Waveguide antenna assembly precision visual calibration method and system

Abstract

The application discloses a waveguide antenna assembly precision visual calibration method and system, and belongs to the technical field of waveguide antenna assembly. In the state that the waveguide antenna and the docking component are not in contact, a multi-view high-resolution industrial camera and a structured light projection are used to synchronously collect a waveguide flange surface image, and a three-dimensional appearance is reconstructed through phase demodulation; based on the image and the point cloud, a port center axis, a waveguide flange surface normal vector and a positioning reference are extracted, and flatness and edge continuity are calculated; geometric features are matched with a standard assembly model, a six-degree-of-freedom pose deviation is solved through singular value decomposition, visual guidance instructions are generated according to a preset tolerance threshold, a closed-loop dynamic calibration is driven by an adjustment platform, and the process is paused when there is an edge defect. The application changes the assembly calibration from a posteriori mechanical contact to a prospective geometric alignment based on visual detection, realizes intelligent assembly of non-contact real-time guidance, and significantly improves the assembly precision and consistency of the waveguide system.

Description

Technical Field

[0001] This invention relates to the field of waveguide antenna assembly technology, and more specifically to a visual calibration method and system for waveguide antenna assembly accuracy. Background Technology

[0002] As a key component of the radio frequency front-end, the essential function of waveguide antennas is to constrain the propagation path of electromagnetic waves in physical space while maintaining the phase consistency and polarization directionality of the wavefront. In phased array radar, satellite communication, and millimeter-wave systems, the physical connection between waveguide ports constitutes the boundary continuity condition for electromagnetic wave transmission. According to microwave transmission line theory, the field distribution of electromagnetic waves propagating within a waveguide is strictly determined by the boundary geometry. When two waveguide ports are connected, the gap between the waveguide flanges, the lateral offset of the axis, and the angular tilt directly alter the continuity of the boundary conditions, leading to the following physical phenomena: lateral offset causes the fundamental mode energy to convert to higher-order modes; axial tilt increases the reflection coefficient; and the gap between the waveguide flanges creates a resonant cavity effect, resulting in power leakage at specific frequencies. The essence of these phenomena is geometric perturbation in the electromagnetic field boundary value problem. Micrometer-level deviations can generate significant reactive components in the millimeter-wave band, disrupting the original impedance matching state of the system.

[0003] In existing technologies, such as CN223770293U, the connection between the calibration device and the waveguide antenna is improved through auxiliary connectors, and an analyzer is integrated to enhance ease of operation. However, calibration still relies on physical insertion and lacks the ability to perceive the geometric state before assembly. While CN121432302A constructs an error model that includes time-varying characteristics across the entire link to improve the accuracy of electrical performance calibration, it is essentially an indirect deduction in the electromagnetic domain and cannot directly obtain spatial pose information such as the bonding state of the waveguide flange, axis offset, or angle tilt. None of the above solutions introduce visual or optical sensing methods, making it impossible to achieve non-contact, visualized, and real-time monitoring and dynamic guidance of the assembly process. This makes it difficult to meet the process requirements of modern high-density, high-reliability waveguide systems for "one-time assembly and immediate use."

[0004] Therefore, there is an urgent need for a visual calibration method that integrates high-resolution imaging and intelligent image analysis to accurately identify and guide the correction of assembly deviations before physical connection, thereby breaking through the bottlenecks of traditional calibration technology in terms of accuracy, efficiency and applicability. Summary of the Invention

[0005] The purpose of this invention is to provide a visual calibration method and system for waveguide antenna assembly accuracy, which solves the technical problem that existing waveguide assembly calibration relies on mechanical contact or a posteriori RF feedback, and cannot obtain the spatial pose of the waveguide flange surface through intelligent visual detection before physical connection to achieve look-ahead geometric alignment.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A visual calibration method for waveguide antenna assembly accuracy includes the following steps: With the waveguide antenna and the docking component in a non-contact state, at least two sets of high-resolution industrial cameras are used to simultaneously acquire images of the waveguide flange surface and its surrounding structure from different perspectives. A specific coded pattern is then projected onto the surface of the waveguide flange surface using a structured light projection device, and the three-dimensional morphology information of the waveguide flange surface is reconstructed through a phase demodulation algorithm. Based on the image data and three-dimensional topography information, the central axis of the waveguide port, the outer contour of the waveguide flange surface, and the positioning holes or reference marks are identified, and the normal vector, flatness, and continuity of the port edge of the waveguide flange surface are calculated. The extracted geometric features are aligned with the preset standard assembly model in the coordinate system, and the singular value decomposition algorithm is used to solve the six-degree-of-freedom pose deviation of the current waveguide port relative to the target docking position. The six-degree-of-freedom pose deviation includes translational deviation along the X, Y, and Z axes and rotational angle deviation around each axis. Based on the positional deviation and combined with the preset assembly tolerance threshold model, it is determined whether the current state meets the docking conditions. If there is an out-of-tolerance, a visual guidance interface is generated to instruct the operator or automatic actuator to adjust the position and attitude of the waveguide antenna along a specific direction. At the same time, based on the calculated port edge continuity, if there is edge defect or discontinuity, an alarm containing defect location information is generated and the calibration process is paused. The aforementioned steps are repeated continuously during the adjustment process until the positional deviation converges to the allowable range, completing high-precision alignment and triggering physical connection action.

[0007] In one embodiment of the present invention, the high-resolution industrial camera is equipped with a telecentric lens with adjustable focal length, and provides uniform illumination in conjunction with a ring LED light source. The structured light projection device projects a sequence of sinusoidal stripes, and performs phase demodulation through a four-step phase shifting method. The reconstructed three-dimensional point cloud spatial sampling density is not less than 50 points per square millimeter, and the depth accuracy is better than 5 micrometers.

[0008] In one embodiment of the present invention, the identification of the waveguide port center axis includes: detecting the circular contour on the waveguide flange surface by Hough transform, accurately locating the contour boundary by combining a sub-pixel edge detection algorithm, and fitting a least-squares circle to determine the port center; the waveguide flange surface normal vector is obtained by performing principal component analysis on the three-dimensional point cloud data to perform plane fitting, and the flatness error is defined as the difference between the maximum distance and the minimum distance from all points to the fitted plane.

[0009] In one embodiment of the present invention, the preset standard assembly model includes the ideal geometric parameters of the waveguide port and the definition of the global coordinate system. The number of feature points participating in the singular value decomposition algorithm is no less than 16, and they are evenly distributed on the waveguide flange surface and the positioning structure. The pose calculation update frequency is no less than 10 Hz.

[0010] In one embodiment of the present invention, the assembly tolerance threshold model is dynamically set according to the waveguide operating frequency band, and the axial alignment tolerance, angular deflection tolerance and waveguide flange surface parallelism tolerance corresponding to the high-frequency operating frequency band are respectively set to preset thresholds; the visual guidance interface is superimposed on the real-time video stream in an augmented reality manner, displaying deviation vector arrows and numerical prompts, and supports manual fine-tuning or automatic servo control.

[0011] In one embodiment of the present invention, the adjustment process is performed by a six-degree-of-freedom precision adjustment platform, which has a three-axis translational stroke in X / Y / Z and a three-axis rotational stroke, with a repeatability accuracy better than ±2 micrometers, an angular resolution better than 0.005 degrees, and a command response delay of less than 1 millisecond.

[0012] In one embodiment of the present invention, an environmental interference suppression step is also included: before image acquisition, environmental parameters are collected in real time by a temperature sensor and a vibration monitoring unit. When the rate of temperature change exceeds a preset temperature change rate threshold or the vibration acceleration exceeds a preset vibration threshold, the calibration process is paused and an online calibration compensation algorithm is started to dynamically correct the camera's intrinsic and extrinsic parameters.

[0013] In one embodiment of the present invention, the online calibration compensation algorithm uses a polynomial regression model to correct the camera focal length, principal point offset, and rotation and translation matrix based on historical calibration data and current environmental parameters, so as to ensure that the accuracy of 3D reconstruction is not affected by environmental disturbances.

[0014] In one embodiment of the present invention, a multi-port collaborative calibration step is also included: when processing a multi-channel waveguide array, image data of all ports are acquired synchronously, a global coordinate system is established, and the relative pose relationship between each port is coordinated through a graph optimization algorithm to ensure the phase consistency of the overall array. After calibration, the axis parallelism deviation between each port is not greater than a preset parallelism threshold.

[0015] In addition, the present invention also discloses a waveguide antenna assembly accuracy visual calibration system, including a multi-view high-resolution imaging subsystem, a structured light three-dimensional reconstruction subsystem, an environmental perception and interference suppression module, a six-degree-of-freedom precision adjustment platform, and a central processing and control unit. The multi-view high-resolution imaging subsystem includes at least two sets of industrial cameras, which are arranged symmetrically at an angle on both sides of the waveguide flange surface. The structured light 3D reconstruction subsystem projects a sequence of sinusoidal fringes and synchronizes with the imaging subsystem; The environmental perception and interference suppression module integrates a temperature sensor and a triaxial accelerometer; The six-degree-of-freedom precision adjustment platform is used to perform pose adjustment; The central processing and control unit runs a vision processing software stack to execute the steps of the waveguide antenna assembly accuracy vision calibration method described above.

[0016] Furthermore, the central processing and control unit communicates with the robotic arm controller via industrial Ethernet, employs a time-sensitive networking protocol, has an end-to-end communication latency of less than 1 millisecond, and integrates an augmented reality visualization engine to generate a guide interface overlaid on the real-time video stream.

[0017] The multi-view high-resolution imaging subsystem and the structured light 3D reconstruction subsystem achieve nanosecond-level synchronization through a unified clock source. All data is exchanged with low latency through a shared memory pool. The system supports one-click start of the calibration process, and a single complete calibration takes no more than 8 seconds.

[0018] Furthermore, it also includes an environmental adaptive dynamic compensation step: real-time acquisition of multiple environmental parameters, construction of an error prediction model that integrates physical models and data-driven methods, incorporating the predicted system drift into pose deviation calculation, and generating feedforward compensation instructions that are superimposed on the adjustment amount; the error prediction model uses a Kalman filter to fuse temperature, vibration and humidity sensor data, and combines a thermal-structural coupled finite element model to update the estimated values ​​of camera intrinsic parameters and adjustment platform drift in real time.

[0019] Furthermore, the state vector of the Kalman filter includes the equivalent focal length of the camera, the principal point coordinates, the six-degree-of-freedom drift of the adjustment platform and its rate of change; the observation vector includes the temperature sensor readings, the vibration sensor readings and the visual measurement pose of the reference target fixed on the base; the prediction equation is based on the heat conduction and mechanical physical model, and the update equation corrects the state estimate according to the observation values, thereby obtaining the optimal real-time estimate of the camera intrinsic parameters and the platform drift.

[0020] Furthermore, the multi-port collaborative calibration also includes a digital twin-based intelligent path planning step: constructing a digital twin model of the waveguide array, synchronizing the physical system state in real time, adopting a model predictive control algorithm, simulating and evaluating the future results of multiple adjustment sequences at each step, selecting the adjustment action that minimizes the global error and has the shortest execution time, and dynamically optimizing the adjustment sequence and adjustment amount of each port.

[0021] Furthermore, the digital twin model includes a geometric model, a kinematic model, and an error propagation model; in model predictive control, the state is defined as the current six-degree-of-freedom deviation of each port, the action is the selection of the port to be adjusted and the adjustment amount, and the reward function is a linear combination of the negative value of the sum of squared errors after adjustment and the adjustment time; by simulating future multi-step actions, the sequence with the largest cumulative reward is selected to execute the first step, and this process is repeated until calibration is completed.

[0022] Compared with the prior art, the present invention has the following beneficial effects: Traditional calibration methods rely on mechanical locating pins or guide grooves to achieve physical constraints. Their accuracy is limited by fundamental solid mechanical properties such as material hardness, coefficient of friction, and coefficient of thermal expansion, and they inherently cannot eliminate uncertainties caused by machining tolerances and wear. This invention uses a high-resolution industrial camera and a structured light projection device to directly acquire the spatial geometric information of the waveguide flange surface. Utilizing the principles of rectilinear propagation and phase modulation of light, it constructs the three-dimensional morphology of the waveguide flange surface before physical contact occurs. This transformation restores the calibration benchmark from a volatile solid surface to a stable optical wavefront, fundamentally avoiding plastic deformation and cumulative errors caused by mechanical contact.

[0023] Traditional RF feedback calibration requires indirect inference of assembly quality after physical connection is completed, through measuring VSWR or insertion loss. This is essentially an inverse problem based on electromagnetic field boundary conditions, resulting in multiple solutions and hysteresis. This invention extracts geometric invariants such as the waveguide flange normal vector and the port center axis, and directly solves the six-degree-of-freedom rigid body transformation matrix using singular value decomposition, transforming the solution for assembly deviations into a point set registration problem in three-dimensional space. This logical shift allows the evaluation benchmark for assembly quality to return to the Euclidean geometric axiomatic system, achieving quantitative traceability and real-time visualization of deviations.

[0024] Traditional manual assembly relies on the operator's experience and judgment, involving multiple trial insertions. Essentially, it is a discrete, open-loop control process. This invention constructs a continuous information-physical closed loop of "image acquisition - pose calculation - deviation compensation - adjustment execution," ensuring that each movement of the adjustment platform is based on vector decomposition and synthesis of the difference between the current measured pose and the ideal pose. This closed-loop control process follows the principle of negative feedback adjustment, gradually reducing the pose deviation through multiple iterations until it converges to a preset tolerance band, ensuring that every physical docking is based on rigorously verified geometric alignment.

[0025] Traditional precision assembly systems assume constant environmental parameters, making their accuracy susceptible to thermal deformation caused by temperature gradients and rigid body displacement disturbances induced by foundation vibrations. This invention introduces an environmental interference suppression module that monitors the rate of temperature change and vibration acceleration in real time. When environmental parameters exceed steady-state thresholds, it compensates for changes in the optical path caused by thermal expansion by online correction of the camera's internal and external parameters. This mechanism is based on quantitative analysis of each physical component in the measurement chain (lens refraction, sensor thermal noise, platform support stiffness), enabling the entire measurement system to maintain metrological traceability even in non-ideal environments.

[0026] This invention fundamentally transforms the physical principles of the four basic steps in the assembly process—"contact-measurement-judgment-adjustment"—achieving a leap from manual trial and error based on experience to closed-loop alignment based on geometric axioms. Attached Figure Description

[0027] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained from these drawings without creative effort.

[0028] Figure 1 To develop an overall flowchart.

[0029] Figure 2 This is a simplified diagram of the core calibration process of this invention.

[0030] Figure 3 This is a simplified flowchart of the multi-port collaborative process of the present invention. Detailed Implementation

[0031] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the embodiments of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0032] The following is in conjunction with the appendix Figures 1-3 The embodiments of the present invention will be described in detail below.

[0033] Example 1: This example discloses a visual calibration method and system for waveguide antenna assembly accuracy, which is applied to the high-precision docking and assembly of waveguide antennas and feed networks in millimeter-wave phased array radar systems.

[0034] The waveguide port operates at a frequency of 38 GHz, requiring an axial alignment error of no more than 50 micrometers, a waveguide flange surface parallelism deviation of no more than 30 micrometers, and high-speed operation with a single-piece calibration time of ≤8 seconds on an automated production line.

[0035] In practical implementation, the waveguide antenna assembly accuracy visual calibration system deployed in this embodiment includes the following core hardware modules: (1) Multi-view high-resolution imaging subsystem: It consists of two sets of industrial cameras, each containing a global shutter CMOS image sensor (such as the Sony IMX253 chip) with a pixel resolution of 8 megapixels (3296×2472) and a frame rate of ≥30fps, paired with a telecentric lens with adjustable focal length (25~50 mm) to eliminate perspective distortion and ensure distortion-free imaging at the edge of the waveguide flange. The two cameras are symmetrically arranged on both sides of the waveguide flange at an angle of approximately 60°, with their optical axes intersecting at the center of the waveguide flange to form a stereoscopic visual baseline. Each camera is equipped with an independent ring LED light source (color temperature 5600K, illuminance uniformity ≥95%), and precise timing coordination with the structured light projection device is achieved through a pulse synchronization controller.

[0036] (2) Structured light 3D reconstruction subsystem: A blue laser diode (wavelength 450 nm) is used as the light source to drive a digital micromirror device (DMD) to project a sinusoidal fringe sequence onto the waveguide flange surface; the fringe pattern is generated using a four-step phase-shifting method (with phases of 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 ... , , The projection resolution reaches 1920×1080, and the refresh rate is ≥60 Hz. This subsystem is synchronized with the imaging subsystem through hardware trigger signals to ensure that each frame of the image corresponds to a unique phase code, thereby supporting the subsequent phase demodulation algorithm to reconstruct the 3D point cloud.

[0037] (3) Environmental perception and interference suppression module: The system integrates a high-precision temperature sensor (PT1000, accuracy ±0.1℃) and a triaxial MEMS accelerometer (range ±2g, resolution 0.001g), which are mounted on the camera bracket and the adjustment platform base, respectively, to monitor the ambient temperature change rate and mechanical vibration level in real time. When the temperature change rate is detected to be >0.5℃ / min or the vibration acceleration in any direction is >0.1g, the system automatically pauses image acquisition and starts an online calibration compensation algorithm to dynamically correct the camera's intrinsic parameters (focal length, principal point offset) and extrinsic parameters (rotation and translation matrix).

[0038] (4) Six-degree-of-freedom precision adjustment platform: Employing a parallel mechanism driven by piezoelectric ceramics (Stewart platform configuration), it has the capability of ±5mm translational travel in the X / Y / Z axes and ±2° rotational travel around the X / Y / Z axes; the platform's repeatability is better than ±2 μm, its angular resolution is better than 0.005°, it has a built-in high-bandwidth position feedback encoder (0.1 μm resolution), and communicates with the main control unit via EtherCAT bus, with a command response delay of <1 ms.

[0039] (5) Central processing and control unit: Composed of an embedded industrial computer (Intel i7-12700E CPU + NVIDIA RTX A2000 GPU), running a real-time operating system (RTOS) and a customized vision processing software stack; the unit connects to the robotic arm controller (such as KUKA KR AGILUS) and servo driver via gigabit industrial Ethernet (IEEE 802.3at), and the communication protocol adopts TCP / IP-based OPC UAover TSN (Time Sensitive Networking) to ensure end-to-end communication latency <1 ms; at the same time, the unit integrates an AR visualization engine to generate a guide interface overlaid on the real-time video stream.

[0040] The modules mentioned above achieve nanosecond-level synchronization through a unified clock source (10 MHz OCXO thermostatic crystal oscillator) and are uniformly scheduled by the central processing unit. All image data, 3D point clouds, environmental parameters, and pose commands are exchanged with low latency through a shared memory pool, avoiding the jitter caused by traditional message queues.

[0041] After completing the static architecture deployment, the system performs the following dynamic calibration process: Step (1): Obtain high-resolution image data of the waveguide port; The central processing unit first sends an acquisition enable signal to the pulse synchronization controller, which then synchronously triggers two industrial cameras and a structured light projector. The structured light projector sequentially projects four phase-shifted sinusoidal fringes (spatial frequency of 8 cycles / mm), each lasting 8 ms, and the two cameras synchronously capture the corresponding images. The raw images are transmitted to the GPU memory via the CameraLink HS interface, and then a phase demodulation algorithm is executed: for each pixel, the wrapping phase is calculated using a four-step phase shift formula. ; in, Pixel coordinates of waveguide flange surface The phase value of the package at that location; : The two-dimensional pixel x and y coordinates of the waveguide flange surface image; Phase is The sine stripe image in pixels The grayscale value at that location; Phase is The sine stripe image in pixels The grayscale value at that location; Phase is The sine stripe image in pixels The grayscale value at that location; Phase is The sine stripe image in pixels The grayscale value at that location.

[0042] Absolute phase is obtained through multi-frequency heterodyne method or quality map-guided phase expansion algorithm. Finally, by combining the system calibration parameters (projection-camera geometry), the phase is mapped to three-dimensional coordinates. The point cloud of the waveguide flange surface was reconstructed, with a spatial sampling density of 50 points / mm² and a depth accuracy better than 5 μm.

[0043] Step (2), extract key geometric features; After obtaining the 3D point cloud and 2D image, the system performs multimodal feature extraction in parallel: Center axis identification: First, Canny edge detection and Hough circle transform are applied to initially screen the outer contour of the waveguide flange surface in the 2D image; then, a set of points with Z coordinates within ±50 μm of the waveguide flange surface is extracted from the 3D point cloud, and sub-pixel interpolation technology is used to improve the edge positioning accuracy to 0.1 pixels; finally, least squares circle fitting is performed on the screened boundary points, and the coordinates of the circle center are the center of the waveguide port. ; Normal vector and flatness calculation: Apply principal component analysis (PCA) to the above subset of point clouds to find the eigenvector corresponding to the smallest eigenvalue of the covariance matrix. That is, the normal vector of the waveguide flange surface; the distance from all points to the fitting plane. Flatness error is defined as ,like If the diameter is greater than 20 μm, it is marked as an "anomaly of waveguide flange warping". in: : The first point cloud of the waveguide flange surface The perpendicular distance from each point to the fitting plane; : The first point cloud of the waveguide flange surface The three-dimensional spatial coordinates of each point; : The three-dimensional spatial coordinates of the waveguide port center; : Normal vector of the waveguide flange fitting plane (obtained by performing principal component analysis plane fitting on 3D point cloud data); Absolute value operation; Dot product operation of vectors.

[0044] : Flatness error value of waveguide flange surface; Distance from all points on the waveguide flange surface to the fitted plane The maximum value in; Distance from all points on the waveguide flange surface to the fitted plane The minimum value in; Reference marker positioning: If the waveguide flange surface has positioning holes or laser-etched cross marks, the system accurately positions its three-dimensional coordinates through template matching and ellipse fitting, which are used as high-weight feature points for subsequent pose calculation.

[0045] Port Edge Continuity Assessment: To detect defects or burrs at port edges, the system simultaneously calculates the continuity of the port edges. Based on the outer contour of the waveguide flange extracted from the 2D image, samples are taken at equal intervals along the contour line, and the gray-level gradient change between adjacent sampling points is calculated. Simultaneously, combined with the 3D point cloud, height jumps at edge points are detected. If the gray-level gradient exceeds 50 gray levels or the height jump is greater than 10 μm, the edge is determined to be discontinuous and marked as a potential defect. Its location information is recorded for subsequent quality control.

[0046] Step (3), construct the spatial pose model; The system loads a preset standard assembly model, which is stored in STEP format and contains CAD geometric data of the ideal waveguide port and a global coordinate system definition (the origin is located at the theoretical docking center, and the Z-axis is along the waveguide propagation direction).

[0047] The at least 16 feature points extracted in step (2) (including the port center, 3 positioning holes, and 12 uniformly distributed waveguide flange edge points) are matched with the corresponding point set predefined in the standard assembly model. The coordinates of the uniform sampling points of the ideal port center, the ideal positioning hole center, and the ideal waveguide flange edge have been marked in the standard model.

[0048] Subsequently, the Singular Value Decomposition (SVD) algorithm is used to solve for the optimal rigid body transformation matrix. , making Minimum; in: : Rigid body transformation matrix in three-dimensional space ; In the preset standard assembly model, the first The ideal three-dimensional spatial coordinates of each feature point; The first result obtained from visual inspection and 3D reconstruction Measured three-dimensional spatial coordinates of each feature point; The square of the L2 norm of a vector; Feature point index: The number of feature points involved in the calculation is no less than 16 and they are evenly distributed on the waveguide flange surface and positioning structure.

[0049] Specifically, let , Given the ideal and measured point sets (centroids removed), respectively, the covariance matrix is... ,in, The covariance matrix between the ideal point set and the measured point set; The ideal feature point set matrix after centroid removal. , This represents the number of feature points; The measured feature point set matrix after centroid removal. , This represents the number of feature points; : Matrix transpose operation.

[0050] Its SVD decomposition yields Rotation matrix (like (Then correct the last column to be negative), translation vector Therefore, the six-degree-of-freedom deviations can be obtained: , , (Unit: μm) and , , (Unit: degrees), update frequency ≥ 10 Hz; : The translational deviation of the current waveguide port relative to the target docking position along the X, Y, and Z axes, in micrometers (μm); : The rotational angle deviation of the current waveguide port relative to the target docking position around the X, Y, and Z axes, in degrees (°).

[0051] in: Covariance matrix The matrix obtained through singular value decomposition; The rotation matrix (3×3 orthogonal matrix) of a rigid body transformation, if If so, then correct the last column to be negative; Rotation matrix The determinant of; Translation vector of a rigid body transformation; Ideal feature point set The three-dimensional coordinates of the centroid; : Measured feature point set The three-dimensional coordinates of the centroid.

[0052] Step (4): Generate a deviation analysis report and guidance instructions; The system calls a preset assembly tolerance threshold model based on the waveguide's operating frequency band (38 GHz): axial offset tolerance ±50 μm, angular deflection tolerance ±0.1°, and waveguide flange parallelism tolerance ±30 μm. If any deviation exceeds the limit, the central processing unit immediately generates an augmented reality (AR) guided interface: a colored vector arrow (red indicates the direction of the deviation, green indicates the allowable range) is overlaid on the real-time video stream, and the current deviation value and target value are displayed in a floating window; simultaneously, the system determines the operation mode. If it is in manual mode, only a visual prompt is output; if it is in automatic mode, the required adjustment amount is calculated based on the current measured pose and the target pose. Let the current measured pose transformation matrix be... The target pose transformation matrix is Then it must satisfy Solving for The transformation matrix is ​​decomposed into translation components along the X, Y, and Z axes. and rotational components about each axis The motion commands for the six-degree-of-freedom precision adjustment platform are sent to the servo driver via the EtherCAT bus. in: : The rigid body transformation matrix required for the waveguide port to adjust from its current pose to its target pose; Current measured pose transformation matrix The inverse matrix; Matrix multiplication.

[0053] In addition, based on the port edge continuity calculated in step (2), if there are edge defects or discontinuities (i.e., areas with grayscale gradients exceeding 50 gray levels or height jumps greater than 10 μm), the system immediately generates an alarm containing defect location information and suspends the calibration process to avoid incorrect pose calculations based on defect features. Operators can check and handle the defects according to the alarm information and then restart the calibration.

[0054] Step (5), Dynamic closed-loop calibration: After receiving the command, the six-degree-of-freedom precision adjustment platform completes the pose adjustment within 100 ms. After the adjustment is completed, the system immediately triggers a new round of image acquisition, forming a closed loop of "perception → analysis → guidance → adjustment".

[0055] This process continues iteratively until all deviations converge within the tolerance range (e.g.: , (etc.), at which point the system determines that the alignment is successful and sends a "physical connection allowed" signal to the robotic arm controller, triggering the locking mechanism of the waveguide flange to complete the final assembly. The entire closed-loop process is usually completed within 3 to 5 iterations, with a total calibration time of ≤7.5 seconds, meeting the 8-second cycle requirement.

[0056] Furthermore, if the environmental disturbance suppression module detects excessive vibration or temperature drift during any iteration cycle, the system will pause the closed loop and invoke the online calibration compensation algorithm: based on historical calibration data and current environmental parameters, a multinomial regression model is used to correct the camera intrinsic parameters (e.g., ...). ,in: The corrected camera Directional equivalent focal length; :camera Initial equivalent focal length in the direction; : Polynomial regression coefficients; Changes in ambient temperature; : Environmental vibration acceleration value. ), to ensure that the accuracy of subsequent 3D reconstruction is not affected.

[0057] After calibration, the system automatically generates a log file containing initial deviation, number of iterations, final pose, and environmental parameters for quality traceability.

[0058] In summary, this embodiment achieves non-contact, visualized, and closed-loop high-precision calibration of waveguide antennas before assembly by deeply integrating high-precision visual perception, real-time 3D reconstruction, robust pose calculation, and precision servo control. This effectively solves the core pain points of traditional methods, such as reliance on a posteriori verification, insufficient accuracy, and low efficiency.

[0059] Example 2: This example demonstrates multi-port collaborative calibration, applied to the assembly of a phased array antenna array containing 4×4 waveguide ports. The system deploys 8 sets of high-resolution industrial cameras (each covering a 2×2 port area), simultaneously acquiring images and structured light projection data from all ports. The system hardware modules are basically the same as in Example 1, only increasing the number of cameras and corresponding image acquisition card channels.

[0060] Steps (1) to (2): Process each port in parallel, performing the same high-resolution image acquisition, 3D reconstruction, and geometric feature extraction as in Example 1, to obtain the results for each port. The measured feature point set and its initial pose estimation.

[0061] Step (3) Constructing the global pose graph: Establishing the world coordinate system Each port The measured pose is recorded as (From the port local coordinate system to) (transformation), the ideal pose of each port in the standard model is denoted as . Using the poses of each port as nodes, construct two types of edge constraints: Absolute constraint: The deviation of each port from its ideal pose, i.e., the error term. ;in: : No. The absolute pose error term of each waveguide port; : No. The measured rigid body transformation matrix from the local coordinate system to the world coordinate system for each port; : No. Measured rigid body transformation matrix at each port The inverse matrix; : No. An ideal rigid body transformation matrix from the local coordinate system to the world coordinate system for each port; : Index of the waveguide port.

[0062] Relative constraints: The relative pose between adjacent ports should be consistent with the design value, i.e. .

[0063] Graph optimization algorithms (such as Levenberg-Marquardt) are used to iteratively solve all... Make the total error function: ; in: : Total error value of multi-port pose graph optimization; : No. The squared L2 norm of the absolute pose error term of each port; The set of adjacent port pairs in a multi-port array; : No. The measured rigid body transformation matrix from the local coordinate system to the world coordinate system for each port; : No. The inverse matrix of the ideal rigid body transformation matrix for each port; : No. An ideal rigid body transformation matrix from the local coordinate system to the world coordinate system for each port; The square of the 2-norm of a matrix; Waveguide port index express , These are adjacent ports.

[0064] Step (4): Calculate the required adjustment for each port based on the optimized pose. If a certain port... pose deviation If the tolerance exceeds the preset single-port tolerance, the operator will be guided through the AR interface or the multi-axis robotic arm will adjust it sequentially. Simultaneously, the axis parallelism deviation between ports is calculated, defined as any two ports... The propagation direction vector (i.e. If the maximum parallelism deviation between the three columns exceeds 50 μm (equivalent arcseconds), then continue to optimize and adjust. in: : No. The pose deviation matrix of each waveguide port; : No. The inverse matrix of the ideal rigid body transformation matrix for each port.

[0065] Step (5): Repeat the above process until all port pose deviations and port parallelism meet the requirements, completing the overall array alignment. Finally, trigger the synchronous physical connection of each port.

[0066] By optimizing the coordination through graphs, the cumulative errors caused by port-by-port calibration can be avoided, ensuring the phase consistency of the array.

[0067] Example 3: Based on Example 1, this example introduces an environmental adaptive dynamic compensation mechanism to further improve the calibration accuracy and robustness of the system in harsh environments.

[0068] First, an error prediction model integrating a physical model and data-driven approaches is constructed. The system collects the following environmental parameters in real time: temperature values ​​from a temperature sensor array (distributed cameras, platform, and structured light projector). (j=1,2,…,m), vibration acceleration vector measured by a triaxial accelerometer and the relative humidity measured by the humidity sensor. All data was recorded synchronously at a sampling rate of 1 kHz.

[0069] The model building process is as follows: Physical model section: Based on thermo-structural coupled finite element analysis, thermal deformation models of key components (camera lens, camera mount, adjustment platform) were pre-established. For the camera, temperature changes... Causes changes in lens focal length and principal point offset The relationship is approximately linear: , , ; in: The change in the focal length of the camera lens; : The coefficient of thermal expansion of the camera lens focal length (obtained from calibration experiments); Temperature change of the camera lens (current temperature - reference temperature); : The offset of the horizontal coordinate of the camera's principal point; : The coefficient of thermal expansion of the principal point x-coordinate of the camera (obtained from calibration experiments); : The offset of the camera's principal point ordinate; : The coefficient of thermal expansion of the principal point ordinate of the camera (obtained from calibration experiments); Temperature change of the camera sensor (current temperature - reference temperature).

[0070] For the platform, uneven temperature distribution causes platform attitude drift, which is modeled as the thermal deformation vectors of each joint. ,in This is the thermal influence coefficient matrix. This represents the temperature difference vector of each temperature sensor relative to the reference temperature.

[0071] Data-driven part: A Kalman filter is used to fuse multi-sensor data to estimate the system state in real time, including the true values ​​of camera intrinsic parameters and the true pose drift of the platform. The state vector is defined as: ; in: : No. The Kalman filter state vector of the step; The camera is , Equivalent focal length in direction; : The x and y coordinates of the camera's principal point; The six-degree-of-freedom drift vector of a six-degree-of-freedom precision adjustment platform; The rate of change of the drift vector of a six-degree-of-freedom precision-adjustable platform; : Transpose of a vector; : The number of iterations in the Kalman filter.

[0072] The observation vector includes temperature sensor readings, vibration sensor readings, and the pose changes of a reference target fixed on the base, obtained through visual measurements. The Kalman filter prediction equation is based on the thermal and mechanical models of the physical model, and the update equation corrects the state estimate based on the observations. Specifically, the prediction steps are as follows: ; ; in: : No. The prior state estimation vector for the step; : No. The state transition matrix of the step (derived from the heat conduction and mechanical physical model); : No. The posterior state estimation vector of the step; : No. The control matrix of the step; : No. The environmental parameter input vector for the step (temperature change rate, vibration acceleration, etc.); : No. The process noise vector of the step; : No. The prior covariance matrix of the step; : No. The posterior covariance matrix of the step; : No. Step process noise The covariance matrix.

[0073] Update steps: ; ; ; in: : No. The Kalman gain matrix of the step; : No. The observation matrix of the step; : No. The covariance matrix of the step observation noise; Inverse operation of a matrix; : No. The posterior state estimation vector of the step; : No. The measured observation vectors of each step (temperature sensor readings, vibration sensor readings, visual measurement pose of the reference target, etc.); : The identity matrix that matches the dimension of the state vector; : No. The posterior covariance matrix of the step.

[0074] The model training process is as follows: During the offline phase, multiple calibration experiments were conducted on a temperature control chamber and a vibration table to collect sensor data and visual measurement data under different temperature and vibration conditions, which were used to initially determine the physical model parameters. And the noise covariance matrix of the Kalman filter. During the online phase, the filter continuously updates its state, while simultaneously using new observation data to adaptively adjust the model parameters (e.g., using recursive least squares updates). (etc.) to enable the model to learn itself.

[0075] Model application process: In each cycle of the calibration process, the following steps are performed: Collect current environmental parameters and run Kalman filtering to update the state estimate.

[0076] Based on the estimated camera intrinsic parameters, the coordinate transformation of the 3D reconstruction is corrected, that is, using the currently estimated... Recalculate the 3D point cloud.

[0077] Based on the estimated platform drift The pose deviation calculated in step (3) is compensated: the actual deviation should be... (Considering the impact of platform drift); where: : Actual pose deviation matrix after eliminating the influence of platform drift; The pose deviation matrix directly calculated by visual inspection; : The inverse matrix of the drift transformation matrix of the six-degree-of-freedom adjustment platform.

[0078] When generating adjustment instructions, in addition to the original pose correction amount Additional feedforward compensation term The predicted changes are used to offset impending environmental drift. That is, the final instruction issued is... ,in: : The final pose adjustment matrix issued to the six-degree-of-freedom precision adjustment platform; The basic adjustment matrix based on pose deviation calculation; : Feedforward compensation matrix for environmental drift.

[0079] By integrating physical models with data-driven Kalman filter compensation, the system can proactively predict and offset measurement and positioning errors caused by environmental changes, significantly improving the stability and accuracy of calibration.

[0080] Technical effect comparison: Under the same environmental conditions (temperature change rate 1℃ / min, vibration amplitude 0.15g), the original scheme (using only pause calibration + polynomial regression) without the compensation of this embodiment is compared with the scheme of this embodiment. The results are shown in Table 1 below: As can be seen, this embodiment significantly improves calibration performance in complex environments through environmental adaptive dynamic compensation.

[0081] Example 4: Based on Example 2, this example introduces digital twin technology to simulate and optimize the calibration process of the multi-port array, realize intelligent path planning, further shorten the calibration time and improve consistency.

[0082] First, a digital twin model of the waveguide array is constructed, which includes the following parts: Geometric model: An accurate CAD model of the waveguide array, including the geometric dimensions and relative positions of all ports and waveguide flanges.

[0083] Kinematic model: Describes the kinematics of a six-DOF precision adjustment platform (or robotic arm), including the range of motion and speed limits of each joint, as well as the mutual influences when adjusting ports (e.g., adjusting one port may slightly affect adjacent ports through platform coupling).

[0084] Error propagation model: Based on historical data and finite element analysis, an error propagation function is established during the adjustment process to predict the impact of adjustment actions on the final pose. This model can be expressed as: ; in: : The expected pose deviation vector of the waveguide port after the adjustment action, with dimension . ( (Number of ports); Error transfer function (obtained by system identification or simplified based on physical model); : Adjust the current pose deviation vector of the front waveguide port, with dimension . ( (Number of ports); : Single adjustment motion vector (including the port index to be adjusted and the translation / rotation adjustment amount).

[0085] The digital twin model is synchronized with the physical system in real time: the current pose, environmental parameters and platform status of each port are acquired in real time through sensors, and the status of the twin model is updated.

[0086] Model Predictive Control (MPC) optimizes the adjustment sequence: At each decision point, based on the current state, the system uses a digital twin model to predict the results of different adjustment sequences over several future steps (e.g., 3 steps), and selects the optimal sequence to execute the first step. Specifically: Define state , : No. Step model predicts the state of control; Waveguide array, from the 1st to the 1st The current six-degree-of-freedom deviation of each port; The number of iterations for model predictive control; : The total number of ports in the waveguide array.

[0087] Define Action To select the port index to be adjusted and adjustment vector (Translation and rotation), where the adjustment amount is selected from a preset discrete set (e.g., fine-tuning step size).

[0088] Define reward function Taking into account both the reduction in error after adjustment and the adjustment time: ; in: : Reward value for a single adjustment action; : Weighting coefficients of the error term (positive coefficients, obtained from calibration); The total number of ports in the waveguide array; : No. Each port executes the adjusted pose deviation vector; : No. The squared L2 norm of the pose deviation vector after port adjustment; : Weighting coefficient of the time term (positive coefficient, obtained from calibration); : Execute the Step adjustment movement Time required; : No. Step adjustment action (select the port to be adjusted + adjustment amount); : Number of iterations for model predictive control.

[0089] At each step, the system uses a digital twin model to represent the current state. For all possible actions Perform simulation to predict the next state. The immediate reward is calculated; then, a recursive search is performed on the subsequent steps to select the action sequence that maximizes the cumulative reward. Due to the large action space, heuristic pruning or a reinforcement learning-based policy network can be used to accelerate the process. In practical applications, this can be simplified: adjustments are considered for each port individually, sorted based on error magnitude, and then the effects of different sequences are verified using a Siamese model to select the optimal sequence.

[0090] Specific implementation steps: 1. Initialization: Obtain the initial pose deviation of each port and construct the state of the digital twin model.

[0091] 2. Iterative optimization: Using the MPC algorithm, considering the future. Step (e.g.) ), generate candidate action sequences.

[0092] For each candidate sequence, a digital twin model is invoked to simulate the state after each adjustment step, and the reward is accumulated.

[0093] Select the action sequence with the highest cumulative reward and execute the first step (i.e., adjust a certain port).

[0094] After adjustment, the new pose is actually measured and the state of the twin model is updated (due to model error, there may be a difference between the actual measurement and the prediction, and the state can be corrected).

[0095] 3. Repeat step 2 until all port deviations meet the requirements.

[0096] 4. Calibration complete.

[0097] Model training process: In the offline phase, a fast prediction model (such as a neural network) is trained using historical calibration data or Monte Carlo simulation data as a simplified alternative to the digital twin model for rapid evaluation in MPC. Simultaneously, a policy network (such as a deep Q-network) can be trained to directly output the optimal action; however, this embodiment focuses on MPC, combined with twin model simulation.

[0098] Technical Performance Comparison: Table 2 shows the results of 100 simulated calibration tests on a 4×4 waveguide array, comparing the results of using only graph optimization (Example 2) with incorporating MPC path planning (this example). Table 2: It is evident that the combination of digital twins and MPC significantly improves the efficiency and accuracy of multi-port calibration, while reducing overshoot and repeated adjustments.

[0099] Environmental adaptive compensation enables the system to operate stably in complex industrial environments, avoiding calibration failures caused by environmental changes; intelligent path planning greatly shortens the calibration time of multi-port arrays, meeting the demands of high-speed production. The combination of these two (for example, environmental compensation can also be introduced in Example 4) enables all-weather, high-efficiency, and high-precision automated assembly, promoting the development of high-end manufacturing such as phased array radar and satellite communications.

[0100] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention.

[0101] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. It should be noted that any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A visual calibration method for waveguide antenna assembly accuracy, characterized in that, Includes the following steps: With the waveguide antenna and the docking component in a non-contact state, at least two sets of high-resolution industrial cameras are used to simultaneously acquire images of the waveguide flange surface and its surrounding structure from different perspectives. A specific coded pattern is then projected onto the surface of the waveguide flange surface using a structured light projection device, and the three-dimensional morphology information of the waveguide flange surface is reconstructed through a phase demodulation algorithm. Based on the image data and three-dimensional topography information, the central axis of the waveguide port, the outer contour of the waveguide flange surface, and the positioning holes or reference marks are identified, and the normal vector, flatness, and continuity of the port edge of the waveguide flange surface are calculated. The extracted geometric features are aligned with the preset standard assembly model in the coordinate system, and the singular value decomposition algorithm is used to solve the six-degree-of-freedom pose deviation of the current waveguide port relative to the target docking position. The six-degree-of-freedom pose deviation includes translational deviation along the X, Y, and Z axes and rotational angle deviation around each axis. Based on the positional deviation and combined with the preset assembly tolerance threshold model, it is determined whether the current state meets the docking conditions. If there is an out-of-tolerance, a visual guidance interface is generated to instruct the operator or automatic actuator to adjust the position and attitude of the waveguide antenna along a specific direction. At the same time, based on the calculated port edge continuity, if there is edge defect or discontinuity, an alarm containing defect location information is generated and the calibration process is paused. The aforementioned steps are repeated continuously during the adjustment process until the positional deviation converges to the allowable range, completing high-precision alignment and triggering physical connection action.

2. The waveguide antenna assembly accuracy visual calibration method according to claim 1, characterized in that, The high-resolution industrial camera is equipped with a telecentric lens with adjustable focal length, and provides uniform illumination with a ring LED light source. The structured light projection device projects a sequence of sinusoidal stripes, and performs phase demodulation through a four-step phase shifting method. The reconstructed three-dimensional point cloud spatial sampling density is not less than 50 points per square millimeter, and the depth accuracy is better than 5 micrometers.

3. The waveguide antenna assembly accuracy visual calibration method according to claim 1, characterized in that, The identification of the waveguide port center axis includes: detecting the circular contour on the waveguide flange surface through Hough transform, accurately locating the contour boundary by combining a sub-pixel edge detection algorithm, and fitting a least-squares circle to determine the port center; the waveguide flange surface normal vector is obtained by performing principal component analysis on the three-dimensional point cloud data to perform plane fitting, and the flatness error is defined as the difference between the maximum distance and the minimum distance from all points to the fitted plane.

4. The waveguide antenna assembly accuracy visual calibration method according to claim 1, characterized in that, The preset standard assembly model includes the ideal geometric parameters of the waveguide port and the definition of the global coordinate system. The number of feature points participating in the singular value decomposition algorithm is no less than 16, and they are evenly distributed on the waveguide flange surface and the positioning structure. The pose calculation update frequency is no less than 10 Hz.

5. The waveguide antenna assembly accuracy visual calibration method according to claim 1, characterized in that, The assembly tolerance threshold model is dynamically set according to the waveguide operating frequency band. The axial alignment tolerance, angular deflection tolerance and waveguide flange parallelism tolerance corresponding to the high-frequency operating frequency band are set to preset thresholds respectively. The visual guidance interface is superimposed on the real-time video stream in an augmented reality manner, displaying deviation vector arrows and numerical prompts, and supports manual fine-tuning or automatic servo control.

6. The waveguide antenna assembly accuracy visual calibration method according to claim 1, characterized in that, The adjustment process is performed by a six-degree-of-freedom precision adjustment platform, which has three-axis translational strokes (X / Y / Z) and three-axis rotational strokes. The repeatability is better than ±2 micrometers, the angular resolution is better than 0.005 degrees, and the command response delay is less than 1 millisecond.

7. The waveguide antenna assembly accuracy visual calibration method according to claim 1, characterized in that, It also includes an environmental interference suppression step: before image acquisition, environmental parameters are collected in real time by temperature sensors and vibration monitoring units. When the rate of temperature change exceeds the preset temperature change rate threshold or the vibration acceleration exceeds the preset vibration threshold, the calibration process is paused and an online calibration compensation algorithm is started to dynamically correct the camera's intrinsic and extrinsic parameters.

8. The waveguide antenna assembly accuracy visual calibration method according to claim 7, characterized in that, The online calibration compensation algorithm is based on historical calibration data and current environmental parameters. It uses a multinomial regression model to correct the camera focal length, principal point offset, and rotation and translation matrix, ensuring that the accuracy of 3D reconstruction is not affected by environmental disturbances.

9. The waveguide antenna assembly accuracy visual calibration method according to claim 1, characterized in that, It also includes a multi-port collaborative calibration step: when processing a multi-channel waveguide array, image data of all ports are acquired synchronously, a global coordinate system is established, and the relative pose relationship between each port is coordinated through a graph optimization algorithm to ensure the phase consistency of the overall array. After calibration, the axis parallelism deviation between each port is not greater than the preset parallelism threshold.

10. A visual calibration system for waveguide antenna assembly accuracy, characterized in that, It includes a multi-view high-resolution imaging subsystem, a structured light 3D reconstruction subsystem, an environmental perception and interference suppression module, a six-degree-of-freedom precision adjustment platform, and a central processing and control unit; The multi-view high-resolution imaging subsystem includes at least two sets of industrial cameras, which are arranged symmetrically at an angle on both sides of the waveguide flange surface. The structured light 3D reconstruction subsystem projects a sequence of sinusoidal fringes and synchronizes with the imaging subsystem; The environmental perception and interference suppression module integrates a temperature sensor and a triaxial accelerometer; The six-degree-of-freedom precision adjustment platform is used to perform pose adjustment; The central processing and control unit runs a vision processing software stack to execute the steps of the waveguide antenna assembly accuracy vision calibration method according to any one of claims 1 to 9.

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