Deburring equipment of tool jig and automatic burr positioning method

Abstract

The invention relates to the technical field of deburring, in particular to deburring equipment of a tool jig and an automatic burr positioning method. The method comprises the following steps: acquiring a plurality of monitoring images of a to-be-deburred tool jig under each view angle; performing Gaussian pyramid scale transformation on each monitoring image, performing angular point detection, and dividing the angular points in each monitoring image into two categories according to the presentation condition of the angular points in a scale image; obtaining a texture expression value of each angular point in the scale image by combining the relative distance between each angular point and other angular points in the corresponding scale image of each monitoring image and the edge change condition; and obtaining a texture feature value of each angular point in the monitoring image by combining the texture representation value of each angular point in the corresponding scale image in all the monitoring images under the same visual angle, and further dividing and screening a burr region for each category. According to the invention, the positioning accuracy of the burr area and the burr removal effect are improved.

Description

Technical Field

[0001] The invention relates to the technical field of deburring, and in particular to a deburring device for a tooling fixture and a burr automatic positioning method. Background Art

[0002] Tooling and fixtures are devices used to fix, support, position or limit the position and state of products during processing in the manufacturing process. They play an important role in ensuring product quality, improving production efficiency and ensuring operational safety. Tooling and fixtures use various mechanical processing equipment in the production process, such as lathes, milling machines, grinders, and EDM machines, to perform processing operations such as cutting, drilling, and grinding on materials to make various parts of tooling and fixtures. Burrs will be generated during the processing, which will affect the processing accuracy of the product and the quality during use.

[0003] Binocular vision generally uses dual cameras to simultaneously acquire two digital images of an object from different angles, extract the pixel coordinates of the points on the two images, find the corresponding points on the two images, calculate the offset between the corresponding points, and restore the depth information of the object based on the principle of triangulation. When deburring the tooling, it is necessary to locate the burr area in the acquired parts. When positioning, the binocular camera can be used to capture part information at different angles, and the spatial depth of the product can be determined by the matching of the binocular camera. However, when the binocular camera is positioned, the image changes caused by the burrs may lead to incorrect feature point matching, which will directly affect the calculation of the disparity map and the accuracy of the depth information. At the same time, the three-dimensional burrs may be blocked by the structure of the product itself or other burrs, which will make it impossible for the binocular camera to fully capture the burrs, resulting in inaccurate positioning, which in turn affects the subsequent burr removal effect. Summary of the invention

[0004] In order to solve the problem that the burr area is not accurately positioned when deburring the jig in the existing method, which affects the burr removal effect, the purpose of the present invention is to provide a deburring device for the jig and a burr automatic positioning method. The technical scheme adopted is as follows:

[0005] In a first aspect, the present invention provides a method for automatically locating burrs, the method comprising the following steps:

[0006] Acquire several monitoring images of the tooling fixture to be deburred at each viewing angle;

[0007] Perform Gaussian pyramid scale transformation on each surveillance image and perform corner point detection. According to the presentation of the corner points in each surveillance image in the scale map, the corner points in each surveillance image are divided into two categories. Combined with the relative distance and edge change between each corner point and other corner points in the corresponding scale map of each surveillance image, the texture representation value of each corner point in the scale map is obtained.

[0008] The texture representation value of each corner point in the corresponding scale map of all monitoring images under the same viewing angle is combined to obtain the texture feature value of each corner point in the monitoring image; the texture feature value is used to filter the burr area for each category division.

[0009] Preferably, the step of dividing the corner points in each surveillance image into two categories according to the presentation of the corner points in each surveillance image in the scalar map includes:

[0010] For any corner point in the candidate monitoring image, the total number of times the corner point is judged as a corner point in all scale images corresponding to the candidate monitoring image is recorded as the scale feature value of the corner point;

[0011] Based on the scale feature values ​​of all corner points in the candidate monitoring image, the maximum inter-class variance method is used to divide all corner points in the candidate monitoring image into two categories; the candidate monitoring image is any monitoring image.

[0012] Preferably, the step of combining the relative distance between each corner point and other corner points and edge changes in the corresponding scalar map of each surveillance image to obtain the texture representation value of each corner point in the scalar map includes:

[0013] Record the corner point directly connected to the candidate corner point through the edge line as the reference corner point of the candidate corner point; calculate the discrete degree of the slope of all pixel points on the edge line between the candidate corner point and each of its reference corner points, and record it as the first eigenvalue between the candidate corner point and each of its reference corner points;

[0014] Obtaining a texture representation value of the candidate corner point according to the Euclidean distance between the candidate corner point and all reference corner points thereof and the first eigenvalue, wherein the Euclidean distance is negatively correlated with the texture representation value, and the first eigenvalue is positively correlated with the texture representation value;

[0015] The candidate corner point is any corner point in any scale map.

[0016] Preferably, the calculation of the discrete degree of the slope of all pixel points on the edge line between the candidate corner point and each of its reference corner points includes: taking the variance of the slope of all pixel points on the edge line connecting the candidate corner point and each of its reference corner points as the corresponding discrete degree.

[0017] Preferably, the step of combining the texture representation value of each corner point in the corresponding scale map in all surveillance images at the same viewing angle to obtain the texture feature value of each corner point in the surveillance image includes:

[0018] The variance of the texture representation value of the corresponding position of the corner point to be analyzed in all scale images is recorded as the correction coefficient;

[0019] Using the correction coefficient, the texture representation values ​​of the positions of the corner points to be analyzed in all monitoring images at the same viewing angle in all scale images are corrected to obtain the texture feature values ​​of the positions of the corner points to be analyzed in the monitoring images;

[0020] The corner point to be analyzed is any corner point in any monitoring image.

[0021] Preferably, the correction coefficient is used to correct the texture representation values ​​of the positions of the corner points to be analyzed in all the monitoring images at the same viewing angle in all the scale images to obtain the texture feature values ​​of the positions of the corner points to be analyzed in the monitoring images, including:

[0022] The cumulative sum of the products of the texture representation values ​​of the positions of the corner points to be analyzed in all monitoring images under the same viewing angle in all scale images and the correction coefficient is determined as the texture feature value of the position of the corner point to be analyzed in the monitoring image.

[0023] Preferably, the method of using the texture feature value to divide and filter the burr area into each category includes:

[0024] For any surveillance image:

[0025] Calculate the average of the scale characteristic values ​​of all corner points in each category and record it as the average scale characteristic value of each category; record the category with the largest average scale characteristic value as the first category, and record the category with the smallest average scale characteristic value as the second category;

[0026] Based on the texture feature values ​​of all corner points in each category in each surveillance image, the maximum inter-class variance method is used to divide all corner points in each category in each surveillance image into two subcategories again;

[0027] Calculate the average value of the texture feature values ​​of all corner points in each subclass and record it as the average texture feature value of each subclass; record the subclass with the largest average texture feature value in the first class as the first subclass, and record the subclass with the largest average texture feature value in the second class as the second subclass;

[0028] A burr area is determined according to the first subcategory and the second subcategory.

[0029] Preferably, determining the burr area according to the first subcategory and the second subcategory includes:

[0030] All corner points in the first subclass and edge lines between the corner points form a first burr area;

[0031] All corner points in the second subclass and edge lines between the corner points form a second burr area;

[0032] The burr region includes a first burr region and a second burr region.

[0033] In a second aspect, the present invention provides a deburring device for a tooling fixture, the device comprising:

[0034] An image acquisition module is used to obtain a number of monitoring images of the tooling fixture to be deburred at each viewing angle;

[0035] The texture representation value determination module is used to perform Gaussian pyramid scale transformation on each surveillance image and perform corner point detection. According to the presentation of the corner points in each surveillance image in the scale map, the corner points in each surveillance image are divided into two categories; the texture representation value of each corner point in the scale map is obtained by combining the relative distance and edge change between each corner point and other corner points in the corresponding scale map of each surveillance image;

[0036] The burr area screening module is used to combine the texture representation value of each corner point in the corresponding scale map of all monitoring images under the same viewing angle to obtain the texture feature value of each corner point in the monitoring image; and use the texture feature value to screen the burr area for each category;

[0037] The deburring module is used to screen the obscured structural feature points on the tooling and fixture to be deburred according to the relative distance between each position of the tooling and fixture to be deburred and the burr area and the situation that each position is judged as a corner point in the monitoring image at the same viewing angle; reconstruct the three-dimensional model of the tooling and fixture to be deburred by combining the obscured structural feature points and all corner points in all monitoring images at different viewing angles; and plan the deburring route of the deburring equipment and perform deburring processing based on the three-dimensional model and the position of the burr area.

[0038] Preferably, the screening of blocked structural feature points on the tooling and fixture to be deburred according to the relative distance between each position of the tooling and fixture to be deburred and the burr area and the situation that each position is determined to be a corner point in the monitoring image at the same viewing angle includes:

[0039] For any position of the tooling to be deburred:

[0040] Calculate the minimum value of the distance between the position and all corner points in the first subclass; count the number of times the position is determined as a corner point in each surveillance image at the same viewing angle; obtain the probability that the position in each surveillance image belongs to an occluded structure point according to the minimum value and the number of times the position is determined as a corner point in each surveillance image at the same viewing angle;

[0041] If the probability is greater than the probability threshold, it is determined that the position in the corresponding monitoring image belongs to an obscured structure point.

[0042] The present invention has at least the following beneficial effects:

[0043] The present invention first performs Gaussian pyramid scale transformation on each monitoring image of the tooling fixture to be deburred to obtain a corresponding scale map, performs corner point detection on each monitoring image and the scale map, and then divides the corner points in the monitoring image into two categories based on the presentation of the corner points in the scale map, one of which is stable points with obvious scale characteristics, that is, structural corner points or large burr corner points of the tooling fixture itself to be deburred, and the other is points with unstable scale characteristics, that is, structural corner points or small burr corner points of the tooling fixture itself; in combination with the relative distance between each corner point and other corner points in the scale map and the edge change, the texture distribution of the corner points in the scale map is evaluated to obtain the texture expression value, and in combination with the texture expression value of each corner point in the corresponding scale map of all monitoring images at the same viewing angle, the texture feature value of each corner point in the monitoring image is obtained, and the texture feature value is used to screen the corner points in each category again to determine the burr area, thereby realizing the subsequent deburring process, making the positioning of the burr area more accurate, and improving the burr removal effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1 A flow chart of a burr automatic location method provided by an embodiment of the present invention;

[0046] Figure 2 This is a structural block diagram of a deburring device for a tooling fixture provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, a deburring device for a tooling fixture and an automatic burr positioning method proposed in accordance with the present invention are described in detail below in combination with the accompanying drawings and preferred embodiments.

[0048] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0049] The following is a detailed description of a deburring device for a tooling fixture and a specific solution of an automatic burr positioning method provided by the present invention in conjunction with the accompanying drawings.

[0050] An embodiment of a burr automatic positioning method:

[0051] The specific scenario targeted by this embodiment is: in the process of deburring the tooling fixture by using the deburring equipment, it is necessary to first locate the burr area on the tooling fixture to be deburred. This embodiment uses a binocular camera to collect image information of the tooling fixture to be deburred that needs to be polished, obtains corner points in images taken at different angles through the SIFT algorithm, determines the burr distribution information in space by analyzing the corner points, and then performs the burr polishing operation based on the acquired spatial information.

[0052] This embodiment proposes a burr automatic positioning method, such as Figure 1 As shown, a burr automatic positioning method of this embodiment includes the following steps:

[0053] Step S1, obtaining a plurality of monitoring images of the tooling fixture to be deburred at each viewing angle.

[0054] First, in this embodiment, when acquiring image data of the jig to be deburred, the binocular camera is installed on the upper side and the lower side of the part to be polished. When the jig to be deburred is polished, the jig to be deburred will rotate. In specific applications, the implementer sets the image acquisition frequency according to the time required for the jig to be deburred to rotate one circle. The acquisition frequency and field of view of all cameras of the binocular camera are the same, that is, all cameras at the same time collect images of the same area of ​​the jig to be deburred, and the image collected by one camera is an image under one viewing angle. This embodiment is explained by taking the image of an area of ​​the jig to be deburred as an example, and the method provided in this embodiment can be used to process other areas. In this embodiment, 20 images of the jig to be deburred at each viewing angle are collected respectively. In specific applications, the implementer can set the number of collected images according to the specific situation. It should be noted that: in the process of image acquisition, the field of view of each camera is fixed. Each collected surveillance image is preprocessed and background information is removed, and the processed image is recorded as a surveillance image. The image preprocessing and background information removal process are both prior arts and will not be described in detail here.

[0055] So far, this embodiment has collected multiple monitoring images of the tooling fixture to be deburred at different viewing angles.

[0056] Step S2, perform Gaussian pyramid scale transformation on each surveillance image, and perform corner point detection, and divide the corner points in each surveillance image into two categories according to the presentation of the corner points in the scale map; combine the relative distance and edge change between each corner point and other corner points in the corresponding scale map of each surveillance image to obtain the texture representation value of each corner point in the scale map.

[0057] When the binocular camera locates burrs, since the tooling to be deburred is a complex three-dimensional geometric structure, one part of the product may block another part. The blocked part may not be captured by two cameras at the same time, resulting in incomplete three-dimensional information of this part. Therefore, the product is rotated during the inspection process to capture images from multiple angles to obtain complete three-dimensional information.

[0058] When performing spatial positioning based on the collected surveillance images, it is necessary to extract feature points from the surveillance images. These feature points will be used to determine the corresponding relationship in stereo matching, and the corresponding feature points in different surveillance images are found through the stereo matching algorithm. This step is based on the disparity principle, that is, the position difference of the same object in two images. The disparity map is calculated using the matched feature point pairs, and then the depth information of each feature point is calculated based on the internal and external parameters of the camera. Based on the depth information and image features, image processing algorithms (such as edge detection, morphological operations, etc.) are used to identify and locate burrs.

[0059] When matching information in an image, it is impossible for all points to participate in the matching, so this embodiment uses the SIFT (Scale-Invariant Feature Transform) algorithm to extract feature points with scale invariance in the image, that is, corner points.

[0060] Specifically, first, Gaussian pyramid scale transformation is applied to each surveillance image to obtain multiple scale maps corresponding to each surveillance image. The parameters of Gaussian pyramid scale transformation are set by the implementer according to the specific situation, and the surveillance image is converted into image information of different scales by setting different parameters. Gaussian pyramid scale transformation is a prior art and will not be described in detail here. Then, SIFT corner point detection is performed on each surveillance image and each scale map obtained, respectively, to obtain multiple corner points in each surveillance image and multiple corner points in each scale map. SIFT corner point detection is a prior art and will not be described in detail here.

[0061] When acquiring SIFT feature points, the acquired burrs may block or change the original feature points, causing these feature points to be lost in the image. At the same time, the surface of the tooling fixture to be deburred is not a smooth metal structure, but has a certain frosted texture, which will cause a certain deviation in the acquired corner points. Therefore, the acquired corner points cannot be used directly and need further screening.

[0062] The grayscale of the surface of the tooling fixture to be deburred is inherently uneven. When acquiring corner points on the uneven product surface, it will result in obtaining too much useless corner point information, affecting the subsequent matching accuracy. When matching the acquired corner points, it is necessary to filter the corner point information and select valid corner points for subsequent matching between images acquired at different positions.

[0063] Next, any surveillance image is taken as an example for description. Other surveillance images can also be processed using the method provided in this embodiment.

[0064] Specifically, any monitoring image is recorded as a candidate monitoring image, and for any corner point in the candidate monitoring image, the total number of times the corner point is judged as a corner point in all scale images corresponding to the candidate monitoring image is recorded as the scale feature value of the corner point. By adopting this method, the scale feature value of each corner point in the candidate monitoring image can be obtained. Based on the scale feature values ​​of all corner points in the candidate monitoring image, the maximum inter-class variance method is used to divide all corner points in the candidate monitoring image into two categories. The maximum inter-class variance method is a prior art and will not be described in detail here. The average value of the scale feature values ​​of all corner points in each category is calculated and recorded as the average scale feature value of each category; the category with the largest average scale feature value is recorded as the first category, and the category with the smallest average scale feature value is recorded as the second category; the corner points in the first category are stable points with obvious scale features, that is, structural corner points or large burr corner points of the tooling fixture to be deburred, and the corner points in the second category are points with unstable scales, that is, structural corner points or small burr corner points of the tooling fixture to be deburred.

[0065] In this embodiment, by performing Gaussian pyramid scale transformation on each monitoring image, the uneven grayscale area in the monitoring image of the tooling fixture to be deburred can be smoothed out. However, when the burr is too small, the scale change will cause it to be smoothed out and the feature points in the burr area may not be obtained. When the burr is too large, it will lead to the acquisition of feature points at the edge of the shape of the tooling fixture to be deburred. Therefore, only the correspondence between the corner points in the scale map can be used to preliminarily screen the acquired corner points, that is, only part of the data can be screened out, and it is impossible to directly extract the feature points of the burr area and the feature points caused by the structural changes of the part itself.

[0066] In subsequent matching, the texture features of the corner points in images of different scales can be first obtained, and then the initial matching features of each corner point for matching are constructed by combining the texture features and the above-mentioned scale features, that is, the spatial texture change features in the image.

[0067] The size of burrs can range from tiny to large, and the shape of burrs may be irregular, with sharp edges or protruding corners. In the image, burrs will show edge discontinuities, that is, protrusions or depressions appear where the edge should be smooth. Burrs may change the texture characteristics of the local area. For example, an originally smooth surface may become rough due to the appearance of burrs.

[0068] The Canny edge detection algorithm is used to perform edge detection on each monitoring image and the scale map corresponding to each monitoring image to obtain the edge line in each image. The Canny edge detection algorithm is an existing technology and will not be described in detail here.

[0069] Next, this embodiment is described by taking any corner point in any scalar image as an example. The method provided in this embodiment can be used to process other corner points in the scalar image or all corner points in other scalar images.

[0070] Specifically, any corner point in any scale map is recorded as a candidate corner point, and the corner point directly connected to the candidate corner point through the edge line is recorded as the reference corner point of the candidate corner point; the discrete degree of the slope of all pixels on the edge line between the candidate corner point and each of its reference corner points is calculated, and recorded as the first eigenvalue between the candidate corner point and each of its reference corner points; in this embodiment, the variance of the slope of all pixels on the edge line connected to the candidate corner point and each of its reference corner points is respectively used as the discrete degree of the slope of all pixels on the edge line between the candidate corner point and each of its reference corner points. According to the Euclidean distance between the candidate corner point and all of its reference corner points and the first eigenvalue, the texture representation value of the candidate corner point is obtained, the Euclidean distance is negatively correlated with the texture representation value, and the first eigenvalue is positively correlated with the texture representation value.

[0071] Among them, a positive correlation means that the dependent variable will increase as the independent variable increases, and the dependent variable will decrease as the independent variable decreases. It can be an additive relationship, a multiplicative relationship, etc., which is determined by actual application; a negative correlation means that the dependent variable will decrease as the independent variable increases, and the dependent variable will increase as the independent variable decreases. It can be a subtractive relationship, a division relationship, etc., which is determined by actual application.

[0072] In this embodiment, a specific calculation formula for the texture representation value is given, and the texture representation value of the j-th corner point in any scale map can be expressed as:

[0073]

[0074] Among them, B j represents the texture representation value of the jth corner point in the scale map, A represents the number of reference corner points of the jth corner point in the scale map, and ε j,a Indicates the discrete degree of the slope of all pixels on the edge line between the jth corner point and its ath reference corner point, l j,a represents the Euclidean distance between the jth corner point and its ath reference corner point.

[0075] The greater the discreteness of the slopes of all pixels on the edge line between the jth corner point and its ath reference corner point and the smaller the Euclidean distance between the jth corner point and its reference corner point, the greater the texture representation value of the jth corner point in the scale map.

[0076] By using the above method, the texture representation value of each corner point in each scale map can be obtained.

[0077] Step S3, combining the texture representation value of each corner point in the corresponding scale map in all monitoring images under the same viewing angle, obtain the texture feature value of each corner point in the monitoring image; and use the texture feature value to filter the burr area for each category.

[0078] The texture representation value obtained in step S2 of this embodiment can only reflect the texture change characteristics of the corresponding position in the plane, but the edge characteristics may also exist in the texture of the product surface, so it is not easy to distinguish between texture and burrs. According to the morphological changes of the burr structure in the product as the convex part in the space, it can be known that the texture changes are small during the rotation of the tooling fixture to be deburred, and its burrs may have obvious differences. Based on the above characteristics, this embodiment will combine the texture representation value of each corner point in the corresponding scale map in all monitoring images at the same viewing angle to obtain the texture feature value of each corner point in the monitoring image.

[0079] Next, we will still use a corner point as an example to explain. Specifically, any corner point in any monitoring image is recorded as the corner point to be analyzed. Calculate the variance of the texture representation values ​​of the corresponding positions of the corner point to be analyzed in all scale maps. It should be noted that the corner point to be analyzed may only exist in some scale maps. Therefore, when calculating the variance, only the variance of all texture representation values ​​of the position in the scale map where the position exists is calculated. The variance of the texture representation values ​​of the corresponding positions of the corner point to be analyzed in all scale maps is recorded as the correction coefficient. The texture representation values ​​of the corresponding positions of the corner point to be analyzed in all monitoring images at the same viewing angle in all scale maps are corrected using the correction coefficient to obtain the texture feature value of the position of the corner point to be analyzed in the monitoring image. Specifically, the cumulative sum of the products of the texture representation values ​​of the corresponding positions of the corner point to be analyzed in all monitoring images at the same viewing angle in all scale maps and the correction coefficient is determined as the texture feature value of the position of the corner point to be analyzed in the monitoring image.

[0080] In this embodiment, a specific calculation formula for the texture feature value is given, and the texture feature value at the i-th corner point position can be expressed as:

[0081]

[0082] Among them, T i Represents the texture feature value of the i-th corner point, B i ′ ,z,x represents the texture representation value of the i-th corner point in the z-th scale map corresponding to the x-th surveillance image under the same viewing angle, q i represents the correction coefficient of the i-th corner point position, S represents the number of surveillance images under the same viewing angle, and m represents the number of scale maps corresponding to the x-th surveillance image under the same viewing angle.

[0083] It should be noted that: for any position in the monitoring image, after the monitoring image is transformed, the position may not appear in some scale maps, and the texture feature value of the position in the scale map where it does not appear is 0.

[0084] By using the above method, the texture feature value of each corner point in the monitoring image is obtained.

[0085] For any surveillance image: based on the texture eigenvalues ​​of all corner points in each category in each surveillance image, the maximum inter-class variance method is used to divide all corner points in each category in each surveillance image into two subcategories again; the average value of the texture eigenvalues ​​of all corner points in each subcategory is calculated and recorded as the average texture eigenvalue of each subcategory; the subcategory with the largest average texture eigenvalue in the first category is recorded as the first subcategory, and the subcategory with the largest average texture eigenvalue in the second category is recorded as the second subcategory; all corner points in the first subcategory and the edge lines between the corner points form a first burr area; all corner points in the second subcategory and the edge lines between the corner points form a second burr area, the first burr area is the large burr area, and the second burr area is the small burr area.

[0086] So far, the burr area has been screened out using the method provided in this embodiment.

[0087] In this embodiment, a Gaussian pyramid scale transformation is first performed on each monitoring image of the tooling fixture to be deburred to obtain a corresponding scale map, and corner point detection is performed on each monitoring image and the scale map. Then, based on the presentation of the corner points in the scale map, the corner points in the monitoring image are divided into two categories, one of which is a stable point with obvious scale characteristics, that is, a structural corner point or a large burr corner point of the tooling fixture itself to be deburred, and the other is a point with unstable scale characteristics, that is, a structural corner point or a small burr corner point of the tooling fixture itself; combined with the relative distance and edge change between each corner point in the scale map and other corner points, the texture distribution of the corner point in the scale map is evaluated to obtain a texture expression value, and combined with the texture expression value of each corner point in the corresponding scale map in all monitoring images at the same viewing angle, the texture feature value of each corner point in the monitoring image is obtained, and the texture feature value is used to screen the corner points in each category again to determine the burr area, thereby realizing subsequent deburring processing, making the positioning of the burr area more accurate, and improving the burr removal effect.

[0088] A deburring device embodiment of a tooling fixture:

[0089] like Figure 2 As shown, the figure shows a structural block diagram of a deburring device for a tooling fixture, the device includes an image acquisition module, a texture representation value determination module, a burr area screening module and a deburring module;

[0090] The image acquisition module is used to obtain a number of monitoring images of the tooling fixture to be deburred at each viewing angle;

[0091] The texture representation value determination module is used to perform Gaussian pyramid scale transformation on each surveillance image and perform corner point detection. According to the presentation of the corner points in each surveillance image in the scale map, the corner points in each surveillance image are divided into two categories; the texture representation value of each corner point in the scale map is obtained by combining the relative distance and edge change between each corner point and other corner points in the corresponding scale map of each surveillance image;

[0092] The burr area screening module is used to combine the texture representation value of each corner point in the corresponding scale map of all monitoring images under the same viewing angle to obtain the texture feature value of each corner point in the monitoring image; and use the texture feature value to screen the burr area for each category;

[0093] The deburring module is used to screen the obscured structural feature points on the tooling and fixture to be deburred according to the relative distance between each position of the tooling and fixture to be deburred and the burr area and the situation that each position is judged as a corner point in the monitoring image at the same viewing angle; reconstruct the three-dimensional model of the tooling and fixture to be deburred by combining the obscured structural feature points and all corner points in all monitoring images at different viewing angles; and plan the deburring route of the deburring equipment and perform deburring processing based on the three-dimensional model and the position of the burr area.

[0094] The image acquisition module, texture expression value determination module and burr area screening module of the deburring equipment of the tooling fixture are used to execute the steps in the embodiment of the burr automatic positioning method, and the deburring module of the deburring equipment of the tooling fixture is used to execute the following steps: according to the relative distance between each position of the tooling fixture to be deburred and the burr area and the situation that each position is judged as a corner point in the monitoring image under the same viewing angle, the obscured structural feature points on the tooling fixture to be deburred are screened; the three-dimensional model of the tooling fixture to be deburred is reconstructed by combining the obscured structural feature points and all corner points in all monitoring images under different viewing angles; based on the three-dimensional model and the position of the burr area, the deburring route of the deburring equipment is planned and the deburring process is performed.

[0095] The purpose of extracting corner points from surveillance images is to match the content between surveillance images taken by different cameras. Corner points can be used to match images. When matching images taken at different positions, the area where the large burrs are located may block the structural feature points at the original position. At the same time, since the two cameras cannot fully capture all the information of the tooling and fixture, the tooling and fixture needs to be rotated during the shooting process. By rotating the camera at the next position, image information of different areas can be captured. When performing the above matching, the image information captured by the camera itself can be matched.

[0096] For any position of the tooling to be deburred:

[0097] Calculate the minimum value of the distance between the position and all corner points in the first subclass; count the number of times the position is determined to be a corner point in each surveillance image at the same viewing angle; and obtain the probability that the position in each surveillance image belongs to an occluded structure point based on the minimum value and the number of times the position is determined to be a corner point in each surveillance image at the same viewing angle.

[0098] In this embodiment, a specific calculation formula for the probability of belonging to an obscured structure point is given. The probability that the r-th pixel position in any monitoring image belongs to an obscured structure point can be expressed as:

[0099]

[0100] Among them, Z r It indicates the probability that the rth pixel position in the monitoring image belongs to the occluded structure point, min(L r,x ) represents the minimum distance between the rth pixel position in the surveillance image and all corner points in the first subclass, S represents the number of surveillance images under the same viewing angle, and D rx It represents the corner point feature value corresponding to the r-th pixel position in the monitoring image, min() represents the minimum function, λ represents the preset adjustment parameter, and norm() represents the normalization function.

[0101] The process of obtaining the corner point feature value corresponding to the r-th pixel position in the monitoring image is as follows: if the r-th pixel position in the monitoring image is determined to be a corner point, then its corresponding corner point feature value is 1; if the r-th pixel position in the monitoring image is not determined to be a corner point, then its corresponding corner point feature value is 0, that is, the corner point feature value represents the number of times the corresponding position is determined to be a corner point.

[0102] The smaller the minimum value of the distance between the rth pixel position and all corner points in the first subclass, the more times the rth pixel position is judged as a corner point, and the greater the probability that the rth pixel position in the monitoring image belongs to an occluded structure point. It should be noted that the probability that the pixels at the same position in all monitoring images at the same viewing angle belong to an occluded structure point is the same.

[0103] By using the above method, the probability that each position in the monitoring image belongs to an obscured structure point can be obtained. If the probability is greater than the probability threshold, it is determined that the position in the corresponding monitoring image belongs to an obscured structure point, that is, multiple obscured structure points are screened. In this embodiment, the probability threshold is 0.75. In specific applications, the implementer can set it according to specific circumstances.

[0104] The occluded structural points and corner points are used to match the images taken by different cameras at the same time. The specific matching operation is an existing method and will not be described in detail here. The successfully matched point pairs are used to calculate the disparity, that is, the horizontal position difference of the same point in different images. The depth information of each position is calculated based on the disparity and the parameters of the binocular camera (such as focal length, baseline distance, etc.), and the feature points in the two-dimensional image coordinate system are converted to the three-dimensional world coordinate system to obtain the three-dimensional coordinates of each position, that is, to construct a three-dimensional model of the tooling fixture to be deburred. The reconstruction method of the three-dimensional model is an existing technology and will not be described in detail here. The location of the burr in the three-dimensional model of the tooling fixture to be deburred is marked based on the location of the burr area in the image.

[0105] Trajectory planning plans the linear trajectory of the end of the robot arm in Cartesian space according to the requirements of the grinding equipment to meet the actual task requirements. The MoveIt Kinematics and Dynamics Library (KDL) plug-in is used to obtain the inverse kinematic solution of the end position. For example, if the starting coordinates of the end of the robot arm are (x1, y1, z1), the end coordinates are (x2, y2, z2), and it moves from the starting point to the end point along a straight line, the number of interpolations is N, and the KDL plug-in is used to calculate the joint amount corresponding to each straight line trajectory, and the joint information is passed to the controller. The deburring trajectory is planned based on the obtained corner point information, which completes the planning of the deburring route of the deburring equipment, and then the deburring process is performed.

[0106] It should be understood that Figure 2 The structural block diagram of a deburring device for a tooling fixture shown and its modules can be implemented in various ways. For example, in some embodiments, the device and its modules can be implemented by hardware, software, or a combination of software and hardware. Among them, the hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. Those skilled in the art can understand that the above-mentioned method and system can be implemented using computer executable instructions and / or included in a processor control code, for example, such codes are provided on a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of this specification can not only be implemented by hardware circuits such as ultra-large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software (for example, firmware).

[0107] For more details about the above modules, please refer to other places in this manual and will not be repeated here.

[0108] In other embodiments, a medium is also provided, wherein the medium stores at least one program executable by a computer, and when the at least one program is executed by a computer, the computer executes the steps of the burr automatic location method in the above embodiment, and the medium may be a computer-readable storage medium.

[0109] Among them, the provided equipment and media are used to execute the corresponding methods provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding methods provided above, and will not be repeated here.

[0110] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A burr automatic positioning method, characterized in that: The method comprises the following steps: Acquire several monitoring images of the tooling fixture to be deburred at each viewing angle; Perform Gaussian pyramid scale transformation on each surveillance image and perform corner point detection. According to the presentation of the corner points in each surveillance image in the scale map, the corner points in each surveillance image are divided into two categories. Combined with the relative distance and edge change between each corner point and other corner points in the corresponding scale map of each surveillance image, the texture representation value of each corner point in the scale map is obtained. The texture representation value of each corner point in the corresponding scale map of all monitoring images under the same viewing angle is combined to obtain the texture feature value of each corner point in the monitoring image; the texture feature value is used to filter the burr area for each category division.

2. The burr automatic positioning method according to claim 1, characterized in that: According to the presentation of the corner points in each surveillance image in the scale map, the corner points in each surveillance image are divided into two categories, including: For any corner point in the candidate monitoring image, the total number of times the corner point is judged as a corner point in all scale images corresponding to the candidate monitoring image is recorded as the scale feature value of the corner point; Based on the scale feature values ​​of all corner points in the candidate monitoring image, the maximum inter-class variance method is used to divide all corner points in the candidate monitoring image into two categories; the candidate monitoring image is any monitoring image.

3. The burr automatic positioning method according to claim 1, characterized in that: The step of combining the relative distance and edge change between each corner point and other corner points in the corresponding scale map of each surveillance image to obtain the texture representation value of each corner point in the scale map includes: Record the corner point directly connected to the candidate corner point through the edge line as the reference corner point of the candidate corner point; calculate the discrete degree of the slope of all pixel points on the edge line between the candidate corner point and each of its reference corner points, and record it as the first eigenvalue between the candidate corner point and each of its reference corner points; Obtaining a texture representation value of the candidate corner point according to the Euclidean distance between the candidate corner point and all reference corner points thereof and the first eigenvalue, wherein the Euclidean distance is negatively correlated with the texture representation value, and the first eigenvalue is positively correlated with the texture representation value; The candidate corner point is any corner point in any scale map.

4. The burr automatic positioning method according to claim 3, characterized in that: The calculating the discrete degree of the slope of all the pixels on the edge line between the candidate corner point and each of its reference corner points includes: taking the variance of the slope of all the pixels on the edge line connecting the candidate corner point and each of its reference corner points as the corresponding discrete degree.

5. The burr automatic positioning method according to claim 1, characterized in that: The texture feature value of each corner point in the surveillance image is obtained by combining the texture representation value of each corner point in the corresponding scale map in all surveillance images under the same viewing angle, including: The variance of the texture representation value of the corresponding position of the corner point to be analyzed in all scale images is recorded as the correction coefficient; Using the correction coefficient, the texture representation values ​​of the positions of the corner points to be analyzed in all monitoring images at the same viewing angle in all scale images are corrected to obtain the texture feature values ​​of the positions of the corner points to be analyzed in the monitoring images; The corner point to be analyzed is any corner point in any monitoring image.

6. The method for burr automatic location according to claim 5, characterized in that: The method of using the correction coefficient to correct the texture representation values ​​of the positions of the corner points to be analyzed in all the monitoring images at the same viewing angle in all the scale images to obtain the texture feature values ​​of the positions of the corner points to be analyzed in the monitoring images includes: The cumulative sum of the products of the texture representation values ​​of the positions of the corner points to be analyzed in all monitoring images under the same viewing angle in all scale images and the correction coefficient is determined as the texture feature value of the position of the corner point to be analyzed in the monitoring image.

7. The method for burr automatic location according to claim 2, characterized in that: The method of using the texture feature value to divide and filter the burr area for each category includes: For any surveillance image: Calculate the average of the scale characteristic values ​​of all corner points in each category and record it as the average scale characteristic value of each category; record the category with the largest average scale characteristic value as the first category, and record the category with the smallest average scale characteristic value as the second category; Based on the texture feature values ​​of all corner points in each category in each surveillance image, the maximum inter-class variance method is used to divide all corner points in each category in each surveillance image into two subcategories again; Calculate the average value of the texture feature values ​​of all corner points in each subclass and record it as the average texture feature value of each subclass; record the subclass with the largest average texture feature value in the first class as the first subclass, and record the subclass with the largest average texture feature value in the second class as the second subclass; A burr area is determined according to the first subcategory and the second subcategory.

8. The method for burr automatic location according to claim 7, characterized in that: The step of determining the burr area according to the first subcategory and the second subcategory includes: All corner points in the first subclass and edge lines between the corner points form a first burr area; All corner points in the second subclass and edge lines between the corner points form a second burr area; The burr region includes a first burr region and a second burr region.

9. A deburring device for a tooling fixture, characterized in that: The burr area screening module of the device is used to implement the method according to any one of claims 7 to 8. The device comprises: An image acquisition module is used to obtain a number of monitoring images of the tooling fixture to be deburred at each viewing angle; The texture representation value determination module is used to perform Gaussian pyramid scale transformation on each surveillance image and perform corner point detection. According to the presentation of the corner points in each surveillance image in the scale map, the corner points in each surveillance image are divided into two categories; the texture representation value of each corner point in the scale map is obtained by combining the relative distance and edge change between each corner point and other corner points in the corresponding scale map of each surveillance image; The burr area screening module is used to combine the texture representation value of each corner point in the corresponding scale map of all monitoring images under the same viewing angle to obtain the texture feature value of each corner point in the monitoring image; and use the texture feature value to screen the burr area for each category; The deburring module is used to screen the obscured structural feature points on the tooling and fixture to be deburred according to the relative distance between each position of the tooling and fixture to be deburred and the burr area and the situation that each position is judged as a corner point in the monitoring image at the same viewing angle; reconstruct the three-dimensional model of the tooling and fixture to be deburred by combining the obscured structural feature points and all corner points in all monitoring images at different viewing angles; and plan the deburring route of the deburring equipment and perform deburring processing based on the three-dimensional model and the position of the burr area.

10. The deburring device for a tooling fixture according to claim 9, characterized in that: The method of screening the blocked structural feature points on the tooling fixture to be deburred according to the relative distance between each position of the tooling fixture to be deburred and the burr area and the situation that each position is determined to be a corner point in the monitoring image at the same viewing angle includes: For any position of the tooling to be deburred: Calculate the minimum value of the distance between the position and all corner points in the first subclass; count the number of times the position is determined as a corner point in each surveillance image at the same viewing angle; obtain the probability that the position in each surveillance image belongs to an occluded structure point according to the minimum value and the number of times the position is determined as a corner point in each surveillance image at the same viewing angle; If the probability is greater than the probability threshold, it is determined that the position in the corresponding monitoring image belongs to an obscured structure point.

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