Adaptive variable-structure duct robot motion control method based on environmental sensing

Abstract

An adaptive variable-structure duct robot motion control method based on environmental sensing. A duct robot is a dual-swing-arm tracked mechanism, and onboard sensing devices include a depth camera, an IMU, a wheel speed sensor and a TOF module. The method comprises: a depth camera capturing an image of the interior of a duct to perform scenario recognition, segmenting planar and curved surfaces in the image on the basis of scenario recognition results, and extracting edge lines of the duct; using the edge lines to calculate the width of a straight duct, the curvature of a curved duct, the angle of a slope and the height of a step, in order to determine the passability of a robot; designing a path planner and a swing arm planner to generate a reference trajectory and a swing arm angle sequence of the robot, performing smoothing processing, and inputting the smoothed reference trajectory and swing arm angle sequence into an MPC motion controller; using data from the depth camera, an IMU and a wheel speed sensor to estimate the position and state of the robot by means of an ESKF algorithm, and inputting estimation results and a collision warning signal from a TOF module into the MPC motion controller in real time; and finally, outputting control signals for a locomotion electric motor and a swing arm electric motor. Thus, the adaptive variable-structure and autonomous motion of a robot in different scenarios is realized.

Description

An adaptive modifiable pipeline robot motion control method based on environmental perception Technical Field

[0001] The present invention relates to the field of robotics technology, and in particular to a motion control method of an adaptive variable-configuration pipeline robot based on environment perception. Background Art

[0002] Ventilation ducts are critical infrastructure in factories and high-rise buildings. Pollutant accumulation and corrosion within these ducts can cause respiratory infections and other illnesses. However, because ventilation ducts are often located between mezzanines or high above a building structure, their inspection and maintenance present challenges. The development of duct robots to replace manual inspections within ducts is a current trend.

[0003] In complex pipeline environments, pipeline robots need to navigate a variety of terrain scenarios, including straight pipes, curved pipes, slopes, and steps. This is especially true in narrow environments, which places high demands on the operator's operational proficiency. There are also issues with misoperation caused by blind spots and excessive fatigue from long inspections. Currently, autonomous robot inspections and autonomous motion control have been extensively researched in conventional terrestrial environments. However, in narrow pipeline environments, on the one hand, the accuracy of existing perception and positioning systems is affected by factors such as high reflectivity and low texture in the pipeline environment. On the other hand, pipeline robots need to automatically plan their configurations to adapt to a variety of terrain scenarios. Learning and model-based methods can only solve a single scenario and have poor robustness. Therefore, existing methods are unable to achieve autonomous inspections and adaptive motion control for robots in pipeline environments.

[0004] The Chinese patent application number is: CN202211176185.2, and the patent document with the authorization date of July 4, 2023 discloses an autonomous intelligent pipeline inspection robot. The toe angle is adjusted by the toe control panel to realize automatic main bus tracking of the pipeline robot at various operating speeds. The Hall sensor is used to count and measure distance to avoid measurement errors caused by wear or failure of the relatively poor working conditions of the inner wall of the pipeline. However, in a smooth pipeline environment, the mileage error caused by slippage cannot be solved, and adjusting the toe angle can only solve the autonomous movement in the straight pipe scenario.

[0005] The Chinese patent application number is: CN201710719668.5, and the patent document with the authorization date of June 20, 2023 discloses an adaptive crawler walking system and an adaptive crawler robot. When encountering obstacles, the suspension mechanism is used to automatically change the shape of the crawler to adapt to the changing terrain. It has the characteristics of simple structure and strong adaptability. However, this passive adaptive method can only be applied to scenes with small terrain undulations during straight-line travel, and cannot solve the problems of adaptive motion control in corners and passing through step scenes.

[0006] At present, how to use the data collected by multi-sensor equipment to design an adaptive configuration method for pipeline robots to realize autonomous inspection of robots in various pipeline scenarios is an urgent problem that needs to be solved. Summary of the Invention

[0007] To solve the above technical problems, the present invention proposes a motion control method for an adaptive variable-configuration pipeline robot based on environmental perception. The pipeline robot is a double-swing-arm crawler mechanism. The sensing equipment it carries includes a depth camera, an IMU, a wheel speed recorder, and three TOF modules for ranging. The depth camera is installed in front of the robot, and the three TOF modules are installed on the left and right sides and the top of the robot respectively. The multi-sensor fusion method is used to perceive the pipeline environment and the robot status, and an adaptive variable-configuration method for the pipeline robot is proposed to realize autonomous inspection of the robot in various pipeline scenarios.

[0008] To achieve the above object, the technical solution adopted by the present invention is:

[0009] A motion control method for an adaptive variable-configuration pipeline robot based on environmental perception comprises the following steps:

[0010] Step 1: The pipeline robot is placed inside the pipeline. The depth camera captures an image of the pipeline in front. After image noise reduction preprocessing, the pipeline scene is recognized using a neural network. Based on the recognition results, the planes and curved surfaces in the image are segmented. The region segmentation results on the two-dimensional image are projected into three-dimensional space using a camera projection model, and the pipeline edge lines are extracted.

[0011] Step 2: Use the edge line to calculate the straight pipe width, bend curvature, slope angle, and step height to analyze the robot's passability;

[0012] Step 3: Design a path planner and an arm planner to generate the robot's reference trajectory and arm angle sequence, perform smoothing, and input them into the MPC motion controller;

[0013] Step 4: The data from the depth camera, IMU, and wheel shorthand are used to estimate the robot's position and state through the ESKF algorithm. The estimation results and the collision warning signal from the TOF module are input into the MPC motion controller in real time.

[0014] Step 5: In the MPC motion controller, the kinematic model of the double-swing-arm crawler robot is used, combined with the reference trajectory and the swing-arm angle sequence, to construct a quadratic objective function, which is solved using an iterative method to output the robot's adaptive motion control signal.

[0015] Furthermore, the pipeline robot is a double-swing-arm crawler mechanism, and the sensing equipment it carries includes a depth camera, IMU, wheel speed recorder, and three TOF modules for ranging. The depth camera is installed in front of the robot, and the three TOF modules are installed on the left and right sides and top of the robot respectively.

[0016] Furthermore, the specific steps of step 1 are:

[0017] The depth camera collects images inside the rectangular pipe, uses bilateral filtering and backlight compensation to reduce noise and suppress strong light in the image, completes the image preprocessing, and uses the lightweight ResNet18 neural network to perform scene recognition on the processed image. The recognition results are classified into four scenes: straight pipe, curved pipe, slope, and step. Based on the classification results, the DeepLabv3 network is used to segment the two-dimensional image into multiple plane areas P and curved surface areas S. Three points on each area are randomly selected along the x-axis of the pixel coordinate system, and the camera's intrinsic parameter matrix K and the scale information Z provided by the depth camera are used to calculate the selected point p in the pixel coordinate system. uv Convert p to the camera coordinate system C : Zp uv =Kp C

[0018] Combined with the geometric structure characteristics of the pipeline environment, the three-dimensional coordinates of the sampling points in each area are used to fit each plane area P in the space i and surface area S j The pipeline edge formed by the intersection of each area is a straight line or a curve, and the regional equation is used to calculate the spatial equation of each line and curve.

[0019] Furthermore, the specific steps of step 2 are:

[0020] The length, width and height of the measuring pipeline robot are L l ,L w ,L h The radius of the driving wheel is R, the radius of the driven wheel on the swing arm is r, and the wheelbase of the front swing arm is l F , the wheelbase of the rear swing arm is l R , the wheelbase of the moving body is l B After obtaining the equation of each edge line, the geometric characteristics of the pipe are used to calculate the pipe width using two straight lines on the bottom surface and the pipe height using two straight lines on the side surface. These values ​​are compared with the robot's outer envelope size to determine the passability of the straight pipe environment ahead. In a sloped environment, the angle between the side edges is calculated as the slope angle θ. S , the friction coefficient between the robot and the pipe wall is μ, if arctan(μ)<θ S, then the front slope is considered passable; in the step scene, the distance between the edge of the step surface and the edge of the bottom surface is calculated as the step height H. It is considered that the steps ahead are passable. In the pipe bend environment, the curvature λ of the inner line of the bend is calculated using the two curves of the bottom surface. in and the outer curvature λ out , and then judge the passing conditions of the elbow:

[0021] Furthermore, the specific steps of step 3 are:

[0022] In straight pipe and curved pipe scenarios, motion control only needs to consider the track. The path planner is designed to calculate the equidistant lines of the left and right side edges as the reference trajectory of the robot, and perform sampling and smoothing. In slope and step scenarios, in addition to controlling the track, the angles of the front and rear swing arms must also be adjusted. The kinematic model of the dual-swing arm tracked pipeline robot is simplified to a contact edge model, with the front swing arm contacting the edge l f , rear swing arm touches the edge line l r Contact edge with chassis l b composition,

[0023] l r The two end points of p r With p br , l f The two end points of p f With p bf , l b The two end points are p br With p bf , l r With l b The angle is θ r , l f With l b The angle is θ f ; Use the simplified model to contact the terrain and calculate θ during the movement r and θ f , and mapped to the swing arm joint angle θ R and θ F , complete the control of the swing arm.

[0024] Furthermore, in step 3, solving θ r and θ f There are the following constraints:

[0025] 1) At least two points on the three simplified edges are in contact with the terrain;

[0026] 2) The three simplified edges do not interfere with the terrain;

[0027] 3) The vertical line of the robot's center of gravity is at p br With p bf between;

[0028] In θ r and θ f When there are multiple solutions, the state when the robot's center of gravity is at its lowest is taken as the optimal state. After obtaining the swing arm angle sequence in the discrete state during the obstacle crossing process, the swing arm angle change rate is used as the motion cost. The dynamic programming algorithm is used to optimize the cumulative cost to obtain the optimal discrete swing arm angle sequence. The generated optimal discrete swing arm state sequence is sampled and smoothed using a Bezier curve. The smoothed reference path and swing arm angle are sent to the MPC motion controller.

[0029] Furthermore, the specific steps of step 4 are:

[0030] To obtain the real-time position and state information of the robot, the ESKF motion equations of the nominal state and error state in discrete time are derived based on the IMU measurement model, and then the ESKF prediction process is executed, including the calculation of the nominal state prediction and the error state prediction. On the one hand, the left and right track speeds obtained by the encoder are used in the update phase. On the other hand, in order to alleviate the influence of cumulative errors, the distance between the pipeline connection and the vehicle body is used as the position observation to complete the update of the covariance matrix and the error state. The distance of the pipeline connection is obtained by using a depth camera to extract the edge features of the pipeline connection, and then calculated using rectangular constraints and camera projection models. TOF ranging modules are installed on the left and right sides and the top of the robot for collision warning. The real-time position and state of the robot and the collision warning signal are input into the MPC motion controller.

[0031] Furthermore, the specific steps of step 5 are:

[0032] Set the system state to x=[x r ,y r ,z r ,θ yaw ,θ pitch ,θ F ,θ R ] T , where x r ,y r ,z r ,θ yaw ,θ pitch They represent the robot’s three-dimensional spatial coordinates, yaw angle, and pitch angle respectively; the system input is u=[v L ,v R ,Δθ F ,Δθ R ] T , v L ,vR ,Δθ F ,Δθ R They represent the left track input speed, right track input speed, front swing arm angle increment, and rear swing arm angle increment respectively; the reference state is X ref ={x0,x1,x2,…,x end}, x0 is the initial state of the system, x end is the desired final state of the system; the kinematic model f of the double-swing arm crawler robot is used to predict the system state in the next N cycles at time i The difference between the predicted state and the reference state is constructed into a quadratic objective function, and iterative optimization is performed to minimize it, and the system input u within the optimized N cycles is obtained. i→i+N-1 During the system status update process, in order to obtain more accurate three-dimensional spatial coordinates of the robot, the robot position and status information obtained in step 4 is used; a sliding window is used to optimize the system input u in real time.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] 1. The present invention uses two-dimensional image region segmentation and camera projection model to calculate the regional spatial equation. Combined with the geometric structure of the pipeline environment, the spatial equation of the intersecting lines or curves in the region is obtained, and then a simplified pipeline spatial configuration can be obtained for analyzing the robot's passability and generating motion reference trajectories. Compared with the method of directly extracting the edge features of the two-dimensional image, this method extracts the edge with higher accuracy, is not affected by the strong light reflection from the inner wall, and is more robust.

[0035] 2. The present invention designs robot passability constraints in various scenarios and uses spatial edges and robot kinematic models to analyze the center of gravity stability of the pipeline robot, which can avoid the operator's misjudgment of the robot's passability.

[0036] 3. The present invention adopts an edge contact model to simplify the contact motion between the robot and the terrain, and designs a path planner and a swing arm planner to generate a reference trajectory and a swing arm angle sequence. The ESKF algorithm is used to fuse the depth camera, IMU, and wheel speed recorder to estimate the robot's position and state. The estimation result and the collision warning signal of the TOF module are input into the MPC motion controller in real time. A quadratic objective function is constructed, and the input control signal is iteratively optimized. This can realize adaptive variable motion control of the pipeline robot, which helps to improve the efficiency of autonomous inspection. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] FIG1 shows a framework diagram of a motion control method for an adaptive variable-configuration pipeline robot based on environment perception proposed by the present invention;

[0038] Figure 2 shows the trajectory and arm planning diagram of the dual-swing-arm crawler pipeline robot through four scenarios: straight pipe, curved pipe, slope, and step. (a) is the straight pipe motion planning, (b) is the curved pipe motion planning, (c) is the slope motion planning, and (d) is the step motion planning.

[0039] Figure 3 shows a schematic diagram of simplifying the double-swing-arm crawler pipeline robot model into a contact edge model;

[0040] Figure 4 shows a schematic diagram of the six stages of the robot's simplified edge model passing through the stairs. DETAILED DESCRIPTION

[0041] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0042] The present invention provides a motion control method for an adaptive variable-configuration pipeline robot based on environmental perception, the method comprising the following steps:

[0043] Step 1: The pipeline robot is placed inside the pipeline. The depth camera captures an image of the pipeline in front. After image noise reduction preprocessing, the pipeline scene is recognized using a neural network. Based on the recognition results, the planes and curved surfaces in the image are segmented. The region segmentation results on the two-dimensional image are projected into three-dimensional space using a camera projection model, and the pipeline edge lines are extracted.

[0044] Step 2: Use the edge line to calculate the straight pipe width, bend curvature, slope angle, and step height to analyze the robot's passability;

[0045] Step 3: Design a path planner and an arm planner to generate the robot's reference trajectory and arm angle sequence, perform smoothing, and input them into the MPC motion controller;

[0046] Step 4: The data from the depth camera, IMU, and wheel shorthand are used to estimate the robot's position and state through the ESKF algorithm. The estimation results and the collision warning signal from the TOF module are input into the MPC motion controller in real time.

[0047] Step 5: In the MPC motion controller, the kinematic model of the dual-swing-arm tracked robot is used, combined with the reference trajectory and the swing-arm angle sequence, to construct a quadratic objective function. The iterative method is used to solve it and output the robot's adaptive motion control signal. The framework of the robot motion control method is shown in Figure 1.

[0048] First, the image inside the rectangular pipe captured by the depth camera is processed. Bilateral filtering and backlight compensation are used to reduce noise and suppress strong light, completing the image preprocessing.

[0049] The processed images were then subjected to scene recognition using a lightweight ResNet18 neural network. The recognition results were classified into four scenarios: straight pipe, curved pipe, slope, and steps.

[0050] According to the classification results, the DeepLabv3 network is used to segment the two-dimensional image into multiple plane regions P and curved surface regions S;

[0051] Randomly select three points in each area along the x-axis of the pixel coordinate system, and use the camera's intrinsic parameter matrix K and the scale information Z provided by the depth camera to convert the selected point p in the pixel coordinate system into uv Convert p to the camera coordinate system C : Zp uv =Kp C (1);

[0052] Combined with the geometric structure characteristics of the pipeline environment, the three-dimensional coordinates of the sampling points in each area are used to fit each plane area P in the space i and surface area S j The pipeline edge formed by the intersection of each area is a straight line or a curve, and the regional equation is used to calculate the spatial equation of each line and curve.

[0053] The length, width and height of the measuring pipeline robot are L l ,L w ,L h The radius of the driving wheel is R, the radius of the driven wheel on the swing arm is r, and the wheelbase of the front swing arm is l F , the wheelbase of the rear swing arm is l R , the wheelbase of the moving body is l B After obtaining the equations for each edge, the robot uses the geometric characteristics of the pipe to calculate the pipe width using two straight lines on the bottom surface and the pipe height using two straight lines on the side surface. These values ​​are then compared with the robot's outer envelope to determine the passability of the straight pipe environment ahead.

[0054] In a slope environment, the angle between the side edges is calculated as the slope angle θ S , the friction coefficient between the robot and the pipe wall is μ, if arctan(μ)<θ S , then the slope ahead is considered passable;

[0055] In the step scene, the distance between the edge of the step surface and the edge of the bottom surface is calculated as the step height H. If It is considered that the steps ahead are passable.

[0056] In the pipe bend environment, the curvature λ of the inner line of the bend is calculated using the two curves of the bottom surface. in and the outer curvature λout , and then judge the passing conditions of the elbow:

[0057] As shown in Figure 2, it is a schematic diagram of the trajectory and swing arm planning of the dual-swing arm crawler pipeline robot through four scenarios: straight pipe, curved pipe, slope, and step. (a) is the straight pipe motion planning, (b) is the curved pipe motion planning, (c) is the slope motion planning, and (d) is the step motion planning.

[0058] In straight and curved pipe scenarios, motion control only requires the track. A path planner is designed to calculate the equidistant lines between the left and right side edges as the robot's reference trajectory, and perform sampling and smoothing.

[0059] In slope and step scenarios, in addition to controlling the moving crawler, the angles of the front and rear swing arms must also be adjusted simultaneously. The kinematic model of the dual-swing-arm crawler pipeline robot is simplified to a contact edge model, as shown in Figure 3. f , rear swing arm touches the edge line l r Contact edge with chassis l b composition:

[0060] l r The two end points of p r With p br , l f The two end points of p f With p bf , l b The two end points are p br With p bf , l r With l b The angle is θ r , l f With l b The angle is θ f ;

[0061] Using the simplified model to contact the terrain, calculate θ during the process of moving r and θ f , and mapped to the swing arm joint angle θ R and θ F , complete the control of the swing arm;

[0062] Solving for θ r and θ f The following constraints apply: 1) At least two points on the three simplified edges are in contact with the terrain; 2) The three simplified edges do not interfere with the terrain; 3) The vertical line of the robot's center of gravity is at p br With p bf between;

[0063] In θr and θ f When there are multiple solutions, the state when the robot's center of gravity is the lowest is taken as the optimal state. As shown in Figure 4, it is a schematic diagram of the six stages of the robot's simplified edge model passing through the steps.

[0064] After obtaining the swing arm angle sequence in the discrete state during the obstacle crossing process, the swing arm angle change rate is used as the motion cost. The dynamic programming algorithm is used to optimize the cumulative cost to obtain the optimal discrete swing arm angle sequence. The generated optimal discrete swing arm state sequence is sampled and smoothed using a Bezier curve. The smoothed reference path and swing arm angle are sent to the MPC motion controller.

[0065] To obtain the real-time position and state information of the robot, the ESKF motion equations of the nominal state and error state in discrete time are derived based on the IMU measurement model. Then, the ESKF prediction process can be performed, including the calculation of the prediction of the nominal state and the prediction of the error state.

[0066] During the update phase, the left and right track speeds acquired by the encoders are used. Furthermore, to mitigate the impact of cumulative errors, the distance between the pipe connection and the vehicle body is used as a position observation to update the covariance matrix and error state. The distance to the pipe connection is calculated using rectangular constraints and a camera projection model after extracting the edge features of the pipe connection using a depth camera.

[0067] TOF ranging modules are installed on the left and right sides and top of the robot for collision warning; the robot's real-time position and status as well as the collision warning signal are input into the MPC motion controller.

[0068] Set the system state to x=[x r ,y r ,z r ,θ yaw ,θ pitch ,θ F ,θ R ] T , where x r ,y r ,z r ,θ yaw ,θ pitch They represent the robot’s three-dimensional spatial coordinates, yaw angle, and pitch angle respectively; the system input is u=[v L ,v R ,Δθ F ,Δθ R ] T , v L ,v R ,Δθ F ,Δθ RThey represent the left track input speed, right track input speed, front swing arm angle increment, and rear swing arm angle increment respectively; the reference state is X ref ={x0,x1,x2,…,x end}, x0 is the initial state of the system, x end is the desired final state of the system;

[0069] Using the kinematic model f of the double-swing arm crawler robot, the system state in the next N cycles is predicted at time i. The difference between the predicted state and the reference state is constructed into a quadratic objective function, and iterative optimization is performed to minimize it, and the system input u within the optimized N cycles is obtained. i→i+N-1 ;

[0070] During the system status update process, in order to obtain more accurate three-dimensional spatial coordinates of the robot, the robot position and status information obtained in step 4 is used; a sliding window is used to optimize the system input u in real time.

[0071] The above description is merely a preferred embodiment of the present invention and does not constitute any other form of limitation to the present invention. Any modification or equivalent variation based on the technical essence of the present invention shall still fall within the scope of protection claimed by the present invention.

Claims

1. A motion control method for an adaptive variable-configuration pipeline robot based on environmental perception, characterized in that: The following steps are involved: Step 1: The pipeline robot is placed inside the pipeline. The depth camera captures an image of the pipeline in front. After image noise reduction preprocessing, the pipeline scene is recognized using a neural network. Based on the recognition results, the planes and curved surfaces in the image are segmented. The region segmentation results on the two-dimensional image are projected into three-dimensional space using a camera projection model, and the pipeline edge lines are extracted. Step 2: Use the edge line to calculate the straight pipe width, bend curvature, slope angle, and step height to analyze the robot's passability; Step 3: Design a path planner and an arm planner to generate the robot's reference trajectory and arm angle sequence, perform smoothing, and input them into the MPC motion controller; Step 4: The data from the depth camera, IMU, and wheel shorthand are used to estimate the robot's position and state through the ESKF algorithm. The estimation results and the collision warning signal from the TOF module are input into the MPC motion controller in real time. Step 5: In the MPC motion controller, the kinematic model of the double-swing-arm crawler robot is used, combined with the reference trajectory and the swing-arm angle sequence, to construct a quadratic objective function, which is solved using an iterative method to output the robot's adaptive motion control signal.

2. The method for controlling motion of an adaptive variable-configuration pipeline robot based on environmental perception according to claim 1 is characterized in that: The pipeline robot is a double-swing-arm crawler mechanism, and the sensing equipment it carries includes a depth camera, IMU, wheel speed recorder, and three TOF modules for ranging. The depth camera is installed in front of the robot, and the three TOF modules are installed on the left and right sides and top of the robot respectively.

3. The method for controlling motion of an adaptive, variable-configuration pipeline robot based on environmental perception according to claim 1 or 2, characterized in that: The specific steps of step 1 are: The depth camera captures images inside a rectangular pipe, uses bilateral filtering and backlight compensation to reduce noise and suppress strong light, completing image preprocessing. The processed images are then subjected to scene recognition using a lightweight ResNet18 neural network, classifying the resulting scenes into four categories: straight pipe, curved pipe, slope, and steps. Based on the classification results, the DeepLabv3 network is used to segment the two-dimensional image into multiple planar regions P and curved regions S. Randomly select three points in each area along the x-axis of the pixel coordinate system, and use the camera's intrinsic parameter matrix K and the scale information Z provided by the depth camera to convert the selected point p in the pixel coordinate system into uv Convert p to the camera coordinate system C : Zp uv =Kp C Combined with the geometric structure characteristics of the pipeline environment, the three-dimensional coordinates of the sampling points in each area are used to fit each plane area P in the space i and surface area S j The pipeline edge formed by the intersection of each area is a straight line or a curve, and the regional equation is used to calculate the spatial equation of each line and curve.

4. The method for controlling motion of an adaptive, variable-configuration pipeline robot based on environmental perception according to claim 1 or 2, characterized in that: The specific steps of step 2 are: The length, width and height of the measuring pipeline robot are L l ,L w ,L h The radius of the driving wheel is R, the radius of the driven wheel on the swing arm is r, and the wheelbase of the front swing arm is l F , the wheelbase of the rear swing arm is l R , the wheelbase of the moving body is l B After obtaining the equation of each edge line, the geometric characteristics of the pipe are used to calculate the pipe width using two straight lines on the bottom surface and the pipe height using two straight lines on the side surface. These values are compared with the robot's outer envelope size to determine the passability of the straight pipe environment ahead. In a sloped environment, the angle between the side edges is calculated as the slope angle θ. S , the friction coefficient between the robot and the pipe wall is μ, if arctan(μ)<θ S , then the front slope is considered passable; in the step scene, the distance between the edge of the step surface and the edge of the bottom surface is calculated as the step height H. It is considered that the steps ahead are passable. In the pipe bend environment, the curvature λ of the inner line of the bend is calculated using the two curves of the bottom surface. in and the outer curvature λ out , and then judge the passing conditions of the elbow:

5. The method for controlling motion of an adaptive, variable-configuration pipeline robot based on environmental perception according to claim 1 or 2, characterized in that: The specific steps of step 3 are: In straight pipe and curved pipe scenarios, motion control only needs to consider the track. The path planner is designed to calculate the equidistant lines of the left and right side edges as the reference trajectory of the robot, and perform sampling and smoothing. In slope and step scenarios, in addition to controlling the track, the angles of the front and rear swing arms must also be adjusted. The kinematic model of the dual-swing arm tracked pipeline robot is simplified to a contact edge model, with the front swing arm contacting the edge l f , rear swing arm touches the edge line l r Contact edge with chassis l b composition, i f =arccos(l R / (Rr))+θ F i r =arccos(l R / (Rr))+θ R l r The two end points of p r With p br , l f The two end points of p f With p bf , l b The two end points are p br With p bf , l r With l b The angle is θ r , l f With l b The angle is θ f ; Use the simplified model to contact the terrain and calculate θ during the movement r and θ f , and mapped to the swing arm joint angle θ R and θ F , complete the control of the swing arm.

6. The method for controlling motion of an adaptive, variable-configuration pipeline robot based on environmental perception according to claim 5 is characterized by: Solve θ in step 3 r and θ f There are the following constraints: 1) At least two points on the three simplified edges are in contact with the terrain; 2) The three simplified edges do not interfere with the terrain; 3) The vertical line of the robot's center of gravity is at p br With p bf between; In θ r and θ f When there are multiple solutions, the state when the robot's center of gravity is lowest is taken as the optimal state; after obtaining the swing arm angle sequence in the discrete state during the obstacle crossing process, the swing arm angle change rate is used as the motion cost, and the dynamic programming algorithm is used to optimize the cumulative cost to obtain the optimal discrete swing arm angle sequence. The generated optimal discrete swing arm state sequence is sampled and smoothed using a Bezier curve; the smoothed reference path and swing arm angle are sent to the MPC motion controller.

7. The method for controlling motion of an adaptive, variable-configuration pipeline robot based on environmental perception according to claim 1 or 2, characterized in that: The specific steps of step 4 are: To obtain the real-time position and state information of the robot, the ESKF motion equations of the nominal state and error state in discrete time are derived based on the IMU measurement model, and then the ESKF prediction process is executed, including the calculation of the nominal state prediction and the error state prediction. On the one hand, the left and right track speeds obtained by the encoder are used in the update phase. On the other hand, in order to alleviate the influence of cumulative errors, the distance between the pipeline connection and the vehicle body is used as the position observation to complete the update of the covariance matrix and the error state. The distance of the pipeline connection is obtained by using a depth camera to extract the edge features of the pipeline connection, and then calculated using rectangular constraints and camera projection models. TOF ranging modules are installed on the left and right sides and the top of the robot for collision warning. The real-time position and state of the robot and the collision warning signal are input into the MPC motion controller.

8. The method for controlling motion of an adaptive, conformational, pipeline robot based on environmental perception according to claim 1 or 2, characterized in that: The specific steps of step 5 are: Set the system state to x=[x r ,y r ,z r ,θ yaw ,θ pitch ,θ F ,θ R ] T , where x r ,y r ,z r ,θ yaw ,θ pitch They represent the robot’s three-dimensional spatial coordinates, yaw angle, and pitch angle respectively; the system input is u=[v L ,v R ,Δθ F ,Δθ R ] T , v L ,v R ,Δθ F ,Δθ R They represent the left track input speed, right track input speed, front swing arm angle increment, and rear swing arm angle increment respectively; the reference state is X ref ={x0,x1,x2,…,x end }, x0 is the initial state of the system, x end is the desired final state of the system; the kinematic model f of the double-swing arm crawler robot is used to predict the system state in the next N cycles at time i The difference between the predicted state and the reference state is constructed into a quadratic objective function, and iterative optimization is performed to minimize it, and the system input u within the optimized N cycles is obtained. i→i+N-1 During the system status update process, in order to obtain more accurate three-dimensional spatial coordinates of the robot, the robot position and status information obtained in step 4 is used; a sliding window is used to optimize the system input u in real time.

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