Automatic mirror positioning method based on optical processing robot, computer device and computer readable storage medium

Abstract

The present application relates to the technical field of robot error calibration, and particularly relates to an automatic mirror positioning method based on an optical machining robot, a computer device and a computer readable storage medium, the automatic mirror positioning method comprises the following steps: determining a workpiece coordinate system of a mechanical arm, an initial attitude point position and a center thickness of a to-be-machined element; establishing an affine transformation relationship between the workpiece coordinate system plane and an image plane, realizing nine-point calibration, and outputting a calibration file; photographing and identifying, and outputting the coordinates and coordinate displacement of the workpiece coordinate system of the mechanical arm; and realizing automatic mirror positioning; the method provided by the present application does not have a repeatability requirement for the position of the to-be-machined element, can directly take a photo to realize center positioning for the to-be-machined element in a large field of view, and is more convenient for a visual calibration process, does not need the mechanical arm to move multiple times to find an edge for positioning and calibration, the whole calibration and positioning process does not need human intervention, realizes high automation, and improves the efficiency of center positioning.

Description

Technical Field

[0001] This invention belongs to the field of non-imaging optical design technology, and particularly relates to an automated mirror positioning method based on an optical processing robot, a computer device, and a computer-readable storage medium. Background Technology

[0002] Optical processing robots are used to process aspherical optical mirrors. Compared to CNC machine tools, robotic equipment has the advantages of high repeatability and positioning accuracy, but low absolute positioning accuracy. The processing of aspherical optical mirrors using optical processing robots often requires human operation or other mechanical assistance to center the workpiece and establish a machining coordinate system. The absolute positioning accuracy of the robotic processing equipment directly affects the calibration accuracy of the machining coordinate system and the positioning accuracy of machining points during the processing.

[0003] The patent disclosed in CN112192370A, "A Robotic Arm Positioning Method for Polishing Spatial Curved Surfaces of Optical Components," involves using a robotic processing device to station three points on the surface to be processed, with the positions being right angles. The center point of the stationed position is measured using a laser interferometer, and the coordinate data of the robotic arm stationed at the three stationed points are combined to match the robot tool coordinate system with the processing coordinate system, thereby achieving the positioning work in the early stage of processing. However, the patent specifies that the repeatability positioning accuracy needs to reach 30µm. The positioning method has stringent requirements for the repeatability accuracy of the workpiece's placement on the optical processing platform. Although the method can achieve a good positioning effect for the workpiece, the operation process is relatively complex, the implementation is difficult, and the technical requirements are high, making it difficult to guarantee the implementation efficiency during the processing.

[0004] The patent disclosed in CN119115935A, titled "A Vision-Based Composite Robot Hand-Eye Calibration and Workpiece Positioning Device and Method," uses a checkerboard pattern and Aruco QR codes for hand-eye calibration and TCP calibration to unify the relationship between the workpiece coordinate system and the robotic arm coordinate system, thereby enabling the robotic arm to identify the position of the workpiece and perform grasping operations. However, this method requires first constructing a complex support fixture based on the robotic arm's working space and the binocular camera's field of view. Then, it necessitates intrinsic parameter calibration of the two cameras, checkerboard hand-eye calibration, and subsequent QR code pose error correction. The operation process is complex and inefficient, with a small positioning space. Furthermore, this patented method, used in conjunction with a grasping tool head, cannot complete TCP calibration of optical processing tools, making it difficult to achieve center positioning for optical processing robots.

[0005] Existing robotic optical machining positioning schemes mainly rely on the known geometrical relationship between the geometrical center of the optical machining tool head and the geometrical pose of the end flange center of the robotic arm. The robotic arm moves the tool head to a predetermined position to determine the edge points to be processed. Then, the center point is calculated based on the geometrical characteristics of the component to be processed. Finally, the calculated center point is used as the origin of the machining coordinate system. This positioning method suffers from parallax, has low positioning efficiency for large-diameter components, and has methodological limitations for positioning irregularly shaped components (shapes whose center point is difficult to calculate through geometric features). Summary of the Invention

[0006] In view of this, the present invention provides an automated mirror positioning method based on an optical processing robot, which can achieve more convenient visual calibration, can calibrate the feature points of various large-diameter and irregularly shaped components to be processed, and improve the positioning efficiency of the optical processing robot for mirror feature points.

[0007] To achieve the above objectives, the technical solution created by this invention is implemented as follows: This invention provides an automated mirror positioning method based on an optical processing robot. The optical processing robot includes a robotic arm, an optical processing platform, a monocular vision camera lens module, a monocular vision positioning module, and a support fixture. The optical processing platform is used to place the component to be processed, the monocular vision positioning module is used to position the monocular vision camera lens module, and the support fixture is used to support the installation of the monocular vision camera lens module. The automated mirror positioning method based on the optical processing robot includes the following steps: S1. Determine the workpiece coordinate system of the robotic arm and determine the initial posture points of the robotic arm. and the center thickness d of the component to be processed; S2. Establish the affine transformation relationship between the workpiece coordinate system plane and the image plane of the robotic arm, realize nine-point calibration, and output calibration file; S3. Based on the calibration file, the target on the optical processing platform is photographed and identified, and the coordinates of the workpiece coordinate system of the robotic arm and the coordinate displacement of the robotic arm for control and positioning are output; thus realizing automated mirror positioning.

[0008] Furthermore, the automated mirror positioning method based on optical processing robots also includes the following steps: S4. Perform positioning accuracy verification: After achieving automated mirror positioning, calculate the plane error and verify the positioning accuracy by comparing the visual positioning results with the contact measurement reference value; S4 is located after S3, or between S2 and S3.

[0009] Furthermore, the automated mirror positioning method based on optical processing robots also includes the following steps: S0. Determine whether the component to be processed is a small-diameter component or a large-diameter component, wherein the diameter of the small-diameter component is ≤0.5m and the diameter of the large-diameter component is >0.5m; If the component to be processed is a small-diameter component, the monocular vision camera lens module is installed at the end of the robotic arm using the support fixture; If the component to be processed is a large-diameter component, the monocular vision camera lens module is mounted on a fixed reference by the support fixture. The fixed reference is independent of the robotic arm and is located outside the robotic arm. S0 is located before S1, or between S1 and S2.

[0010] Furthermore, the component to be processed is a small-diameter component, and the reference module used for the nine-point calibration is a standard cylinder, which is fixedly mounted on the optical processing platform; the nine-point calibration process includes: S21. Control the robotic arm to move nine points. , Nine photos were taken at nine different locations, resulting in nine images; initial pose location. That is, the nine-point mark. Location; S22. During image processing of the nine images, each image is binarized to highlight and fit the edge of the standard cylinder. Based on the edge fitting extraction of the image, the center pixel coordinates of the standard cylinder are calculated using the least squares algorithm. , ; S23. The affine transformation relationship between the workpiece coordinate system plane of the robotic arm and the image pixel coordinate system plane is established as follows: ; The coordinates of the center of the reference module in the image pixel coordinate system; Let be the coordinates of the center of the end probe of the robotic arm in the workpiece coordinate system of the robotic arm; The rotation angle from the image pixel coordinate system to the robot arm workpiece coordinate system; and These represent the scaling factors from the image pixel coordinate system to the robotic arm workpiece coordinate system in the u-axis and v-axis directions, respectively. and These represent the translation amounts from the image pixel coordinate system to the robot arm workpiece coordinate system; , , , These are the parameters of the comprehensive transformation matrix, which includes rotation and scaling. ; ; ; ; T is the two-dimensional affine transformation matrix from the image pixel coordinate system to the robot arm workpiece coordinate system. use and The affine transformation relationship T between the two coordinate systems is calculated from the point data, and the calibration file is output.

[0011] Furthermore, after outputting the calibration file, step S3 includes: S31. Control the robotic arm to return to its initial posture position. ; S32. Place the component to be processed at any position on the optical processing platform, and obtain the pixel coordinates of the feature points of the component to be processed by taking pictures through the monocular vision camera lens module. ; S33. Calculate and output the workpiece coordinate system coordinates of the robotic arm using the calibration file. ; S34. Control the descent height of the robotic arm. , The height in the z-direction at the initial attitude point is used to establish the positioning point of the component to be processed. .

[0012] Furthermore, after outputting the calibration file, before S3, the automated mirror positioning method based on optical processing robots also includes the following steps: S41. Arbitrarily change the position of the standard cylinder on the optical processing platform, and use the probe at the end of the robotic arm to achieve automatic three-point contact positioning to center the standard cylinder, thereby obtaining the horizontal coordinates of the robotic arm end-effector posture in the workpiece coordinate system based on the center alignment of the probe. This is the contact measurement reference value; S42. Control the robotic arm at the initial posture point. The optical processing platform is photographed, and the pixel coordinates of the center of the standard cylinder at the current position are extracted. and workpiece coordinate system coordinates This refers to the comparison of visual localization results; S43. Compare coordinate data With coordinate data Calculate the plane error and verify the positioning accuracy; the plane error is the Euclidean distance D: .

[0013] Furthermore, the component to be processed is a large-diameter component, and the reference module used for the nine-point calibration is a cylindrical protrusion. The cylindrical protrusion is fixedly disposed at the center position of the nine-point calibration plate, forming an integral part with the nine-point calibration plate. The nine-point calibration plate is placed on the optical processing platform, and the nine-point calibration process includes: S211. Take photos of the nine centers of the nine-point calibration plate to obtain nine images; during image processing, binarize each image to highlight and fit the edges of the cylindrical protrusions, extract the edges based on the image edge fitting, and calculate the center pixel coordinates of the nine centers using the least squares algorithm. , ; S222. Utilizing the initial attitude point The robot arm's movement was calculated, along with the workpiece coordinates at the nine center positions. , The center point is / Each center includes a circular marker, and the coordinates of the nine centers in the workpiece coordinate system of the robotic arm are as follows: ; ; ; ; ; ; ; ; ; in, These are the coordinates of the origin of the basic coordinate system obtained through contact positioning; The design spacing between the centers of adjacent circular marks on the calibration plate; The design diameter of the circular logo; S233. The affine transformation relationship between the workpiece coordinate system plane of the robotic arm and the image pixel coordinate system plane is established as follows: ; The coordinates of the center of the reference module in the image pixel coordinate system; Let be the coordinates of the center of the end probe of the robotic arm in the workpiece coordinate system of the robotic arm; The rotation angle from the image pixel coordinate system to the robot arm workpiece coordinate system; and These represent the scaling factors from the image pixel coordinate system to the robotic arm workpiece coordinate system in the u-axis and v-axis directions, respectively. and These represent the translation amounts from the image pixel coordinate system to the robot arm workpiece coordinate system; , , , These are the parameters of the comprehensive transformation matrix, which includes rotation and scaling. ; ; ; ; T is the two-dimensional affine transformation matrix from the image pixel coordinate system to the robot arm workpiece coordinate system. use and The affine transformation relationship T between the two coordinate systems is calculated from the point data, and the calibration file is output.

[0014] Furthermore, after outputting the calibration file, step S3 includes: S311. Place the component to be processed at any position on the optical processing platform, and obtain the pixel coordinates of the feature points of the component to be processed by taking pictures through the monocular vision camera lens module. ; S322. Through the affine transformation relation T, according to the calculation formula... ; Pixel coordinates of feature points Transform to horizontal coordinates in the workpiece coordinate system of the robotic arm ; S333. Determine the Z-axis height. Control the robotic arm to adjust the height difference = , The initial attitude point is the height in the z-direction, which enables the positioning point of the component to be processed. .

[0015] Furthermore, after outputting the calibration file and before S3, the automated mirror positioning method based on optical processing robots also includes the following steps: S411. Using the probe at the end of the robotic arm to detect the cylindrical protrusion for center positioning, obtain the horizontal coordinates of the robotic arm end-effector posture in the workpiece coordinate system based on the center alignment of the probe. This is the contact measurement reference value; S422. Control the robotic arm at the initial posture point. The optical processing platform is photographed, and the pixel coordinates of the center of the cylindrical protrusion at the current position are extracted. and workpiece coordinate system coordinates This refers to the comparison of visual localization results; S433. Compare coordinate data With coordinate data Calculate the plane error and verify the positioning accuracy; the plane error is the Euclidean distance D: .

[0016] The present invention also provides a computer device, comprising: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform any of the automated mirror positioning methods based on optical processing robots.

[0017] The present invention also provides a computer-readable storage medium storing computer instructions for causing the computer to execute any of the described automated mirror positioning methods based on optical processing robots.

[0018] Compared with the prior art, the present invention can achieve the following beneficial effects: The automated mirror positioning method based on optical processing robots provided by this invention is adaptable to visual sensing modules with dual working modes and differentiated calibration strategies. For components of different sizes to be processed, two configurable visual working modes and calibration processes are clearly proposed. Specifically, for small-diameter components with a diameter ≤0.5m, the "eye on hand" mode is adopted, where the camera is mounted on the end of the robotic arm using a support fixture; for large-diameter components with a diameter >0.5m, the "eye outside hand" mode is adopted, where the camera is fixedly mounted on a fixed reference independent of the robotic arm. This achieves "single contact, global calculation" for calibration data acquisition, greatly improving calibration efficiency and reducing cumulative errors caused by multiple movements of the robotic arm. Moreover, both modes include a "positioning accuracy verification" step, which quantitatively evaluates the system's positioning accuracy by comparing the visual positioning results with the reference true value obtained through contact measurement, forming a complete and verifiable automated positioning closed loop.

[0019] This invention addresses the need for automated, non-contact, millimeter-level centering of meter-diameter aspherical optical mirrors using optical processing robots. It provides an automated mirror positioning method based on optical processing robots, enabling more convenient visual calibration and calibrating the feature points of various large-diameter and irregularly shaped components. Specifically, the method eliminates the requirement for repetitive positioning of the component on the optical processing platform. For components with a large field of view, centering can be achieved directly by taking photographs. The visual calibration process is more convenient, eliminating the need for multiple edge-finding movements of the robotic arm, simplifying the optical processing centering process, and enabling the identification and calibration of feature points of irregularly shaped components. The entire calibration and positioning process requires no human intervention, achieving a high degree of automation and improving the efficiency of centering. Attached Figure Description

[0020] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments and descriptions of the invention are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a schematic diagram of the basic process of the automated mirror positioning method based on an optical processing robot in a specific embodiment of the present invention; Figure 2 This is a schematic diagram of the automated mirror positioning and positioning accuracy verification method based on an optical processing robot in a specific embodiment of the present invention; Figure 3 This is a schematic diagram of a nine-point calibration plate used for eyes outside the hands in a specific embodiment of the present invention; Figure 4 This is a schematic diagram of the central vision module of an optical processing robot with its "eyes on its hands" in a specific embodiment of the present invention. Figure 5 This is a schematic diagram of the central vision module of an optical processing robot with its eyes outside its hands, as described in a specific embodiment of the present invention. Figure 6 A schematic diagram of a computer device for the end-to-end error calibration method of an optical processing robot platform in an embodiment of the present invention.

[0021] Figure label: 1. First robotic arm; 2. First optical processing platform; 3. First monocular vision positioning module; 4. Optical processing tool head; 5. First camera lens module; 6. First support fixture; 11. Second monocular vision positioning module; 12. Second robotic arm; 13. Second optical processing platform; 14. Second support fixture; 15. Robotic arm end probe; 41. Computer equipment; 42. Peripheral devices; 43. Processing unit; 44. Bus; 45. Network adapter; 46. I / O interface; 47. Display; 48. System memory; 49. RAM; 50. Cache; 51. Storage system; 52. Program / utility; 53. Program module. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not constitute a limitation thereof.

[0023] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0024] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing this invention 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, and therefore should not be construed as a limitation on this invention. Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0025] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0026] like Figure 1The diagram shows a basic flowchart of an automated mirror positioning method based on an optical processing robot according to a specific embodiment of the present invention. The optical processing robot includes a robotic arm, an optical processing platform, a monocular vision camera lens module, a monocular vision positioning module, and a support fixture. The optical processing platform is used to place the component to be processed, the monocular vision positioning module is used to position the monocular vision camera lens module, and the support fixture is used to support the installation of the monocular vision camera lens module. As can be seen from the diagram, the automated mirror positioning method based on the optical processing robot includes the following steps: S1. Determine the workpiece coordinate system of the robotic arm and determine the initial posture points of the robotic arm. and the center thickness d of the component to be processed; S2. Establish the affine transformation relationship between the workpiece coordinate system plane and the image plane of the robotic arm, realize nine-point calibration, and output calibration file; S3. Based on the calibration file, the target on the optical processing platform is photographed and identified, and the coordinates of the workpiece coordinate system of the robotic arm and the coordinate displacement of the robotic arm for control and positioning are output; thus realizing automated mirror positioning.

[0027] In a specific implementation, the automated mirror positioning method based on an optical processing robot further includes the following steps: S4. Perform positioning accuracy verification: After achieving automated mirror positioning, calculate the plane error and verify the positioning accuracy by comparing the visual positioning results with the contact measurement reference value; S4 is located after S3, or between S2 and S3.

[0028] In a specific implementation, the automated mirror positioning method based on an optical processing robot further includes the following steps: S0. Determine whether the component to be processed is a small-diameter component or a large-diameter component, wherein the diameter of the small-diameter component is ≤0.5m and the diameter of the large-diameter component is >0.5m; If the component to be processed is a small-diameter component, the monocular vision camera lens module is installed at the end of the robotic arm using the support fixture; If the component to be processed is a large-diameter component, the monocular vision camera lens module is mounted on a fixed reference by the support fixture. The fixed reference is independent of the robotic arm and is located outside the robotic arm. S0 is located before S1, or between S1 and S2.

[0029] In a specific implementation, if the component to be processed is a small-diameter component, a small-field-of-view vision camera positioning scheme is adopted, i.e., an "eye-on-hand" mode is used. The reference module used for the nine-point calibration is a standard cylinder, which is fixedly set on the optical processing platform. The nine-point calibration process includes: S21. Control the robotic arm to move nine points. , Nine images were taken at nine different locations; initial pose location. That is, the nine-point mark. Position; In the "eye in hand" working mode, the monocular vision camera moves with the robotic arm, and the initial shooting posture position coordinates of the monocular vision camera are: At this point, the robotic arm needs to move nine points to obtain the workpiece coordinates and the corresponding image coordinates. According to the definition of the point positions in the solution, the center position of the nine points is... The coordinates of the camera's initial shooting posture are consistent (the positions overlap); and This indicates the position of the center of the standard cylinder on the horizontal plane of the workpiece coordinate system. This indicates the height at which the robotic arm hovers on this plane; in this mode, this height is the position height adapted to the working field of view of the monocular vision camera.

[0030] S22. During image processing of the nine images, each image is binarized to highlight and fit the edge of the standard cylinder. Based on the edge fitting extraction of the image, the center pixel coordinates of the standard cylinder are calculated using the least squares algorithm. , ; S23. The affine transformation relationship between the workpiece coordinate system plane of the robotic arm and the image pixel coordinate system plane is established as follows: ; The coordinates of the center of the reference module in the image pixel coordinate system; in this embodiment, the coordinates of the center of the standard cylinder in the image pixel coordinate system are also given. Let be the coordinates of the center of the end probe of the robotic arm in the workpiece coordinate system of the robotic arm; The rotation angle from the image pixel coordinate system to the robot arm workpiece coordinate system; and These represent the scaling factors from the image pixel coordinate system to the robotic arm workpiece coordinate system in the u-axis and v-axis directions, respectively. and These represent the translation amounts from the image pixel coordinate system to the robot arm workpiece coordinate system; , , , These are the parameters of the comprehensive transformation matrix, which includes rotation and scaling. ; ; ; ; T is the two-dimensional affine transformation matrix from the image pixel coordinate system to the robot arm workpiece coordinate system. use and The affine transformation relationship T between the two coordinate systems is calculated from the point data, and the calibration file is output.

[0031] In a specific implementation, after outputting the calibration file, step S3 includes: S31. Control the robotic arm to return to its initial posture position. ; S32. Place the component to be processed at any position on the optical processing platform, and obtain the pixel coordinates of the feature points of the component to be processed by taking pictures through the monocular vision camera lens module. ; S33. Calculate and output the workpiece coordinate system coordinates of the robotic arm using the calibration file. ; S34. Control the descent height of the robotic arm. , The height in the z-direction at the initial attitude point is used to establish the positioning point of the component to be processed. .

[0032] In a preferred embodiment, after outputting the calibration file, before or after step S3, the following step is further included: S41. Arbitrarily change the position of the standard cylinder on the optical processing platform, and use the probe at the end of the robotic arm to achieve automatic three-point contact positioning, thereby centering the standard cylinder and obtaining the robotic arm end-effector posture based on the center alignment of the probe. This is the contact measurement reference value; S42. Control the robotic arm at the initial posture point. The optical processing platform is photographed, and the pixel coordinates of the center of the standard cylinder at the current position are extracted. and workpiece coordinate system coordinates This refers to the comparison of visual localization results; S43. Compare coordinate data With coordinate data Calculate the plane error and verify the positioning accuracy; the plane error is the Euclidean distance D: .

[0033] By repeatedly changing the position of the reference module and repeating the above process, the average positioning accuracy and repeatability accuracy of the system can be statistically obtained, thereby verifying the performance of the visual positioning system and the positioning accuracy of the positioning method of the present invention.

[0034] In a specific implementation, if the component to be processed is a large-diameter component, a large field-of-view vision camera positioning scheme is adopted, i.e., an "eye outside the hand" mode. The reference module used for the nine-point calibration is a cylindrical protrusion, which is fixedly set at the center of the nine-point calibration plate, forming an integral part with the nine-point calibration plate. The nine-point calibration plate is placed on the optical processing platform, and the nine-point calibration process includes: S211. Take photos of the nine centers of the nine-point calibration plate to obtain nine images; during image processing, binarize each image to highlight and fit the edges of the cylindrical protrusions, extract the edges based on the image edge fitting, and calculate the center pixel coordinates of the nine centers using the least squares algorithm. , ; S222. Utilizing the initial attitude point The robot arm's movement was calculated, along with the workpiece coordinates at the nine center positions. , The center point is / In the "eye outside the hand" mode, the monocular vision camera is in a separate image-taking position. The robotic arm needs to determine the center point of the cylindrical protrusion on the calibration plate through contact measurement. This point is the center position of the nine points. It can also be the hovering position of the robotic arm before positioning. ; This indicates the center position of the cylindrical protrusion on the horizontal plane of the workpiece coordinate system. This indicates the hovering height of the robotic arm on this plane; in this mode, this height is the safe hovering height for the robotic arm. Each circle includes a circular marker, and the coordinates of the nine circles in the robotic arm's workpiece coordinate system are as follows: ; ; ; ; ; ; ; ; ; in, These are the coordinates of the origin of the basic coordinate system obtained through contact positioning; The design spacing between the centers of adjacent circular marks on the calibration plate; The design diameter of the circular logo; S233. The affine transformation relationship between the workpiece coordinate system plane of the robotic arm and the image pixel coordinate system plane is established as follows: ; The coordinates of the center of the reference module in the image pixel coordinate system; in this embodiment, the coordinates of the center of the cylindrical protrusion in the image pixel coordinate system are also given. Let be the coordinates of the center of the end probe of the robotic arm in the workpiece coordinate system of the robotic arm; The rotation angle from the image pixel coordinate system to the robot arm workpiece coordinate system; and These represent the scaling factors from the image pixel coordinate system to the robotic arm workpiece coordinate system in the u-axis and v-axis directions, respectively. and These represent the translation amounts from the image pixel coordinate system to the robot arm workpiece coordinate system; , , , These are the parameters of the comprehensive transformation matrix, which includes rotation and scaling. ; ; ; ; T is the two-dimensional affine transformation matrix from the image pixel coordinate system to the robot arm workpiece coordinate system. use and The point data is used to calculate the affine transformation relationship T between the two coordinate systems and output the calibration file. The main difference between the eye-in-hand mode and the eye-on-hand mode is that the operation process for obtaining the calibration data for {Qi,Ci} is different.

[0035] In a specific implementation, after outputting the calibration file, step S3 includes: S311. Place the component to be processed at any position on the optical processing platform, and obtain the pixel coordinates of the feature points of the component to be processed by taking pictures through the monocular vision camera lens module. ; S322. Through the affine transformation relation T, according to the calculation formula... ; Pixel coordinates of feature points Transform to horizontal coordinates in the workpiece coordinate system of the robotic arm ); S333. Determine the Z-axis height. Control the robotic arm to adjust the height difference = , The height in the z-direction at the initial attitude point is used to establish the positioning point of the component to be processed. .

[0036] After outputting the calibration file and before S3, the automated mirror positioning method based on optical processing robots further includes the following steps: S411. Using the probe at the end of the robotic arm to detect the cylindrical protrusion for center positioning, the robotic arm end-effector posture based on the center alignment of the probe is obtained. This is the contact measurement reference value; S422. Control the robotic arm at the initial posture point. The optical processing platform is photographed, and the pixel coordinates of the center of the cylindrical protrusion at the current position are extracted. and workpiece coordinate system coordinates This refers to the comparison of visual localization results; S433. Compare coordinate data With coordinate data Calculate the plane error and verify the positioning accuracy; the plane error is the Euclidean distance D: .

[0037] By repeatedly changing the position of the reference module and repeating the above process, the average positioning accuracy and repeatability accuracy of the system can be statistically obtained, thereby verifying the performance of the visual positioning system and the positioning accuracy of the positioning method of the present invention.

[0038] This invention also provides a computer device, comprising: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform any of the automated mirror positioning methods based on optical processing robots.

[0039] The specific embodiments of the present invention further provide a computer-readable storage medium storing computer instructions, specifically a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are configured to cause a computer to execute any one of the above-mentioned automatic mirror alignment and positioning methods based on an optical processing robot.

[0040] Aiming at the automatic, non-contact millimeter-level center positioning requirement for a to-be-processed element of a meter-aperture aspheric optical reflector based on an optical processing robot, the present invention provides an automatic mirror alignment and positioning method based on an optical processing robot, which can realize more convenient visual calibration and can calibrate feature points of various large-aperture and special-shaped to-be-processed elements. Specifically, there is no repetitive requirement for the position of the to-be-processed element on the optical processing platform, the center positioning of the to-be-processed element in a large field of view can be realized by directly taking a photograph, the visual calibration process is more convenient, no multiple movements of the mechanical arm to find the edge for positioning and calibration are needed, the positioning process of the optical processing center is simplified, and the identification and calibration of the feature points of the special-shaped to-be-processed element can be realized. The whole calibration and positioning process does not require human intervention, which achieves a high degree of automation and improves the efficiency of center positioning.

[0041] As Figure 4 shown in the figure, it is a schematic diagram of an eye-in-hand centering vision module of the optical processing robot in the specific embodiment of the present invention. It can be seen from the figure that in this mode, the optical processing robot comprises a first mechanical arm 1 equipped with a monocular vision camera, and a first optical processing platform 2 matched with the eye-in-hand mode; the monocular vision camera comprises a first camera lens module 5 under the required field of view condition; a first monocular vision positioning module 3, a first supporting fixture 6 for matching the vision module with the mechanical arm, and an optical processing tool head 4, with other components not marked and shown. The optical processing tool head 4 is used for processing the to-be-processed element. For the feature point positioning scenario of the to-be-processed element with a small field of view and a small aperture, the first supporting fixture 6 can ensure that the camera is mounted at the end of the six-axis mechanical arm in the "eye-in-hand" form for use.

[0042] Figure 5This is a schematic diagram of the centering vision module of the eye-to-hand optical processing robot in the specific embodiment of the present invention. As can be seen from the figure, in this mode, the optical processing robot includes a second manipulator 12, a second optical processing platform 13 supporting the eye-to-hand mode, a second monocular vision positioning module 11 and a second support fixture 14, with other components not marked and shown; the second camera lens module (not shown in the figure) is mounted on the second support fixture 14 via the second monocular vision positioning module 11, the second support fixture 14 is fixed through a fixed reference (not shown in the figure), and the fixed reference is independent of the second manipulator 12 and arranged outside the second manipulator 12. For the feature point positioning of large-field-of-view, meter-scale elements to be processed, the second support fixture 14 is mounted on the fixed reference to ensure that the monocular vision camera is fixedly installed in the "eye-to-hand" form. Specifically, the optical axis of the monocular vision camera should be perpendicular or approximately perpendicular to the plane of the second optical processing platform 13, and its field of view can completely cover all feature areas or predetermined processing areas of the element to be processed; at the same time, the monocular vision camera and its fixed reference should be placed outside the full working space of the second manipulator 12, or maintain a sufficient safety distance from the second manipulator 12, the workpiece, and the second optical processing platform 13, so as to ensure that there is no risk of collision during the entire processing movement of the second manipulator 12; in this mode, the second support fixture 14 is fixedly installed on a fixed reference independent of the second manipulator 12, and its field of view covers the processing area on the second optical processing platform 13; typical installation positions include being fixed on an independent bracket at the side of the second optical processing platform 13, the wall of the workshop, or a special upright post.

[0043] The automated mirror alignment positioning method based on an optical processing robot provided by the specific embodiment of the present invention realizes automated, non-contact millimeter-level centering positioning of a meter-aperture aspheric optical reflector element to be processed by the optical processing robot based on monocular vision, and assists the unification of the processing coordinate system and the manipulator workpiece coordinate system during the grinding and polishing of the aspheric optical reflector.

[0044] Figure 2 This is a schematic flowchart of the automated mirror alignment positioning and positioning accuracy verification method based on an optical processing robot in the specific embodiment of the present invention. As can be seen from the figure, in the specific embodiment, the automated mirror alignment positioning and positioning accuracy verification process includes: (1) Adjust the pose of the manipulator and the field of view of the camera; (2) Control the probe through manipulator programming to perform centering positioning on the standard module; (3) Record the center position of the standard module, and set the initial calibration pose; (4) Perform nine-point calibration and output a calibration file; (5) Restore the initial pose, change the position of the standard module, and position its center; (6) Control the probe to center the standard module and compare the visual positioning accuracy.

[0045] This invention first establishes a monocular vision detection module based on the working scenario of meter-level components to be processed. Then, based on nine-point calibration, it quickly establishes a mapping relationship between the image and the workpiece coordinate system of the robotic arm, realizes the recognition of image feature points and the output of the coordinate data of feature points in the workpiece coordinate system of the robotic arm, and achieves non-contact and automated center positioning effect when processing large-diameter optical components.

[0046] In a specific implementation, the robotic arm is first operated to ensure that the workpiece coordinate system is consistent with the plane of the optical processing platform. The positional relationship between the optical processing robot's processing tool head and the robotic arm is determined by TCP (tool center point) calibration method (four-point method, etc.). The center thickness d of the workpiece is calibrated and measured by three-coordinate calibration. Finally, the image capture status of the camera can be used to observe whether the vision camera remains fixed and the field of view covers the optical processing platform. The image capture status when the camera is fixed well and the field of view meets the requirements is that the edge contrast in the image is clear, without overexposure or dark spots, and the worktable is presented in the image.

[0047] In one specific embodiment, for center positioning of small-field-of-view, small-diameter components, the vision module is installed in an "eye-on-hand" configuration. In the workpiece coordinate system of the robotic arm, a standard cylindrical reference module is located on the optical processing platform. The position of the standard cylinder serving as the reference module remains fixed. A probe is installed at the end effector of the robotic arm via a specialized tool, such as a quick-change connector or fixture. The probe at the end effector of the robotic arm achieves automatic three-point contact positioning to center the standard cylindrical reference module. Simultaneously, ensuring the optical processing platform is within the field of view, the focal length is adjusted to ensure clear field-of-view information, resulting in the robotic arm end effector posture based on probe center alignment, i.e., the initial posture point. .

[0048] Then, nine-point calibration is performed to achieve an affine transformation between the workpiece coordinate system plane (xy coordinate system) and the image pixel coordinate system plane (uv image coordinate system). Specifically, the image pixel coordinate system plane refers to the two-dimensional pixel coordinate system (i.e., uv coordinate system) corresponding to the digital image obtained after the monocular vision camera takes a picture of the optical processing platform plane containing the standard cylindrical reference module. The robot arm is then controlled to move nine points. ;in, Photos were taken at nine locations, including the initial pose location. That is, the nine-point mark. In image processing, the image is binarized to highlight and fit the edges of the reference module. Based on the edge fitting extraction, the center pixel coordinates of the standard cylindrical reference module are calculated using the least squares algorithm. ;in, .

[0049] The expression based on the principle of affine transformation is as follows: ; : The coordinates of the center of the reference module in the image pixel coordinate system.

[0050] : The coordinates of the probe center at the end of the robotic arm in the XY plane of the workpiece coordinate system of the robotic arm.

[0051] : Represents the rotation angle from the image coordinate system (uv) to the robot arm workpiece coordinate system (xy).

[0052] and : These represent the scaling factors from the image coordinate system to the robot arm workpiece coordinate system in the u-axis and v-axis directions, respectively.

[0053] and : These represent the translation amounts from the image coordinate system to the robot arm workpiece coordinate system.

[0054] , , , These are the parameters of the comprehensive transformation matrix, which includes rotation and scaling. ; ; ; .

[0055] T: is the two-dimensional affine transformation matrix from the image pixel coordinate system to the robot arm workpiece coordinate system.

[0056] use and The affine transformation relationship T between the two coordinate systems is calculated from the point data, and the calibration file is output.

[0057] In a specific implementation, the position of the standard cylindrical reference module on the optical processing platform is arbitrarily changed, and a probe is used to achieve automatic three-point contact positioning to center the standard cylindrical reference module, thereby obtaining the end effector posture of the robotic arm based on the probe center alignment. These coordinates will serve as the baseline for subsequent evaluation of visual positioning accuracy. Then, the robotic arm is controlled to return to its initial position. The optical processing platform is photographed, and its center pixel coordinates are extracted through image processing. The aforementioned calibration file T is then used to transform the coordinates to the workpiece coordinate system of the robotic arm, thus obtaining the visual positioning coordinates. This coordinate will be used as the visual positioning result value for subsequent evaluation of visual positioning accuracy, and compared with the coordinate data. With coordinate data The accuracy of the positioning is evaluated numerically. Specifically, the accuracy evaluation involves calculating the Euclidean distance between the two points in the XY plane: ; This distance D (unit: mm) represents the single-point visual positioning error. By repeatedly changing the position of the reference module and repeating the above process, the average positioning accuracy and repeatability accuracy of the system can be statistically obtained, thereby verifying the performance of the visual positioning system.

[0058] In a specific implementation, the robotic arm is then controlled to return to its initial posture position. The component to be processed is placed at any position on the optical processing platform, and the camera takes pictures to obtain the pixel coordinates of the feature points of the component. The workpiece coordinate system coordinates of the robotic arm are calculated and output using the calibration file. Positioning the feature points of the component to be processed in the z-direction requires controlling the descent height of the robotic arm. , The initial attitude point is the height in the z-direction, and the final positioning point is... .

[0059] In another specific implementation, for the center positioning of large field of view and large diameter components to be processed, the vision module is installed in the form of "eye outside the hand". The process and principle of positioning the mirror feature points are the same, but the operation of the nine-point calibration process is different.

[0060] In a specific implementation, a nine-point calibration plate with a cylindrical protrusion in the center is customized, such as... Figure 3As shown, this is used for initial attitude determination and pre-calibration preparation for large-field-of-view, large-diameter components to be processed. The cylindrical protrusion on the calibration plate serves as the reference module. Unlike the independent, movable standard cylindrical reference module used in the "eye in hand" mode, the reference module used in the large-field-of-view mode is a cylindrical protrusion fixedly integrated into the center of the nine-point calibration plate. This cylindrical protrusion is consistent with the independent standard cylinder in terms of physical dimensions and accuracy requirements, but it forms a whole with the calibration plate, and its position is fixed and known (located at the center of the calibration plate pattern). This design achieves the physical integration of the reference module (used to establish the origin of the coordinate system) and the calibration feature points (used to calculate coordinate transformations). In the "eye in hand" mode, a single standard cylindrical reference module can meet the nine-point calibration requirements because the camera is integrated on the robotic arm, and the active motion capability of the robotic arm compensates for the static limitations of the camera's field of view, eliminating the need for pre-made multiple marker points. In the "eye outside the hand" mode, since the camera position is fixed, all calibration point information needs to be obtained through a single shot. Therefore, a composite calibration plate integrating fixed marker points (such as nine circles) and a central cylindrical protrusion is used to ensure that the positional relationship of all calibration points is known and can be measured in one go.

[0061] In a specific implementation, the robotic arm end-effector probe 15 is first used to achieve automatic three-point contact positioning, and the cylindrical protrusion reference module is centered to obtain the robotic arm end-effector posture based on the center alignment of the robotic arm end-effector probe 15. Raising the robotic arm allows the optical processing platform to reach a height that does not obstruct camera shooting. That is, the initial position of the robotic arm at this time is The camera takes pictures of the nine-point calibration board, and image processing yields the center pixel coordinates of the nine circles on the calibration board. ;in, .

[0062] The initial attitude points can be directly used to design the dimensions based on the tooling positions of the nine points on the calibration board. The workpiece coordinate system position coordinates were calculated when the robotic arm moved to the nine center positions on the calibration plate. ;in, The center point is / The top left and top right points are respectively The coordinates of the remaining points can be deduced similarly, forming a pattern. A 3×3 grid centered on the workpiece. The nine-point calibration plate contains regularly arranged points; the calculated coordinate system points for the nine robotic arm workpieces are as follows: ; ; ; ; ; ; ; ; ; in: : These are the coordinates of the origin of the basic coordinate system obtained through contact positioning.

[0063] : This refers to the design spacing between the centers of adjacent circular markers on the calibration plate.

[0064] : The design diameter of the circular logo.

[0065] Z-axis components of all coordinates This ensures consistency because the calibration plates are located on the same horizontal plane. Using the above formula, the precise theoretical coordinates of all nine calibration points in the workpiece coordinate system can be obtained in one go without moving the robotic arm for multiple physical contacts. These coordinates correspond to the pixel coordinates obtained through image processing. A calibration pair is formed to solve for the affine transformation matrix T. This method fully utilizes prior knowledge of the known geometric relationships of the fixed calibration plate, transforming the complex multi-point spatial measurement problem into a simple geometric calculation, significantly improving calibration efficiency and reliability, and is especially suitable for applications with a large field of view and fixed cameras.

[0066] In a specific implementation, the feature points within a large field of view are located by calibrating the output mapping relationship. Specifically, the automated feature point location process is as follows: First, image acquisition and feature extraction are performed. After completing the nine-point calibration and obtaining the affine transformation matrix T, the nine-point calibration plate is removed. The large optical component to be processed (such as a meter-scale aspherical mirror) is placed at any position on the optical processing platform. A fixedly mounted camera ("eye outside the hand") captures an image containing the component.

[0067] Then, pixel coordinates are obtained: using image processing algorithms (such as edge detection, contour extraction, template matching, etc.), the pixel coordinates of preset feature points on the component (such as specific vertices on the edge of the mirror, the center of the positioning hole on the mirror blank, or the center of the pre-pasted marker point) are automatically identified and extracted in the image. .

[0068] Next, coordinate transformation calculations are performed: using the calibrated affine transformation matrix T, the pixel coordinates of the feature points are transformed to the horizontal coordinates in the workpiece coordinate system of the robotic arm. The calculation method is as follows: This coordinate This refers to the target position of the feature point within the plane of the optical processing platform. For scenarios requiring three-dimensional positioning, the Z-axis height can be determined using known workpiece thickness information, and the robotic arm can be controlled to adjust the height difference. = , The height in the z-direction at the initial attitude point is used to establish the positioning point of the component to be processed. .

[0069] Finally, the robotic arm's motion and positioning are achieved, and the robotic arm control program receives the target coordinates. The system plans a path and drives the end effector of the robotic arm to precisely move to the feature point, completing the positioning. This concludes the vision-based automated positioning process, providing a coordinate reference for subsequent processing operations (such as grinding and polishing).

[0070] The above positioning process is completed by the robotic arm control program and the MATLAB image processing program, which can realize the automated positioning process of the robotic arm for the mirror feature points.

[0071] This invention utilizes an ABB6700 robotic arm equipped with a Hikvision industrial camera to perform feature point positioning tests on small-diameter components under test on a 2m optical processing platform. Based on images acquired by a monocular vision camera, a coordinate system mapping relationship is established using the nine-point calibration principle. The average translation pixel error in image processing is 1.451373, and the image reprojection error is (0.02766 pixels, 0.05313 pixels). After calibration, the feature point coordinate transformation accuracy error is (0.075mm, 0.096mm). Using the positioning accuracy of a ruby ​​spherical probe as a reference (positioning accuracy at the μm level), the final feature point positioning effect of 0.5mm to 1mm is achieved.

[0072] For the "eye outside the hand" (large field of view, large aperture) working mode, the same nine-point calibration and coordinate mapping method described above is used. Within a typical meter-level field of view, by optimizing the calibration plate design (e.g., using higher-precision marker points) and installation (reducing perspective distortion), and employing the automated mirror positioning method based on optical processing robots provided in this invention's specific embodiment, sub-millimeter-level (1mm~2mm) feature point positioning effects can also be achieved, meeting the process requirements for coarse positioning of large-aperture optical components. Actual accuracy can be verified and calibrated through the same testing procedure as the "eye in the hand" mode.

[0073] Accordingly, according to embodiments of the present invention, the present invention also provides a computer device, a computer-readable storage medium, and a computer program product.

[0074] Figure 6 This is a schematic diagram of the structure of a computer device 41 provided in an embodiment of the present invention. Figure 6 A block diagram of an exemplary computer device 41 suitable for implementing embodiments of the present invention is shown. Figure 6 The computer device 41 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0075] like Figure 6 As shown, computer device 41 is represented in the form of a general-purpose computing device. Computer device 41 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0076] The components of the computer device 41 may include, but are not limited to: one or more processors or processing units 43, system memory 48, and bus 44 connecting different system components (including system memory 48 and processing unit 43).

[0077] Bus 44 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0078] Computer device 41 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by computer device 41, including volatile and non-volatile media, removable and non-removable media.

[0079] System memory 48 may include computer system readable media in the form of volatile memory, such as random access memory, i.e., RAM 49, and / or cache memory 50. Computer device 41 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 51 may be used to read and write non-removable, non-volatile magnetic media (…). Figure 6 Not shown; usually referred to as a "hard drive"). Although Figure 6Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 44 via one or more data media interfaces. System memory 48 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0080] A program / utility 52 having a set (at least one) of program modules 53 may be stored, for example, in system memory 48. Such program modules 53 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 53 typically perform the functions and / or methods described in the embodiments of the present invention.

[0081] Computer device 41 can also communicate with one or more external devices 42 (e.g., keyboard, pointing device, display 47, etc.), and with one or more devices that enable a user to interact with computer device 41, and / or with any device that enables computer device 41 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed through an input / output interface, i.e., I / O interface 46. Furthermore, computer device 41 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 45.

[0082] like Figure 6 As shown, network adapter 45 communicates with other modules of computer device 41 via bus 44. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with computer device 41, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0083] The processing unit 43 executes various functional applications and data processing by running programs stored in the system memory 48, such as implementing the automated mirror positioning method based on an optical processing robot provided in the embodiments of the present invention.

[0084] In a specific embodiment of the present invention, a non-transitory computer-readable storage medium storing computer instructions is also provided, on which a computer program is stored, wherein the program, when executed by a processor, is the automated mirror positioning method based on an optical processing robot provided in all embodiments of the present application.

[0085] The computer storage medium of this invention can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. More specific examples (a non-exhaustive list) of computer-readable storage media include: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0086] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0087] The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof. The computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0088] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described automated mirror positioning method based on an optical processing robot.

[0089] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this invention disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this invention can be achieved, and this is not limited herein.

[0090] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. An automated mirror positioning method based on an optical processing robot, characterized in that: The optical processing robot includes a robotic arm, an optical processing platform, a monocular vision camera lens module, a monocular vision positioning module, and a support fixture; the optical processing platform is used to place the component to be processed, the monocular vision positioning module is used to position the monocular vision camera lens module, and the support fixture is used to support the installation of the monocular vision camera lens module; the automated mirror positioning method based on the optical processing robot includes the following steps: S1. Determine the workpiece coordinate system of the robotic arm and determine the initial posture points of the robotic arm. and the center thickness d of the component to be processed; S2. Establish the affine transformation relationship between the workpiece coordinate system plane and the image plane of the robotic arm, realize nine-point calibration, and output calibration file; S3. Based on the calibration file, the target on the optical processing platform is photographed and identified, and the coordinates of the workpiece coordinate system of the robotic arm and the coordinate displacement of the robotic arm for control and positioning are output; thus realizing automated mirror positioning. It also includes the following steps: S0. Determine whether the component to be processed is a small-diameter component or a large-diameter component, wherein the diameter of the small-diameter component is ≤0.5m and the diameter of the large-diameter component is >0.5m; If the component to be processed is a small-diameter component, the monocular vision camera lens module is installed at the end of the robotic arm using the support fixture; If the component to be processed is a large-diameter component, the monocular vision camera lens module is mounted on a fixed reference by the support fixture. The fixed reference is independent of the robotic arm and is located outside the robotic arm. S0 is located before S1, or between S1 and S2; If the component to be processed is a small-diameter component, a small field-of-view vision camera positioning scheme is adopted. The reference module used for the nine-point calibration is a standard cylinder, which is fixedly set on the optical processing platform. During the calibration of the standard cylinder, the edge of the standard cylinder is highlighted and fitted. Based on the edge fitting extraction of the image, the center pixel coordinates of the standard cylinder are calculated using the least squares algorithm. If the component to be processed is a large-diameter component, a large field-of-view vision camera positioning scheme is adopted. The reference module used for the nine-point calibration is a cylindrical protrusion, which is fixedly set at the center of the nine-point calibration plate and forms an integral part with the nine-point calibration plate. The nine-point calibration plate is placed on the optical processing platform. During the calibration of the nine-point calibration plate, the edge of the cylindrical protrusion is highlighted and fitted. Based on the edge fitting extraction of the image, the center pixel coordinates of the nine circles are calculated using the least squares algorithm.

2. The automated mirror positioning method based on an optical processing robot according to claim 1, characterized in that: The automated mirror positioning method based on optical processing robots also includes the following steps: S4. Perform positioning accuracy verification: After achieving automated mirror positioning, calculate the plane error and verify the positioning accuracy by comparing the visual positioning results with the contact measurement reference value; S4 is located after S3, or between S2 and S3.

3. The automated mirror positioning method based on an optical processing robot according to claim 1, characterized in that: The nine-point calibration process includes: S21. Control the robotic arm to move nine points. , Nine photos were taken at nine different locations, resulting in nine images; initial pose location. For nine o'clock Location; S22. During image processing of the nine images, each image is binarized to highlight and fit the edge of the standard cylinder. Based on the edge fitting extraction of the image, the center pixel coordinates of the standard cylinder are calculated using the least squares algorithm. , ; S23. The affine transformation relationship between the workpiece coordinate system plane of the robotic arm and the image pixel coordinate system plane is established as follows: ； The coordinates of the center of the reference module in the image pixel coordinate system; Let be the coordinates of the center of the end probe of the robotic arm in the workpiece coordinate system of the robotic arm; The rotation angle from the image pixel coordinate system to the robot arm workpiece coordinate system; and These represent the scaling factors from the image pixel coordinate system to the robotic arm workpiece coordinate system in the u-axis and v-axis directions, respectively. and These represent the translation amounts from the image pixel coordinate system to the robot arm workpiece coordinate system; , , , These are the parameters of the comprehensive transformation matrix, which includes rotation and scaling. ; ; ; ; T is the two-dimensional affine transformation matrix from the image pixel coordinate system to the robot arm workpiece coordinate system. use and The affine transformation relationship T between the two coordinate systems is calculated from the point data, and the calibration file is output.

4. The automated mirror positioning method based on an optical processing robot according to claim 3, characterized in that: After outputting the calibration file, step S3 includes: S31. Control the robotic arm to return to its initial posture position. ; S32. Place the component to be processed at any position on the optical processing platform, and obtain the pixel coordinates of the feature points of the component to be processed by taking pictures through the monocular vision camera lens module. ; S33. Calculate and output the workpiece coordinate system coordinates of the robotic arm using the calibration file. ; S34. Control the descent height of the robotic arm. , The height in the z-direction at the initial attitude point is used to establish the positioning point of the component to be processed. .

5. The automated mirror positioning method based on an optical processing robot according to claim 4, characterized in that: The automated mirror positioning method based on optical processing robots also includes the following steps: S41. Arbitrarily change the position of the standard cylinder on the optical processing platform, and use the probe at the end of the robotic arm to achieve automatic three-point contact positioning, thereby centering the standard cylinder and obtaining the robotic arm end-effector posture based on the center alignment of the probe. This is the contact measurement reference value; S42. Control the robotic arm at the initial posture point. The optical processing platform is photographed, and the pixel coordinates of the center of the standard cylinder at the current position are extracted. and workpiece coordinate system coordinates This refers to the comparison of visual localization results; S43. Compare coordinate data With coordinate data Calculate the plane error and verify the positioning accuracy; the plane error is the Euclidean distance D: 。 6. The automated mirror positioning method based on an optical processing robot according to claim 1, characterized in that: The nine-point calibration process includes: S211. Take photos of the nine centers of the nine-point calibration plate to obtain nine images; during image processing, binarize each image to highlight and fit the edges of the cylindrical protrusions, extract the edges based on the image edge fitting, and calculate the center pixel coordinates of the nine centers using the least squares algorithm. , ; S222. Utilizing the initial attitude point The robot arm's movement was calculated, along with the workpiece coordinates at the nine center positions. , The center point is / Each center includes a circular marker, and the coordinates of the nine centers in the workpiece coordinate system of the robotic arm are as follows: ； ； ； ； ； ； ； ； ； in, These are the coordinates of the origin of the basic coordinate system obtained through contact positioning; The design spacing between the centers of adjacent circular marks on the calibration plate; The design diameter of the circular logo; S233. The affine transformation relationship between the workpiece coordinate system plane of the robotic arm and the image pixel coordinate system plane is established as follows: ； The coordinates of the center of the reference module in the image pixel coordinate system; Let be the coordinates of the center of the end probe of the robotic arm in the workpiece coordinate system of the robotic arm; The rotation angle from the image pixel coordinate system to the robot arm workpiece coordinate system; and These represent the scaling factors from the image pixel coordinate system to the robotic arm workpiece coordinate system in the u-axis and v-axis directions, respectively. and These represent the translation amounts from the image pixel coordinate system to the robot arm workpiece coordinate system; , , , These are the parameters of the comprehensive transformation matrix, which includes rotation and scaling. ; ; ; ; T is the two-dimensional affine transformation matrix from the image pixel coordinate system to the robot arm workpiece coordinate system. use and The affine transformation relationship T between the two coordinate systems is calculated from the point data, and the calibration file is output.

7. The automated mirror positioning method based on an optical processing robot according to claim 6, characterized in that: After outputting the calibration file, step S3 includes: S311. Place the component to be processed at any position on the optical processing platform, and obtain the pixel coordinates of the feature points of the component to be processed by taking pictures through the monocular vision camera lens module. ; S322. Through the affine transformation relation T, according to the calculation formula... ; Pixel coordinates of feature points Transform to horizontal coordinates in the workpiece coordinate system of the robotic arm ; S333. Determine the Z-axis height. Control the robotic arm to adjust the height difference = , The initial attitude point is the height in the z-direction, which enables the positioning point of the component to be processed. .

8. A computer device, characterized in that, include: At least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the automated mirror positioning method based on an optical processing robot as described in any one of claims 1 to 7.

9. A computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to execute the automated mirror positioning method based on an optical processing robot as described in any one of claims 1 to 7.

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