CN106181162B - A kind of real-time weld joint tracking detection method based on machine vision - Google Patents
A kind of real-time weld joint tracking detection method based on machine vision Download PDFInfo
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- CN106181162B CN106181162B CN201610672098.4A CN201610672098A CN106181162B CN 106181162 B CN106181162 B CN 106181162B CN 201610672098 A CN201610672098 A CN 201610672098A CN 106181162 B CN106181162 B CN 106181162B
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- 238000003466 welding Methods 0.000 claims abstract description 76
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- 238000012545 processing Methods 0.000 claims abstract description 9
- 238000004422 calculation algorithm Methods 0.000 claims description 16
- 238000001914 filtration Methods 0.000 claims description 14
- 238000003708 edge detection Methods 0.000 claims description 10
- 230000003628 erosive effect Effects 0.000 claims description 9
- 230000010339 dilation Effects 0.000 claims description 8
- 230000000877 morphologic effect Effects 0.000 claims description 8
- 238000012937 correction Methods 0.000 claims description 7
- 238000003709 image segmentation Methods 0.000 claims description 7
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- 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
- B23K37/00—Auxiliary devices or processes, not specially adapted to a procedure covered by only one of the preceding main groups
- B23K37/02—Carriages for supporting the welding or cutting element
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- 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
- B23K37/00—Auxiliary devices or processes, not specially adapted to a procedure covered by only one of the preceding main groups
- B23K37/02—Carriages for supporting the welding or cutting element
- B23K37/0252—Steering means
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Abstract
The real-time weld joint tracking detecting system based on machine vision that the invention discloses a kind of, it includes：It includes：Industrial personal computer as system core control device；The image acquisition units being made of light source controller, LED light source, image pick-up card and CCD camera；And the motion control unit being made of robot controller, welding robot；The real-time weld joint tracking detection based on machine vision that the invention also includes a kind of, including Image Acquisition → image procossing → data processing → motion control and etc.；The method that the present invention uses real-time online detection need not manually carry out extra operation, it is expected that seam track and the comparison of actual welds track are rectified a deviation what is generated by groove image；And in the case that site welding environment has different with manual teaching environment, off-line programing simulated environment, real-time weld joint tracking can be carried out, all kinds of interference of welding scene is overcome, ensures the accuracy and reliability of welding effect.
Description
Technical field
The present invention relates to a kind of real-time weld joint tracking detecting system and method based on machine vision, belong to machine vision and
Technical field of mechanical automation.
Background technology
Welding plays always extremely most important role in the industrial production, and as robot and vision-based detection are handled
The automatization level of the development of technology, welding technique increasingly improves, and weld joint tracking detection method also develops into meter from artificial detection
Calculation machine intelligent measurement.
Existing welding robot belongs to on-site manual teaching type or off-line programing type mostly, welding robot in both methods
The welding parameter and path of welding of people is planned before production, makes welding robot can be high-precision in working space
Degree ground repeats programming movement.
And traditional weld joint tracking detection method compares in the weld seam path of planning with the weld seam path actually generated
The tracking of weld seam is realized in error correction.Such welding seam tracking method can ensure the high-precision fortune of welding robot in planning region
Row and weld joint tracking, but can only be poor for the adaptability to changes of emergency case to planning path into line trace.
During actual welding, the interference of welding scene is more, the shape of weldment can with the variation of production environment and
The collision etc. to happen suddenly in variation, such as the high temperature of molten bath generation, lower assembly precision, welding process, all can be to welding production
It causes to influence accordingly；When site welding environment and manual teaching environment, off-line programing simulated environment have the case where different
Under, the effect of conventional weld tracking is very undesirable, and this restrict the further development of existing THE WELD SEAM TRACKING TECHNOLOGY.
Invention content
The real-time weld joint tracking inspection based on machine vision that in view of the above existing problems in the prior art, the present invention provides a kind of
Examining system and method can carry out real-time weld joint tracking, overcome all kinds of of welding scene with high accuracy and adaptivity
Interference, to ensure the accuracy and reliability of welding effect.
To achieve the goals above, the technical solution adopted by the present invention is：A kind of real-time weld seam based on machine vision with
Track detection method, it includes：
Industrial personal computer as system core control device；
The image acquisition units being made of light source controller, LED light source, image pick-up card and CCD camera；
And the motion control unit being made of robot controller, welding robot；
The industrial personal computer is responsible for the processing and calculating of image, data；The light-source controller controls LED light source is with weldering
The movement of rifle and move, so that light source is covered at weld seam and groove to be welded, ensure the daylighting degree of image acquisition region；
The CCD camera includes CCD camera one, CCD camera two；CCD camera one, CCD camera two are distinguished
Mounted on the both sides of welding robot welding gun, moved with the movement of welding gun；And CCD camera one is to be welded towards welding gun front end
At groove, welding position below two positive Butt welding gun of CCD camera；
It is further comprising the steps of：
1) Image Acquisition：Before welding starts, light-source controller controls LED light source is irradiated to groove in a manner of front illumination
And commissure, CCD camera acquire the image in groove region to be welded at the beginning, after time t, CCD camera two starts acquisition weldering
The weld image of socket part point, two initial pictures are transferred in industrial personal computer；
Wherein t is prolonged after taking the welding of groove region by CCD camera one for groove region by what CCD camera two was shot
The slow time；
2) image procossing：Groove is obtained by image filtering, image segmentation, Morphological scale-space and edge detection process method
With the edge image of welded seam area partial enlargement, image procossing detailed process is as follows：
(1) gaussian filtering method is used to carry out denoising to initial pictures；
Digital picture can be expressed as the form f (x, y) of two-dimensional array, and x, y indicate pixel point coordinates, f (x, y) table respectively
The gray scale of diagram picture, wherein G (x, y) are two-dimensional Gaussian function；
fs(x, y)=G (x, y) * f (x, y)
fs(x, y) indicates the digital picture after gaussian filtering；
(2) maximum variance between clusters are used to carry out image segmentation to image：
Entire data are divided into two classes using a threshold value, if the variance between two classes is maximum, then this threshold
Value is exactly best threshold value, wherein T for pixel in image gray value；
I.e. selection makes maximum σ2(T) T*As optimal segmenting threshold；
(3) dilation erosion algorithm is used to carry out Morphological scale-space to image：
Dilation operation first is carried out to f (x, y) with structural element, erosion operation then is carried out to result with structural element；Profit
The extra hole of target object can be eliminated with dilation erosion algorithm, connects similar object, while filling up tiny on contour line
Indent wedge angle with the boundary of smooth object；
(4) Canny edge detection algorithms are used to carry out edge detection into image：
Gradient magnitude and the direction of filtering image are calculated,
Gradient magnitude：
Gradient direction：
Again to gradient magnitude carry out non-maxima suppression, if the value of M (x, y) be less than pixel any two neighborhood it
One, then enable gN(x, y)=0；Otherwise, g is enabledN(x, y)=M (x, y)；gN(x, y) indicates to carry out the image after non-maximum suppression；
Finally use dual threashold value-based algorithm process decision chart as marginal point：Set a threshold value upper bound THWith threshold value lower bound TL, in image
Pixel if it is greater than the threshold value upper bound, that is, gN(x,y)>THThen think necessarily boundary, is less than threshold value lower bound, that is, gN(x,y)<TL
It is not boundary then to think inevitable, between the two be then considered candidate item；
3) data processing：The edge image for handling groove region partial enlargement, judges groove class according to algorithm for pattern recognition
Type calculates planned expectation seam track；Handle the weld image of welding portion, the weld seam rail that computing device actually generates
Mark obtains two smooth Weld pipe mill tracks, two seam tracks is compared and calculate tracking correction amount after filtered；
4) motion control：Industrial personal computer according to correction amount carry out real-time deviation correcting, control welding robot movement and further
Welding, to realize the accurate tracking of weld seam.
Compared with existing welding seam tracking method, the method and system that the present invention uses has the following advantages and effect：
1. the present invention need not manually carry out extra operation, after welding starts using the method for real-time online detection
Welding robot can fully automatically carry out weld joint tracking detection；
2. the present invention is generated using groove image it is expected seam track, strong to the adaptivity of site welding environment, when existing
In the case that field welding surroundings have different with manual teaching environment, off-line programing simulated environment, real-time weld seam can be carried out
Tracking, overcomes all kinds of interference of welding scene, ensures the accuracy and reliability of welding effect.
Description of the drawings
Fig. 1 is the solution of the present invention general principles schematic diagram.
Fig. 2 is the system principle schematic diagram of the present invention.
Specific implementation mode
The invention will be further described below in conjunction with the accompanying drawings.
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation describes, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
If Fig. 2 is a kind of real-time weld joint tracking detecting system based on machine vision, it includes：
Industrial personal computer as system core control device；
The image acquisition units being made of light source controller, LED light source, image pick-up card and CCD camera；
And the motion control unit being made of robot controller, welding robot；
The industrial personal computer is responsible for the processing and calculating of image, data；The light-source controller controls LED light source is with weldering
The movement of rifle and move, so that light source is covered at weld seam and groove to be welded, ensure the daylighting degree of image acquisition region；
The CCD camera includes CCD camera one, CCD camera two；CCD camera one, CCD camera two are distinguished
Mounted on the both sides of welding robot welding gun, moved with the movement of welding gun；And CCD camera one is to be welded towards welding gun front end
At groove, welding position below two positive Butt welding gun of CCD camera.
It is as shown in Figure 1 a kind of real-time weld joint tracking detection method based on machine vision, uses above-mentioned system, wraps
Include following steps：
1) Image Acquisition：Before welding starts, light-source controller controls LED light source is irradiated to groove in a manner of front illumination
And commissure, CCD camera acquire the image in groove region to be welded at the beginning, after time t, CCD camera two starts acquisition weldering
The weld image of socket part point, the data of two initial pictures are transferred in industrial personal computer；
When wherein t is that groove region takes the delay shot by CCD camera two after the welding of region by CCD camera one
Between；
2) image procossing：Groove is obtained by image filtering, image segmentation, Morphological scale-space and edge detection process method
With the edge image of welded seam area partial enlargement；
3) data processing：The edge image for handling groove region partial enlargement, judges groove class according to algorithm for pattern recognition
Type calculates planned expectation seam track, handles the weld image of welding portion, the weld seam rail that computing device actually generates
Mark obtains two smooth Weld pipe mill tracks, two seam tracks is compared and calculate tracking correction amount after filtered.
4) motion control：Industrial personal computer according to correction amount carry out real-time deviation correcting, control welding robot movement and further
Welding, to realize the accurate tracking of weld seam.
In the above scheme, step 1), 2) region, 3) different to two are detected and handle：Groove area to be welded
Domain and welded seam region.
Groove region to be welded passes through root face and rest part gray feature value using the root face at groove as application
Difference, extract the morphological feature of groove part root face, judge groove type and calculate desired Weld pipe mill track；Welding weldering
Region is stitched using weld seam as application, actual welds centrode is calculated by the marginal information of weld seam.
In the above scheme, if step 1) does not collect root face information, i.e. welding object square groove, then in step 3)
In Data Data processing, with the set of object both sides notch central point to be welded for desired Weld pipe mill track.
In the above scheme, t is that groove region is taken after groove region is welded by CCD camera one by CCD camera
It is the delay time of two shootings, related with the welding procedure, speed of welding, computer disposal speed of welding equipment etc., ensure that institute is right
The seam track that the expectation of ratio, actual welds track are the same area, are mutually matched.
Wherein, the image procossing of step 2) is broadly divided into image filtering, image segmentation, Morphological scale-space and edge detection 4
Part, detailed process are as follows：
(1) gaussian filtering method is used to carry out denoising to initial pictures；
Digital picture can be expressed as the form f (x, y) of two-dimensional array, and x, y indicate pixel point coordinates, f (x, y) table respectively
The gray scale of diagram picture；
fs(x, y)=G (x, y) * f (x, y)
fs(x, y) indicates the digital picture after gaussian filtering；
(2) maximum variance between clusters are used to carry out image segmentation to image：
Entire data are divided into two classes using a threshold value, if the variance between two classes is maximum, then this threshold
Value is exactly best threshold value；
I.e. selection makes σ2(Tb) maximum T*As optimal segmenting threshold；
(3) dilation erosion algorithm is used to carry out Morphological scale-space to image：
Dilation operation first is carried out to f (x, y) with structural element, erosion operation then is carried out to result with structural element.Profit
The extra hole of target object can be eliminated with dilation erosion algorithm, connects similar object, while filling up tiny on contour line
Indent wedge angle with the boundary of smooth object；
(4) Canny edge detection algorithms are used to carry out edge detection into image：
Gradient magnitude and the direction of filtering image are calculated,
Gradient magnitude：
Gradient direction：
Again to gradient magnitude carry out non-maxima suppression, if the value of M (x, y) be less than pixel any two neighborhood it
One, then enable gN(x, y)=0；Otherwise, g is enabledN(x, y)=M (x, y).gN(x, y) indicates to carry out the image after non-maximum suppression；
Finally use dual threashold value-based algorithm process decision chart as marginal point：Set a threshold value upper bound THWith threshold value lower bound TL, in image
Pixel then think necessarily boundary (g if it is greater than the threshold value upper boundN(x, y) >=TH), then think necessarily not less than threshold value lower bound
It is boundary (gN(x, y)≤TL), between the two be then considered candidate item.
In the above scheme, step 3) judges groove type by algorithm for pattern recognition, further according to the welding of different grooves
Feature calculates in conjunction with the groove image that step 2) obtains and it is expected seam track；It is expected that seam track with actual welds track through excellent
It is compared after changing smoothing processing, Optimal calculation is carried out to its departure, bias direction and tolerance speed.
Compared with existing welding seam tracking method, the method and system that the present invention uses has the following advantages and effect：
1. the present invention need not manually carry out extra operation, after welding starts using the method for real-time online detection
Welding robot can fully automatically carry out weld joint tracking detection；
2. the present invention is generated using groove image it is expected seam track, strong to the adaptivity of site welding environment, when existing
In the case that field welding surroundings have different with manual teaching environment, off-line programing simulated environment, real-time weld seam can be carried out
Tracking, overcomes all kinds of interference of welding scene, ensures the accuracy and reliability of welding effect.
It is obvious to a person skilled in the art that invention is not limited to the details of the above exemplary embodiments, Er Qie
In the case of without departing substantially from spirit or essential attributes of the invention, the present invention can be realized in other specific forms.Therefore, no matter
From the point of view of which point, the present embodiments are to be considered as illustrative and not restrictive, and the scope of the present invention is by appended power
Profit requires rather than above description limits, it is intended that all by what is fallen within the meaning and scope of the equivalent requirements of the claims
Variation is included within the present invention.Any reference signs in the claims should not be construed as limiting the involved claims.
The above, only presently preferred embodiments of the present invention, are not intended to limit the invention, every skill according to the present invention
Art essence should be included in technical solution of the present invention to any trickle amendment, equivalent replacement and improvement made by above example
Protection domain within.
Claims (1)
1. a kind of real-time weld joint tracking detection method based on machine vision, which is characterized in that it includes：
Industrial personal computer as system core control device；
The image acquisition units being made of light source controller, LED light source, image pick-up card and CCD camera；
And the motion control unit being made of robot controller, welding robot；
The industrial personal computer is responsible for the processing and calculating of image, data；The light-source controller controls LED light source is with welding gun
It moves and moves, so that light source is covered at weld seam and groove to be welded, ensure the daylighting degree of image acquisition region；
The CCD camera includes CCD camera one, CCD camera two；CCD camera one, CCD camera two are installed respectively
In the both sides of welding robot welding gun, moved with the movement of welding gun；And CCD camera one is towards welding gun front end groove to be welded
Place, two positive Butt welding gun of CCD camera lower section welding position；
It is further comprising the steps of：
1) Image Acquisition：Before welding starts, light-source controller controls LED light source is irradiated to groove and weldering in a manner of front illumination
At seam, CCD camera acquires the image in groove region to be welded at the beginning, and after time t, CCD camera two starts to acquire weld part
The weld image divided, two initial pictures are transferred in industrial personal computer；
When wherein t is that groove region takes the delay shot by CCD camera two after the welding of groove region by CCD camera one
Between；
2) image procossing：Groove and weldering are obtained by image filtering, image segmentation, Morphological scale-space and edge detection process method
The edge image of region partial enlargement is stitched, image procossing detailed process is as follows：
(1) gaussian filtering method is used to carry out denoising to initial pictures；
Digital picture can be expressed as the form f (x, y) of two-dimensional array, and x, y indicate that pixel point coordinates, f (x, y) indicate respectively
The gray scale of image, wherein G (x, y) are two-dimensional Gaussian function；
fs(x, y)=G (x, y) * f (x, y)
fs(x, y) indicates the digital picture after gaussian filtering；
(2) maximum variance between clusters are used to carry out image segmentation to image：
Entire data are divided into two classes using a threshold value, if the variance between two classes is maximum, then this threshold value is just
It is best threshold value, wherein T is the gray value of pixel in image；
I.e. selection makes maximum σ2(T) T*As optimal segmenting threshold；
(3) dilation erosion algorithm is used to carry out Morphological scale-space to image：
Dilation operation first is carried out to f (x, y) with structural element, erosion operation then is carried out to result with structural element；Using swollen
Swollen erosion algorithm can eliminate the extra hole of target object, connect similar object, while fill up tiny interior on contour line
Recessed wedge angle is with the boundary of smooth object；
(4) Canny edge detection algorithms are used to carry out edge detection into image：
Gradient magnitude and the direction of filtering image are calculated,
Gradient magnitude：
Gradient direction：
Non-maxima suppression is carried out to gradient magnitude again, if the value of M (x, y) is less than one of any two neighborhood of pixel,
Then enable gN(x, y)=0；Otherwise, g is enabledN(x, y)=M (x, y)；gN(x, y) indicates to carry out the image after non-maximum suppression；
Finally use dual threashold value-based algorithm process decision chart as marginal point：Set a threshold value upper bound THWith threshold value lower bound TL, the picture in image
Vegetarian refreshments is if it is greater than the threshold value upper bound, that is, gN(x,y)>THThen think necessarily boundary, is less than threshold value lower bound, that is, gN(x,y)<TLThen recognize
For it is inevitable be not boundary, between the two be then considered candidate item；
3) data processing：The edge image for handling groove region partial enlargement, judges groove type according to algorithm for pattern recognition, counts
Calculate planned expectation seam track；Handle the weld image of welding portion, the seam track that computing device actually generates, warp
Two smooth Weld pipe mill tracks are obtained after filtering, and two seam tracks are compared and calculate tracking correction amount；
4) motion control：Industrial personal computer carries out real-time deviation correcting according to correction amount, controls the movement of welding robot and further weldering
It connects, to realize the accurate tracking of weld seam.
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