Non-destructive Detection and Positioning Method for Hyperbolic Glass Curtain Wall Based on Mobile Robot

Abstract

The present application provides a non-destructive detection and positioning method for hyperbolic glass curtain walls based on mobile robots. The method includes mounting a Lamb wave detection device, a SLAM module, and an adaptive curved surface fitting mechanism on a preset self-mobile robot; controlling the self-mobile robot to move in the set environment corresponding to the hyperbolic glass curtain wall to collect the environmental data of the hyperbolic glass curtain wall in real time through the SLAM module; constructing a three-dimensional map based on the environmental data and the corresponding curtain wall three-dimensional model of the hyperbolic glass curtain wall; generating an optimal detection path corresponding to the hyperbolic glass curtain wall in the three-dimensional map; controlling the adaptive curved surface fitting mechanism of the self-mobile robot to move according to the optimal detection path, and controlling the Lamb wave detection device to emit Lamb waves and receive reflection signals; obtaining the positioning information recorded by the SLAM module when the Lamb waves corresponding to the reflection signals are emitted; calculating the damage position corresponding to the hyperbolic glass curtain wall based on the reflection signals and the positioning information, and completing the non-destructive detection and positioning of the hyperbolic glass curtain wall.

Description

Technical Field

[0001] The present application relates to the technical field of glass curtain walls, and particularly to a non-destructive detection and positioning method for hyperbolic glass curtain walls based on a mobile robot. Background Art

[0002] With the development of modern architectural design, hyperbolic glass curtain walls, as a unique building exterior material, are widely used in large buildings due to their aesthetic appearance and unique shape. The curved surface shape of hyperbolic glass curtain walls increases the complexity of their structure and poses higher requirements for damage detection. Traditional glass curtain wall detection methods, such as visual inspection, vibration detection, thermal imaging, and ultrasonic detection, etc., cannot adapt to the special geometric shape of hyperbolic glass curtain walls and it is difficult to achieve autonomous, efficient, and comprehensive detection of hyperbolic glass curtain walls as well as rapid and accurate detection and positioning of damage to hyperbolic glass curtain walls. Existing damage detection has at least the following problems:

[0003] 1. Low detection accuracy: Hyperbolic curtain walls have complex geometric shapes, and the accuracy of traditional detection equipment is limited. In a complex hyperbolic surface environment, there is strong signal interference, making it difficult to accurately obtain the damage information of hyperbolic glass curtain walls and it is hard to achieve precise positioning of damage.

[0004] 2. Insufficient adaptability: Hyperbolic glass curtain walls have complex shapes and diverse curvature changes. Traditional detection equipment is difficult to adapt to the complex shape of hyperbolic surfaces and may not be able to closely fit the curtain wall surface during the detection process, resulting in the emergence of detection blind spots and it is difficult to achieve comprehensive detection of the entire hyperbolic glass curtain wall.

[0005] 3. Unable to achieve autonomous detection and positioning: Existing automated detection means have deficiencies in path planning and autonomous control and it is difficult to autonomously plan the optimal detection path according to the actual situation of the curtain wall.

[0006] Therefore, there is an urgent need for a method to solve at least one of the above problems. Summary of the Invention

[0007] The present application provides a non-destructive detection and positioning method for hyperbolic glass curtain walls based on a mobile robot, aiming to solve the problems of existing damage detection, including at least the following problems: low detection accuracy, insufficient adaptability, and inability to achieve autonomous detection and positioning.

[0008] In a first aspect, the present application provides a non-destructive detection and positioning method for hyperbolic glass curtain walls based on a mobile robot, including:

[0009] Mounting a Lamb wave detection device, a SLAM module, and an adaptive surface fitting mechanism on a preset self-mobile robot;

[0010] Control the self - moving robot to move in the corresponding setting environment of the hyperbolic glass curtain wall, so as to collect the environmental data of the hyperbolic glass curtain wall in real time through the SLAM module;

[0011] Construct a three - dimensional map according to the environmental data and the three - dimensional model of the curtain wall corresponding to the hyperbolic glass curtain wall;

[0012] Generate an optimal detection path corresponding to the hyperbolic glass curtain wall in the three - dimensional map;

[0013] Control the adaptive surface fitting mechanism of the self - moving robot to move according to the optimal detection path, and control the Lamb wave detection device to emit Lamb waves and receive reflection signals;

[0014] Obtain the positioning information recorded by the SLAM module when the Lamb wave corresponding to the reflection signal is emitted;

[0015] Calculate the damage position corresponding to the hyperbolic glass curtain wall according to the reflection signal and the positioning information, and complete the non - destructive detection positioning of the hyperbolic glass curtain wall.

[0016] In some embodiments, generating the optimal detection path corresponding to the hyperbolic glass curtain wall in the three - dimensional map includes: extracting the surface curvature gradient corresponding to the hyperbolic glass curtain wall in the three - dimensional map; generating a global detection path according to the surface curvature gradient and the detection coverage rate constraint condition corresponding to the hyperbolic glass curtain wall; obtaining obstacle information according to the three - dimensional map; generating the optimal detection path according to the global detection path and the obstacle information.

[0017] Exemplarily, generating the optimal detection path according to the global detection path and the obstacle information includes: obtaining the obstacle density according to the obstacle information; obtaining the curtain wall curvature complexity and the curvature change amount of adjacent path points of the hyperbolic glass curtain wall according to the surface curvature gradient; optimizing based on the improved ant colony algorithm with curvature field constraint according to the obstacle density, curtain wall curvature complexity and curvature change amount of adjacent path points to generate the optimal detection path.

[0018] In some embodiments, calculating the damage position corresponding to the hyperbolic glass curtain wall according to the reflection signal and the positioning information includes: obtaining the propagation time difference between the Lamb wave and the reflection signal, the phase difference of the receiver array corresponding to the reflection signal, and the pose data collected by the SLAM module; inputting the propagation time difference, receiver array phase difference, pose data, positioning information and reflection signal into an extended particle filter to output the damage position.

[0019] In some embodiments, the adaptive surface fitting mechanism includes a bionic adsorption foot array, and the bionic adsorption foot array includes a plurality of adsorption units. Each adsorption unit includes: a vacuum negative pressure adsorption module, a multi-degree-of-freedom flexible joint, and a contact pressure feedback sensor. Each adsorption unit is used to achieve adaptive fitting with the curved surface of the hyperbolic glass curtain wall.

[0020] In some embodiments, before calculating the damage position corresponding to the hyperbolic glass curtain wall according to the reflection signal and the positioning information, it further includes: performing six-layer wavelet basis function decomposition on the reflection signal; determining an effective frequency band in the decomposed reflection signal based on the energy entropy threshold method; performing signal reconstruction according to the effective frequency band and extracting the joint time-frequency domain features in the reconstructed signal; so as to calculate the damage position according to the joint time-frequency domain features and the corresponding positioning information.

[0021] In some embodiments, the Lamb wave detection device includes an adjustable frequency broadband excitation array, and the excitation frequency range of the adjustable frequency broadband excitation array is from 50 kHz to 1 MHz.

[0022] Exemplarily, the expression of the optimal excitation frequency corresponding to the adjustable frequency broadband excitation array includes: ; is the optimal excitation frequency, is the propagation speed of Lamb waves in the hyperbolic glass curtain wall, is the thickness of the hyperbolic glass curtain wall, is the modal order corresponding to the adjustable frequency broadband excitation array.

[0023] In some embodiments, after calculating the damage position corresponding to the hyperbolic glass curtain wall according to the reflection signal and the corresponding positioning information, it further includes: mapping the damage position to the three-dimensional map; generating and outputting a visual detection result according to the mapped three-dimensional map, so as to give an early warning according to the visual detection result.

[0024] In a second aspect, the present application provides a non-destructive testing and positioning device for a hyperbolic glass curtain wall based on a mobile robot, including:

[0025] A device carrying unit for carrying a Lamb wave detection device, a SLAM module, and an adaptive surface fitting mechanism on a preset mobile robot;

[0026] A first control unit for controlling the mobile robot to move in the set environment corresponding to the hyperbolic glass curtain wall, so as to collect the environmental data of the hyperbolic glass curtain wall in real time through the SLAM module;

[0027] A map construction unit, configured to construct a three-dimensional map according to the environmental data and the three-dimensional curtain wall model corresponding to the hyperbolic glass curtain wall;

[0028] A path generation unit, configured to generate an optimal detection path corresponding to the hyperbolic glass curtain wall in the three-dimensional map;

[0029] A second control unit, configured to control the adaptive surface fitting mechanism of the self-mobile robot to move according to the optimal detection path, and control the Lamb wave detection device to emit Lamb waves and receive reflection signals;

[0030] A positioning acquisition unit, configured to acquire the positioning information recorded by the SLAM module when the Lamb wave corresponding to the reflection signal is emitted;

[0031] A damage acquisition unit, configured to calculate the damage position corresponding to the hyperbolic glass curtain wall according to the reflection signal and the positioning information, and complete the non-destructive detection and positioning of the hyperbolic glass curtain wall.

[0032] In a third aspect, the present application provides a computer device, including a memory and a processor; the memory is used to store a computer program; the processor is configured to execute the computer program and implement the method provided in any embodiment of the present application when executing the computer program.

[0033] In a fourth aspect, the present application provides a computer-readable storage medium, the computer-readable storage medium stores a computer program, and when the computer-readable instruction is executed by the processor, one or more processors are enabled to execute the method provided in any embodiment of the present application.

[0034] A non-destructive detection and positioning method for a hyperbolic glass curtain wall based on a mobile robot provided by an embodiment of the present application. The method provided by the present application realizes high-precision and high-adaptability non-destructive detection and positioning of a hyperbolic glass curtain wall by mounting a Lamb wave detection device, a SLAM (Simultaneous Localization and Mapping) module, and an adaptive surface fitting mechanism on a self-mobile robot. The specific steps are as follows:

[0035] Device integration: Lamb wave detection device: used to emit and receive Lamb wave signals, and detect damage to the glass curtain wall by analyzing reflection signals. SLAM module: used to collect environmental data in real time, construct a three-dimensional map, and record the position information of the robot at the same time. Adaptive surface fitting mechanism: enables the robot to closely fit the surface of the hyperbolic glass curtain wall to ensure effective contact of the detection device.

[0036] Environmental data collection: Control the self-mobile robot to move in the set environment corresponding to the hyperbolic glass curtain wall, and collect environmental data in real time through the SLAM module. These data include, but are not limited to, laser scan data, visual images, etc., and are used to construct a three-dimensional map.

[0037] Three-dimensional map construction: Construct a detailed three-dimensional map based on the collected environmental data and the known three-dimensional model of the hyperbolic glass curtain wall. This map not only contains the geometric information of the environment but also includes the specific structural features of the curtain wall.

[0038] Optimal detection path generation: Generate an optimal detection path in the constructed three-dimensional map. This path takes into account factors such as the geometric shape of the curtain wall, the movement ability of the robot, and the detection efficiency, ensuring that the robot can efficiently cover the entire surface of the curtain wall.

[0039] Detection and positioning: Control the adaptive surface fitting mechanism of the self-mobile robot to move according to the optimal detection path. At the same time, control the Lamb wave detection device to emit Lamb waves and receive the reflected signals. When obtaining the reflected signals, the SLAM module records the positioning information at this time. Based on the reflected signals and the positioning information, calculate the damage position of the hyperbolic glass curtain wall to complete non-destructive testing and positioning.

[0040] Suppose there is a building with a hyperbolic glass curtain wall that needs non-destructive testing and positioning. The following are the specific implementation steps: Install the Lamb wave detection device, SLAM module, and adaptive surface fitting mechanism on the self-mobile robot. Ensure that all devices are working properly and perform necessary calibrations. Place the self-mobile robot near the hyperbolic glass curtain wall and start the SLAM module. The robot moves within the preset area, and the SLAM module collects environmental data in real time, including laser scan data and visual images. Transmit the collected data to the computer system. Use professional 3D modeling software to construct a detailed three-dimensional map in combination with the existing three-dimensional model of the curtain wall. In the three-dimensional map, use a path planning algorithm to generate an optimal detection path. This path should cover all areas of the curtain wall as much as possible while avoiding obstacles and unstable areas. Control the robot to move along the optimal path, and the adaptive surface fitting mechanism ensures that the robot always adheres tightly to the surface of the curtain wall. The Lamb wave detection device emits Lamb waves and receives the reflected signals, and the SLAM module records the position information of each detection point. Analyze the reflected signals to determine the damage position and record the results in the three-dimensional map. Display the detection results in a visual manner on the three-dimensional map, marking all damage positions. Generate a detection report and provide it to the maintenance personnel for further processing.

[0041] The method provided has at least the following beneficial effects:

[0042] High-precision detection: Through the collaborative work of the Lamb wave detection device and the SLAM module, high-precision damage detection of the hyperbolic glass curtain wall can be achieved.

[0043] High adaptability: The adaptive surface fitting mechanism enables the robot to adapt to various complex surfaces, improving the scope of application of the detection.

[0044] Autonomous detection and positioning: The robot can move autonomously and complete the detection task, reducing manual intervention and improving the detection efficiency.

[0045] Real-time monitoring: The SLAM module can collect environmental data in real time to ensure dynamic adjustment and optimization during the detection process.

[0046] Visualized results: The detection results are displayed through a 3D map, which is intuitive and easy to understand, facilitating subsequent maintenance and management.

[0047] In summary, the present application provides an efficient non-destructive detection and positioning method for hyperbolic glass curtain walls, with significant technical advantages and application prospects.

[0048] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. Brief Description of the Drawings

[0049] To more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0050] Figure 1 It is a schematic flowchart of the steps of a non-destructive detection and positioning method for a hyperbolic glass curtain wall based on a mobile robot provided by an embodiment of the present application;

[0051] Figure 2 It is a schematic block diagram of the structure of a non-destructive detection and positioning device for a hyperbolic glass curtain wall based on a mobile robot provided by an embodiment of the present application;

[0052] Figure 3 It is a schematic block diagram of the structure of a computer device provided by an embodiment of the present application.

[0053] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. Detailed Embodiments

[0054] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0055] The flowcharts shown in the accompanying drawings are only illustrative examples, and do not necessarily include all the content and operations / steps, nor do they necessarily need to be executed in the described order. For example, some operations / steps can also be decomposed, combined or partially merged, so the actual execution order may change according to the actual situation.

[0056] It should be understood that in order to facilitate the clear description of the technical solutions in the embodiments of the present invention, in the embodiments of the present invention, terms such as "first" and "second" are used to distinguish the same items or similar items with basically the same functions and effects. Those skilled in the art can understand that the terms "first", "second", etc. do not limit the quantity and execution order, and the terms "first", "second", etc. do not necessarily mean different.

[0057] It should be understood that the terms used in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification of the present application and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0058] It should also be understood that the term "and / or" used in the specification of the present application and the appended claims refers to any combination and all possible combinations of one or more of the related listed items, and includes these combinations.

[0059] Next, some embodiments of the present application will be described in detail in conjunction with the accompanying drawings. Without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0060] With the development of modern architectural design, the hyperbolic glass curtain wall, as a unique building facade material, is widely used in large buildings due to its aesthetic appearance and unique shape. The curved shape of the hyperbolic glass curtain wall increases the complexity of its structure, and at the same time poses higher requirements for damage detection. Traditional glass curtain wall detection methods, such as visual inspection, vibration detection, thermal imaging and ultrasonic detection, etc., cannot adapt to the special geometric shape of the hyperbolic glass curtain wall, and it is difficult to achieve autonomous, efficient and comprehensive detection of the hyperbolic glass curtain wall and rapid and accurate detection and positioning of the damage of the hyperbolic glass curtain wall. The existing damage detection has at least the following problems:

[0061] 1. Low detection accuracy: The hyperbolic curtain wall has a complex geometric shape. The accuracy of traditional detection equipment is limited. In a complex hyperbolic environment, there is strong signal interference, making it difficult to accurately obtain the damage information of the hyperbolic glass curtain wall and hard to achieve precise positioning of the damage.

[0062] 2. Insufficient adaptability: The hyperbolic glass curtain wall has a complex shape and diverse curvature changes. Traditional detection equipment is difficult to adapt to the complex shape of the hyperboloid. During the detection process, it may not be able to closely fit the surface of the curtain wall, resulting in the emergence of detection blind spots and making it difficult to achieve a comprehensive detection of the entire hyperbolic glass curtain wall.

[0063] 3. Unable to achieve autonomous detection and positioning: Existing automated detection means have deficiencies in path planning and autonomous control, and it is difficult to autonomously plan the optimal detection path according to the actual situation of the curtain wall.

[0064] Therefore, there is an urgent need for a method to solve at least one of the above problems.

[0065] To solve the above problems, please refer to Figure 1 as Figure 1 shown. The non-destructive detection and positioning method for hyperbolic glass curtain walls based on mobile robots includes steps S101 to S107. This non-destructive detection and positioning method for hyperbolic glass curtain walls based on mobile robots is executed by computer equipment, which can be a single server or a server cluster, or can be a handheld terminal, a laptop, a wearable device, or a robot, etc.

[0066] As Figure 1 shown, the details of steps S101 - S107 are as follows:

[0067] Step S101. Mount a Lamb wave detection device, a SLAM module, and an adaptive surface fitting mechanism on a preset self-mobile robot.

[0068] Specifically, a self - moving robot with high - precision positioning and navigation functions is selected, usually based on wheeled or tracked designs. The robot should have sufficient load - carrying capacity to carry all necessary equipment. For example, high - performance robot platforms such as Spot from Boston Dynamics or RoboMaster EP from DJI can be chosen. It includes ultrasonic transmitters and receivers for generating and receiving Lamb waves. Lamb waves are elastic waves that propagate in thin plates and are suitable for non - destructive testing. The transmitter generates Lamb waves that propagate through the curtain wall, and the receiver captures the reflected signals. The SLAM module (Simultaneous Localization and Mapping) integrates sensors such as lidar, cameras, and IMUs (Inertial Measurement Units) to build an environmental map in real - time and perform self - localization. The SLAM module can provide accurate position information and environmental data, providing basic support for subsequent steps. The adaptive surface - fitting mechanism is an adjustable robotic arm or suction cup device that can adjust its posture according to the curvature of the hyperbolic glass curtain wall to ensure that the Lamb - wave detection device is in close contact with the curtain - wall surface. This mechanism should have multiple degrees of freedom and can be adjusted through servo motors or pneumatic systems.

[0069] Exemplarily, select a suitable self - moving robot platform according to actual needs. These platforms have high - precision positioning and navigation functions and can carry heavy equipment. Install the Lamb - wave detection device on the top or side of the robot to ensure that it can cover the detection area. When installing, the fixing method of the device needs to be considered to ensure that it does not loosen during movement. Install lidar, cameras, and other sensors and connect them to the main control system of the robot. The lidar is used to generate point - cloud data, the cameras are used to capture image data, and the IMU records acceleration and angular - velocity data. The data from these sensors will be fused together for building an environmental map and performing self - localization. Design and install the adaptive surface - fitting mechanism: Design a robotic arm or suction cup device with multiple degrees of freedom that can be adjusted through servo motors or pneumatic systems. This mechanism needs to be able to adjust its posture according to the curvature of the curtain wall to ensure that the Lamb - wave detection device is in close contact with the curtain - wall surface. When installing, the movement range and stability of the robotic arm need to be considered.

[0070] This step has the following beneficial effects: improving the detection efficiency and accuracy and reducing manual operations. The adaptive surface - fitting mechanism ensures good contact between the Lamb - wave detection device and the curtain - wall surface, improving the signal quality. The SLAM module provides accurate positioning information, which helps with subsequent data processing and analysis. By integrating multiple sensors and technologies, the reliability and flexibility of the system are improved.

[0071] Step S102. Control the self - moving robot to move in the set environment corresponding to the hyperbolic glass curtain wall, so as to collect the environmental data of the hyperbolic glass curtain wall in real time through the SLAM module.

[0072] Specifically, control the self - moving robot to move near the hyperbolic glass curtain wall according to a predetermined path or a random path through programming or remote control. Path planning algorithms such as A* or RRT (Rapidly - exploring Random Tree) can be used. The SLAM module collects point cloud data, image data, and IMU data in the environment in real time, constructs a local map, and performs self - positioning. The SLAM module generates a high - precision environmental map and provides real - time position information by fusing data from multiple sensors.

[0073] For example, write a control program to enable the robot to move autonomously around the hyperbolic glass curtain wall. Path planning algorithms such as A* or RRT can be used. The path planning algorithm can help the robot avoid obstacles and efficiently cover the entire detection area. Start the SLAM module to collect environmental data in real time. The lidar generates point cloud data, the camera captures image data, and the IMU records acceleration and angular velocity data. These data will be fused together to construct a local map and update the robot's position information. Through the SLAM algorithm, these data are fused to generate a local map and update the robot's position information. The SLAM algorithm can process a large amount of sensor data in real time, generate a high - precision map, and provide accurate position information.

[0074] By obtaining environmental data in real time, it provides accurate basic information for subsequent steps. Through the SLAM module, the robot can navigate autonomously in an unknown environment, improving the flexibility and adaptability of the system. The high - precision map and position information are helpful for subsequent path planning and detection.

[0075] Step S103. Construct a three - dimensional map based on the environmental data and the three - dimensional model of the curtain wall corresponding to the hyperbolic glass curtain wall.

[0076] Specifically, environmental data: includes point cloud data, image data, and IMU data. Three - dimensional model of the curtain wall: A three - dimensional geometric model of the hyperbolic glass curtain wall established in advance, usually generated by CAD software. Three - dimensional map construction: Use the SLAM algorithm and three - dimensional reconstruction technology to align the environmental data with the three - dimensional model of the curtain wall to generate a detailed three - dimensional map. By combining point cloud data and image data, a high - precision three - dimensional map can be generated.

[0077] If a point cloud processing software (such as PCL - Point Cloud Library) is used to filter, register, and segment the collected point cloud data. Filtering can remove noise, registration can align multiple frames of point cloud data, and segmentation can divide the point cloud data into different parts. The processed point cloud data is aligned with the curtain wall 3D model using the ICP (Iterative Closest Point) algorithm or other alignment algorithms. The ICP algorithm improves the alignment accuracy by iteratively optimizing the matching between the point cloud data and the model. The 3D reconstruction technology (such as SfM - Structure from Motion) is used to combine the image data and the point cloud data to generate a detailed 3D map. The SfM technology can extract feature points from multi-view images and reconstruct the 3D structure. The generated 3D map is stored in the database for subsequent steps. The 3D map can be used for path planning, detection path generation, and damage localization.

[0078] By generating a detailed 3D map, accurate spatial information is provided for subsequent path planning and detection. Combining with the curtain wall 3D model improves the accuracy and reliability of the map. The 3D map can be used for visualization and further analysis, improving the overall performance of the system.

[0079] Step S104. Generate the optimal detection path corresponding to the hyperbolic glass curtain wall in the 3D map.

[0080] Specifically, the optimal detection path is to generate a shortest and most efficient detection path that covers the entire curtain wall based on the 3D map and the curtain wall 3D model. The optimal path should cover all the detection points as much as possible while avoiding repeated detection. Path planning algorithms such as A*, Dijkstra, RRT, etc. can be used and optimized in combination with the geometric characteristics of the curtain wall. The path planning algorithm needs to consider the curvature and obstacle distribution of the curtain wall to generate the optimal path.

[0081] For example, extract the geometric information of the curtain wall surface from the 3D map to determine the area to be detected. The geometric information includes the shape, curvature, and obstacle distribution of the curtain wall. Apply the path planning algorithm to generate a shortest path that covers the entire curtain wall. The A* algorithm can be used, which finds the shortest path through heuristic search. The RRT algorithm can also be used, which generates a path through random sampling. Smooth the generated path to ensure that the robot can move smoothly. The smoothing process can remove sharp corners and unnecessary turns in the path, improving the smoothness of the path. Store the generated optimal detection path in the database for subsequent steps. The optimal path can be used to guide the movement and detection of the robot.

[0082] Improve the detection efficiency and coverage rate by generating the optimal detection path. Smoothing ensures that the robot can move smoothly, reducing mechanical wear. The optimal path can reduce the detection time and improve the overall efficiency of the system.

[0083] Step S105. Control the adaptive surface fitting mechanism of the self-mobile robot to move according to the optimal detection path, and control the Lamb wave detection device to emit Lamb waves and receive the reflected signals.

[0084] Specifically, according to the optimal detection path, adjust the attitude to maintain close contact with the curtain wall surface. The adaptive surface fitting mechanism needs to be able to adjust the attitude in real time to ensure good contact between the Lamb wave detection device and the curtain wall surface. The Lamb wave detection device emits Lamb waves and receives the reflected signals for detecting the damage inside the curtain wall. The Lamb wave detection device detects the defects inside the curtain wall by emitting ultrasonic signals and receiving the reflected signals.

[0085] For example, read the stored optimal detection path from the database and control the self-mobile robot to move according to the path. The robot needs to reach each detection point in sequence according to the path. At each detection point, the adaptive surface fitting mechanism adjusts the attitude according to the curvature of the curtain wall to ensure close contact between the Lamb wave detection device and the curtain wall surface. When adjusting the attitude, the curvature of the curtain wall and the flatness of the surface need to be considered. Control the Lamb wave detection device to emit Lamb waves and receive the reflected signals. Record the emission time and the reception time. The emission time is used to calculate the signal propagation time, and the reception time is used to analyze the reflected signal. Store the reflected signal in the database for subsequent analysis. The reflected signal can be used to identify the damage inside the curtain wall.

[0086] Ensure good contact between the Lamb wave detection device and the curtain wall surface through the adaptive surface fitting mechanism to improve the detection accuracy. Move according to the optimal detection path to improve the detection efficiency and coverage rate. The Lamb wave detection device can accurately detect the damage inside the curtain wall, improving the reliability of non-destructive testing.

[0087] Step S106. Obtain the positioning information recorded by the SLAM module when the Lamb wave corresponding to the reflected signal is emitted.

[0088] Specifically, reflected signal: the reflected signal received by the Lamb wave detection device. Positioning information: the robot position information recorded by the SLAM module when the Lamb wave is emitted. The positioning information provided by the SLAM module is of high precision and can be used to determine the specific position of the reflected signal.

[0089] Read the reflected signal and the corresponding transmission time from the database. The reflected signal contains information about the internal structure of the curtain wall. From the data recorded by the SLAM module, find the position information that matches the transmission time. The SLAM module records the real-time position of the robot, which can be matched through timestamps. Associate the reflected signal with the corresponding position information and store it in the database. After association, the specific position of the reflected signal can be determined.

[0090] By accurately recording the position information corresponding to the reflected signal, it provides an accurate basis for subsequent calculation of the damage position. Through the high-precision positioning information provided by the SLAM module, the accuracy of damage positioning is improved. Associating the reflected signal and the positioning information facilitates subsequent data analysis and processing.

[0091] Step S107. Calculate the damage position corresponding to the hyperbolic glass curtain wall based on the reflected signal and the positioning information, and complete the non-destructive testing and positioning of the hyperbolic glass curtain wall.

[0092] Specifically, by analyzing the reflected signal, identify abnormal signals and determine whether there is damage. Abnormal signals in the reflected signal usually indicate defects inside the curtain wall. Damage position calculation: Combine the reflected signal and the positioning information to calculate the specific position of the damage. Through triangulation or other positioning algorithms, the specific position of the damage can be determined.

[0093] Read the reflected signal and the corresponding position information from the database. The reflected signal and the position information have been associated. Use signal processing techniques (such as Fourier transform, wavelet transform, etc.) to analyze the reflected signal and identify abnormal signals. Signal processing techniques can extract useful information from the reflected signal and identify abnormal signals. Combine the abnormal signal and the position information to calculate the specific position of the damage. Triangulation or other positioning algorithms can be used to calculate the specific position of the damage through the signals and position information of multiple detection points. Mark the calculated damage position on a 3D map and generate a detection report. The detection report contains information such as the specific position, type, and severity of the damage.

[0094] By accurately identifying and positioning the damage position of the curtain wall, the accuracy and reliability of non-destructive testing are improved. Generate a detailed detection report to provide a basis for subsequent repair and maintenance. Through signal processing and positioning algorithms, the damage position can be accurately determined, improving the accuracy of detection. The marks on the 3D map and the detection report provide intuitive visualization results, facilitating understanding and analysis. Through the above detailed technical content and specific implementation methods, efficient and accurate non-destructive testing of hyperbolic glass curtain walls can be achieved, improving the detection efficiency and reliability, and providing strong support for the maintenance and management of curtain walls.

[0095] In some embodiments, generating the optimal detection path corresponding to the hyperbolic glass curtain wall in the three-dimensional map includes: extracting the surface curvature gradient corresponding to the hyperbolic glass curtain wall in the three-dimensional map; generating a global detection path according to the surface curvature gradient and the detection coverage constraint condition corresponding to the hyperbolic glass curtain wall; obtaining obstacle information according to the three-dimensional map; and generating the optimal detection path according to the global detection path and the obstacle information.

[0096] By extracting the surface curvature gradient of the hyperbolic glass curtain wall from the three-dimensional map. The curvature gradient represents the degree of curvature and the rate of change of the surface, and can be obtained by calculating the Gaussian curvature and the mean curvature at each point. Use geometric processing software (such as Open3D or MeshLab) to process the three-dimensional map and extract the curvature gradient information. Generate a global detection path according to the surface curvature gradient and the detection coverage constraint condition. The detection coverage constraint condition ensures that the path can cover the entire curtain wall surface. Use path planning algorithms (such as A*, RRT, etc.), combined with the curvature gradient information, to generate a path that covers the entire curtain wall and is the shortest. Consider that areas with larger curvature gradients require denser detection points. Extract obstacle information from the data recorded by the SLAM module. The obstacle information includes the position, shape, and size of the obstacles. Use point cloud data and image data to identify and mark the obstacles, and store their position information in the database. Generate the optimal detection path according to the global detection path and the obstacle information. The optimal path should avoid obstacles and cover all detection points as much as possible. Use an improved ant colony algorithm (Ant Colony Optimization, ACO) for optimization to generate the optimal path.

[0097] Exemplarily, generating the optimal detection path according to the global detection path and the obstacle information includes: obtaining the obstacle density according to the obstacle information; obtaining the curtain wall curvature complexity and the curvature change amount of adjacent path points of the hyperbolic glass curtain wall according to the surface curvature gradient; and optimizing according to the obstacle density, the curtain wall curvature complexity, and the curvature change amount of adjacent path points based on the improved ant colony algorithm with curvature field constraint to generate the optimal detection path.

[0098] Calculate the obstacle density from the obstacle information. The obstacle density represents the number of obstacles per unit area. Use statistical methods to calculate the obstacle density of each area, for example, divide the 3D map into multiple small grids by the grid method and calculate the number of obstacles in each grid. Extract the curtain wall curvature complexity and the curvature change amount of adjacent path points from the surface curvature gradient. The curvature complexity represents the degree of bending of the surface, and the curvature change amount of adjacent path points represents the curvature change on the path. Use mathematical formulas to calculate the curvature complexity and the curvature change amount, for example, use the combination of Gaussian curvature and mean curvature. The improved ant colony algorithm based on the curvature field constraint is optimized according to the obstacle density, the curtain wall curvature complexity, and the curvature change amount of adjacent path points to generate the optimal detection path.

[0099] In some embodiments, calculating the damage position corresponding to the hyperbolic glass curtain wall according to the reflection signal and the positioning information includes: obtaining the propagation time difference between the Lamb wave and the reflection signal, the phase difference of the receiver array corresponding to the reflection signal, and the pose data collected by the SLAM module; inputting the propagation time difference, the phase difference of the receiver array, the pose data, the positioning information, and the reflection signal into an extended particle filter to output the damage position.

[0100] Obtain the Lamb wave emission time and the reflection signal reception time, and calculate the propagation time difference. Calculate the phase difference of the reflection signal received by the receiver array. Collect the pose data of the robot when the Lamb wave is emitted from the SLAM module, including position and attitude information. Input the propagation time difference, the phase difference of the receiver array, the pose data, the positioning information, and the reflection signal into an extended particle filter. The extended particle filter outputs the damage position through a probability estimation method.

[0101] Through the extended particle filter, the damage position can be accurately calculated. Combining multiple sensor data improves the accuracy of damage positioning. The particle filter can handle non-linear and non-Gaussian noise and is suitable for non-destructive testing in complex environments.

[0102] In some embodiments, the adaptive surface fitting mechanism includes a bionic adsorption foot array, and the bionic adsorption foot array includes a plurality of adsorption units. Each adsorption unit includes: a vacuum negative pressure adsorption module, a multi-degree-of-freedom flexible joint, and a contact pressure feedback sensor. Each adsorption unit is used to achieve surface adaptive fitting with the hyperbolic glass curtain wall.

[0103] Design a bionic adsorption foot array containing multiple adsorption units. Each adsorption unit includes a vacuum negative pressure adsorption module, a multi-degree-of-freedom flexible joint, and a contact pressure feedback sensor. The vacuum negative pressure adsorption module is used to generate negative pressure to make the adsorption unit closely contact the curtain wall surface. The multi-degree-of-freedom flexible joint allows the adsorption unit to flexibly adjust its posture on surfaces with different curvatures. The contact pressure feedback sensor is used to monitor the contact pressure between the adsorption unit and the curtain wall surface to ensure a good contact state.

[0104] By controlling the multi-degree-of-freedom flexible joint, the adsorption unit can automatically adjust its posture according to the curvature of the curtain wall. The contact pressure is monitored in real time through the contact pressure feedback sensor, and the working state of the vacuum negative pressure adsorption module is adjusted to ensure a stable adsorption force.

[0105] The bionic adsorption foot array can adapt to curtain wall surfaces with different curvatures, improving the contact quality between the detection device and the curtain wall surface. The multi-degree-of-freedom flexible joint and the contact pressure feedback sensor ensure the flexibility and stability of the adsorption unit. The signal quality and detection accuracy of the Lamb wave detection device are improved.

[0106] In some embodiments, before calculating the damage position corresponding to the hyperbolic glass curtain wall according to the reflection signal and the positioning information, it further includes: performing 6-layer wavelet basis function decomposition on the reflection signal; determining the effective frequency band in the decomposed reflection signal based on the energy entropy threshold method; performing signal reconstruction according to the effective frequency band and extracting the time-frequency domain joint features in the reconstructed signal; so as to calculate the damage position according to the time-frequency domain joint features and the corresponding positioning information.

[0107] Perform 6-layer wavelet basis function decomposition on the reflection signal. Wavelet transform can decompose the signal into components of different frequency bands, facilitating subsequent analysis. Commonly used wavelet basis functions (such as db4, haar, etc.) are used for decomposition.

[0108] Based on the energy entropy threshold method, determine the effective frequency band in the decomposed reflection signal. The energy entropy threshold method selects the frequency band with a higher energy entropy as the effective frequency band by calculating the energy entropy of each frequency band. The effective frequency band usually contains more useful information, which helps to improve the detection accuracy.

[0109] Perform signal reconstruction according to the effective frequency band to remove noise and interference. The reconstructed signal is clearer and more convenient for feature extraction.

[0110] Extract the time-frequency domain joint features in the reconstructed signal. The time-frequency domain joint features include time-domain features (such as peak value, mean value, etc.) and frequency-domain features (such as frequency distribution, spectrogram, etc.). Through the time-frequency domain joint features, the characteristics of the reflection signal can be more comprehensively described.

[0111] Calculate the damage location based on the joint time-frequency domain features and the corresponding positioning information. Use signal processing and positioning algorithms to determine the specific location of the damage.

[0112] By performing wavelet transform and signal reconstruction on the reflected signal, the quality and signal-to-noise ratio of the signal are improved. The joint time-frequency domain features provide richer information, which helps to improve the accuracy of damage location. The robustness and reliability of the system are enhanced, making it suitable for non-destructive testing in complex environments.

[0113] In some embodiments, the Lamb wave detection device includes an adjustable frequency wideband excitation array, and the excitation frequency range of the adjustable frequency wideband excitation array is from 50 kHz to 1 MHz.

[0114] Design an adjustable frequency wideband excitation array with an excitation frequency range from 50 kHz to 1 MHz. This array can adjust the excitation frequency according to actual needs. The selection of the excitation frequency has an important impact on the propagation characteristics and detection effect of Lamb waves. Adjust the excitation frequency of the excitation array according to the calculated optimal excitation frequency. Transmit Lamb waves and receive the reflected signals for non-destructive testing.

[0115] Exemplarily, the expression of the optimal excitation frequency corresponding to the adjustable frequency wideband excitation array includes: ; is the optimal excitation frequency, is the propagation speed of Lamb waves in the hyperbolic glass curtain wall, is the thickness of the hyperbolic glass curtain wall, is the modal order corresponding to the adjustable frequency wideband excitation array.

[0116] Obtain the propagation speed of Lamb waves in the hyperbolic glass curtain wall through experiments or literature. Obtain the thickness of the hyperbolic glass curtain wall through measurement or other methods. Determine the modal order of the excitation array according to the detection requirements and the characteristics of the curtain wall. According to the calculation results, adjust the excitation frequency of the excitation array so that it works at the optimal frequency.

[0117] By adjusting the excitation frequency, the propagation characteristics and detection effect of Lamb waves can be optimized. The optimal excitation frequency can improve the signal resolution and detection accuracy. The adjustable frequency wideband excitation array has higher flexibility and adaptability, and is suitable for the detection of different types of curtain walls.

[0118] In some embodiments, after calculating the damage location corresponding to the hyperbolic glass curtain wall based on the reflected signal and the corresponding positioning information, it further includes: mapping the damage location to the three-dimensional map; generating and outputting a visual detection result according to the mapped three-dimensional map for early warning based on the visual detection result.

[0119] Map the calculated damage locations onto a 3D map. Through coordinate transformation, convert the damage locations from the local coordinate system to the global coordinate system. Mark the damage locations on the 3D map, using different colors or symbols to represent different damage types and severities.

[0120] Generate visual inspection results: Based on the mapped 3D map, generate visual inspection results. The visual results can include various forms such as 3D models, 2D floor plans, heat maps, etc. The visual results should clearly display the damage locations, types, and severities, facilitating user understanding and analysis.

[0121] Output the visual inspection results: Output the generated visual inspection results to the user interface or a report file. The user interface can be a graphical user interface (GUI), and the report file can be in formats such as PDF, Word, etc.

[0122] Early warning: Conduct an early warning based on the visual inspection results. If severe damage is detected, the system can automatically send warning messages to relevant personnel. The warning messages can include the damage location, type, severity, and recommended repair measures.

[0123] Through the visual inspection results, users can intuitively understand the damage situation of the curtain wall. The visual results are convenient for users to understand and analyze, improving the readability and usability of the inspection results. The automatic early warning function can notify relevant personnel in a timely manner, accelerating the repair response speed and reducing potential safety hazards. It improves the overall performance and user experience of the system.

[0124] A non-destructive inspection and positioning method for hyperbolic glass curtain walls based on mobile robots provided by an embodiment of this application. The method provided by this application realizes high-precision and highly adaptable non-destructive inspection and positioning of hyperbolic glass curtain walls by mounting Lamb wave detection equipment, a SLAM (Simultaneous Localization and Mapping) module, and an adaptive curved surface fitting mechanism on a self-mobile robot. The specific steps are as follows:

[0125] Equipment integration: Lamb wave detection equipment: Used to transmit and receive Lamb wave signals, and detect damage to the glass curtain wall by analyzing the reflected signals. SLAM module: Used to collect environmental data in real time, construct a 3D map, and record the position information of the robot at the same time. Adaptive curved surface fitting mechanism: Enables the robot to closely fit the surface of the hyperbolic glass curtain wall to ensure effective contact of the detection equipment.

[0126] Environmental data collection: Control the self-mobile robot to move in the corresponding set environment of the hyperbolic glass curtain wall, and collect environmental data in real time through the SLAM module. These data include, but are not limited to, laser scan data, visual images, etc., and are used to construct a 3D map.

[0127] 3D Map Construction: Based on the collected environmental data and the known 3D model of the hyperbolic glass curtain wall, construct a detailed 3D map. This map not only contains the geometric information of the environment but also includes the specific structural features of the curtain wall.

[0128] Optimal Inspection Path Generation: In the constructed 3D map, generate an optimal inspection path. This path takes into account factors such as the geometric shape of the curtain wall, the movement ability of the robot, and the inspection efficiency to ensure that the robot can efficiently cover the entire surface of the curtain wall.

[0129] Inspection and Positioning: Control the adaptive surface fitting mechanism of the self - moving robot to move along the optimal inspection path. At the same time, control the Lamb wave detection device to emit Lamb waves and receive the reflected signals. When acquiring the reflected signals, the SLAM module records the positioning information at this time. Based on the reflected signals and the positioning information, calculate the damage location of the hyperbolic glass curtain wall to complete non - destructive testing and positioning.

[0130] Suppose there is a building with a hyperbolic glass curtain wall that needs non - destructive testing and positioning. The following are the specific implementation steps: Install the Lamb wave detection device, SLAM module, and adaptive surface fitting mechanism on the self - moving robot. Ensure that all devices are working properly and perform necessary calibrations. Place the self - moving robot near the hyperbolic glass curtain wall and start the SLAM module. The robot moves within the preset area, and the SLAM module real - time collects environmental data, including laser scan data and visual images. Transmit the collected data to the computer system. Use professional 3D modeling software, combined with the existing 3D model of the curtain wall, to construct a detailed 3D map. In the 3D map, use a path planning algorithm to generate an optimal inspection path. This path should cover all areas of the curtain wall as much as possible while avoiding obstacles and unstable areas. Control the robot to move along the optimal path, and the adaptive surface fitting mechanism ensures that the robot always adheres tightly to the surface of the curtain wall. The Lamb wave detection device emits Lamb waves and receives the reflected signals, and the SLAM module records the position information of each detection point. Analyze the reflected signals to determine the damage location and record the results in the 3D map. Display the detection results in a visual way on the 3D map, marking all damage locations. Generate a detection report and provide it to the maintenance personnel for further processing.

[0131] The provided method has at least the following beneficial effects:

[0132] High - precision Detection: Through the collaborative work of the Lamb wave detection device and the SLAM module, high - precision damage detection of the hyperbolic glass curtain wall can be achieved.

[0133] High Adaptability: The adaptive surface fitting mechanism enables the robot to adapt to various complex surfaces, improving the applicable range of the detection.

[0134] Autonomous Detection and Positioning: The robot can move autonomously and complete the detection task, reducing manual intervention and improving the detection efficiency.

[0135] Real-time Monitoring: The SLAM module can collect environmental data in real time to ensure dynamic adjustment and optimization during the detection process.

[0136] Visualized Results: The detection results are presented through a 3D map, which is intuitive and easy to understand, facilitating subsequent maintenance and management.

[0137] In summary, the present application provides an efficient non-destructive detection and positioning method for hyperbolic glass curtain walls, which has significant technical advantages and application prospects.

[0138] Please refer to Figure 2 as shown Figure 2 It is a schematic structural diagram of a non-destructive detection and positioning device 200 for hyperbolic glass curtain walls based on a mobile robot provided by an embodiment of the present application. The non-destructive detection and positioning device 200 for hyperbolic glass curtain walls based on a mobile robot is used to execute the steps of the non-destructive detection and positioning method for hyperbolic glass curtain walls based on a mobile robot shown in the above embodiments. The non-destructive detection and positioning device 200 for hyperbolic glass curtain walls based on a mobile robot can be a single server or a server cluster, or the non-destructive detection and positioning device 200 for hyperbolic glass curtain walls based on a mobile robot can be a terminal, and the terminal can be a handheld terminal, a laptop computer, a wearable device or a robot, etc.

[0139] As Figure 2 shown, the non-destructive detection and positioning device 200 for hyperbolic glass curtain walls based on a mobile robot includes:

[0140] A device carrying unit 201 for carrying a Lamb wave detection device, a SLAM module and an adaptive curved surface fitting mechanism on a preset self-mobile robot;

[0141] A first control unit 202 for controlling the self-mobile robot to move in the set environment corresponding to the hyperbolic glass curtain wall, so as to collect the environmental data of the hyperbolic glass curtain wall in real time through the SLAM module;

[0142] A map construction unit 203 for constructing a 3D map according to the environmental data and the curtain wall 3D model corresponding to the hyperbolic glass curtain wall;

[0143] A path generation unit 204 for generating an optimal detection path corresponding to the hyperbolic glass curtain wall in the 3D map;

[0144] A second control unit 205, configured to control the adaptive surface fitting mechanism of the self-moving robot to move according to the optimal detection path, and control the Lamb wave detection device to emit Lamb waves and receive reflection signals;

[0145] A positioning acquisition unit 206, configured to acquire the positioning information recorded by the SLAM module when the Lamb wave corresponding to the reflection signal is emitted;

[0146] A damage acquisition unit 207, configured to calculate the damage position corresponding to the hyperbolic glass curtain wall according to the reflection signal and the positioning information, and complete the non-destructive detection positioning of the hyperbolic glass curtain wall.

[0147] In some embodiments, generating the optimal detection path corresponding to the hyperbolic glass curtain wall in the three-dimensional map includes: extracting the surface curvature gradient corresponding to the hyperbolic glass curtain wall in the three-dimensional map; generating a global detection path according to the surface curvature gradient and the detection coverage constraint condition corresponding to the hyperbolic glass curtain wall; obtaining obstacle information according to the three-dimensional map; and generating the optimal detection path according to the global detection path and the obstacle information.

[0148] Exemplarily, generating the optimal detection path according to the global detection path and the obstacle information includes: obtaining the obstacle density according to the obstacle information; obtaining the curtain wall curvature complexity and the curvature change amount of adjacent path points of the hyperbolic glass curtain wall according to the surface curvature gradient; and optimizing based on the improved ant colony algorithm with curvature field constraint according to the obstacle density, the curtain wall curvature complexity and the curvature change amount of adjacent path points to generate the optimal detection path.

[0149] In some embodiments, calculating the damage position corresponding to the hyperbolic glass curtain wall according to the reflection signal and the positioning information includes: obtaining the propagation time difference between the Lamb wave and the reflection signal, the phase difference of the receiver array corresponding to the reflection signal, and the pose data collected by the SLAM module; and inputting the propagation time difference, the phase difference of the receiver array, the pose data, the positioning information and the reflection signal into an extended particle filter to output the damage position.

[0150] In some embodiments, the adaptive surface fitting mechanism includes a bionic adsorption foot array, and the bionic adsorption foot array includes a plurality of adsorption units. Each adsorption unit includes: a vacuum negative pressure adsorption module, a multi-degree-of-freedom flexible joint and a contact pressure feedback sensor, and each adsorption unit is used to realize the surface adaptive fitting with the hyperbolic glass curtain wall.

[0151] In some embodiments, before calculating the damage position corresponding to the hyperbolic glass curtain wall according to the reflection signal and the positioning information, the method further includes: decomposing the reflection signal by using a six-layer wavelet basis function; determining an effective frequency band in the decomposed reflection signal based on an energy entropy threshold method; performing signal reconstruction according to the effective frequency band and extracting joint time-frequency domain features from the reconstructed signal; and calculating the damage position according to the joint time-frequency domain features and the corresponding positioning information.

[0152] In some embodiments, the Lamb wave detection device includes an adjustable frequency wide-band excitation array, and the excitation frequency range of the adjustable frequency wide-band excitation array is from 50 kHz to 1 MHz.

[0153] Exemplarily, the expression of the optimal excitation frequency corresponding to the adjustable frequency wide-band excitation array includes: ; is the optimal excitation frequency, is the propagation speed of Lamb waves in the hyperbolic glass curtain wall, is the thickness of the hyperbolic glass curtain wall, is the modal order corresponding to the adjustable frequency wide-band excitation array.

[0154] In some embodiments, after calculating the damage position corresponding to the hyperbolic glass curtain wall according to the reflection signal and the corresponding positioning information, the method further includes: mapping the damage position to the three-dimensional map; generating and outputting a visual detection result according to the mapped three-dimensional map, so as to issue a warning according to the visual detection result.

[0155] It should be noted that those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described non-destructive detection and positioning device for hyperbolic glass curtain walls based on a mobile robot and each module can refer to the corresponding processes in the embodiments of the non-destructive detection and positioning method for hyperbolic glass curtain walls based on a mobile robot described above, and will not be elaborated herein.

[0156] The above non-destructive detection and positioning method for hyperbolic glass curtain walls based on a mobile robot can be implemented in the form of a computer program, and the computer program can run on a device as shown in Figure 2 shown.

[0157] Please refer to Figure 3 , Figure 3 which is a schematic block diagram of the structure of a computer device provided by an embodiment of the present application. The computer device includes a processor, a memory, and a network interface connected through a device bus. Among them, the memory may include a storage medium and an internal memory.

[0158] The storage medium can store an operating device and a computer program. The computer program includes program instructions which, when executed, can cause the processor to execute any one of the non-destructive inspection and positioning methods for hyperbolic glass curtain walls based on mobile robots.

[0159] The processor is used to provide computing and control capabilities to support the operation of the entire computer device.

[0160] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor, it can cause the processor to execute any one of the non-destructive inspection and positioning methods for hyperbolic glass curtain walls based on mobile robots.

[0161] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art can understand that Figure 3 the structure shown in is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the terminal to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0162] It should be understood that the processor can be a Central Processing Unit (CPU), and the processor can also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0163] Among them, in one embodiment, the processor is used to run the computer program stored in the memory to implement the following steps:

[0164] Mount a Lamb wave detection device, a SLAM module, and an adaptive surface fitting mechanism on a preset self-mobile robot;

[0165] Control the self-mobile robot to move in the setting environment corresponding to the hyperbolic glass curtain wall to collect the environmental data of the hyperbolic glass curtain wall in real time through the SLAM module;

[0166] Construct a three-dimensional map according to the environmental data and the three-dimensional curtain wall model corresponding to the hyperbolic glass curtain wall;

[0167] Generate an optimal detection path corresponding to the hyperbolic glass curtain wall in the three-dimensional map;

[0168] Control the adaptive surface fitting mechanism of the self-moving robot to move according to the optimal detection path, and control the Lamb wave detection device to emit Lamb waves and receive reflection signals;

[0169] Obtain the positioning information recorded by the SLAM module when the Lamb wave corresponding to the reflection signal is emitted;

[0170] Calculate the damage position corresponding to the hyperbolic glass curtain wall according to the reflection signal and the positioning information, and complete the non-destructive detection and positioning of the hyperbolic glass curtain wall.

[0171] In some embodiments, generating the optimal detection path corresponding to the hyperbolic glass curtain wall in the three-dimensional map includes: extracting the surface curvature gradient corresponding to the hyperbolic glass curtain wall in the three-dimensional map; generating a global detection path according to the surface curvature gradient and the detection coverage constraint condition corresponding to the hyperbolic glass curtain wall; obtaining obstacle information according to the three-dimensional map; generating the optimal detection path according to the global detection path and the obstacle information.

[0172] Exemplarily, generating the optimal detection path according to the global detection path and the obstacle information includes: obtaining the obstacle density according to the obstacle information; obtaining the curtain wall curvature complexity and the curvature change amount of adjacent path points of the hyperbolic glass curtain wall according to the surface curvature gradient; optimizing based on the improved ant colony algorithm with curvature field constraint according to the obstacle density, curtain wall curvature complexity and curvature change amount of adjacent path points to generate the optimal detection path.

[0173] In some embodiments, calculating the damage position corresponding to the hyperbolic glass curtain wall according to the reflection signal and the positioning information includes: obtaining the propagation time difference between the Lamb wave and the reflection signal, the phase difference of the receiver array corresponding to the reflection signal, and the pose data collected by the SLAM module; inputting the propagation time difference, the phase difference of the receiver array, the pose data, the positioning information and the reflection signal into an extended particle filter to output the damage position.

[0174] In some embodiments, the adaptive surface fitting mechanism includes a bionic adsorption foot array, and the bionic adsorption foot array includes a plurality of adsorption units. Each adsorption unit includes: a vacuum negative pressure adsorption module, a multi-degree-of-freedom flexible joint and a contact pressure feedback sensor. Each adsorption unit is used to realize the surface adaptive fitting with the hyperbolic glass curtain wall.

[0175] In some embodiments, before calculating the damage position corresponding to the hyperbolic glass curtain wall based on the reflection signal and the positioning information, the method further includes: decomposing the reflection signal by using a six-layer wavelet basis function; determining an effective frequency band in the decomposed reflection signal based on an energy entropy threshold method; performing signal reconstruction according to the effective frequency band and extracting joint time-frequency domain features in the reconstructed signal; and calculating the damage position according to the joint time-frequency domain features and the corresponding positioning information.

[0176] In some embodiments, the Lamb wave detection device includes an adjustable frequency wideband excitation array, and the excitation frequency range of the adjustable frequency wideband excitation array is from 50 kHz to 1 MHz.

[0177] Exemplarily, the expression of the optimal excitation frequency corresponding to the adjustable frequency wideband excitation array includes: ; is the optimal excitation frequency, is the propagation speed of Lamb waves in the hyperbolic glass curtain wall, is the thickness of the hyperbolic glass curtain wall, is the modal order corresponding to the adjustable frequency wideband excitation array.

[0178] In some embodiments, after calculating the damage position corresponding to the hyperbolic glass curtain wall based on the reflection signal and the corresponding positioning information, the method further includes: mapping the damage position to the three-dimensional map; generating and outputting a visual detection result according to the mapped three-dimensional map, and giving an early warning according to the visual detection result.

[0179] It should be noted that those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the above-described computer device and each module can refer to the corresponding processes in the embodiments of the non-destructive detection and positioning method for hyperbolic glass curtain walls based on mobile robots described in the above embodiments, and will not be elaborated here.

[0180] The present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the processor is caused to implement the steps of the non-destructive detection and positioning method for hyperbolic glass curtain walls based on mobile robots provided in any embodiment of the present application.

[0181] Among them, the computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiments, such as the hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a SmartMedia Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the computer device.

[0182] Exemplarily, the medium is used to implement the following steps:

[0183] Mount a Lamb wave detection device, a SLAM module, and an adaptive surface fitting mechanism on a preset self-mobile robot;

[0184] Control the self-mobile robot to move in the installation environment corresponding to the hyperbolic glass curtain wall, so as to collect the environmental data of the hyperbolic glass curtain wall in real time through the SLAM module;

[0185] Construct a three-dimensional map according to the environmental data and the three-dimensional curtain wall model corresponding to the hyperbolic glass curtain wall;

[0186] Generate an optimal detection path corresponding to the hyperbolic glass curtain wall in the three-dimensional map;

[0187] Control the adaptive surface fitting mechanism of the self-mobile robot to move according to the optimal detection path, and control the Lamb wave detection device to emit Lamb waves and receive reflection signals;

[0188] Obtain the positioning information recorded by the SLAM module when the Lamb wave corresponding to the reflection signal is emitted;

[0189] Calculate the damage position corresponding to the hyperbolic glass curtain wall according to the reflection signal and the positioning information, and complete the non-destructive detection and positioning of the hyperbolic glass curtain wall.

[0190] It should be noted that those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the above-described storage medium and each module can refer to the corresponding processes in the embodiments of the non-destructive detection and positioning method for hyperbolic glass curtain walls based on mobile robots described in the above embodiments, and will not be repeated here.

[0191] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or substitutions within the technical scope disclosed by the present application, and these modifications or substitutions should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A non-destructive testing and positioning method for hyperbolic glass curtain walls based on mobile robots, characterized in that Including: Mount a Lamb wave detection device, a SLAM module and an adaptive surface fitting mechanism on a preset self - moving robot; Control the self - moving robot to move in the set environment corresponding to the hyperbolic glass curtain wall, so as to collect the environmental data of the hyperbolic glass curtain wall in real time through the SLAM module; Construct a three - dimensional map according to the environmental data and the three - dimensional curtain wall model corresponding to the hyperbolic glass curtain wall; Generate the optimal detection path corresponding to the hyperbolic glass curtain wall in the three - dimensional map, including: extract the surface curvature gradient corresponding to the hyperbolic glass curtain wall in the three - dimensional map; generate a global detection path according to the surface curvature gradient and the detection coverage rate constraint condition corresponding to the hyperbolic glass curtain wall; obtain obstacle information according to the three - dimensional map; generate the optimal detection path according to the global detection path and the obstacle information; Control the adaptive surface fitting mechanism of the self - moving robot to move according to the optimal detection path, and control the Lamb wave detection device to emit Lamb waves and receive reflection signals; Obtain the positioning information recorded by the SLAM module when the Lamb wave corresponding to the reflection signal is emitted; Calculate the damage position corresponding to the hyperbolic glass curtain wall according to the reflection signal and the positioning information, including: obtain the propagation time difference between the Lamb wave and the reflection signal, the phase difference of the receiver array corresponding to the reflection signal, and the pose data collected by the SLAM module; input the propagation time difference, the phase difference of the receiver array, the pose data, the positioning information and the reflection signal into an extended particle filter to output the damage position; complete the non - destructive testing and positioning of the hyperbolic glass curtain wall; before calculating the damage position corresponding to the hyperbolic glass curtain wall according to the reflection signal and the positioning information, it also includes: decompose the reflection signal by a 6 - layer wavelet basis function; determine the effective frequency band in the decomposed reflection signal based on the energy entropy threshold method; perform signal reconstruction according to the effective frequency band and extract the time - frequency domain joint features in the reconstructed signal; calculate the damage position according to the time - frequency domain joint features and the corresponding positioning information; after calculating the damage position corresponding to the hyperbolic glass curtain wall according to the reflection signal and the corresponding positioning information, it also includes: map the damage position to the three - dimensional map; generate and output a visual detection result according to the mapped three - dimensional map to give an early warning according to the visual detection result.

2. The method according to claim 1, characterized in that, The generating the optimal detection path according to the global detection path and the obstacle information includes: Obtain the obstacle density according to the obstacle information; Obtain the curtain wall curvature complexity and the curvature change amount of adjacent path points of the hyperbolic glass curtain wall according to the surface curvature gradient; Optimize based on the improved ant colony algorithm with curvature field constraint according to the obstacle density, the curtain wall curvature complexity and the curvature change amount of adjacent path points to generate the optimal detection path.

3. The method according to claim 1, characterized in that The adaptive surface fitting mechanism includes a bionic adsorption foot array, and the bionic adsorption foot array includes a plurality of adsorption units, and each adsorption unit includes: Vacuum negative pressure adsorption module, multi-degree-of-freedom flexible joint and contact pressure feedback sensor, and each of the adsorption units is used to achieve adaptive fitting with the curved surface of the hyperbolic glass curtain wall.

4. The method according to claim 1, characterized in that, The Lamb wave detection device includes an adjustable frequency broadband excitation array, and the excitation frequency range of the adjustable frequency broadband excitation array is 50 kHz to 1 MHz.

5. The method according to claim 4, wherein The expression of the optimal excitation frequency corresponding to the adjustable frequency broadband excitation array includes: ； is the optimal excitation frequency, is the propagation speed of Lamb waves in the hyperbolic glass curtain wall, is the thickness of the hyperbolic glass curtain wall, is the modal order corresponding to the tunable frequency wideband excitation array.

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