An augmented reality based assembly stress control method

Abstract

The application belongs to the field of aircraft strength design, and particularly relates to an assembly stress control method based on augmented reality. The method comprises the following steps: step one, capturing a real-time image from a camera, the image being a real assembly scene image containing at least one ARuco two-dimensional code; step two, pre-processing the image; step three, correcting the distortion of the pre-processed image; step four, detecting the ARuco two-dimensional code in the image, calculating the three-dimensional space posture of the ARuco two-dimensional code relative to the camera, and obtaining space data; and step five, obtaining a preset stress model, loading the stress model into an entity three-dimensional model according to the space data, and generating a three-dimensional stress model under the real assembly scene. The application provides an assembly planning environment to analyze and understand the stress borne by an object in an assembly process, so that the assembly of key parts of an aircraft structure has state perception, the assembly quality of the aircraft is improved, and the assembly precision and efficiency of a new aircraft model are improved.

Description

Technical Field

[0001] This application belongs to the field of aircraft strength design, and specifically relates to an augmented reality-based assembly stress control method. Background Technology

[0002] Assembly quality control in aircraft assembly is crucial for ensuring aircraft performance and structural integrity. However, due to the large number of components, large dimensions, high requirements, and complex forming processes involved in aircraft assembly, assembly defects such as assembly gaps are inevitable. These defects can lead to structural assembly stress, reducing the aircraft's fatigue life and causing stress corrosion cracking or vibration cracking, thus affecting the aircraft's strength and quality. Assembly stress control involves design, materials, processes, and management, among which assembly stress detection during the assembly process is the most direct and effective measure for controlling assembly stress.

[0003] Currently, in the field of digital aircraft assembly in China, gap control technology using shims for assembly stress compensation has been gradually developed. However, research on related gap control and shim compensation methods in actual assembly processes is still insufficient. Often, assembly stress is measured manually, which is time-consuming and reliant on human experience, increasing the uncertainty of measurement results and making it difficult to guarantee measurement accuracy. Repeated trial and error during shim addition leads to poor consistency among assembled aircraft from the same batch. Such assembly processes and stress control technologies are ill-suited to the assembly precision and efficiency requirements of modern aircraft models.

[0004] Therefore, there is an urgent need for a technical solution to overcome or mitigate at least one of the aforementioned defects in the existing technology. Summary of the Invention

[0005] The purpose of this application is to provide an augmented reality-based assembly stress control method to solve the problems of high uncertainty, low accuracy, and low efficiency in current aircraft assembly stress control, which relies on human experience.

[0006] The technical solution of this application is:

[0007] The first aspect of this application provides an augmented reality-based assembly stress control method, comprising:

[0008] Step 1: Capture a real-time image from the camera. The image is a real-world assembly scene image containing at least one ARuco QR code.

[0009] Step 2: Preprocess the image;

[0010] Step 3: Correct distortion in the preprocessed image;

[0011] Step 4: Detect the ARuco QR code in the image, calculate the three-dimensional spatial pose of the ARuco QR code relative to the camera, and obtain spatial data;

[0012] Step 5: Obtain the preset stress model, load the stress model into the solid 3D model according to the spatial data, and generate a 3D stress model in the real assembly scene.

[0013] In at least one embodiment of this application, in step one, real-time images are captured from a camera via an OpenCV interface.

[0014] In at least one embodiment of this application, in step two, the preprocessing method includes at least one of noise reduction, sharpening, color correction, and brightness adjustment.

[0015] In at least one embodiment of this application, in step three, distortion correction includes correcting barrel distortion and / or pincushion distortion of the camera.

[0016] In at least one embodiment of this application, in step four, the ARuco QR code in the image is detected by the OpenCV ARuco module.

[0017] In at least one embodiment of this application, in step five, model overlay rendering is performed using OpenGL.

[0018] A second aspect of this application provides an augmented reality-based assembly stress control system, comprising:

[0019] The image acquisition module is used to capture real-time images from the camera, which are images of a real assembly scene containing at least one ARuco QR code;

[0020] The preprocessing module is used to preprocess the image;

[0021] The distortion correction module is used to correct distortion in the preprocessed image.

[0022] The spatial data acquisition module detects ARuco QR codes in the image, calculates the three-dimensional spatial pose of the ARuco QR code relative to the camera, and obtains spatial data.

[0023] The model overlay rendering module is used to obtain a preset stress model, and overlay and render the stress model onto the solid 3D model based on spatial data to generate a 3D stress model in the real assembly scene.

[0024] The invention has at least the following beneficial technical effects:

[0025] The augmented reality-based assembly stress control method proposed in this application provides a more intuitive, convenient, fast, accurate, realistic, and interactive assembly planning environment to analyze and understand the stress that an object bears during the assembly process. This enables the assembly of key parts of the aircraft structure to have state awareness, improves the quality of aircraft assembly, and enhances the assembly accuracy and efficiency of new aircraft models. Attached Figure Description

[0026] Figure 1 This is a flowchart of an augmented reality-based assembly stress control method according to one embodiment of this application;

[0027] Figure 2 This is a schematic diagram of ARuco QR code detection according to one embodiment of this application;

[0028] Figure 3 This is a schematic diagram of a three-dimensional stress model in a real assembly scenario according to one embodiment of this application. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be described in more detail below with reference to the accompanying drawings. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are some, but not all, embodiments of this application. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. The embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0030] In the description of this application, it should be understood that the terms "center", "longitudinal", "lateral", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting the scope of protection of this application.

[0031] The following is in conjunction with the appendix Figures 1 to 3 This application will be described in further detail.

[0032] The first aspect of this application provides an augmented reality-based assembly stress control method, such as... Figure 1 As shown, it includes the following steps:

[0033] Step 1: Capture a real-time image from the camera. The image is a real-world assembly scene image containing at least one ARuco QR code.

[0034] Step 2: Preprocess the image;

[0035] Step 3: Correct distortion in the preprocessed image;

[0036] Step 4: Detect the ARuco QR code in the image, calculate the three-dimensional spatial pose of the ARuco QR code relative to the camera, and obtain spatial data;

[0037] Step 5: Obtain the preset stress model, and overlay and render the stress model onto the solid 3D model based on the spatial data to generate a 3D stress model in the real assembly scene.

[0038] The augmented reality-based assembly stress control method of this application firstly involves capturing real-time images from a camera via an OpenCV interface in step one. This process is dynamic and adaptable to various ambient lighting and camera conditions. An advanced image sensor is used to ensure that the captured image quality and resolution meet the requirements of subsequent processing.

[0039] In the augmented reality-based assembly stress control method of this application, step two involves performing a series of preprocessing steps on the captured image. Preprocessing methods include, but are not limited to, image denoising, sharpening, color correction, and brightness adjustment. The purpose of preprocessing is to improve image quality, ensure clearer image details, and provide a more stable foundation for ARuco QR code recognition.

[0040] The augmented reality-based assembly stress control method of this application then, in step three, performs distortion correction on the preprocessed image. Distortion correction includes correcting barrel distortion and / or pincushion distortion of the camera. For camera distortion and perspective error, advanced image correction algorithms are used to correct distortion caused by the lens, ensuring the geometric fidelity of the final image.

[0041] The augmented reality-based assembly stress control method of this application further includes, in step four, using OpenCV's ARuco module to detect ARuco QR codes in the image. Figure 2 As shown, by finding the marker boundary and decoding the marker, its pose in three-dimensional space is estimated, and the position and orientation of the ARuco QR code relative to the camera are calculated to obtain spatial data.

[0042] In the augmented reality-based assembly stress control method of this application, in step five, a realistic three-dimensional stress model is generated through OpenGL model overlay rendering. Figure 3As shown, based on the spatial data of the ARuco QR code, the position and orientation are adjusted to ensure that the stress model is aligned with the actual object. Multiple preset stress models built into the system are then loaded into the solid 3D model to achieve the effect of generating a 3D stress model in a real-world scene.

[0043] This application presents an augmented reality-based assembly stress control method that utilizes the Python programming language, combined with OpenCV's image processing capabilities, OpenGL's 3D rendering technology, and ARuco QR code spatial positioning technology to create an augmented reality experience. By recognizing specific ARuco QR codes through software, a detailed 3D stress model is rendered on the screen in real time. These models are integrated with real-world objects and the environment, providing a novel visual interaction method.

[0044] This application presents an augmented reality-based assembly stress control method that can visually represent abstract stress data, significantly improving data understandability and accessibility. It can also quickly and accurately display assembly stress distribution, helping to shorten analysis time and improve design and production efficiency. Furthermore, it provides real-time, dynamic stress analysis, supporting more precise and efficient decision-making processes. This enables state-aware assembly of critical aircraft structural components, improving structural assembly quality and demonstrating practicality.

[0045] A second aspect of this application provides an augmented reality-based assembly stress control system, comprising:

[0046] The image acquisition module is used to capture real-time images from the camera, which are images of a real assembly scene containing at least one ARuco QR code;

[0047] The preprocessing module is used to preprocess the image;

[0048] The distortion correction module is used to correct distortion in the preprocessed image.

[0049] The spatial data acquisition module detects ARuco QR codes in the image, calculates the three-dimensional spatial pose of the ARuco QR code relative to the camera, and obtains spatial data.

[0050] The model overlay rendering module is used to obtain a preset stress model, and overlay and render the stress model onto the solid 3D model based on spatial data to generate a 3D stress model in the real assembly scene.

[0051] The augmented reality-based assembly stress control system of this application is designed in detail for each module with reference to the augmented reality-based assembly stress control method, which will not be described in detail here.

[0052] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An assembly stress control method based on augmented reality, characterized in that, include: Step 1: Capture a real-time image from the camera. The image is a real-world assembly scene image containing at least one ARuco QR code. Step 2: Preprocess the image; Step 3: Correct distortion in the preprocessed image; Step 4: Detect the ARuco QR code in the image, calculate the three-dimensional spatial pose of the ARuco QR code relative to the camera, and obtain spatial data; Step 5: Obtain the preset stress model, and overlay and render the stress model onto the solid 3D model based on the spatial data to generate a 3D stress model in the real assembly scene.

2. The augmented reality-based assembly stress control method according to claim 1, characterized in that, In step one, real-time images are captured from the camera using the OpenCV interface.

3. The augmented reality-based assembly stress control method according to claim 2, characterized in that, In step two, the preprocessing methods include at least one of noise reduction, sharpening, color correction, and brightness adjustment.

4. The augmented reality-based assembly stress control method according to claim 3, characterized in that, In step three, distortion correction includes correcting barrel distortion and / or pincushion distortion of the camera.

5. The augmented reality-based assembly stress control method according to claim 4, characterized in that, In step four, the ARuco QR code in the image is detected using OpenCV's ARuco module.

6. The augmented reality-based assembly stress control method according to claim 5, characterized in that, In step five, model overlay rendering is performed using OpenGL.

7. An augmented reality-based assembly stress control system, characterized in that, include: The image acquisition module is used to capture real-time images from the camera, which are images of a real assembly scene containing at least one ARuco QR code; The preprocessing module is used to preprocess the image; The distortion correction module is used to correct distortion in the preprocessed image. The spatial data acquisition module detects ARuco QR codes in the image, calculates the three-dimensional spatial pose of the ARuco QR code relative to the camera, and obtains spatial data. The model overlay rendering module is used to obtain a preset stress model, and overlay and render the stress model onto the solid 3D model based on spatial data to generate a 3D stress model in the real assembly scene.

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