Large pressure vessel automatic detection robot

Abstract

Large pressure container automatic detection robot relates to adsorption robot posture adjustment technical field. In order to solve the technical defects of the existing wall climbing robot posture adjustment technology, the technical scheme provided by the present application is as follows: including: drive system, including two driving wheels installed on the front and rear central axis of the robot and two driven universal wheels installed on the front and rear ends of the robot; adsorption system, including modularly arranged permanent magnets, the permanent magnets are installed at the bottom of the robot; nondestructive testing module, including eddy current probe sliding table; camera swing arm module, arranged at the top of the robot, the camera is installed at the end of the swing arm, for real-time monitoring of the robot environment and adjusting the center of gravity through the angle of the swing arm; inertial sensor module, for real-time monitoring of the posture information of the robot on the wall. It is suitable for application in the posture adjustment work of the wall climbing robot.

Description

Technical Field

[0001] This relates to the field of posture adjustment technology for adsorption robots, specifically to the posture adjustment of automatic inspection robots for large pressure vessels. Background Technology

[0002] Wall-climbing robots, as specialized robots capable of adapting to complex wall environments, have wide applications in industrial inspection, maintenance, and cleaning. Their ability to move on vertical metal walls or inclined structural surfaces makes them ideal tools for replacing manual labor. To achieve efficient and stable movement, wall-climbing robots need reliable adhesion capabilities, flexible movement patterns, and precise posture adjustment capabilities.

[0003] In terms of attitude adjustment, existing technologies mainly focus on the following research directions:

[0004] 1. Applications of inertial sensors

[0005] Wall-climbing robots are generally equipped with inertial sensors (such as accelerometers and gyroscopes) to acquire their acceleration and angular velocity data in real time. These sensors can monitor the robot's tilt angle, center of gravity changes, and motion status. By collecting sensor data, existing research attempts to use angle changes to determine abnormal robot postures and make adjustments. For example, some studies have proposed a real-time tilt angle monitoring system based on inertial sensor data to issue an alarm when the robot deviates from its posture. However, most existing methods remain at the detection stage, and the adjustment measures after posture abnormalities occur are relatively simple.

[0006] 2. Center of gravity optimization method

[0007] During wall movement, the robot's center of gravity position has a significant impact on its adsorption state. Existing technologies attempt to improve adsorption stability through fixed center of gravity design or structural optimization. For example, some robots use fixed counterweights to keep the center of gravity close to the wall, but this method lacks flexibility and cannot cope with posture anomalies caused by real-time center of gravity shifts.

[0008] 3. The adaptive ability of the musculoskeletal system

[0009] To address posture anomalies, some technologies restore stability by adjusting the speed and direction of the robot's drive system. For example, differential-driven robots can mitigate the effects of center-of-gravity shift by adjusting the speed ratio of the drive wheels to change their trajectory. However, existing technologies lack automated adjustment strategies for posture anomalies, relying mostly on fixed programs or manual control, which is ill-suited for complex and dynamic wall environments.

[0010] 4. Current Status of Control Algorithm Research

[0011] Control algorithms are the core of attitude adjustment. Existing research is mostly based on proportional-integral-derivative (PID) control algorithms, which process data collected by sensors and adjust the motion parameters of the drive motor. However, traditional PID control suffers from response lag, especially when attitude changes drastically, making it difficult to achieve fast and accurate adjustments. In addition, most existing control algorithms only adjust a single variable, lacking multi-variable collaborative optimization of adsorption force, center of gravity, and drive system.

[0012] Despite the progress made in attitude adjustment technology, the following key issues still exist in practical applications:

[0013] 1. Insufficient real-time performance of attitude anomaly detection: Although existing methods can acquire attitude data through inertial sensors, their accuracy and real-time performance in detecting abnormal states are limited. Especially in complex wall environments, sensor noise and data delays can cause attitude anomalies to go undetected in a timely manner.

[0014] 2. Insufficient flexibility in center of gravity adjustment: Traditional methods rely heavily on fixed center of gravity design, which makes it difficult to deal with dynamic center of gravity shifts, causing the robot's adhesion to decrease or even fall off during movement.

[0015] 3. Limited attitude adjustment methods: Existing technologies mainly correct attitude by adjusting the speed and direction of the drive system, lacking multi-dimensional collaborative adjustment methods (such as combining center of gravity optimization and adsorption force optimization).

[0016] 4. Control algorithm response lag: Traditional control algorithms lack response speed and sensitivity in attitude adjustment, making it difficult to achieve precise control in complex environments. Summary of the Invention

[0017] To address the shortcomings of existing wall-climbing robot posture adjustment technologies, such as insufficient real-time performance in posture anomaly detection, inadequate flexibility in center of gravity adjustment, and limited posture adjustment methods, the technical solution provided by this invention is as follows:

[0018] Automated inspection robots for large pressure vessels, including:

[0019] The drive system includes two drive wheels mounted on the front and rear central axes of the robot and two driven omnidirectional wheels mounted on the front and rear ends of the robot;

[0020] An adsorption system includes modularly arranged permanent magnets mounted on the bottom of a robot;

[0021] Non-destructive testing module, including eddy current probe slide;

[0022] The camera swing arm module is located on the top of the robot, with a camera installed at the end of the swing arm. It is used to monitor the robot's environment in real time and optimize the center of gravity by adjusting the angle of the swing arm.

[0023] An inertial sensor module is used to monitor the robot's posture information on the wall in real time.

[0024] Furthermore, a preferred embodiment is provided in which the drive wheel is driven by an independent motor and adopts a differential drive method, and the omnidirectional wheel has an elastic telescopic structure for auxiliary support and steering.

[0025] Furthermore, a preferred embodiment is provided, wherein the slide is a cross-shaped slide rail structure used to cover the target detection area.

[0026] Furthermore, a preferred embodiment is provided in which the length and swing angle of the swing arm are adjustable.

[0027] Furthermore, a preferred embodiment is provided, comprising two eddy current probe slides.

[0028] Based on the same inventive concept, this invention also provides a method for adjusting the posture of an automatic inspection robot for large pressure vessels. The method is used to adjust the robot's posture, including:

[0029] The steps for collecting robot posture data on a wall surface;

[0030] Steps to determine if the tilt angle exceeds the safe range;

[0031] If the abnormal state conditions are met, the center of gravity optimization and attitude adjustment process will be triggered:

[0032] The steps for adjusting the camera's swing arm are derived based on the center of gravity offset and tilt parameters.

[0033] The steps for obtaining the adjustment parameters of the differential speed driven by the motor are based on the real-time tilt angle and center of gravity offset after adjusting the camera's swing arm angle.

[0034] Based on the same inventive concept, the present invention also provides a posture adjustment device for an automatic inspection robot of a large pressure vessel, the device being used to adjust the posture of the robot, comprising:

[0035] A module for collecting robot posture data on a wall surface;

[0036] A module for determining whether the tilt angle exceeds the safe range;

[0037] If the abnormal state conditions are met, the center of gravity optimization and attitude adjustment process will be triggered:

[0038] A module that obtains the adjustment angle of the camera's swing arm based on the center of gravity offset and tilt parameters;

[0039] This module obtains the adjustment parameters for the differential speed driven by the motor based on the real-time tilt angle and center of gravity offset after adjusting the camera's arm angle.

[0040] Based on the same inventive concept, the present invention also provides a computer storage medium for storing a computing program, wherein when the computer program is read by a computer, the computer executes the method described thereon.

[0041] Based on the same inventive concept, the present invention also provides a computer, including a processor and a storage medium, wherein when the processor reads a computer program stored in the storage medium, the computer executes the method described thereon.

[0042] Based on the same inventive concept, the present invention also provides a computer program product, which, when executed, implements the method described.

[0043] Compared with the prior art, the advantages of the technical solution provided by the present invention are as follows:

[0044] By monitoring attitude changes in real time using inertial sensors, accurate detection of robot posture anomalies was achieved. Compared to existing systems that rely solely on basic angle changes for detection, this approach can quickly capture tilt and displacement in complex wall environments, greatly improving the real-time performance and accuracy of detection.

[0045] The robot's center of gravity distribution was optimized by dynamically adjusting the camera arm. This approach overcomes the shortcomings of fixed center of gravity designs in existing studies, allowing the robot to adjust its center of gravity position in real time according to posture changes, restoring stability when the adhesion force decreases, thereby significantly improving its wall adaptability.

[0046] By adjusting the motor speed and direction using differential drive, rapid correction of the robot's posture is effectively achieved. Compared to traditional fixed-program control, this method combines sensor data and real-time feedback control, enabling more sensitive responses to posture anomalies and reducing the risk of the robot detaching from the wall.

[0047] The coordinated control algorithm enables the optimization of motor drive and swing arm adjustment. Compared with the single-variable control of the traditional PID algorithm, this approach exhibits higher control sensitivity and response speed in multi-variable coordinated optimization, making the robot more flexible and stable in complex paths and dynamic environments.

[0048] By combining permanent magnet adsorption technology and center of gravity optimization, the reliability of the adsorption system is enhanced. This approach overcomes the problem of insufficient adsorption force in existing electromagnetic adsorption systems when power is off, and further improves the safety of the adsorption state through dynamic adjustment of the center of gravity, making the robot more stable during long-term operation.

[0049] Suitable for use in the posture adjustment of wall-climbing robots. Attached Figure Description

[0050] Figure 1 Front view of an automated inspection robot for large pressure vessels;

[0051] Figure 2 This is a schematic diagram of the gear train configuration;

[0052] Figure 3 This is a schematic diagram of a cross slide.

[0053] Figure 4 This is a schematic diagram of a camera ornament. Detailed Implementation

[0054] To make the advantages and benefits of the technical solution provided by the present invention clearer, the technical solution provided by the present invention will now be described in further detail with reference to the accompanying drawings, specifically:

[0055] Implementation Method 1: This implementation method provides an automated inspection robot for large pressure vessels, including:

[0056] The drive system includes two drive wheels mounted on the front and rear central axes of the robot and two driven omnidirectional wheels mounted on the front and rear ends of the robot;

[0057] An adsorption system includes modularly arranged permanent magnets mounted on the bottom of a robot;

[0058] Non-destructive testing module, including eddy current probe slide;

[0059] The camera swing arm module is located on the top of the robot, with a camera installed at the end of the swing arm. It is used to monitor the robot's environment in real time and optimize the center of gravity by adjusting the angle of the swing arm.

[0060] An inertial sensor module is used to monitor the robot's posture information on the wall in real time.

[0061] The drive wheel is driven by an independent motor and uses a differential drive method. The omnidirectional wheel has an elastic telescopic structure for auxiliary support and steering.

[0062] The slide table has a cross-shaped slide rail structure and is used to cover the target detection area.

[0063] The length and swing angle of the swing arm are adjustable.

[0064] Includes two eddy current probe slides.

[0065] It also provides a method for adjusting the posture of an automated inspection robot for large pressure vessels, including:

[0066] The steps for collecting robot posture data on a wall surface;

[0067] Steps to determine if the tilt angle exceeds the safe range;

[0068] If the abnormal state conditions are met, the center of gravity optimization and attitude adjustment process will be triggered:

[0069] The steps for adjusting the camera's swing arm are derived based on the center of gravity offset and tilt parameters.

[0070] The steps for obtaining the adjustment parameters of the differential speed driven by the motor are based on the real-time tilt angle and center of gravity offset after adjusting the camera's swing arm angle.

[0071] Implementation Method Two: Combination Figure 1-4 This embodiment is a further explanation of the technical solution provided in Embodiment 1. Specifically:

[0072] This embodiment provides a wall-climbing robot based on magnetic adsorption technology. It achieves stable movement and posture adjustment on a wall surface through attitude monitoring by inertial sensors, center-of-gravity adjustment by a camera arm, differential speed control by motor drives, and a cooperative control algorithm. The entire solution is implemented according to the following steps:

[0073] Step 1: Attitude Data Acquisition

[0074] The robot uses inertial sensors mounted on its body to collect acceleration and angular velocity data in real time, acquiring its attitude information on the wall surface. This data includes the robot's current tilt angle, center of gravity shift, and adhesion state, providing basic information for subsequent adjustments. The inertial sensors include a three-axis accelerometer and a gyroscope, and their acquisition frequency should be higher than the robot's motion response frequency to ensure data real-time performance and accuracy. The collected data is preprocessed using built-in algorithms to filter out noise and calculate attitude angles, determining whether the robot is tilted or in an abnormal state.

[0075] Step 2: Determining the tilt state and detecting the adsorption force

[0076] Based on the attitude data collected in step 1, the angle between the robot and the wall is analyzed using the attitude angle calculation formula, and the current adhesion state is estimated to be normal using the adhesion force formula. An adhesion force threshold is set; when the adhesion force is less than this threshold and the tilt angle exceeds the safe range, the system determines that the robot is in an abnormal state and needs to enter the attitude adjustment stage. The output of this step is the robot's current attitude state (normal or abnormal) and center of gravity offset parameters.

[0077] Step 3: Dynamic adjustment of the camera arm

[0078] Based on the tilt state and center of gravity offset parameters output in step 2, the system controls the camera arm to dynamically adjust to optimize the robot's center of gravity distribution. The camera arm is driven by a motor, and the center of gravity position is adjusted by changing the arm angle. The adjustment range of the arm angle is determined by design parameters, typically set between 0° and 90°. The adjustment process calculates the required change in arm angle using a formula, and the motor actuator precisely drives the arm to the target position. After the arm adjustment, the center of gravity position is updated in real time, and the new position is used as an input parameter for optimizing the adsorption force.

[0079] Step 4: Adjustment of motor differential drive

[0080] While the camera arm is being adjusted, the system adjusts the motor drive via differential control. Based on the tilt state determined in step 2 and the new center of gravity position parameters, the target rotational speed for differential adjustment is calculated. If the robot needs to resume linear motion, the rotational speeds of the two drive wheels remain consistent; if it needs to adjust the direction of motion or rotate in place, the difference in rotational speeds between the two drive wheels is calculated according to the formula. By adjusting the real-time rotational speed of the motors, the robot can quickly regain balance and enter a stable motion state.

[0081] Step 5: Optimization of Cooperative Control Algorithm

[0082] To ensure coordination between steps 3 and 4, this implementation introduces a cooperative control algorithm that comprehensively processes inertial sensor data, arm adjustment parameters, and motor drive parameters. Based on the attitude angle deviation and center of gravity offset, the cooperative control algorithm calculates the optimal arm adjustment angle and differential motor speed, ensuring the robot achieves rapid attitude recovery and stable adsorption in dynamic environments. The algorithm includes control gain parameters to adjust control sensitivity and response speed, ensuring synchronized arm adjustment and motor drive.

[0083] Step 6: Abnormal Status Monitoring and Stop Judgment

[0084] After implementing the above steps, the system monitors the robot's posture and adhesion status in real time. If the tilt angle fails to return to a safe range, or the adhesion force remains below the set threshold, the system determines that the robot cannot maintain stability, stops all motor drives, and prevents further tilting or detachment. After stopping, the system can attempt to restore the posture again by adjusting the swing arm's center of gravity and optimizing the adhesion force.

[0085] The following details should be noted during implementation:

[0086] Selection and installation of inertial sensors: High-precision models (e.g., 10-bit resolution or higher) should be selected to ensure data accuracy. The sensors should be installed near the robot's center of gravity to reduce errors caused by sensor offset.

[0087] Camera arm design: The rotational inertia and length of the arm should be optimized based on the overall robot design to balance adjustment sensitivity and structural stability. The connection between the arm and the robot body should utilize low-friction transmission bearings to reduce energy consumption of the control motor.

[0088] Motor drive system: The differential drive motor must have a fast response capability, and its minimum speed variation range should be small enough to ensure the stability of the robot in fine-tuning state.

[0089] Cooperative control algorithm implementation: The algorithm needs to combine attitude changes and dynamic adjustments of center of gravity parameters, and optimize through cyclic feedback to ensure control accuracy. PID control is introduced as the basic algorithm module, and an adaptive gain adjustment strategy is superimposed on it to improve performance in complex scenarios.

[0090] Among them, the shape and structure of the robot

[0091] This wall-climbing robot has a rectangular shape, a compact design, and a smooth surface, making it suitable for stable movement on vertical or inclined walls. It also possesses good wind resistance and high reliability. The robot mainly consists of the following components:

[0092] 1. Main Structure

[0093] The robot's main body is a frame structure made of lightweight, high-strength materials (such as aluminum alloy or carbon fiber composites), which ensures that the robot maintains rigidity while minimizing its weight.

[0094] The lower part of the main frame is the drive system installation area, the middle part is the control system and detection equipment installation area, and the upper part is the additional module (such as camera arm and sensor) installation area.

[0095] 2. Drive system

[0096] The robot uses a four-wheeled rhomboid movement method, with two drive wheels and two omnidirectional wheels forming the rhomboid structure:

[0097] Two drive wheels: mounted on the front and rear central axes of the robot body, each driven by a separate motor, responsible for the robot's main movements and posture adjustments.

[0098] Two omnidirectional wheels: installed at the front and rear ends of the robot body respectively, with elastic extension function, used for support and auxiliary steering.

[0099] The outer surface of the drive wheel is coated with a high-friction material to enhance its grip on the wall.

[0100] 3. Adsorption system

[0101] The adsorption system is based on permanent magnet adsorption technology. Multiple high-strength permanent magnets are installed on the bottom of the robot to ensure the robot's adsorption force on the metal wall.

[0102] The magnets are evenly distributed on the bottom frame through a modular design, and there is a certain elastic adjustment device between the magnets and the bottom frame to adapt to the slight deformation or unevenness of different wall surfaces.

[0103] 4. Non-destructive testing module

[0104] Two eddy current probe slides are installed on both sides of the robot's bottom frame. The slides are connected to the robot body via guide rails and can be precisely displaced along the guide rails to perform non-destructive testing tasks.

[0105] The slide table adopts a cross slide rail structure, which can move left and right and make fine adjustments up and down to ensure that the detection probe can cover the target area.

[0106] 5. Camera swing arm module

[0107] A rotatable camera arm is mounted on top of the robot. The arm is driven by a motor and swings to change the robot's center of gravity, thus optimizing the adsorption state.

[0108] The swing arm is made of lightweight, high-strength material, and its length and swing angle are adjustable, with a swing range typically from 0° to 90°.

[0109] A high-definition camera is installed at the end of the swing arm to monitor the robot's surrounding environment in real time, assisting in detection tasks or positioning.

[0110] 6. Control and Sensing Module

[0111] The robot integrates an inertial sensor module, including a three-axis accelerometer and a gyroscope, to monitor the robot's attitude information in real time, such as tilt angle and acceleration changes.

[0112] The control system is located in the middle of the robot and includes an embedded processor and a cooperative control algorithm module, which is used to comprehensively process sensor data and execute posture adjustment commands.

[0113] The robot is also equipped with a communication module (such as a Wi-Fi or 5G module) for data interaction with a remote operator.

[0114] 7. Power supply system

[0115] The robot has a built-in rechargeable battery module installed at the bottom of the main frame to lower its center of gravity. The battery capacity is designed to support the robot's long-term operation, while also featuring overload protection and low battery alarm functions.

[0116] 8. Enclosure and Protective Design

[0117] The robot's shell features an integrated streamlined design and is covered with wear-resistant and corrosion-resistant materials, enabling it to operate normally in harsh environments (such as high humidity or salt spray conditions).

[0118] The housing has excellent sealing properties and an IP65 protection rating, which can effectively prevent dust, rainwater or other impurities from entering the internal system.

[0119] Implementation Method 3: This implementation method further describes the technical solution provided above in detail through specific embodiments, specifically:

[0120] 1. Algorithm Design

[0121] 1.1 Attitude Monitoring and Anomaly Detection Algorithm

[0122] Inertial sensors mounted on the robot can acquire data on the robot's acceleration and angular velocity on the wall. Using this data, this embodiment can calculate the robot's tilt angle (i.e., attitude) and determine its adhesion state using the following formula.

[0123] Attitude angle calculation formula: Let the acceleration measured by the sensor be a. x a y a z (Acceleration components in three directions), this implementation method can calculate the robot's attitude angle using the following formula:

[0124]

[0125] Here, θ represents the robot's tilt angle. By continuously monitoring changes in the angle, this implementation method can determine whether the robot is in a tilted state.

[0126] Adhesion force determination: Based on the adhesion force F between the robot and the wall. ads In this implementation, a threshold F is defined. ads，min If F ads <F ads，min If the adsorption force is insufficient, it is considered that the adsorption may be detached. Adsorption force F ads It can be estimated using the following formula:

[0127] F ads = k·A·B·cos(θ)

[0128] Where k is the magnetic constant, A is the adsorption area, B is the magnetic field strength, and θ is the robot's tilt angle.

[0129] 1.2 Center of Gravity Optimization and Attitude Adjustment Algorithm

[0130] Based on sensor data, the system will calculate the robot's current center of gravity G. x G y Gz The robot's center of gravity (G) is then assessed to determine if it deviates from the normal range. If the robot's center of gravity deviates significantly, the position of the center of gravity needs to be optimized by adjusting the orientation of the camera mount. Assuming the angle of the camera mount is φ, adjusting the angle of the camera mount can change the horizontal position of the robot's center of gravity, thereby affecting the adsorption state.

[0131] Center of gravity optimization formula:

[0132] G′ x =G x +Δx(φ)

[0133] G′ y =G y +Δy(φ)

[0134] Here, Δx(φ) and Δy(φ) represent the amount of center of gravity offset that can be achieved by adjusting the angle φ of the camera ornament.

[0135] 1.3 Motor Drive and Speed ​​Adjustment Algorithm

[0136] When the robot's posture becomes abnormal, the system adjusts the motor speed v to change the robot's motion state, preventing further tilting or loss of adhesion. The motor speed adjustment can be achieved using the following formula:

[0137] Motor speed adjustment formula:

[0138]

[0139] Where v0 is the initial rotational speed, Δθ is the deviation between the current attitude and the standard attitude, and θ max This is the maximum permissible tilt angle.

[0140] Stopping Motion Decision: If an abnormal robot posture is detected and adjusting the motor speed cannot restore stability, the system will choose to stop the robot's movement. The decision criteria are:

[0141] Δθ>θ limit And F ads <F ads,min

[0142] If the above conditions are met, the system will immediately stop the motor drive and wait for the adsorption force to recover.

[0143] 2. Camera ornament and swing arm design

[0144] In order to optimize the robot's center of gravity and adjust its posture, this implementation method requires designing reasonable physical parameters for the camera mount to ensure that it can be flexibly adjusted in changing environments.

[0145] 2.1 Design parameters of the camera ornament

[0146] The design of the camera mount depends on its arm length L, its weight m, and its connection points with other parts of the robot. To achieve the goal of adjusting the center of gravity, the camera mount's movement should possess sufficient flexibility and stability.

[0147] Arm length: The arm length of the camera mount is set to L. This length determines the influence the camera mount can have on the robot's center of gravity. A longer arm can provide more room for center of gravity adjustment.

[0148] Weight of the ornament: Let the mass of the ornament be m. The relationship between the mass and the moment of inertia is:

[0149]

[0150] Where I is the moment of inertia of the swing arm, which determines the power and response speed required for the swing arm to rotate.

[0151] 2.2 Ornament Angle Adjustment Mechanism

[0152] The camera mount is adjusted by a motor, with fine-tuning of the center of gravity achieved by changing the angle of the swing arm. Assume the motor's control angle range is φ. min to φ max The motor can control the rotation angle of the swing arm by adjusting the current.

[0153] Angle adjustment formula:

[0154] φ=φ0+Δφ

[0155] Where φ0 is the initial angle, and Δφ is the angle change obtained by controlling the motor.

[0156] 2.3 Coordinated Control of Ornament and Drive System

[0157] To achieve optimal adsorption, coordinated control is required between the adjustment of the ornament and the motor drive system. The control signals for the motor and the ornament should change synchronously based on the robot's posture and center of gravity changes to achieve the best posture adjustment effect.

[0158] Cooperative control algorithm:

[0159]

[0160] Where v is the motor speed, φ is the angle of the ornament, Δθ is the attitude angle deviation, ΔG is the center of gravity offset, and K1, K2, K3, and K4 are control gain coefficients, which determine the response sensitivity of the motor and ornament adjustment.

[0161] This implementation method, by introducing data and algorithms from inertial sensors and combining them with the camera mount's posture adjustment, center of gravity optimization, and dynamic control of motor drives, achieves a comprehensive adsorption state stability control system. It can monitor the robot's posture in real time, adjust the center of gravity, optimize the adsorption force, and optimize the robot's motion state through precise control algorithms.

[0162] The innovation of this implementation method compared with the prior art lies in:

[0163] Real-time posture anomaly detection: By combining inertial sensors and algorithms, the robot's adsorption state is monitored and determined in real time.

[0164] Dynamic center of gravity adjustment: By adjusting the posture and optimizing the center of gravity of the camera ornament, more efficient control of the adsorption state can be achieved.

[0165] Motor drive and attitude control work together: By adjusting the motor speed and the angle of the camera ornament, the optimal adsorption state is achieved, preventing the robot from falling off.

[0166] The combination of these technologies has greatly improved the stability and flexibility of robots in complex environments, effectively addressing the shortcomings of existing technologies.

[0167] In specific implementation work:

[0168] This embodiment designs a wall-climbing robot based on magnetic adsorption technology. When the robot adheres to the wall and begins to move, if it encounters an unstable working state, the robot needs to adjust its posture, center of gravity, and driving method in real time to avoid falling off. This embodiment uses inertial sensors to monitor the robot's posture, adjusts the camera arm angle to optimize the center of gravity, and uses motor control to adjust the movement state.

[0169] 1. Robot abnormal state detection

[0170] In this embodiment, the robot is equipped with inertial sensors (such as a three-axis accelerometer and a gyroscope). These sensors acquire the robot's attitude data in real time, including acceleration and angular velocity.

[0171] Sensor data: Robot tilt angle θ; measured acceleration a x a y a z Used to calculate the tilt angle θ:

[0172]

[0173] Adsorption force F ads Estimating the adsorption force using acceleration and the magnetic constant k:

[0174] F ads= k·A·B·cos(θ)

[0175] If F ads <F ads，min If the robot's adhesion is insufficient, it may detach.

[0176] Assume the robot detects that θ = 15° (the robot has already tilted slightly) and the suction force F is... ads Less than threshold F ads，min The system has determined this to be an abnormal state and adjustments must be made.

[0177] 2. Center of gravity optimization and camera arm adjustment

[0178] The robotic system needs to optimize its center of gravity by adjusting the angle of the camera arm to ensure the adhesion is restored and to prevent tilting. Assume the camera arm's design parameters are as follows:

[0179] The length of the swing arm is L = 0.3m; the mass of the swing piece is m = 0.2kg.

[0180] Moment of inertia of the swing arm:

[0181] By adjusting the angle φ of the camera arm, the horizontal position of the robot's center of gravity can be changed, thus affecting the adsorption state. The control angle range of the arm is set to φ. min = -15° to φ max =15°.

[0182] Assume the robot's current center of gravity is G x and G y The offset is too large, and the center of gravity position needs to be optimized by adjusting the swing arm angle. The specific adjustment calculation is as follows:

[0183] Center of gravity optimization formula:

[0184] G′ x =G x +Δx(φ), G′ y =G y +Δy(φ)

[0185] If the target center of gravity adjustment amounts Δx(φ) = -0.05m and Δy(φ) = -0.03m are set, then the center of gravity is optimized by adjusting the swing arm angle φ to keep the robot stable.

[0186] During this process, the robot adjusts the swing arm angle in real time by controlling the motor current.

[0187] 3. Motor drive and speed adjustment

[0188] Even after optimizing the center of gravity by adjusting the swing arm angle in real time, the robot still needs to further adjust the motor speed vv to restore a stable state.

[0189] Motor speed adjustment formula:

[0190]

[0191] Assuming an initial rotational speed v0 = 0.5 m / s, a current attitude deviation Δθ = 15° - 5° = 10°, and a maximum tilt angle θ max =15°, then the motor speed is adjusted as follows:

[0192]

[0193] By reducing its rotation speed, the robot slows down its movement, thereby reducing the rate of tilting and helping to restore its adhesion.

[0194] If abnormal posture is detected and the adhesion force does not recover significantly, the system determines that the robot needs to stop moving to avoid further tilting.

[0195] Criteria for stopping motion: If the condition Δθ>10° is met, F ads <F ads，min The system immediately stops the motor drive and waits for the adsorption force to recover.

[0196] 4. Collaborative Control and Feedback

[0197] Through a collaborative control algorithm, the angles of the motor and the ornament need to be adjusted synchronously to ensure the best posture adjustment effect.

[0198] Cooperative control algorithm:

[0199]

[0200] Assuming the control gain coefficients are K1 = 1.2, K2 = 0.8, K3 = 1.0, and K4 = 1.5, the adjustment results are calculated. By synchronously adjusting the motor speed and the swing arm angle, the robot is ensured to eventually return to stability.

[0201] The above description of several specific embodiments further details the technical solution provided by the present invention in order to highlight the advantages and benefits of the technical solution provided by the present invention. However, the above-described specific embodiments are not intended to limit the present invention. Any reasonable modifications and improvements to the present invention, combinations of embodiments, and equivalent substitutions based on the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An automatic inspection robot for large pressure vessels, characterized in that, include: The drive system includes two drive wheels mounted on the front and rear central axes of the robot and two driven omnidirectional wheels mounted on the front and rear ends of the robot; An adsorption system includes modularly arranged permanent magnets mounted on the bottom of a robot; Non-destructive testing module, including eddy current probe slide; The camera swing arm module is located on the top of the robot, with a camera installed at the end of the swing arm. It is used to monitor the robot's environment in real time and optimize the center of gravity by adjusting the angle of the swing arm. An inertial sensor module is used to monitor the robot's attitude information on the wall in real time; Specifically: Let the acceleration measured by the sensor be... , The robot's pose angles are calculated using the following formula: , Indicates the robot's tilt angle; Adsorption force Estimate using the following formula: in, is the magnetic constant. For adsorption area, The magnetic field strength, The tilt angle of the robot; Define a threshold ,like If so, the adsorption force is considered insufficient; Let the robot's center of gravity be at the current position. , By adjusting the angle of the camera mount, the horizontal position of the robot's center of gravity can be changed. Center of gravity optimization formula: and This indicates that by adjusting the angle of the camera ornament... The achievable center of gravity offset; When the posture is abnormal, the system adjusts the motor speed. To change the robot's motion state and prevent it from tilting further or losing its adhesion, the motor speed adjustment formula is as follows: in, The initial rotational speed, The deviation between the current posture and the standard posture. This is the maximum permissible tilt angle; If an abnormal robot posture is detected and adjusting the motor speed cannot restore stability, the system will choose to stop the robot's movement, under the following conditions: If the above conditions are met, the system will immediately stop the motor drive; Cooperative control algorithm: Assume the control gain coefficient is The calculation yields the adjustment results, and by synchronously adjusting the motor speed and swing arm angle, the robot is ensured to eventually regain stability.

2. The automatic inspection robot for large pressure vessels according to claim 1, characterized in that, The drive wheel is driven by an independent motor and uses a differential drive method. The omnidirectional wheel has an elastic telescopic structure for auxiliary support and steering.

3. The automatic inspection robot for large pressure vessels according to claim 1, characterized in that, The slide table has a cross-shaped slide rail structure and is used to cover the target detection area.

4. The automatic inspection robot for large pressure vessels according to claim 1, characterized in that, The length and swing angle of the swing arm are adjustable.

5. The automatic inspection robot for large pressure vessels according to claim 1, characterized in that, Includes two eddy current probe slides.

6. A method for adjusting the posture of an automatic inspection robot for large pressure vessels, characterized in that, The method is used to adjust the posture of the robot according to claim 1, comprising: The steps for collecting robot posture data on a wall surface; Steps to determine if the tilt angle exceeds the safe range; If the abnormal state conditions are met, the center of gravity optimization and attitude adjustment process will be triggered: The steps for adjusting the camera's swing arm are derived based on the center of gravity offset and tilt parameters. The steps for obtaining the adjustment parameters of the differential speed driven by the motor are based on the real-time tilt angle and center of gravity offset after adjusting the camera's swing arm angle.

7. A posture adjustment device for an automatic inspection robot of large pressure vessels, characterized in that, The device is used to adjust the posture of the robot according to claim 1, comprising: A module for collecting robot posture data on a wall surface; A module for determining whether the tilt angle exceeds the safe range; If the abnormal state conditions are met, the center of gravity optimization and attitude adjustment process will be triggered: A module that obtains the adjustment angle of the camera's swing arm based on the center of gravity offset and tilt parameters; This module obtains the adjustment parameters for the differential speed driven by the motor based on the real-time tilt angle and center of gravity offset after adjusting the camera's arm angle.

8. A computer storage medium for storing computing programs, characterized in that, When the computer program is read by the computer, the computer executes the method of claim 6.

9. A computer, comprising a processor and a storage medium, characterized in that, When the processor reads the computer program stored in the storage medium, the computer executes the method of claim 6.

10. A computer program product, as a computer program, is characterized by: When the computer program is executed, it implements the method of claim 6.

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