CN115431284B - A highly versatile automated machining process for a rack-type manipulator - Google Patents
A highly versatile automated machining process for a rack-type manipulator Download PDFInfo
- Publication number
- CN115431284B CN115431284B CN202211148149.5A CN202211148149A CN115431284B CN 115431284 B CN115431284 B CN 115431284B CN 202211148149 A CN202211148149 A CN 202211148149A CN 115431284 B CN115431284 B CN 115431284B
- Authority
- CN
- China
- Prior art keywords
- image
- coordinate system
- workpiece
- processing
- ccd camera
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000003754 machining Methods 0.000 title description 4
- 238000000034 method Methods 0.000 claims abstract description 18
- 238000005516 engineering process Methods 0.000 claims abstract description 16
- 230000009466 transformation Effects 0.000 claims abstract description 12
- 230000000007 visual effect Effects 0.000 claims description 12
- 238000003708 edge detection Methods 0.000 claims description 6
- 239000012636 effector Substances 0.000 claims description 4
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 238000005286 illumination Methods 0.000 claims description 3
- 238000003384 imaging method Methods 0.000 description 8
- 238000010586 diagram Methods 0.000 description 5
- 230000000694 effects Effects 0.000 description 4
- 230000009286 beneficial effect Effects 0.000 description 2
- 239000000284 extract Substances 0.000 description 2
- 238000001914 filtration Methods 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000036544 posture Effects 0.000 description 2
- 238000002360 preparation method Methods 0.000 description 2
- 230000001131 transforming effect Effects 0.000 description 2
- 230000006835 compression Effects 0.000 description 1
- 238000007906 compression Methods 0.000 description 1
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J11/00—Manipulators not otherwise provided for
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J19/00—Accessories fitted to manipulators, e.g. for monitoring, for viewing; Safety devices combined with or specially adapted for use in connection with manipulators
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J19/00—Accessories fitted to manipulators, e.g. for monitoring, for viewing; Safety devices combined with or specially adapted for use in connection with manipulators
- B25J19/02—Sensing devices
- B25J19/021—Optical sensing devices
- B25J19/023—Optical sensing devices including video camera means
Landscapes
- Engineering & Computer Science (AREA)
- Robotics (AREA)
- Mechanical Engineering (AREA)
- Multimedia (AREA)
- Image Processing (AREA)
Abstract
The invention relates to the technical field of manipulators, in particular to an automatic processing technology of a high-universality line-frame manipulator; the processing technology comprises the following steps: coordinate transformation in CCD camera calibration; image acquisition and processing; image noise processing; the method comprises the steps of positioning and transferring a workpiece, introducing a CCD camera to shoot the workpiece, determining the position of the workpiece through an acquired image, thereby effectively improving the recognition accuracy of the workpiece, acquiring the pixel coordinates of the center of the inner circle of the workpiece through gray processing of the image and image enhancement and binarization processing in the recognition process, converting the pixel coordinates into three-dimensional space coordinates in a reference coordinate system, and then matching with a manipulator to realize the accurate recognition of the reference coordinate system of the workpiece, thereby effectively improving the grabbing accuracy of the manipulator to the workpiece.
Description
Technical Field
The invention relates to the technical field of manipulators, in particular to an automatic machining process for a high-universality row-frame manipulator.
Background
When processing the automobile engine column workpieces, carrying work is often needed before processing, the workpieces are carried into a processing unit, and the past carrying is often carried out manually, so that on one hand, the labor intensity of personnel is improved, and on the other hand, the risk of hurting the bodies of the labor personnel exists;
In the prior art, a rack type manipulator is adopted to carry out automatic carrying so as to reduce the labor intensity of labor staff, but in the using process, the fact that the number of workpieces to be carried is large is found, when the workpieces on the conveying belt are grabbed, the workpieces cannot be accurately identified, and the grabbing precision of the manipulator is low is often caused.
Disclosure of Invention
The invention aims to provide a high-universality automatic machining process for a rack type manipulator so as to effectively improve the accuracy of grabbing workpieces by the rack type manipulator.
In order to achieve the above purpose, the invention provides a high-universality line-frame type manipulator automatic processing technology, wherein an actuator is arranged at the tail end of the manipulator, a plurality of light sources for illumination are arranged at the upper side of the manipulator, and a CCD camera for acquiring images of workpieces to be conveyed is also arranged at one side of the light sources;
the processing technology comprises the following steps:
Coordinate transformation in CCD camera calibration, shooting a plurality of images of a calibration plate from different angles to calibrate the CCD camera, so as to obtain a rotation and translation relation between a self coordinate system of the CCD camera and a reference coordinate system;
the method comprises the steps of image acquisition and processing, namely performing gray processing on an acquired image, sequentially performing image enhancement and image binarization processing to acquire pixel coordinates of the center of an inner circle of a workpiece, and converting the pixel coordinates of the center of the inner circle of the workpiece into three-dimensional space coordinates in a reference coordinate system;
Image noise processing, namely performing edge detection by adopting a Sobel operator to reduce errors caused by an image algorithm;
And positioning and transferring the workpiece, overlapping the end effector of the manipulator and the three-dimensional space coordinate of the center of the inner circle of the workpiece to grasp the workpiece, and then conveying the manipulator clamping the workpiece to a processing device through the integral moving frame to finish processing.
On the basis of the prior art, a CCD camera is introduced to shoot a workpiece, the position of the workpiece is determined through an acquired image, so that the recognition accuracy of the workpiece is effectively improved, in the recognition process, the CCD camera part is subjected to coordinate transformation, and the shooting position of the CCD camera is calibrated through matching with a calibration plate, so that the relationship between the self coordinate system of the CCD camera and a reference coordinate system is acquired, then the workpiece image shot by the CCD camera is subjected to gray processing, image enhancement and binarization processing, so that the pixel coordinate of the center of the inner circle of the workpiece is acquired, and is converted into the three-dimensional space coordinate in the reference coordinate system, and then the accurate recognition of the reference coordinate system of the workpiece is realized by matching with a manipulator, so that the grabbing accuracy of the workpiece by the manipulator is effectively improved.
The reference coordinate system is an absolute coordinate system of the objective world to represent three-dimensional space coordinates of the objective world, the CCD camera self coordinate system is a three-dimensional rectangular coordinate system established by taking the CCD camera as a center, the pixel coordinate system is a coordinate system used for digital images, the origin is positioned at the upper left corner of the images, the X axis is rightward, the Y axis is downward, the column number and the row number of a certain pixel in the images are respectively represented, and the reference coordinate system and the CCD camera self coordinate system and the pixel coordinate system and the CCD camera self coordinate system can be mutually transformed.
The linear transformation between the coordinate systems is performed as follows:
The three-dimensional space point xw= (Xw, yw, zw) T is projected onto the two-dimensional imaging plane to obtain an imaging point m= (x i,yi)T), and the pixel coordinate system needs to be transformed as follows in the process of transforming the pixel coordinate system into the reference coordinate system due to the mutual connection between the coordinate systems.
The coordinate transformation of the reference coordinate system and the CCD camera coordinate system is shown in the following formula 1:
in formula 1, xc and Xw are coordinates of a camera coordinate system, R, T are relative positions and postures between a reference coordinate system and the camera coordinate system, wherein R is a 3D rotation matrix, and T is a 3D translation vector.
The transformation between the camera coordinate system and the pixel coordinate system is shown in equation 2:
In equation 2, f is the focal length, i.e., the distance from the focal plane of the camera to the imaging plane, (x i,yi) is the coordinates of the imaging point in the pixel coordinate system.
According to equations 1 and 2, the relationship of any one pixel in the acquired image in two coordinate systems is as follows:
In equation 3, (Cx, cy) is the pixel coordinates of the optical center, sx, sy is the number of pixels per unit distance of the image plane, sx=1/dx, sy=1/dy.
The image graying process is to convert a color image into a gray image, wherein the conversion relation between an RGB color model and a gray model is as follows:
Gray(i，j)＝0.11R(i,j)+0.59G(i,j)+0.3B(i,j)
Where Gray (i, j) represents the Gray value of the converted black-and-white image pixel at the point (i, j).
Where Gray (i, j) represents the Gray value of the converted black and white image pixel at point (i, j).
In order to determine the boundary between a workpiece and a background, the image needs to be subjected to gray processing, the quality of the processing result has great influence on the subsequent processing, the purpose of gray processing is to convert a color image containing brightness and color into a gray image for preparation of the subsequent processing, the image enhancement is to enhance the information required by a user in the image by adopting a related technology, the contrast enhancement is carried out on the researched image, the main purpose is to adjust the contrast of the image, improve the visual quality and highlight important details, the gray processing is required to be carried out on the image before, and the two main purposes are that the image enhancement are: firstly, the definition of the image is improved, and the visual effect of the image is improved; and secondly, the image becomes more beneficial to computer processing, and in the binarization of the image, the gray value of the pixel point on the image is set to be 0 or 255, namely, the whole image is enabled to show the visual effect of only white and black. A common method is to set a threshold T, and divide the pixel group into two parts by T: and respectively assigning values to the pixel groups larger than T and the pixel groups smaller than T, and further performing binarization processing on the gray level image of the workpiece to obtain the information of the workpiece on the image so as to obtain a black-and-white image.
Wherein, the image enhancement means that the image after the graying treatment is adjusted in contrast so as to improve the visual quality and display important details.
The image binarization means that a pixel group is divided into two parts by setting a threshold value, the gray value of the pixel group larger than the threshold value is converted into 255, and the gray value of the pixel group smaller than or equal to the threshold value is converted into 0, so that the image presents the visual effect of only white and black.
The edge detection by adopting the Sobel operator refers to processing a gray level image to obtain a gradient image, dividing the image into areas with characteristics according to the characteristics of the image, and extracting a target.
The Sobel operator extracts edges in the form of a filtering operator, one template is used in each of X and Y directions, and the two templates are combined to form 1 gradient operator. The X-direction templates have the greatest effect on vertical edges and the Y-direction templates have the greatest effect on horizontal edges.
According to the high-universality line-frame type mechanical arm automatic processing technology, the processing flow of the mechanical arm on a workpiece is improved on the basis of the prior art, the CCD camera is additionally arranged, and the image acquired by the CCD camera is processed, so that the reference coordinate system is connected with the image coordinate system, the requirement of the mechanical arm on the carrying accuracy of the workpiece in the processing process is met, and the carrying accuracy of the mechanical arm is further effectively improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a process diagram of an automatic processing process of a high-universality gantry type manipulator.
Fig. 2 is a schematic gray scale diagram of an automated processing technology of a high-versatility gantry manipulator provided by the invention.
Fig. 3 is an image enhancement schematic diagram of an automated processing technology of a high-universality gantry manipulator.
Fig. 4 is a binarization schematic diagram of an automatic processing technology of a high-universality gantry type mechanical arm.
Fig. 5 is a schematic diagram of circle center determination of an automated processing technology of a high-universality gantry manipulator.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention.
Referring to fig. 1 to 5, the invention provides a high-universality gantry type mechanical arm automatic processing technology, wherein an actuator is arranged at the tail end of the mechanical arm, a plurality of light sources for illumination are arranged at the upper side of the mechanical arm, and a CCD camera for acquiring images of workpieces to be carried is also arranged at one side of the light sources;
the processing technology comprises the following steps:
s1: coordinate transformation in CCD camera calibration, shooting a plurality of images of a calibration plate from different angles to calibrate the CCD camera, so as to obtain a rotation and translation relation between a self coordinate system of the CCD camera and a reference coordinate system;
S2: the method comprises the steps of image acquisition and processing, namely performing gray processing on an acquired image, sequentially performing image enhancement and image binarization processing to acquire pixel coordinates of the center of an inner circle of a workpiece, and converting the pixel coordinates of the center of the inner circle of the workpiece into three-dimensional space coordinates in a reference coordinate system;
s3: image noise processing, namely performing edge detection by adopting a Sobel operator to reduce errors caused by an image algorithm;
S4: and positioning and transferring the workpiece, overlapping the end effector of the manipulator and the three-dimensional space coordinate of the center of the inner circle of the workpiece to grasp the workpiece, and then conveying the manipulator clamping the workpiece to a processing device through the integral moving frame to finish processing.
On the basis of the prior art, a CCD camera is introduced to shoot a workpiece, the position of the workpiece is determined through an acquired image, so that the recognition accuracy of the workpiece is effectively improved, in the recognition process, the CCD camera part is subjected to coordinate transformation, and the shooting position of the CCD camera is calibrated through matching with a calibration plate, so that the relationship between the self coordinate system of the CCD camera and a reference coordinate system is acquired, then the workpiece image shot by the CCD camera is subjected to gray processing, image enhancement and binarization processing, so that the pixel coordinate of the center of the inner circle of the workpiece is acquired, and is converted into the three-dimensional space coordinate in the reference coordinate system, and then the accurate recognition of the reference coordinate system of the workpiece is realized by matching with a manipulator, so that the grabbing accuracy of the workpiece by the manipulator is effectively improved.
Further, the reference coordinate system is an absolute coordinate system of the objective world to represent three-dimensional space coordinates of the objective world, the coordinate system of the CCD camera is a three-dimensional rectangular coordinate system formulated by taking the CCD camera as a center, the pixel coordinate system is a coordinate system used for digital images, the origin is positioned at the upper left corner of the images, the X axis is rightward, the Y axis is downward, the column number and the row number of a certain pixel in the images are respectively represented, and the reference coordinate system and the coordinate system of the CCD camera and the pixel coordinate system and the coordinate system of the CCD camera can be mutually transformed.
The linear transformation between the coordinate systems is performed as follows:
The three-dimensional space point xw= (Xw, yw, zw) T is projected onto the two-dimensional imaging plane to obtain an imaging point m= (x i,yi)T), and the pixel coordinate system needs to be transformed as follows in the process of transforming the pixel coordinate system into the reference coordinate system due to the mutual connection between the coordinate systems.
The coordinate transformation of the reference coordinate system and the CCD camera coordinate system is shown in the following formula 1:
in formula 1, xc and Xw are coordinates of a camera coordinate system, R, T are relative positions and postures between a reference coordinate system and the camera coordinate system, wherein R is a 3D rotation matrix, and T is a 3D translation vector.
The transformation between the camera coordinate system and the pixel coordinate system is shown in equation 2:
In equation 2, f is the focal length, i.e., the distance from the focal plane of the camera to the imaging plane, (x i,yi) is the coordinates of the imaging point in the pixel coordinate system.
According to equations 1 and 2, the relationship of any one pixel in the acquired image in two coordinate systems is as follows:
In equation 3, (Cx, cy) is the pixel coordinates of the optical center, sx, sy is the number of pixels per unit distance of the image plane, sx=1/dx, sy=1/dy.
Further, the graying processing of the image is to convert a color image into a gray image, wherein the conversion relationship between the RGB color model and the gray model is as follows:
Gray(i，j)＝0.11R(i,j)+0.59G(i,j)+0.3B(i,j)
Where Gray (i, j) represents the Gray value of the converted black-and-white image pixel at the point (i, j).
Where Gray (i, j) represents the Gray value of the converted black and white image pixel at point (i, j).
In order to determine the boundary between a workpiece and a background, the image needs to be subjected to gray processing, the quality of the processing result has great influence on the subsequent processing, the purpose of gray processing is to convert a color image containing brightness and color into a gray image for preparation of the subsequent processing, the image enhancement is to enhance the information required by a user in the image by adopting a related technology, the contrast enhancement is carried out on the researched image, the main purpose is to adjust the contrast of the image, improve the visual quality and highlight important details, the gray processing is required to be carried out on the image before, and the two main purposes are that the image enhancement are: firstly, the definition of the image is improved, and the visual effect of the image is improved; and secondly, the image becomes more beneficial to computer processing, and in the binarization of the image, the gray value of the pixel point on the image is set to be 0 or 255, namely, the whole image is enabled to show the visual effect of only white and black. A common method is to set a threshold T, and divide the pixel group into two parts by T: and respectively assigning values to the pixel groups larger than T and the pixel groups smaller than T, and further performing binarization processing on the gray level image of the workpiece to obtain the information of the workpiece on the image so as to obtain a black-and-white image.
Further, the image enhancement refers to adjusting contrast of the image subjected to the graying treatment so as to improve visual quality and display important details.
Further, the image binarization refers to dividing the pixel group into two parts by setting a threshold value, converting the gray value of the pixel group larger than the threshold value into 255, and converting the gray value of the pixel group smaller than or equal to the threshold value into 0, so that the image presents the visual effect of only white and black.
Further, the edge detection by using the Sobel operator refers to processing a gray image to obtain a gradient image, dividing the image into regions with characteristics according to the characteristics of the image, and extracting a target.
The Sobel operator extracts edges in the form of a filtering operator, one template is used in each of X and Y directions, and the two templates are combined to form 1 gradient operator. The X-direction templates have the greatest effect on vertical edges and the Y-direction templates have the greatest effect on horizontal edges.
Further, the end effector comprises a cylinder, a connecting plate, a paw, a compression mechanism and a synchronization mechanism.
According to the high-universality line-frame type mechanical arm automatic processing technology, the processing flow of the mechanical arm on a workpiece is improved on the basis of the prior art, the CCD camera is additionally arranged, and the image acquired by the CCD camera is processed, so that the reference coordinate system is connected with the image coordinate system, the requirement of the mechanical arm on the carrying accuracy of the workpiece in the processing process is met, and the carrying accuracy of the mechanical arm is further effectively improved.
The above disclosure is only a preferred embodiment of the present invention, and it should be understood that the scope of the invention is not limited thereto, and those skilled in the art will appreciate that all or part of the procedures described above can be performed according to the equivalent changes of the claims, and still fall within the scope of the present invention.
Claims (1)
1. A high-universality line-frame type mechanical arm automatic processing technology is characterized in that,
The tail end of the manipulator is provided with an actuator, the upper side of the manipulator is provided with a plurality of light sources for illumination, and one side of each light source is also provided with a CCD camera for acquiring images of the workpiece to be conveyed;
the processing technology comprises the following steps:
Coordinate transformation in CCD camera calibration, shooting a plurality of images of a calibration plate from different angles to calibrate the CCD camera, so as to obtain a rotation and translation relation between a self coordinate system of the CCD camera and a reference coordinate system;
the method comprises the steps of image acquisition and processing, namely performing gray processing on an acquired image, sequentially performing image enhancement and image binarization processing to acquire pixel coordinates of the center of an inner circle of a workpiece, and converting the pixel coordinates of the center of the inner circle of the workpiece into three-dimensional space coordinates in a reference coordinate system;
Image noise processing, namely performing edge detection by adopting a Sobel operator to reduce errors caused by an image algorithm;
positioning and transferring the workpiece, overlapping an end effector of the manipulator with the three-dimensional space coordinate of the center of the inner circle of the workpiece to grasp the workpiece, and then conveying the manipulator clamping the workpiece to a processing device through an integral moving frame to finish processing;
The reference coordinate system is an absolute coordinate system of the objective world to represent three-dimensional space coordinates of the objective world, the coordinate system of the CCD camera is a three-dimensional rectangular coordinate system established by taking the CCD camera as a center, the pixel coordinate system is a coordinate system used for digital images, the origin is positioned at the upper left corner of the images, the X axis is rightward, the Y axis is downward, the column number and the row number of a certain pixel in the images are respectively represented, and the reference coordinate system and the coordinate system of the CCD camera as well as the pixel coordinate system and the coordinate system of the CCD camera can be mutually transformed;
the graying processing of the image is to convert a color image into a gray image, wherein the conversion relation between an RGB color model and a gray model is as follows:
，
wherein Gray (i, j) represents the Gray value of the converted black-and-white image pixel point at the point (i, j);
the image enhancement means that the image subjected to the graying treatment is subjected to contrast adjustment so as to improve visual quality and display important details;
The image binarization means that a pixel group is divided into two parts by setting a threshold value, the gray value of the pixel group larger than the threshold value is converted into 255, and the gray value of the pixel group smaller than or equal to the threshold value is converted into 0, so that the image presents the visual effect of only white and black;
the edge detection by adopting the Sobel operator refers to processing a gray level image to obtain a gradient image, dividing the image into areas with characteristics according to the characteristics of the image, and extracting a target.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211148149.5A CN115431284B (en) | 2022-09-20 | 2022-09-20 | A highly versatile automated machining process for a rack-type manipulator |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211148149.5A CN115431284B (en) | 2022-09-20 | 2022-09-20 | A highly versatile automated machining process for a rack-type manipulator |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115431284A CN115431284A (en) | 2022-12-06 |
| CN115431284B true CN115431284B (en) | 2024-11-22 |
Family
ID=84248673
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211148149.5A Active CN115431284B (en) | 2022-09-20 | 2022-09-20 | A highly versatile automated machining process for a rack-type manipulator |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115431284B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103702607A (en) * | 2011-07-08 | 2014-04-02 | 修复型机器人公司 | Calibration and transformation of a camera system's coordinate system |
| CN109859277A (en) * | 2019-01-21 | 2019-06-07 | 陕西科技大学 | A kind of robotic vision system scaling method based on Halcon |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111251336B (en) * | 2019-06-29 | 2022-01-28 | 浙江大学 | Double-arm cooperative intelligent assembly system based on visual positioning |
| CN111604909A (en) * | 2020-06-24 | 2020-09-01 | 辽宁工业大学 | A vision system of a four-axis industrial palletizing robot |
-
2022
- 2022-09-20 CN CN202211148149.5A patent/CN115431284B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103702607A (en) * | 2011-07-08 | 2014-04-02 | 修复型机器人公司 | Calibration and transformation of a camera system's coordinate system |
| CN109859277A (en) * | 2019-01-21 | 2019-06-07 | 陕西科技大学 | A kind of robotic vision system scaling method based on Halcon |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115431284A (en) | 2022-12-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110497187B (en) | Sunflower module assembly system based on vision guidance | |
| CN108109174B (en) | Robot monocular guidance method and system for randomly sorting scattered parts | |
| CN114029946A (en) | Method, device and equipment for guiding robot to position and grab based on 3D grating | |
| CN111645074A (en) | Robot grabbing and positioning method | |
| CN110648367A (en) | Geometric object positioning method based on multilayer depth and color visual information | |
| CN109018591A (en) | A kind of automatic labeling localization method based on computer vision | |
| CN109859277A (en) | A kind of robotic vision system scaling method based on Halcon | |
| CN101286235A (en) | A Camera Calibration Method Based on Flexible Stereo Target | |
| CN111768453A (en) | Navigation and positioning device and method in spacecraft cluster ground simulation system | |
| CN113843797A (en) | Automatic dismounting method for part hexagon bolt in non-structural environment based on monocular and binocular mixed vision | |
| CN106964907A (en) | A kind of method and apparatus of laser cutting | |
| CN115830018B (en) | Carbon block detection method and system based on deep learning and binocular vision | |
| CN113822810A (en) | Method for positioning workpiece in three-dimensional space based on machine vision | |
| CN109916914A (en) | A kind of product defects detection method and device | |
| CN115187556A (en) | Method for positioning parts and acquiring point cloud on production line based on machine vision | |
| CN102750698B (en) | Texture camera calibration device, texture camera calibration method and geometry correction method of texture image of texture camera | |
| CN110060304A (en) | A kind of organism three-dimensional information acquisition method | |
| CN115431284B (en) | A highly versatile automated machining process for a rack-type manipulator | |
| CN119554980B (en) | Multi-vision automobile flange plate thread detection device and method based on mechanical arm | |
| WO2020133407A1 (en) | Structured-light-based locating method and apparatus for industrial robot, and controller and medium | |
| CN117635607B (en) | Recognition method for welding quality of circuit board | |
| CN113470117A (en) | Unit attitude three-dimensional structured light calibration system and method based on spherical reverse perspective projection | |
| CN112995641B (en) | 3D module imaging device and method and electronic equipment | |
| CN111798524A (en) | A calibration system and method based on an inverted low-resolution camera | |
| CN112734846A (en) | Mobile phone accessory positioning and attaching method based on shape matching |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |