered and iterated to output the fused attitude angle. The movement path is planned based on the position and the fused attitude angle; The pipeline robot is controlled to move according to the stated movement path.

2. The multi-sensor-based motion control method for a pipeline robot according to claim 1, characterized in that, The target ranging value is obtained through the following steps: Determine the theoretical ranging values ​​measured by the four laser sensors a; For each laser sensor a, the most recent N theoretical ranging values ​​with a detection signal of 1 are selected and filtered to obtain N filtered ranging values; wherein, the filtered ranging values ​​are used as the target ranging values.

3. The multi-sensor-based motion control method for a pipeline robot according to claim 2, characterized in that, The method further includes the following steps: Establish a global reference system with the center of the pipe cross-section as the origin; The coordinates of the center of the pipeline robot relative to the global reference system are calculated using N target ranging values ​​and used as the center coordinates; The initial attitude angle of the pipeline robot is calculated based on the center coordinates; Calculate the difference between the fused attitude angle and the initial attitude angle; If the difference reaches a preset threshold, the process returns to calibrating the target angular velocity of the roll angle acquired by the IMU unit to obtain the calibrated angular velocity, and then recalculates the fused attitude angle; 2N target ranging values ​​are obtained by filtering and screening, and the center coordinates are recalculated using the 2N target ranging values, and then the initial attitude angle is recalculated using the recalculated center coordinates until the difference is less than the preset threshold.

4. The multi-sensor-based motion control method for a pipeline robot according to claim 3, characterized in that, The calculation of the initial attitude angle of the pipeline robot based on the center coordinates includes the following steps: Establish the coordinate system of the pipeline robot, and determine the installation orientation angles of the four laser sensors a in the coordinate system of the pipeline robot as θ=[0°,90°,180°,270°]; Construct a design matrix A and an observation vector b; where b includes bi, and bi = R d i,filter R is the pipe radius. d i,filter The target ranging value is denoted by ; i is the serial number of the laser sensor a; Solving the linear least squares problem ,get ; Calculate the initial attitude angle .

5. The multi-sensor-based motion control method for a pipeline robot according to claim 1, characterized in that, The calibration of the target angular velocity of the roll angle acquired by the IMU unit to obtain the calibrated angular velocity includes the following steps: The original angular velocity of the roll angle is filtered using a low-pass filter to obtain the filtered angular velocity; The average value of the filter angular velocities of the set number is used as the zero bias value; The target angular velocity is obtained by subtracting the zero bias value from the filtered angular velocity.

6. The method for motion control of a pipeline robot based on multiple sensors according to claim 1, characterized in that, The process of constructing a state vector using the calibrated angular velocity and its time-varying drift includes the following steps: Constructing state vectors X k : ;in, φ k for k The calibrated angular velocity at time [time], b k The time-varying drift of the calibrated angular velocity; The state transition equation is constructed as follows: Among them, the state transition matrix , T s It is the synchronous sampling time interval between the laser sensor and the IMU. T s Used to construct the transition relationship between states at adjacent time points, process noise. ; Constructing the coprocess noise variance matrix Q for ; To calibrate angular velocity k The noise variance is used to quantify the random error in angular velocity measurements; For time-varying drift b k The noise variance is used to quantify the uncertainty of IMU drift; Constructing observation equations Z k for: , ls,k The original attitude angle output by the IMU at time k; ω x,cal,k The angular velocity after calibration at time k; V k For observation noise; where, the observation matrix The observation matrix is ​​based on It is derived that; Construct observation noise as ; Construct the observation noise covariance matrix as follows ; This represents the noise variance of the observations, used to quantify the errors in the observation data from the lidar and IMU. The noise variance of the raw IMU data is used to quantify the random fluctuations in the IMU's own measurements; The step of filtering and iterating based on the state vector to output the fused attitude angle includes the following steps: The prediction construction steps are as follows: ;in, Here, P represents the state estimate, and P is the state estimate covariance matrix. The Kalman gain is constructed as follows: ; The build and update steps are as follows: ; Output the fused attitude angle for: .

7. A multi-sensor-based motion control method for a pipeline robot according to any one of claims 1 to 6, characterized in that, Determining the position of the pipeline robot within the pipeline based on the target ranging value where the detection signal is 1 measured by each of the laser sensors includes the following steps: If the detection signal corresponding to laser sensor A is 0, and the detection signal corresponding to the target ranging value collected by laser sensors a1, a2, a3, and a4 is 1, then it is determined that the pipeline robot is in a position without diameter change or tee within the pipeline. If the detection signal corresponding to laser sensor A is 0, the detection signal corresponding to the target ranging value collected by one of laser sensors a1, a2, a3, and a4 is 0, and the detection signals of the other three are 1, then it is determined that the pipeline robot is in the tee position of the main pipeline inside the pipeline. If the detection signal corresponding to laser sensor A is 0, and the detection signals corresponding to the target ranging values ​​collected by two of laser sensors a1, a2, a3, and a4 are 0, while the detection signals of the other two are 1, then it is determined that the pipeline robot is in the tee position of the branch pipeline inside the pipeline.

8. A motion control device for a pipeline robot based on multiple sensors, characterized in that, One laser sensor A is arranged in front of the pipeline robot, and four laser sensors a are evenly spaced around the front of the pipeline robot, denoted as a1, a2, a3, and a4 respectively; when the distance between any of the laser sensors and the pipe wall is within the measurement range, the corresponding detection signal is 1; when the distance between any of the laser sensors and the pipe wall is outside the measurement range, the corresponding detection signal is 0. The pipeline robot is equipped with an IMU unit; The device includes: The position determination unit is used to determine the position of the pipeline robot in the pipeline based on the target ranging value where the detection signal is 1 measured by each of the laser sensors. An angular velocity calibration unit is used to calibrate the target angular velocity of the roll angle acquired by the IMU unit to obtain a calibrated angular velocity. A state variable construction unit is used to construct a state vector using the calibrated angular velocity and the time-varying drift of the calibrated angular velocity; The attitude angle fusion unit is used to perform filtering iteration based on the state vector, and then output the fused attitude angle; A path planning unit is used to plan a movement path based on the position and the fused attitude angle; A robot control unit is used to control the movement of the pipeline robot according to the movement path.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 7.

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