Hyperbolic glass curtain wall nondestructive testing and positioning method based on mobile robot

Abstract

The invention provides a hyperbolic glass curtain wall nondestructive testing and positioning method based on a mobile robot. The hyperbolic glass curtain wall nondestructive testing and positioning method comprises the following steps: carrying Lamb wave detection equipment, an SLAM module and a self-adaptive curved surface fitting mechanism on a preset self-mobile robot; controlling the self-moving robot to move in a setting environment corresponding to the hyperbolic glass curtain wall so as to acquire environment data of the hyperbolic glass curtain wall in real time through an SLAM module; constructing a three-dimensional map according to the environment data and the curtain wall three-dimensional model corresponding to the hyperbolic glass curtain wall; generating an optimal detection path corresponding to the hyperbolic glass curtain wall in the three-dimensional map; a self-adaptive curved surface fitting mechanism of the self-moving robot is controlled to move according to the optimal detection path, and Lamb wave detection equipment is controlled to emit Lamb waves and receive reflection signals; acquiring positioning information recorded by the SLAM module when Lamb waves corresponding to the reflected signals are transmitted; and calculating a damage position corresponding to the hyperbolic glass curtain wall according to the reflection signal and the positioning information, and completing nondestructive detection positioning of the hyperbolic glass curtain wall.

Description

Technical Field

[0001] The present application relates to the technical field of glass curtain walls, and in particular to a nondestructive 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 facade material, are widely used in large buildings due to their aesthetics and unique shape. The curved shape of the hyperbolic glass curtain wall increases the complexity of its structure and puts higher requirements on damage detection. Traditional glass curtain wall detection methods, such as visual inspection, vibration detection, thermal imaging and ultrasonic detection, cannot adapt to the special geometric form of hyperbolic glass curtain walls. It is difficult to achieve autonomous and efficient all-round detection of hyperbolic glass curtain walls and rapid and accurate detection and positioning of hyperbolic glass curtain wall damage. Existing damage detection has at least the following problems:

[0003] 1. Low detection accuracy: The hyperbolic curtain wall has a complex geometric shape, and the accuracy of traditional detection equipment is limited. In a complex hyperbolic environment, the signal interference is large, and it is difficult to accurately obtain the damage information of the hyperbolic glass curtain wall, and it is difficult to accurately locate the damage.

[0004] 2. Insufficient adaptability: The shape of the hyperbolic glass curtain wall is complex and the curvature varies. Traditional inspection equipment is difficult to adapt to the complex shape of the hyperbolic surface. During the inspection process, it may not fit closely with the curtain wall surface, resulting in the appearance of a blind spot in the inspection, making it difficult to achieve a comprehensive inspection of the entire hyperbolic glass curtain wall.

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

[0006] Therefore, a method is urgently needed to solve at least one of the above problems. Summary of the invention

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

[0008] In a first aspect, the present application provides a nondestructive detection positioning method for a hyperbolic glass curtain wall based on a mobile robot, comprising:

[0009] The preset self-propelled robot is equipped with Lamb wave detection equipment, SLAM module and adaptive surface fitting mechanism;

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

[0011] Constructing 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] Generating an optimal detection path corresponding to the hyperbolic glass curtain wall in the three-dimensional map;

[0013] Controlling the adaptive curved surface fitting mechanism of the self-propelled robot to move according to the optimal detection path, and controlling the Lamb wave detection device to emit Lamb waves and receive reflected signals;

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

[0015] The damage position corresponding to the hyperbolic glass curtain wall is calculated according to the reflection signal and the positioning information, and the nondestructive detection positioning of the hyperbolic glass curtain wall is completed.

[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 constraint condition corresponding to the hyperbolic glass curtain wall; acquiring obstacle information according to the three-dimensional map; and 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 obstacle information includes: obtaining obstacle density according to the obstacle information; obtaining the curtain wall curvature complexity and the curvature change of adjacent path points of the hyperbolic glass curtain wall according to the surface curvature gradient; and optimizing the improved ant colony algorithm based on curvature field constraints according to the obstacle density, curtain wall curvature complexity and the curvature change of adjacent path points to generate the optimal detection path.

[0018] It should be noted that, in some embodiments, the objective function corresponding to the optimization includes:

[0019] ;

[0020] in is the value of the objective function, To detect the path, is the curvature complexity of the curtain wall, is the obstacle density, is the detection efficiency requirement corresponding to the hyperbolic glass curtain wall, is the identifier of the adjacent path point pair, is the total number of adjacent path point pairs, For the For the curvature change between adjacent path points, is the square of the curvature change between adjacent path points, For waypoints The Euclidean distance to the nearest obstacle, Estimated total detection time.

[0021] In some embodiments, calculating the damage position corresponding to the hyperbolic glass curtain wall according to the reflected signal and the positioning information includes: obtaining the propagation time difference corresponding to the Lamb wave and the reflected signal, the receiver array phase difference corresponding to the reflected signal, and the posture data collected by the SLAM module; inputting the propagation time difference, the receiver array phase difference, the posture data, the positioning information, and the reflected signal into an extended particle filter to output the damage position.

[0022] In some embodiments, the adaptive curved surface bonding mechanism includes a bionic adsorption foot array, 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 of the adsorption units is used to achieve adaptive bonding with the curved surface of the hyperbolic glass curtain wall.

[0023] 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 also includes: performing a 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; reconstructing the signal according to the effective frequency band and extracting the time-frequency domain joint features from the reconstructed signal; and calculating the damage position according to the time-frequency domain joint features and the corresponding positioning information.

[0024] In some embodiments, the Lamb wave detection device includes a frequency-adjustable broadband excitation array, and the excitation frequency range of the frequency-adjustable broadband excitation array is 50 kHz to 1 MHz.

[0025] 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 wideband excitation array.

[0026] 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 also 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 an early warning according to the visual detection result.

[0027] In a second aspect, the present application provides a nondestructive testing and positioning device for a hyperbolic glass curtain wall based on a mobile robot, comprising:

[0028] An equipment mounting unit, used to mount a Lamb wave detection device, a SLAM module and an adaptive surface fitting mechanism on a preset self-propelled robot;

[0029] A first control unit, used for controlling the self-propelled robot to move in a setting environment corresponding to the hyperbolic glass curtain wall, so as to collect environmental data of the hyperbolic glass curtain wall in real time through the SLAM module;

[0030] A map construction unit, configured to construct a three-dimensional map according to the environmental data and a three-dimensional curtain wall model corresponding to the hyperbolic glass curtain wall;

[0031] A path generation unit, used to generate an optimal detection path corresponding to the hyperbolic glass curtain wall in the three-dimensional map;

[0032] A second control unit is used to control the adaptive curved surface fitting mechanism of the self-propelled robot to move according to the optimal detection path, and control the Lamb wave detection device to emit Lamb waves and receive reflected signals;

[0033] A positioning acquisition unit, used to acquire the positioning information recorded by the SLAM module when the Lamb wave corresponding to the reflected signal is emitted;

[0034] The damage acquisition unit is used 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.

[0035] In a third aspect, the present application provides a computer device comprising a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program and implement a method provided in any embodiment of the present application when executing the computer program.

[0036] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer-readable instructions are executed by the processor, one or more processors execute a method as provided in any embodiment of the present application.

[0037] The embodiment of the present application provides a nondestructive detection and positioning method for a hyperbolic glass curtain wall based on a mobile robot. The method provided in the present application achieves high-precision and highly adaptable nondestructive detection and positioning of a hyperbolic glass curtain wall by equipping a self-propelled robot with a Lamb wave detection device, a SLAM (Simultaneous Localization and Mapping) module, and an adaptive curved surface fitting mechanism. The specific steps are as follows:

[0038] 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, build a three-dimensional map, and record the robot's position information. Adaptive surface fitting mechanism: enables the robot to fit closely to the surface of the hyperbolic glass curtain wall to ensure effective contact of the detection equipment.

[0039] Environmental data collection: Control the self-propelled robot to move in the 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 scanning data, visual images, etc., which are used to build a three-dimensional map.

[0040] 3D map construction: Based on the collected environmental data and the known 3D model of the hyperbolic glass curtain wall, a detailed 3D map is constructed. The map contains not only the geometric information of the environment, but also the specific structural features of the curtain wall.

[0041] Optimal detection path generation: Generate an optimal detection path in the constructed 3D map. This path takes into account factors such as the geometric shape of the curtain wall, the robot's movement ability, and detection efficiency, ensuring that the robot can efficiently cover the entire curtain wall surface.

[0042] Detection and positioning: Control the self-adaptive curved surface fitting mechanism of the self-moving robot to move according to the optimal detection path, and control the Lamb wave detection equipment to emit Lamb waves and receive reflected signals. When obtaining the reflected signal, the SLAM module records the positioning information at this time. Based on the reflected signal and positioning information, the damaged position of the hyperbolic glass curtain wall is calculated to complete the non-destructive detection positioning.

[0043] Suppose there is a building with a hyperbolic glass curtain wall that needs to be non-destructively tested and positioned. The following are the specific implementation steps: Install the Lamb wave detection equipment, SLAM module and adaptive surface fitting mechanism on the self-propelled robot. Ensure that all equipment is working properly and perform necessary calibration. Place the self-propelled robot near the hyperbolic glass curtain wall and start the SLAM module. The robot moves in the preset area, and the SLAM module collects environmental data in real time, including laser scanning data and visual images. The collected data is transmitted to the computer system. Use professional 3D modeling software and combine the existing curtain wall 3D model to build a detailed 3D map. In the 3D map, use the path planning algorithm to generate an optimal detection path. The 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 sticks to the curtain wall surface. The Lamb wave detection equipment emits Lamb waves and receives reflected signals, and the SLAM module records the location information of each detection point. Analyze the reflected signals, 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 test report and provide it to maintenance personnel for further processing.

[0044] The provided method has at least the following beneficial effects:

[0045] High-precision detection: Through the collaborative work of Lamb wave detection equipment and SLAM module, high-precision damage detection of hyperbolic glass curtain walls can be achieved.

[0046] High adaptability: The adaptive surface fitting mechanism enables the robot to adapt to various complex surfaces, thus increasing the scope of application of detection.

[0047] Autonomous detection and positioning: The robot can move autonomously and complete detection tasks, reducing human intervention and improving detection efficiency.

[0048] Real-time monitoring: The SLAM module can collect environmental data in real time to ensure dynamic adjustment and optimization during the detection process.

[0049] Visualization results: The detection results are displayed through a three-dimensional map, which is intuitive and easy to understand, and facilitates subsequent maintenance and management.

[0050] 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.

[0051] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0053] Figure 1 This is a schematic flow chart of the steps of a non-destructive testing and positioning method for a hyperbolic glass curtain wall based on a mobile robot provided in one embodiment of the present application;

[0054] Figure 2 It is a schematic structural diagram of a nondestructive testing and positioning device for a hyperbolic glass curtain wall based on a mobile robot provided in one embodiment of the present application;

[0055] Figure 3 It is a schematic block diagram of the structure of a computer device provided in one embodiment of the present application.

[0056] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0058] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0059] It should be understood that, in order to facilitate the clear description of the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, the words "first", "second", etc. are used to distinguish the same items or similar items with substantially the same functions and effects. Those skilled in the art can understand that the words "first", "second", etc. do not limit the quantity and execution order, and the words "first", "second", etc. do not necessarily limit the difference.

[0060] It should be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0061] It should also be understood that the term “and / or” used in the specification and appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0062] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0063] With the development of modern architectural design, hyperbolic glass curtain walls, as a unique building facade material, are widely used in large buildings due to their aesthetics and unique shape. The curved shape of the hyperbolic glass curtain wall increases the complexity of its structure and puts higher requirements on damage detection. Traditional glass curtain wall detection methods, such as visual inspection, vibration detection, thermal imaging and ultrasonic detection, cannot adapt to the special geometric form of hyperbolic glass curtain walls. It is difficult to achieve autonomous and efficient all-round detection of hyperbolic glass curtain walls and rapid and accurate detection and positioning of hyperbolic glass curtain wall damage. Existing damage detection has at least the following problems:

[0064] 1. Low detection accuracy: The hyperbolic curtain wall has a complex geometric shape, and the accuracy of traditional detection equipment is limited. In a complex hyperbolic environment, the signal interference is large, and it is difficult to accurately obtain the damage information of the hyperbolic glass curtain wall, and it is difficult to accurately locate the damage.

[0065] 2. Insufficient adaptability: The shape of the hyperbolic glass curtain wall is complex and the curvature varies. Traditional inspection equipment is difficult to adapt to the complex shape of the hyperbolic surface. During the inspection process, it may not fit closely with the curtain wall surface, resulting in the appearance of a blind spot in the inspection, making it difficult to achieve a comprehensive inspection of the entire hyperbolic glass curtain wall.

[0066] 3. Unable to achieve autonomous detection and positioning: Existing automated detection methods have deficiencies in path planning and autonomous control, making it difficult to autonomously plan the optimal detection path based on the actual situation of the curtain wall.

[0067] Therefore, a method is urgently needed to solve at least one of the above problems.

[0068] To solve the above problems, please refer to Figure 1 ,like Figure 1 As shown, the provided method for nondestructive testing and positioning of a hyperbolic glass curtain wall based on a mobile robot includes steps S101 to S107. The method for nondestructive testing and positioning of a hyperbolic glass curtain wall based on a mobile robot is executed by a computer device, which may be a single server or a server cluster, or may be a handheld terminal, a laptop computer, a wearable device, or a robot, etc.

[0069] like Figure 1 As shown, the details of steps S101-S107 are as follows:

[0070] Step S101. A Lamb wave detection device, a SLAM module and an adaptive surface fitting mechanism are mounted on a preset self-propelled robot.

[0071] Specifically, select a self-propelled robot with high-precision positioning and navigation capabilities, usually based on a wheeled or tracked design. The robot needs to have sufficient load capacity to carry all the necessary equipment. For example, you can choose a high-performance robot platform such as Boston Dynamics' Spot or DJI's RoboMaster EP. Includes an ultrasonic transmitter and receiver 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 and propagates through the curtain wall, while the receiver captures the reflected signal. The SLAM module (Simultaneous Localization and Mapping) integrates sensors such as lidar, camera, IMU (inertial measurement unit) to build an environmental map in real time and locate itself. The SLAM module can provide accurate location 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 equipment is in close contact with the curtain wall surface. The mechanism should have multiple degrees of freedom and can be adjusted by a servo motor or pneumatic system.

[0072] For example, according to actual needs, choose a suitable self-propelled robot platform, which has high-precision positioning and navigation functions and can carry heavier 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, you need to consider the fixing method of the equipment to ensure that it will not loosen during movement. By installing lidar, camera and other sensors and connecting them to the robot's main control system. Lidar is used to generate point cloud data, camera is used to capture image data, and IMU records acceleration and angular velocity data. The data of these sensors will be fused together to build an environmental map and perform self-positioning. Design and install an adaptive surface fitting mechanism: Design a robotic arm or suction cup device with multiple degrees of freedom that can be adjusted by a servo motor or pneumatic system. The 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. The range of motion and stability of the robotic arm need to be considered during installation.

[0073] This step has the following beneficial effects: Improve detection efficiency and accuracy, reduce manual operation. The adaptive surface fitting mechanism ensures good contact between the Lamb wave detection device and the curtain wall surface, improving signal quality. The SLAM module provides accurate positioning information, which is helpful for subsequent data processing and analysis. By integrating multiple sensors and technologies, the reliability and flexibility of the system are improved.

[0074] Step S102: Control the self-propelled robot to move in a setting environment corresponding to the hyperbolic glass curtain wall, so as to collect environmental data of the hyperbolic glass curtain wall in real time through the SLAM module.

[0075] Specifically, the self-propelled robot is controlled by programming or remote control to move around the hyperbolic glass curtain wall along a predetermined path or a random path. A path planning algorithm such as A* or RRT (Rapidly-exploring RandomTree) can be used. The SLAM module collects point cloud data, image data, and IMU data in the environment in real time, builds a local map, and performs self-positioning. The SLAM module generates a high-precision environmental map by fusing data from multiple sensors and provides real-time location information.

[0076] For example, write a control program to enable the robot to move autonomously around the hyperbolic glass curtain wall. You can use a path planning algorithm, such as A* or RRT. The path planning algorithm can help the robot avoid obstacles and cover the entire detection area efficiently. 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 build a local map and update the robot's position information. The SLAM algorithm fuses these data 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 high-precision maps, and provide accurate position information.

[0077] By acquiring environmental data in real time, accurate basic information is provided for subsequent steps. Through the SLAM module, the robot can navigate autonomously in unknown environments, improving the flexibility and adaptability of the system. High-precision maps and location information are helpful for subsequent path planning and detection.

[0078] Step S103: constructing 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.

[0079] Specifically, environmental data: including point cloud data, image data and IMU data. Curtain wall 3D model: a pre-established 3D geometric model of a hyperbolic glass curtain wall, usually generated by CAD software. 3D map construction: using SLAM algorithm and 3D reconstruction technology, align environmental data with the curtain wall 3D model to generate a detailed 3D map. By combining point cloud data and image data, a high-precision 3D map can be generated.

[0080] For example, point cloud processing software (such as PCL - Point Cloud Library) can be used to filter, align 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. Align the processed point cloud data 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 match between the point cloud data and the model. Use 3D reconstruction technology (such as SfM - Structure from Motion) to combine image data and point cloud data to generate a detailed 3D map. SfM technology can extract feature points from multi-view images and reconstruct the 3D structure. The generated 3D map is stored in a database for use in subsequent steps. The 3D map can be used for path planning, detection path generation and damage location.

[0081] By generating a detailed 3D map, accurate spatial information is provided for subsequent path planning and detection. Combined with the 3D model of the curtain wall, the accuracy and reliability of the map are improved. The 3D map can be used for visualization and further analysis to improve the overall performance of the system.

[0082] Step S104: Generate an optimal detection path corresponding to the hyperbolic glass curtain wall in the three-dimensional map.

[0083] Specifically, the optimal detection path is to generate a shortest and most efficient detection path covering the entire curtain wall based on the 3D map and the 3D model of the curtain wall. The optimal path should cover all detection points as much as possible while avoiding repeated detection. The path planning algorithm can use A*, Dijkstra, RRT and other path planning algorithms, combined with the geometric characteristics of the curtain wall for optimization. The path planning algorithm needs to consider the curvature of the curtain wall and the distribution of obstacles to generate the optimal path.

[0084] For example, the geometric information of the curtain wall surface is extracted from the 3D map to determine the area that needs to be inspected. The geometric information includes the shape, curvature and obstacle distribution of the curtain wall. A path planning algorithm is applied 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. The generated path is smoothed to ensure that the robot can move smoothly. Smoothing can remove sharp corners and unnecessary turns in the path and improve the smoothness of the path. The generated optimal detection path is stored in the database for use in subsequent steps. The optimal path can be used to guide the movement and detection of the robot.

[0085] By generating the optimal inspection path, the inspection efficiency and coverage are improved. Smoothing ensures that the robot can move smoothly and reduces mechanical wear. The optimal path can reduce the inspection time and improve the overall efficiency of the system.

[0086] Step S105: Control the adaptive curved surface fitting mechanism of the self-propelled robot to move according to the optimal detection path, and control the Lamb wave detection device to emit Lamb waves and receive reflected signals.

[0087] Specifically, according to the optimal detection path, the posture is adjusted to maintain close contact with the curtain wall surface. The adaptive surface fitting mechanism needs to be able to adjust the posture 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 reflected signals to detect damage inside the curtain wall. The Lamb wave detection device detects defects inside the curtain wall by emitting ultrasonic signals and receiving reflected signals.

[0088] For example, the optimal detection path stored in the database is read and the self-propelled robot is controlled to move along the path. The robot needs to reach each detection point in sequence along the path. At each detection point, the adaptive surface fitting mechanism adjusts the 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. The curvature of the curtain wall and the flatness of the surface need to be considered when adjusting the posture. The Lamb wave detection device is controlled to emit Lamb waves and receive reflected signals. The emission time and reception time are recorded. The emission time is used to calculate the time of signal propagation, and the reception time is used to analyze the reflected signal. The reflected signal is stored in the database for subsequent analysis. The reflected signal can be used to identify damage inside the curtain wall.

[0089] The adaptive curved surface fitting mechanism ensures good contact between the Lamb wave detection equipment and the curtain wall surface, improving the detection accuracy. Move along the optimal detection path to improve detection efficiency and coverage. The Lamb wave detection equipment can accurately detect damage inside the curtain wall and improve the reliability of non-destructive testing.

[0090] Step S106: Obtain the positioning information recorded by the SLAM module when the Lamb wave corresponding to the reflected signal is emitted.

[0091] Specifically, reflection signal: the reflection 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 highly accurate and can be used to determine the specific location of the reflection signal.

[0092] Read the reflection signal and the corresponding emission time from the database. The reflection signal contains information about the internal structure of the curtain wall. From the data recorded by the SLAM module, find the location information that matches the emission time. The SLAM module records the real-time position of the robot, which can be matched by the timestamp. Associate the reflection signal and the corresponding position information and store them in the database. After association, the specific location of the reflection signal can be determined.

[0093] By accurately recording the position information corresponding to the reflected signal, an accurate basis is provided for the subsequent calculation of the damage location. The high-precision positioning information provided by the SLAM module improves the accuracy of damage positioning. The reflected signal and positioning information are associated to facilitate subsequent data analysis and processing.

[0094] Step S107: Calculate the damage position corresponding to the hyperbolic glass curtain wall according to the reflection signal and the positioning information, and complete the nondestructive detection positioning of the hyperbolic glass curtain wall.

[0095] Specifically, by analyzing the reflected signal, abnormal signals are identified to determine whether there is damage. Abnormal signals in the reflected signal usually indicate defects inside the curtain wall. Damage location calculation: Combine the reflected signal and positioning information to calculate the specific location of the damage. The specific location of the damage can be determined through triangulation or other positioning algorithms.

[0096] Read the reflection signal and the corresponding location information from the database. The reflection signal and the location information are already associated. Use signal processing technology (such as Fourier transform, wavelet transform, etc.) to analyze the reflection signal and identify abnormal signals. Signal processing technology can extract useful information from the reflection signal and identify abnormal signals. Combine the abnormal signal and location information to calculate the specific location of the damage. Triangulation or other positioning algorithms can be used to calculate the specific location of the damage through the signals and location information of multiple detection points. Mark the calculated damage location on the three-dimensional map and generate a test report. The test report contains information such as the specific location, type and severity of the damage.

[0097] By accurately identifying and locating the damaged position of the curtain wall, the accuracy and reliability of non-destructive testing can be improved. A detailed test report can be generated to provide a basis for subsequent repairs and maintenance. Through signal processing and positioning algorithms, the damage location can be accurately determined to improve the accuracy of detection. The markings and test reports on the three-dimensional map provide intuitive visualization results for easy 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, the detection efficiency and reliability can be improved, and strong support can be provided for the maintenance and management of curtain walls.

[0098] 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; acquiring obstacle information according to the three-dimensional map; and generating the optimal detection path according to the global detection path and the obstacle information.

[0099] The curvature gradient of the hyperbolic glass curtain wall is extracted from the three-dimensional map. The curvature gradient indicates the degree of curvature and the rate of change of the surface, which can be obtained by calculating the Gaussian curvature and the mean curvature of each point. The three-dimensional map is processed using geometry processing software (such as Open3D or MeshLab) to extract the curvature gradient information. A global detection path is generated based on the surface curvature gradient and the detection coverage constraint. The detection coverage constraint ensures that the path can cover the entire curtain wall surface. A path planning algorithm (such as A*, RRT, etc.) is used to combine the curvature gradient information to generate a shortest path that covers the entire curtain wall. Consider that areas with larger curvature gradients require more dense detection points. Obstacle information is extracted from the data recorded by the SLAM module. Obstacle information includes the location, shape, and size of the obstacle. Obstacles are identified and marked using point cloud data and image data, and their location information is stored in the database. The optimal detection path is generated based on the global detection path and obstacle information. The optimal path should avoid obstacles and cover all detection points as much as possible. The improved ant colony algorithm (Ant Colony Optimization, ACO) is used for optimization to generate the optimal path.

[0100] Exemplarily, generating the optimal detection path according to the global detection path and obstacle information includes: obtaining obstacle density according to the obstacle information; obtaining the curtain wall curvature complexity and the curvature change of adjacent path points of the hyperbolic glass curtain wall according to the surface curvature gradient; and optimizing the improved ant colony algorithm based on curvature field constraints according to the obstacle density, curtain wall curvature complexity and the curvature change of adjacent path points to generate the optimal detection path.

[0101] Obstacle density is calculated from obstacle information. Obstacle density indicates the number of obstacles per unit area. Obstacle density in each area is calculated using statistical methods, such as dividing the three-dimensional map into multiple small grids through a gridding method, and calculating the number of obstacles in each grid. Curve complexity of the curtain wall and the curvature change of adjacent path points are extracted from the surface curvature gradient. Curvature complexity indicates the degree of curvature of the surface, and the curvature change of adjacent path points indicates the curvature change on the path. Curvature complexity and curvature change are calculated using mathematical formulas, such as a combination of Gaussian curvature and mean curvature. The improved ant colony algorithm based on curvature field constraints is optimized according to obstacle density, curtain wall curvature complexity and curvature change of adjacent path points to generate the optimal detection path.

[0102] It should be noted that, in some embodiments, the objective function corresponding to the optimization includes:

[0103] ;

[0104] in is the value of the objective function, To detect the path, is the curvature complexity of the curtain wall, is the obstacle density, is the detection efficiency requirement corresponding to the hyperbolic glass curtain wall, is the identifier of the adjacent path point pair, is the total number of adjacent path point pairs, For the For the curvature change between adjacent path points, is the square of the curvature change between adjacent path points, For waypoints The Euclidean distance to the nearest obstacle, Estimated total detection time.

[0105] , and It can be adjusted according to actual needs. Use the improved ant colony algorithm for path optimization. When ants move on the path, they choose the next direction according to the objective function. Through iterative optimization, the optimal path is gradually found to minimize the objective function. Generate the optimal detection path to improve detection efficiency and coverage. Consider multiple factors such as curvature gradient, obstacle density and detection efficiency to improve the comprehensive performance of path planning. The improved ant colony algorithm can effectively handle complex path planning problems and generate high-quality optimal paths.

[0106] Specifically, the curvature data set {c1, c2, ..., cn} (multiple curvatures c1 to cn are, for example, Gaussian curvatures or mean curvatures) can be extracted from the curtain wall 3D model. The standard deviation or variance of the curvature distribution of the curvature data set can be calculated:

[0107] ; is the average curvature, n is the number of curvatures, and i is the identifier of the current curvature.

[0108] For example, if 100 points are sampled on a curtain wall surface with an average curvature of 0.5, we can calculate =0.8, indicating a large change in curvature.

[0109] Specifically, we can quantify the constraints on path safety caused by the distribution of obstacles in the environment. We can count the number of obstacles Nobs in the three-dimensional map. We can calculate the obstacle density per unit area: ; is the total area of ​​the detection area. For example, if the detection area is 50m 2 , the number of obstacles is 10, then . Used to balance detection time and path quality (determined by user needs). For example: if the total detection time needs to be limited to Tmax=2 hours, then =1 / Tmax=0.1.

[0110] In some embodiments, calculating the damage position corresponding to the hyperbolic glass curtain wall according to the reflected signal and the positioning information includes: obtaining the propagation time difference corresponding to the Lamb wave and the reflected signal, the receiver array phase difference corresponding to the reflected signal, and the posture data collected by the SLAM module; inputting the propagation time difference, the receiver array phase difference, the posture data, the positioning information, and the reflected signal into an extended particle filter to output the damage position.

[0111] Obtain the Lamb wave emission time and the reflected signal reception time, and calculate the propagation time difference. Calculate the phase difference of the reflected signal received by the receiver array. Collect the robot's posture data when emitting Lamb waves from the SLAM module, including position and posture information. Input the propagation time difference, receiver array phase difference, posture data, positioning information and reflected signal into the extended particle filter. The extended particle filter outputs the damage location through the probability estimation method.

[0112] By extending the particle filter, the damage location can be accurately calculated. Combining multiple sensor data improves the accuracy of damage location. The particle filter can handle nonlinear and non-Gaussian noise and is suitable for non-destructive testing in complex environments.

[0113] In some embodiments, the adaptive curved surface bonding mechanism includes a bionic adsorption foot array, 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 of the adsorption units is used to achieve adaptive bonding with the curved surface of the hyperbolic glass curtain wall.

[0114] A bionic adsorption foot array containing multiple adsorption units is designed. 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 so that the adsorption unit is in close contact with 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.

[0115] By controlling the multi-degree-of-freedom flexible joints, the adsorption unit can automatically adjust its posture according to the curvature of the curtain wall. The contact pressure feedback sensor monitors the contact pressure in real time and adjusts the working state of the vacuum negative pressure adsorption module to ensure stable adsorption force.

[0116] The bionic adsorption foot array can adapt to the curtain wall surface with different curvatures, improving the contact quality between the detection equipment and the curtain wall surface. The multi-degree-of-freedom flexible joint and contact pressure feedback sensor ensure the flexibility and stability of the adsorption unit. The signal quality and detection accuracy of the Lamb wave detection equipment are improved.

[0117] 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 also includes: performing a 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; reconstructing the signal according to the effective frequency band and extracting the time-frequency domain joint features from the reconstructed signal; and calculating the damage position according to the time-frequency domain joint features and the corresponding positioning information.

[0118] The reflected signal is decomposed using a 6-layer wavelet basis function. Wavelet transform can decompose the signal into components of different frequency bands for subsequent analysis. Commonly used wavelet basis functions (such as DB4, Haar, etc.) are used for decomposition.

[0119] Based on the energy entropy threshold method, the effective frequency band is determined in the decomposed reflection signal. The energy entropy threshold method calculates the energy entropy of each frequency band and selects the frequency band with higher energy entropy as the effective frequency band. The effective frequency band usually contains more useful information, which helps to improve the detection accuracy.

[0120] Signals are reconstructed based on the effective frequency band to remove noise and interference. The reconstructed signal is clearer and easier to extract features.

[0121] Extract the time-frequency domain joint features from 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, spectrum diagram, etc.). Through the time-frequency domain joint features, the characteristics of the reflected signal can be more comprehensively described.

[0122] The damage location is calculated based on the joint features in the time-frequency domain and the corresponding positioning information. The specific location of the damage is determined using signal processing and positioning algorithms.

[0123] By performing wavelet transform and signal reconstruction on the reflected signal, the signal quality and signal-to-noise ratio are improved. The joint features in the time and frequency domains provide richer information, which helps to improve the accuracy of damage location. The robustness and reliability of the system are improved, making it suitable for non-destructive testing in complex environments.

[0124] In some embodiments, the Lamb wave detection device includes a frequency-adjustable broadband excitation array, and the excitation frequency range of the frequency-adjustable broadband excitation array is 50 kHz to 1 MHz.

[0125] Design an adjustable wideband excitation array with an excitation frequency range of 50kHz to 1MHz. The array can adjust the excitation frequency according to actual needs. The selection of excitation frequency has an important influence on the propagation characteristics and detection effect of Lamb waves. According to the calculated optimal excitation frequency, adjust the excitation frequency of the excitation array. Transmit Lamb waves and receive reflected signals for nondestructive testing.

[0126] 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 wideband excitation array.

[0127] 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 based on the detection requirements and curtain wall characteristics. According to the calculation results, adjust the excitation frequency of the excitation array so that it works at the optimal frequency.

[0128] By adjusting the excitation frequency, the propagation characteristics and detection effect of Lamb waves can be optimized. The optimal excitation frequency can improve the resolution and detection accuracy of the signal. The adjustable frequency wide-band excitation array has higher flexibility and adaptability, and is suitable for different types of curtain wall detection.

[0129] 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 also 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 an early warning according to the visual detection result.

[0130] Map the calculated damage location onto the 3D map. Transform the damage location from the local coordinate system to the global coordinate system through coordinate transformation. Mark the damage location on the 3D map, using different colors or symbols to indicate different damage types and severity.

[0131] Generate visual inspection results: Generate visual inspection results based on the mapped 3D map. Visualization results can include 3D models, 2D plane maps, thermal maps, etc. Visualization results should clearly show the damage location, type and severity, which is easy for users to understand and analyze.

[0132] Output visual inspection results: Output the generated visual inspection results to a user interface or report file. The user interface can be a graphical user interface (GUI), and the report file can be in PDF, Word, and other formats.

[0133] Early warning: Early warning based on visual inspection results. If serious damage is detected, the system can automatically send early warning information to relevant personnel. The early warning information can include the location, type, severity of the damage and recommended repair measures.

[0134] Through the visualization of the test results, users can intuitively understand the damage of the curtain wall. The visualization results are easy for users to understand and analyze, and improve the readability and usability of the test results. The automatic early warning function can notify relevant personnel in time, speed up the maintenance response speed, and reduce safety hazards. Improve the overall performance of the system and user experience.

[0135] The embodiment of the present application provides a nondestructive detection and positioning method for a hyperbolic glass curtain wall based on a mobile robot. The method provided in the present application achieves high-precision and highly adaptable nondestructive detection and positioning of a hyperbolic glass curtain wall by equipping a self-propelled robot with a Lamb wave detection device, a SLAM (Simultaneous Localization and Mapping) module, and an adaptive curved surface fitting mechanism. The specific steps are as follows:

[0136] 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, build a three-dimensional map, and record the robot's position information. Adaptive surface fitting mechanism: enables the robot to fit closely to the surface of the hyperbolic glass curtain wall to ensure effective contact of the detection equipment.

[0137] Environmental data collection: Control the self-propelled robot to move in the 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 scanning data, visual images, etc., which are used to build a three-dimensional map.

[0138] 3D map construction: Based on the collected environmental data and the known 3D model of the hyperbolic glass curtain wall, a detailed 3D map is constructed. The map contains not only the geometric information of the environment, but also the specific structural features of the curtain wall.

[0139] Optimal detection path generation: Generate an optimal detection path in the constructed 3D map. This path takes into account factors such as the geometric shape of the curtain wall, the robot's movement ability, and detection efficiency, ensuring that the robot can efficiently cover the entire curtain wall surface.

[0140] Detection and positioning: Control the self-adaptive curved surface fitting mechanism of the self-moving robot to move according to the optimal detection path, and control the Lamb wave detection equipment to emit Lamb waves and receive reflected signals. When obtaining the reflected signal, the SLAM module records the positioning information at this time. Based on the reflected signal and positioning information, the damaged position of the hyperbolic glass curtain wall is calculated to complete the non-destructive detection positioning.

[0141] Suppose there is a building with a hyperbolic glass curtain wall that needs to be non-destructively tested and positioned. The following are the specific implementation steps: Install the Lamb wave detection equipment, SLAM module and adaptive surface fitting mechanism on the self-propelled robot. Ensure that all equipment is working properly and perform necessary calibration. Place the self-propelled robot near the hyperbolic glass curtain wall and start the SLAM module. The robot moves in the preset area, and the SLAM module collects environmental data in real time, including laser scanning data and visual images. The collected data is transmitted to the computer system. Use professional 3D modeling software and combine the existing curtain wall 3D model to build a detailed 3D map. In the 3D map, use the path planning algorithm to generate an optimal detection path. The 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 sticks to the curtain wall surface. The Lamb wave detection equipment emits Lamb waves and receives reflected signals, and the SLAM module records the location information of each detection point. Analyze the reflected signals, 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 test report and provide it to maintenance personnel for further processing.

[0142] The provided method has at least the following beneficial effects:

[0143] High-precision detection: Through the collaborative work of Lamb wave detection equipment and SLAM module, high-precision damage detection of hyperbolic glass curtain walls can be achieved.

[0144] High adaptability: The adaptive surface fitting mechanism enables the robot to adapt to various complex surfaces, thus increasing the scope of application of detection.

[0145] Autonomous detection and positioning: The robot can move autonomously and complete detection tasks, reducing human intervention and improving detection efficiency.

[0146] Real-time monitoring: The SLAM module can collect environmental data in real time to ensure dynamic adjustment and optimization during the detection process.

[0147] Visualization results: The detection results are displayed through a three-dimensional map, which is intuitive and easy to understand, and facilitates subsequent maintenance and management.

[0148] 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.

[0149] See also Figure 2 As shown, Figure 21 is a schematic diagram of the structure of a nondestructive testing and positioning device 200 for a hyperbolic glass curtain wall based on a mobile robot provided in an embodiment of the present application. The nondestructive testing and positioning device 200 for a hyperbolic glass curtain wall based on a mobile robot is used to execute the steps of the nondestructive testing and positioning method for a hyperbolic glass curtain wall based on a mobile robot shown in the above embodiments. The nondestructive testing and positioning device 200 for a hyperbolic glass curtain wall based on a mobile robot can be a single server or a server cluster, or the nondestructive testing and positioning device 200 for a hyperbolic glass curtain wall based on a mobile robot can be a terminal, which can be a handheld terminal, a laptop computer, a wearable device or a robot, etc.

[0150] like Figure 2 As shown, the nondestructive testing positioning device 200 for a hyperbolic glass curtain wall based on a mobile robot comprises:

[0151] The equipment mounting unit 201 is used to mount a Lamb wave detection device, a SLAM module and an adaptive curved surface fitting mechanism on a preset self-propelled robot;

[0152] A first control unit 202 is used to control the self-propelled robot to move in a setting environment corresponding to the hyperbolic glass curtain wall, so as to collect environmental data of the hyperbolic glass curtain wall in real time through the SLAM module;

[0153] A map construction unit 203, configured to construct a three-dimensional map according to the environmental data and a three-dimensional curtain wall model corresponding to the hyperbolic glass curtain wall;

[0154] A path generation unit 204, configured to generate an optimal detection path corresponding to the hyperbolic glass curtain wall in the three-dimensional map;

[0155] A second control unit 205 is used to control the adaptive curved surface fitting mechanism of the self-propelled robot to move according to the optimal detection path, and control the Lamb wave detection device to emit Lamb waves and receive reflected signals;

[0156] A positioning acquisition unit 206, used to acquire the positioning information recorded by the SLAM module when the Lamb wave corresponding to the reflected signal is emitted;

[0157] The damage acquisition unit 207 is used 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.

[0158] 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; acquiring obstacle information according to the three-dimensional map; and generating the optimal detection path according to the global detection path and the obstacle information.

[0159] Exemplarily, generating the optimal detection path according to the global detection path and obstacle information includes: obtaining obstacle density according to the obstacle information; obtaining the curtain wall curvature complexity and the curvature change of adjacent path points of the hyperbolic glass curtain wall according to the surface curvature gradient; and optimizing the improved ant colony algorithm based on curvature field constraints according to the obstacle density, curtain wall curvature complexity and the curvature change of adjacent path points to generate the optimal detection path.

[0160] It should be noted that, in some embodiments, the objective function corresponding to the optimization includes:

[0161] ;

[0162] in is the value of the objective function, To detect the path, is the curvature complexity of the curtain wall, is the obstacle density, is the detection efficiency requirement corresponding to the hyperbolic glass curtain wall, is the identifier of the adjacent path point pair, is the total number of adjacent path point pairs, For the For the curvature change between adjacent path points, is the square of the curvature change between adjacent path points, For waypoints The Euclidean distance to the nearest obstacle, Estimated total detection time.

[0163] In some embodiments, calculating the damage position corresponding to the hyperbolic glass curtain wall according to the reflected signal and the positioning information includes: obtaining the propagation time difference corresponding to the Lamb wave and the reflected signal, the receiver array phase difference corresponding to the reflected signal, and the posture data collected by the SLAM module; inputting the propagation time difference, the receiver array phase difference, the posture data, the positioning information, and the reflected signal into an extended particle filter to output the damage position.

[0164] In some embodiments, the adaptive curved surface bonding mechanism includes a bionic adsorption foot array, 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 of the adsorption units is used to achieve adaptive bonding with the curved surface of the hyperbolic glass curtain wall.

[0165] 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 also includes: performing a 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; reconstructing the signal according to the effective frequency band and extracting the time-frequency domain joint features from the reconstructed signal; and calculating the damage position according to the time-frequency domain joint features and the corresponding positioning information.

[0166] In some embodiments, the Lamb wave detection device includes a frequency-adjustable broadband excitation array, and the excitation frequency range of the frequency-adjustable broadband excitation array is 50 kHz to 1 MHz.

[0167] 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 wideband excitation array.

[0168] 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 also 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 an early warning according to the visual detection result.

[0169] It should be noted that technical personnel in the relevant field can clearly understand that, for the convenience and conciseness of description, the above-described hyperbolic glass curtain wall non-destructive testing and positioning device based on a mobile robot and the specific working process of each module can refer to the corresponding process in the embodiment of the hyperbolic glass curtain wall non-destructive testing and positioning method based on a mobile robot described in the above embodiments, and will not be repeated here.

[0170] The above-mentioned nondestructive detection and positioning method of the hyperbolic glass curtain wall based on the mobile robot can be implemented in the form of a computer program. Figure 2 Run on the device shown.

[0171] See also Figure 3 , Figure 3 1 is a schematic block diagram of the structure of a computer device provided in an embodiment of the present application. The computer device includes a processor, a memory and a network interface connected via a device bus, wherein the memory may include a storage medium and an internal memory.

[0172] The storage medium can store an operating device and a computer program. The computer program includes program instructions, and when the program instructions are executed, the processor can execute any one of the non-destructive detection and positioning methods for hyperbolic glass curtain walls based on a mobile robot.

[0173] The processor is used to provide computing and control capabilities and support the operation of the entire computer equipment.

[0174] 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, the processor can execute any one of the non-destructive detection and positioning methods for hyperbolic glass curtain walls based on a mobile robot.

[0175] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the scheme of the present application, and does not constitute a limitation on the terminal to which the scheme of the present 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 a different arrangement of components.

[0176] It should be understood that the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0177] In one embodiment, the processor is used to run a computer program stored in the memory to implement the following steps:

[0178] The preset self-propelled robot is equipped with Lamb wave detection equipment, SLAM module and adaptive surface fitting mechanism;

[0179] Controlling the self-propelled robot to move in a setting environment corresponding to the hyperbolic glass curtain wall, so as to collect environmental data of the hyperbolic glass curtain wall in real time through the SLAM module;

[0180] Constructing 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;

[0181] Generating an optimal detection path corresponding to the hyperbolic glass curtain wall in the three-dimensional map;

[0182] Controlling the adaptive curved surface fitting mechanism of the self-propelled robot to move according to the optimal detection path, and controlling the Lamb wave detection device to emit Lamb waves and receive reflected signals;

[0183] Acquire the positioning information recorded by the SLAM module when the Lamb wave corresponding to the reflected signal is emitted;

[0184] The damage position corresponding to the hyperbolic glass curtain wall is calculated according to the reflection signal and the positioning information, and the nondestructive detection positioning of the hyperbolic glass curtain wall is completed.

[0185] 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; acquiring obstacle information according to the three-dimensional map; and generating the optimal detection path according to the global detection path and the obstacle information.

[0186] Exemplarily, generating the optimal detection path according to the global detection path and obstacle information includes: obtaining obstacle density according to the obstacle information; obtaining the curtain wall curvature complexity and the curvature change of adjacent path points of the hyperbolic glass curtain wall according to the surface curvature gradient; and optimizing the improved ant colony algorithm based on curvature field constraints according to the obstacle density, curtain wall curvature complexity and the curvature change of adjacent path points to generate the optimal detection path.

[0187] It should be noted that, in some embodiments, the objective function corresponding to the optimization includes:

[0188] ;

[0189] in is the value of the objective function, To detect the path, is the curvature complexity of the curtain wall, is the obstacle density, is the detection efficiency requirement corresponding to the hyperbolic glass curtain wall, is the identifier of the adjacent path point pair, is the total number of adjacent path point pairs, For the For the curvature change between adjacent path points, is the square of the curvature change between adjacent path points, For waypoints The Euclidean distance to the nearest obstacle, Estimated total detection time.

[0190] In some embodiments, calculating the damage position corresponding to the hyperbolic glass curtain wall according to the reflected signal and the positioning information includes: obtaining the propagation time difference corresponding to the Lamb wave and the reflected signal, the receiver array phase difference corresponding to the reflected signal, and the posture data collected by the SLAM module; inputting the propagation time difference, the receiver array phase difference, the posture data, the positioning information, and the reflected signal into an extended particle filter to output the damage position.

[0191] In some embodiments, the adaptive curved surface bonding mechanism includes a bionic adsorption foot array, 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 of the adsorption units is used to achieve adaptive bonding with the curved surface of the hyperbolic glass curtain wall.

[0192] 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 also includes: performing a 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; reconstructing the signal according to the effective frequency band and extracting the time-frequency domain joint features from the reconstructed signal; and calculating the damage position according to the time-frequency domain joint features and the corresponding positioning information.

[0193] In some embodiments, the Lamb wave detection device includes a frequency-adjustable broadband excitation array, and the excitation frequency range of the frequency-adjustable broadband excitation array is 50 kHz to 1 MHz.

[0194] 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 wideband excitation array.

[0195] 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 also 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 an early warning according to the visual detection result.

[0196] It should be noted that technical personnel in the relevant field can clearly understand that, for the convenience and conciseness of description, the specific working processes of the computer equipment and each module described above can refer to the corresponding processes in the embodiments of the non-destructive testing and positioning method for hyperbolic glass curtain wall based on mobile robots described in the above embodiments, and will not be repeated here.

[0197] 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 implements the steps of the non-destructive detection and positioning method of a hyperbolic glass curtain wall based on a mobile robot as provided in any embodiment of the present application.

[0198] The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiment, such as a 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 smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc., equipped on the computer device.

[0199] Exemplarily, the medium is used to implement the following steps:

[0200] The preset self-propelled robot is equipped with Lamb wave detection equipment, SLAM module and adaptive surface fitting mechanism;

[0201] Controlling the self-propelled robot to move in a setting environment corresponding to the hyperbolic glass curtain wall, so as to collect environmental data of the hyperbolic glass curtain wall in real time through the SLAM module;

[0202] Constructing 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;

[0203] Generating an optimal detection path corresponding to the hyperbolic glass curtain wall in the three-dimensional map;

[0204] Controlling the adaptive curved surface fitting mechanism of the self-propelled robot to move according to the optimal detection path, and controlling the Lamb wave detection device to emit Lamb waves and receive reflected signals;

[0205] Acquire the positioning information recorded by the SLAM module when the Lamb wave corresponding to the reflected signal is emitted;

[0206] The damage position corresponding to the hyperbolic glass curtain wall is calculated according to the reflection signal and the positioning information, and the nondestructive detection positioning of the hyperbolic glass curtain wall is completed.

[0207] It should be noted that technical personnel in the relevant field can clearly understand that, for the convenience and conciseness of description, the specific working process of the storage medium and each module described above can refer to the corresponding process in the embodiment of the non-destructive testing and positioning method of hyperbolic glass curtain wall based on mobile robot described in the above embodiments, and will not be repeated here.

[0208] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A nondestructive testing and positioning method for a hyperbolic glass curtain wall based on a mobile robot, characterized in that: include: The preset self-propelled robot is equipped with Lamb wave detection equipment, SLAM module and adaptive surface fitting mechanism; Controlling the self-propelled robot to move in a setting environment corresponding to the hyperbolic glass curtain wall, so as to collect environmental data of the hyperbolic glass curtain wall in real time through the SLAM module; Constructing 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; 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-propelled robot to move according to the optimal detection path, and controlling the Lamb wave detection device to emit Lamb waves and receive reflected signals; Acquire the positioning information recorded by the SLAM module when the Lamb wave corresponding to the reflected signal is emitted; The damage position corresponding to the hyperbolic glass curtain wall is calculated according to the reflection signal and the positioning information, and the nondestructive detection positioning of the hyperbolic glass curtain wall is completed.

2. The method according to claim 1, characterized in that: The generating of 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; Generate 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; The optimal detection path is generated according to the global detection path and obstacle information.

3. The method according to claim 2, characterized in that The generating the optimal detection path according to the global detection path and obstacle information comprises: Obtaining obstacle density according to the obstacle information; Acquire the complexity of the curvature of the hyperbolic glass curtain wall and the curvature variation of adjacent path points according to the surface curvature gradient; The improved ant colony algorithm based on curvature field constraint is optimized according to the obstacle density, the complexity of curtain wall curvature and the curvature change of adjacent path points to generate the optimal detection path.

4. The method according to claim 3, characterized in that The objective function corresponding to the optimization includes: ； in is the value of the objective function, To detect the path, is the curvature complexity of the curtain wall, is the obstacle density, is the detection efficiency requirement corresponding to the hyperbolic glass curtain wall, is the identifier of the adjacent path point pair, is the total number of adjacent path point pairs, For the For the curvature change between adjacent path points, is the square of the curvature change between adjacent path points, For waypoints The Euclidean distance to the nearest obstacle, Estimated total detection time.

5. The method according to claim 1, characterized in that The calculating the damage position corresponding to the hyperbolic glass curtain wall according to the reflection signal and the positioning information includes: Acquire the propagation time difference corresponding to the Lamb wave and the reflected signal, the receiver array phase difference corresponding to the reflected signal, and the posture data collected by the SLAM module; The propagation time difference, receiver array phase difference, posture data, positioning information and reflection signal are input into an extended particle filter to output the damage location.

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

7. The method according to claim 1, characterized in that 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: Performing 6-layer wavelet basis function decomposition on the reflected signal; Determine the effective frequency band in the decomposed reflection signal based on the energy entropy threshold method; The signal is reconstructed according to the effective frequency band and the joint features in the time and frequency domains are extracted from the reconstructed signal; and the damage position is calculated according to the joint features in the time and frequency domains and the corresponding positioning information.

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

9. The method according to claim 8, characterized in that 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 frequency-adjustable wideband excitation array.

10. The method according to claim 1, characterized in that 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 location to the three-dimensional map; A visual detection result is generated and outputted according to the mapped three-dimensional map, so as to issue an early warning according to the visual detection result.

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