CN111047702A - Automatic welding method for flange bent pipe based on binocular vision - Google Patents
Automatic welding method for flange bent pipe based on binocular vision Download PDFInfo
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- B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
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- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
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Abstract
The invention discloses a binocular vision-based automatic welding method for a flange bent pipe, which comprises the steps of calibrating binocular sensors by means of a target, strictly and synchronously acquiring and fixing flange images by the two sensors, obtaining a central image coordinate of a mounting hole of a flange by image processing methods such as edge detection, edge repair, morphological refinement, morphological cutting, gradient angle, ellipse fitting and the like, carrying out three-dimensional reconstruction on the center of the mounting hole of the flange by means of an extreme line constraint algorithm, establishing a coordinate value of the mounting hole under a flange coordinate system, and calculating the relative position relation between the flange coordinate system and a camera coordinate system. And finally, converting the coordinate value of the flange measured by the binocular camera under a camera coordinate system into a global coordinate system of the measuring equipment to obtain the coordinate value of the flange in the measuring equipment, and finally calculating a transformation matrix, namely a rotation and translation relation, of the bent pipe coordinate system and the flange coordinate system according to the relative relation between the flange coordinate system and the bent pipe coordinate system to further realize the automatic welding of the flange bent pipe.
Description
Technical Field
The invention belongs to the technical field of computer vision, and particularly relates to the field of automatic welding of flange bent pipes based on binocular vision.
Background
The number of the guide pipes on the airplane is large, and the shape of the pipeline is complex. In a two-engine airplane, the number of guide pipes is more than 2000, and about 30 percent of the guide pipes are welded. Welded type conduits are typically welded from manifolds, pipe fittings or flanges. The welding type conduit includes data of a flange, a pipe joint and a branch pipe, and also includes the position relation among each branch pipe, the flange and the pipe joint, and the data is difficult to directly express. The traditional welding type guide pipe manufacturing method generally adopts a real sample on an airplane, a welding clamp is manufactured according to the real sample, and finally the guide pipe is produced according to the welding clamp, so that the real sample manufacturing period is long, the manufacturing basis is transmitted through the real sample analog quantity, the production quality of the guide pipe cannot be well guaranteed, the guide pipe is repeatedly sampled in the airplane development process, the welding clamp is repeatedly modified, great inconvenience is brought to the production management of the guide pipe, and the development of new machines and aviation industry is severely restricted.
The aircraft welding conduit joint is a three-dimensional space curve, the space position is complex, the welding position changes a lot, and the limitation on the operation position is also a lot. Therefore, the welding difficulty of the aircraft guide pipe robot is high, and the position and the posture of a welding gun need to be automatically adjusted.
Therefore, in order to solve the problem of automatic welding of the airplane guide pipe, a robot automatic welding technology is adopted, and a special welding robot system is developed to realize automatic welding of the airplane guide pipe. The welding technology of the industrial robot is applied to the welding of the airplane guide pipe, the welding speed can be greatly increased, the labor condition of a welder is obviously improved, the welding quality is obviously improved, and the automation level of the welding of the airplane guide pipe is improved.
Disclosure of Invention
The invention provides a binocular vision-based automatic welding method for a flange bent pipe, which aims to realize digital automatic welding of an airplane conduit.
The invention is realized by the following contents:
a binocular vision-based automatic welding method for a flange elbow comprises the following steps:
determining the sensor size and the pixel size of an industrial camera according to the size and the measurement precision requirement of a flange, fixing the flange, calibrating the position relation of the two fixed industrial cameras by utilizing different postures of the swinging of a plane target, and obtaining the internal parameters of the two cameras and the relative position relation of the two cameras and the flange after the calibration is finished;
synchronously triggering two vision sensors to acquire fixed flange images, and acquiring corresponding image coordinates of the centers of all through holes of the flange in two cameras by image processing methods such as edge detection, edge repair, morphological refinement, morphological cutting, gradient angle elimination, ellipse fitting and the like;
thirdly, performing three-dimensional reconstruction on the center of the through hole of the flange by adopting a bidirectional polar line matching algorithm to obtain a space coordinate system of all the through holes of the flange;
and step four, automatically welding the flange bent pipe according to the flange space coordinate system and the bent pipe coordinate system.
In order to better implement the method, the edge detection method further adopts a Canny edge detection algorithm to carry out edge detection on the through holes of the flange image.
In order to better implement the present invention, further, the edge repairing method in the second step performs edge repairing on the image by analyzing the neighborhood characteristics of the pixel points 8 of the edge image.
In order to better implement the present invention, further, the picture to be refined in the morphological refinement method in the second step is a binary image after being subjected to the preprocessing step, and the image has concepts such as a central pixel point, an internal point, an end point, an isolated point, an edge point, and a breakpoint, and the morphological refinement method provides the following refinement basis based on the concepts such as the central pixel point, the internal point, the breakpoint, the isolated point, the edge point, and the breakpoint:
1) interior points cannot be deleted;
2) isolated points cannot be deleted;
3) the endpoint cannot be deleted;
4) if the central pixel point is an edge point, after the central pixel point is removed, if the connected component is not increased, the central pixel point can be deleted;
5) the pixel values of 8 neighborhood pixels are arranged from large to small according to the above to obtain an index table, the table is looked up according to the condition of 8 neighborhood points of a certain point to be processed, if the element in the table is 1, the point can be deleted, otherwise, the point is reserved, because the pixel value can only be 0 or 1, the 8 neighborhood points have 2^8=256 possible conditions, therefore, the size of the index table is 256, and the arrangement sequence is sorted from 255-0.
In order to better implement the present invention, further, the gradient angle elimination method in the second step determines which edge the intersection belongs to by using the gradient angle information, and simultaneously removes the interference edge by the segmentation of the adhesion edge.
In order to better implement the invention, further, the automatic welding in the fourth step is implemented by calculating a transformation matrix of two coordinate systems, namely a rotation and translation relation, by using a flange space coordinate system and a bent pipe coordinate system, performing rotation and translation on the bent pipe, performing butt joint on the bent pipe and a flange, fixing a welding robot, and performing three-point welding.
Compared with the prior art, the invention relates to a binocular vision-based automatic welding method for a flange bent pipe, which has the following advantages and beneficial effects:
(1) the welding precision and the welding quality are improved;
(2) the welding speed is improved;
(3) the labor condition of the welder is improved.
Drawings
FIG. 1 is a general flow chart;
FIG. 2 is a diagram illustrating an example of edge repairing;
FIG. 3 is an exemplary diagram of an 8-neighborhood point binary image of an arbitrary pixel point P;
FIG. 4 is an example graph of gradient angle culling.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments, and therefore should not be considered as a limitation to the scope of protection. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Example 1:
according to the embodiment of the invention, as shown in fig. 1, the method for automatically welding the flange bent pipe based on binocular vision comprises the following steps:
determining the sensor size and the pixel size of an industrial camera according to the size and the measurement precision requirement of a flange, fixing the flange, calibrating the position relation of the two fixed industrial cameras by utilizing different postures of the swinging of a plane target, and obtaining the internal parameters of the two cameras and the relative position relation of the two cameras and the flange after the calibration is finished;
synchronously triggering two vision sensors to acquire fixed flange images, and acquiring corresponding image coordinates of the centers of all through holes of the flange in two cameras by image processing methods such as edge detection, edge repair, morphological refinement, morphological cutting, gradient angle elimination, ellipse fitting and the like;
thirdly, performing three-dimensional reconstruction on the center of the through hole of the flange by adopting a bidirectional polar line matching algorithm to obtain a space coordinate system of all the through holes of the flange;
and step four, automatically welding the flange bent pipe according to the flange space coordinate system and the bent pipe coordinate system.
The working principle is as follows: the invention can confirm the sensor size and the pixel size of the industrial camera through the size and the measurement precision requirement of the flange, can realize the consistency of the measurement precision and the size, and can ensure the measurement accuracy. The flange is fixed, so that the measurement error caused by the movement of the flange can be avoided. And the position relation of two fixed industrial cameras is calibrated by different postures of the swinging of one plane target, data combination can be carried out through calibration under different postures, and finally the internal parameters of the cameras and the relative position relation of the internal parameters and the flange are determined. The fixed flange image information synchronously triggering the two vision sensors to collect is a key point, and if synchronous transmission cannot be guaranteed, signals transmitted by the two sensors cannot be kept consistent, so that measurement errors can be caused. After a series of digital image processing operations, a more accurate flange image can be obtained, the position of each through hole on the flange is determined in the obtained image, the position coordinate of the central point of each through hole is determined, the flange can be subjected to three-dimensional reconstruction by adopting a bidirectional polar line matching algorithm, the space coordinate systems of all the through holes on the flange are obtained, and finally, the flange bent pipe is automatically welded according to the space coordinate systems of the flange and the bent pipe.
Example 2:
in this embodiment, further optimization is performed on the basis of embodiment 1, and a Canny edge detection algorithm is used to perform edge detection on the through holes of the flange image.
Other parts of this embodiment are the same as embodiment 1, and thus are not described again.
Example 3:
in this embodiment, further optimization is performed on the basis of the foregoing embodiment 1 or 2, and as shown in fig. 2, the edge repairing method in the second step described in embodiment 1 performs edge repairing on the image by analyzing the neighborhood characteristics of the pixel 8 of the edge image.
The working principle is as follows: the edge repairing is to refine the result more accurately, because the edge image of the initial Canny edge detection is thicker, and at the same time, as shown in fig. 2, the pixel value of the P point of the edge image is 0, and the pixel points in the 8 neighborhoods centered on the P point are all edge points, and the pixel value is 1, which is unreasonable, and in fact, the P point should also be an edge point. If the edge of the P point is not repaired, that is, the pixel value of the P point is set to 1, the edge topology structure is changed, the subsequent edge refinement result is seriously affected, and difficulty is brought to subsequent detection.
The rest of this embodiment is the same as embodiment 1 or 2, and therefore, the description thereof is omitted.
Example 4:
the present embodiment is further optimized on the basis of any one of the foregoing embodiments 1 to 3, and the morphological refinement in the second step is performed by obtaining a flange through-hole skeleton, where the skeleton is a contour region that only includes one pixel width and then uses an index table refinement algorithm based on mathematical morphology. Specifically, as shown in fig. 3, the image to be refined is a binary image through preprocessing. Suppose that 8 neighborhood points of any pixel point P of the image are identified according to the sequence of P0-P7 in FIG. 3, the value of each pixel point is 0 or 1, wherein the pixel value is 1, the foreground point needing to be refined is, and the pixel value is 0, the background point is. The algorithm based on the index table is to make a look-up table according to a certain judgment basis, look up the table according to the condition of 8 neighborhood points of a certain point to be refined, delete the point if the element in the table is 1, change the point into a background point, and keep if the element in the table is 0, namely the background point. The method comprises the following steps that a picture needing to be refined is a binary image after being subjected to a preprocessing step, concepts such as a central pixel point, an internal point, an end point, an isolated point, an edge point and a breakpoint exist in the image, and the morphological refining method provides the following refining basis based on the concepts such as the central pixel point, the internal point, the breakpoint, the isolated point, the edge point and the breakpoint:
1) interior points cannot be deleted;
2) isolated points cannot be deleted;
3) the endpoint cannot be deleted;
4) if the central pixel point is an edge point, after the central pixel point is removed, if the connected component is not increased, the central pixel point can be deleted.
Arranging pixel values of domain pixel points from large to small to obtain an index table, looking up the table according to the condition of 8 neighborhood points of a certain point to be processed, if an element in the table is 1, indicating that the point can be deleted, otherwise, keeping the element, because the pixel value can only be 0 or 1, the 8 neighborhood points have 2^8=256 possible conditions, therefore, the size of the index table is 256, and the arrangement sequence is sorted from 255-0.
The working principle is as follows: within the 3 × 3 window of fig. 3, the following concept exists for the center pixel point P:
1) if P is a foreground point and only one foreground point is in 8 neighborhoods P0, P1, … and P7, the P is called an end point;
2) if only P in the 8 neighborhoods is a foreground point, the P is called an isolated point;
3) if P is a foreground point and only one background point is selected from P1, P3, P5 and P7, P is called an edge point;
4) if the continuity of the graph is damaged by deleting the foreground point P, the point P is called a breakpoint;
the refining criterion is proposed based on the concepts:
1) interior points cannot be deleted;
2) isolated points cannot be deleted;
3) the endpoint cannot be deleted;
4) if the central pixel point is an edge point, after the central pixel point is removed, if the connected component is not increased, the central pixel point can be deleted;
5) according to the criterion, the pixel values of 8 neighborhood pixel points are arranged from large to small according to the sequence of P7P6P5P4P3P2P1P0 to obtain an index table. And (4) looking up a table according to the condition of 8 neighborhood points of a certain point to be processed, wherein if an element in the table is 1, the point can be deleted, and if not, the point is reserved. Since the pixel value can only be 0 or 1, the 8 neighborhood points have 2^8=256 possible cases, and therefore, the size of the index table is 256, and the arrangement order is sorted from 255-0;
the refining algorithm comprises the following steps:
1) calculating index values for table lookup from the 8 neighborhood values in order:
y = P0*2^0 + P1*2^1+ P2*2^2 + P3*2^3 + P4*2^4 + P5*2^5 + P6*2^6 +P7*2^7
(P0-P7 are 0 or 1, respectively), there are 256 indexes corresponding to the values 0 to 255 one by one;
2) the index table is as follows:
{0,0,1,1,0,0,1,1,1,1,0,1,1,1,0,1,1,1,0,0,1,1,1,1,0,0,0,0,0,0,0,1,
0,0,1,1,0,0,1,1,1,1,0,1,1,1,0,1,1,1,0,0,1,1,1,1,0,0,0,0,0,0,0,1,
1,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
1,1,0,0,1,1,0,0,1,1,0,1,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,1,1,0,0,1,1,1,1,0,1,1,1,0,1,1,1,0,0,1,1,1,1,0,0,0,0,0,0,0,1,
0,0,1,1,0,0,1,1,1,1,0,1,1,1,0,1,1,1,0,0,1,1,1,1,0,0,0,0,0,0,0,0,
1,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,1,1,1,0,0,0,0,0,0,0,0,
1,1,0,0,1,1,0,0,1,1,0,1,1,1,0,0,1,1,0,0,1,1,1,0,1,1,0,0,1,0,0,0};
the index table has 256 elements which respectively correspond to 256 index values y, wherein the index value corresponding to the first element in the table is 255, and the elements in the table respectively correspond to 256 index values of 255-0 according to the descending order of numerical values from left to right and from top to bottom;
3) from top to bottom, from left to right, the binary image shown in fig. 3 is scanned, and each foreground point is processed as follows: judging the left and right neighborhoods of the point, if the left and right neighborhoods are foreground points, the point is not processed; otherwise, 8 neighborhood codes are calculated to be used as indexes, and the index table is checked to judge whether the indexes are deleted; if the foreground point is deleted, skipping the right neighborhood of the point and processing the next point;
4) from left to right, from top to bottom, the binary image is scanned again, and each foreground point is processed as follows: firstly, judging the upper and lower neighborhoods of the point, and if the upper and lower neighborhoods are foreground points, not processing the point; otherwise, 8 neighborhood codes are calculated to be used as indexes, and the index table is checked to judge whether the indexes are deleted; if the point is deleted, skipping the neighborhood below the point and processing the next point;
5) and (4) if the point of the loop is deleted, jumping to (3), otherwise, terminating the loop and finishing the refinement.
Other parts of this embodiment are the same as any of embodiments 1 to 3, and thus are not described again.
Example 5:
this embodiment is further optimized based on any of the above embodiments 1 to 4, as shown in fig. 4, the gradient angle elimination method in the second step determines which edge the intersection belongs to using the gradient angle information, and removes the interference edge by dividing the adhesion edge.
The working principle is as follows: the extracted skeleton has a cross point, and an interference edge is introduced, for example, a point a in fig. 4, the cross point is a pixel point reserved for preventing the connectivity of an edge region from being damaged in a thinning process, and the cross point cannot be accurately eliminated only from the connectivity of 8 neighborhood points, so that the cross point is eliminated from the gradient angle characteristic of a point in the 8 neighborhood range of the cross point.
Other parts of this embodiment are the same as any of embodiments 1 to 4, and thus are not described again.
Example 6:
the present embodiment is further optimized on the basis of any one of the foregoing embodiments 1 to 5, and the automatic welding in the fourth step is implemented by calculating a transformation matrix of two coordinate systems, i.e., a rotation and translation relationship, by using a flange space coordinate system and a bent pipe coordinate system, performing rotation and translation on the bent pipe, performing butt joint with a flange, fixing a welding robot, and performing three-point welding. In order to verify the effectiveness and the correctness of the method, a Manta series camera (resolution: 2048 multiplied by 1088) of AVT company is adopted, an 8mm fine lens of computer company is configured for the camera, and all simulation experiments are realized by adopting Visualstudio 2008 software under a Windows 7 operating system.
Other parts of this embodiment are the same as any of embodiments 1 to 5, and thus are not described again.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, and all simple modifications and equivalent variations of the above embodiments according to the technical spirit of the present invention are included in the scope of the present invention.
Claims (6)
1. A binocular vision-based automatic welding method for a flange elbow is characterized by comprising the following steps:
determining the sensor size and the pixel size of an industrial camera according to the size and the measurement precision requirement of a flange, fixing the flange, calibrating the position relation of the two fixed industrial cameras by utilizing different postures of the swinging of a plane target, and obtaining the internal parameters of the two cameras and the relative position relation of the two cameras and the flange after the calibration is finished;
synchronously triggering two vision sensors to acquire fixed flange images, and acquiring corresponding image coordinates of the centers of all through holes of the flange in two cameras by using image processing methods of edge detection, edge repair, morphological refinement, morphological cutting, gradient angle elimination and ellipse fitting;
thirdly, performing three-dimensional reconstruction on the center of the through hole of the flange by adopting a bidirectional polar line matching algorithm, acquiring a space coordinate system of all the through holes of the flange, and constructing a space coordinate system of a bent pipe coordinate system;
and step four, automatically welding the flange elbow according to the space coordinate system of the flange relative to the elbow.
2. The binocular vision based automatic welding method for the flange elbow according to claim 1, wherein in the second step, the edge detection method adopts a Canny edge detection algorithm to carry out edge detection on through holes of a flange image.
3. The binocular vision based automatic flange elbow welding method according to claim 1, wherein in the second step, the edge repairing method carries out edge repairing on the image by analyzing neighborhood characteristics of 8 pixel points of the edge image.
4. The binocular vision based automatic welding method for the flange bent pipe is characterized in that in the second step, the morphological refining method is to refine the binary image after edge detection and edge repair; the basis for refining the center pixel point, the inner point, the breakpoint, the isolated point, the edge point and the breakpoint in the binary image comprises the following contents:
interior points cannot be deleted;
isolated points cannot be deleted;
the endpoint cannot be deleted;
if the central pixel point is an edge point, after the central pixel point is removed, if the connected component is not increased, the central pixel point can be deleted;
5) obtaining an index table by arranging pixel values of 8 neighborhood pixels from large to small according to the refinement basis, querying the index table according to the condition of 8 neighborhood points of a point to be processed, if an element in the index table is 1, indicating that the point can be deleted, otherwise, keeping the pixel value, because the pixel value can only be 0 or 1, the 8 neighborhood points have 2^8=256 possible conditions, therefore, the number of the elements in the index table is 256, the arrangement sequence of the elements in the index table is sequentially ordered from large to small according to the value of 255-0 from left to right and from top to bottom, and the index table is specifically as follows:
{0,0,1,1,0,0,1,1,1,1,0,1,1,1,0,1,1,1,0,0,1,1,1,1,0,0,0,0,0,0,0,1,
0,0,1,1,0,0,1,1,1,1,0,1,1,1,0,1,1,1,0,0,1,1,1,1,0,0,0,0,0,0,0,1,
1,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
1,1,0,0,1,1,0,0,1,1,0,1,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,1,1,0,0,1,1,1,1,0,1,1,1,0,1,1,1,0,0,1,1,1,1,0,0,0,0,0,0,0,1,
0,0,1,1,0,0,1,1,1,1,0,1,1,1,0,1,1,1,0,0,1,1,1,1,0,0,0,0,0,0,0,0,
1,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,1,1,1,0,0,0,0,0,0,0,0,
1,1,0,0,1,1,0,0,1,1,0,1,1,1,0,0,1,1,0,0,1,1,1,0,1,1,0,0,1,0,0,0}。
5. the binocular vision-based automatic flange elbow welding method according to claim 1, wherein in the second step, the gradient angle eliminating method determines which edge the intersection belongs to by using gradient angle information, and meanwhile, the interference edge is removed through separation of the adhesion edge.
6. The binocular vision-based automatic welding method for the flange and the elbow according to claim 1, wherein the automatic welding in the fourth step is to calculate a transformation matrix of two coordinate systems through a space coordinate system of the flange and a space coordinate system of the elbow so as to butt the elbow with the flange after rotating and translating, fix a welding robot, and realize welding by utilizing three-point welding.
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| CN114331945A (en) * | 2020-09-30 | 2022-04-12 | 北京理工大学 | Large flange assembling alignment method based on multiple local images |
| CN115815939A (en) * | 2023-02-20 | 2023-03-21 | 国网山东省电力公司东营供电公司 | Auxiliary positioning method for capacitor installation |
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