CN119703275A - Fusion method, system and equipment based on laser tracking and arc tracking - Google Patents
Fusion method, system and equipment based on laser tracking and arc tracking Download PDFInfo
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Abstract
本申请涉及管道焊接领域，尤其涉及基于激光跟踪与电弧跟踪的融合方法、系统及设备。获取激光跟踪数据，电弧跟踪数据；通过预设的方法提取焊缝中心线，以及基于预设的焊缝几何模型提取焊缝特征点；检测所述电弧跟踪数据中的局部极值，利用所述局部极值推测焊枪的位置偏差方向以及运用预设的积分法计算所述电弧跟踪数据中的极值差值，利用所述极值差值估算焊枪的偏移量；利用所述预设的焊缝几何模型所提取的焊缝特征点、所述焊枪的位置偏差方向、所述焊枪的偏移量，确定焊枪轨迹，用于焊接指导，有助于提供准确的焊枪位置估计，提高焊接过程的精度和稳定性，适用于复杂的管道全位置焊接作业。
The present application relates to the field of pipeline welding, and in particular to a fusion method, system and device based on laser tracking and arc tracking. Laser tracking data and arc tracking data are obtained; the weld centerline is extracted by a preset method, and weld feature points are extracted based on a preset weld geometry model; local extreme values in the arc tracking data are detected, the position deviation direction of the welding gun is inferred using the local extreme values, and the extreme value difference in the arc tracking data is calculated using a preset integral method, and the offset of the welding gun is estimated using the extreme value difference; the welding feature points extracted by the preset weld geometry model, the position deviation direction of the welding gun, and the offset of the welding gun are used to determine the welding gun trajectory for welding guidance, which helps to provide accurate welding gun position estimation, improve the accuracy and stability of the welding process, and is suitable for complex pipeline all-position welding operations.
Description
Technical Field
The application relates to the field of pipeline welding, in particular to a fusion method, a fusion system and fusion equipment based on laser tracking and arc tracking.
Background
In the field of pipeline welding, ensuring the quality of a welding line is important, and accurate positioning and tracking of the welding line are key links for realizing the target.
The traditional weld tracking method mainly relies on arc tracking, wherein a current tracking method based on a Hall sensor is more commonly used. The principle is that a Hall sensor is used for detecting the magnetic field change generated by current when an arc works, and the magnetic field change is converted into an electric signal to be transmitted to a control system. The system estimates the arc stability and the weld deviation by analyzing the current signal fluctuation, and then adjusts the position of welding equipment so as to keep the arc at the center of the weld. However, this method has a number of problems. On the other hand, the accuracy depends on current fluctuation, and when the arc is stable or the variation range is small, it is difficult to capture a minute arc deviation, and the tracking accuracy is limited. On the other hand, the welding seam spatial characteristics cannot be directly obtained, when the welding seam is complex in shape, irregular in groove and uneven in workpiece surface, the arc is difficult to accurately track, and the welding gun is easy to deviate from the center of the welding seam. In addition, the overall arc tracking system is complex to debug, the current signal is subject to external noise, requires complex algorithmic processing, and is poorly adaptable to specific workpiece materials, such as highly conductive or coated materials.
The line structured light active visual weld tracking technique is an emerging technique for tracking weld position by projecting structured light onto a welding surface, capturing the deformation of the light bar with a camera. Compared with arc tracking, the method can directly acquire geometric information of the surface of the welding seam, such as height, width, groove shape and the like, has higher precision and robustness, and is suitable for complex welding seams. However, the technology also faces challenges, is sensitive to illumination and environmental conditions, and strong light or welding smoke can affect light bar deformation and reflection, so that image acquisition quality is reduced. Meanwhile, the flatness and reflectivity of the surface of the workpiece are required, and rough or low-reflectivity surfaces can influence the light bar projection effect and tracking precision. Moreover, the system is complex, requires high precision cameras and image processing algorithms, requires high computational resources, and may result in loss of tracking information or increased errors due to spatial occlusion or weld pose problems in certain welding environments.
At present, laser tracking and arc tracking are often used independently in the prior art, and the advantages of the laser tracking and the arc tracking are not fully utilized to perform joint sensing and spatial fusion, so that a research direction is provided for further development of a pipeline welding seam tracking technology.
Disclosure of Invention
In view of the above, the present invention aims to provide a fusion method, system and device based on laser tracking and arc tracking, so as to solve the technical problems existing in the prior art.
According to a first aspect of an embodiment of the present invention, there is provided a fusion method based on laser tracking and arc tracking, the method comprising:
acquiring laser tracking data and arc tracking data;
Obtaining first picture information by utilizing the laser tracking data;
extracting a weld joint center line by a preset method by using the first picture information, and extracting weld joint characteristic points based on a preset weld joint geometric model;
detecting a local extremum in the arc tracking data, estimating the position deviation direction of the welding gun by using the local extremum, calculating an extremum difference value in the arc tracking data by using a preset integration method, and estimating the offset of the welding gun by using the extremum difference value;
And fusing the laser tracking data, the arc tracking data, and determining a welding gun track by utilizing the welding seam characteristic points extracted by the preset welding seam geometric model, the position deviation direction of the welding gun and the offset of the welding gun for welding guidance.
Further, the acquiring laser tracking data includes:
projecting laser stripes of a specific mode to the surface of a measured object by using a preset structure light sensor to obtain stripe images;
Capturing the reflected fringe image by using a preset high-speed camera.
Further, the obtaining the first picture information by using the laser tracking data includes:
removing the influence of ambient light and stray light in the stripe image by adopting a median filtering method and a Gaussian filtering method;
And enhancing the stripe contrast in the stripe image and the image definition of the stripe image by using a histogram equalization image enhancement algorithm to obtain first picture information.
Further, extracting the weld center line by using the first picture information through a preset method includes:
Identifying the position and the outline of the laser stripe in the first picture information by utilizing an edge detection algorithm, and distinguishing a welding line from a background area by utilizing area segmentation;
And determining a central line by utilizing a least square curve fitting extracted laser stripe contour, and optimizing the continuity of the central line by utilizing morphological operation.
Further, the extracting weld feature points based on the preset weld geometric model comprises the following steps:
Constructing a weld joint geometric model by using prior knowledge of a welding process, and describing the type, width and angle parameters of the groove by using a polynomial or elliptic mathematical mode;
and extracting key feature points of the welding seam by using the constructed geometric model of the welding seam through a feature extraction algorithm.
Further, the acquiring arc tracking data includes:
collecting welding current signals by using a Hall current sensor or a current transformer, and collecting arc voltage signals by using a voltage transformer or a Hall voltage sensor;
and respectively converting the welding current signal and the arc voltage signal into digital forms by using a preset digital acquisition card to obtain a first welding current digital signal and a first arc voltage digital signal.
Further, the method further comprises:
And respectively removing high-frequency noise in the first welding current digital signal and the first arc voltage digital signal by using a low-pass filter, and respectively removing pulse interference in the first welding current digital signal and the first arc voltage digital signal by using median filtering.
Further, the fusing the laser tracking data and the arc tracking data, and determining a welding gun track by using the welding seam feature points extracted by the preset welding seam geometric model, the position deviation direction of the welding gun and the offset of the welding gun, which is used for welding guidance, includes:
Uniformly converting the laser tracking data and the arc tracking data into the same space coordinate system by using known calibration parameters to perform data fusion, and optimizing the position of a welding gun by using a Kalman filtering algorithm;
And determining a welding gun track by utilizing real-time feedback of the position deviation direction of the welding seam characteristic points extracted by the preset welding seam geometric model and the welding gun and the offset of the welding gun, and using the welding gun track for welding guidance.
According to a second aspect of an embodiment of the present invention, there is provided a fusion system based on laser tracking and arc tracking, applied to the fusion method based on laser tracking and arc tracking described in any one of the above, the system comprising:
The acquisition module is used for acquiring laser tracking data and arc tracking data;
the first processing module is used for obtaining first picture information by utilizing the laser tracking data;
the second processing module is used for extracting a weld joint center line by a preset method by utilizing the first picture information and extracting weld joint characteristic points based on a preset weld joint geometric model;
the third processing module is used for detecting a local extremum in the arc tracking data, estimating the position deviation direction of the welding gun by using the local extremum, calculating an extremum difference value in the arc tracking data by using a preset integration method, and estimating the offset of the welding gun by using the extremum difference value;
And the fourth processing module is used for fusing the laser tracking data and the arc tracking data, and determining a welding gun track by utilizing the welding seam characteristic points extracted by the preset welding seam geometric model, the position deviation direction of the welding gun and the offset of the welding gun for welding guidance.
According to a third aspect of embodiments of the present invention, there is provided a laser tracking and arc tracking based fusion device, the device comprising:
A memory having an executable program stored thereon;
a processor for executing the executable program in the memory to implement the steps of any of the methods described above.
The technical scheme provided by the embodiment of the invention can comprise the following beneficial effects:
It can be understood that the technical scheme provided by the application utilizes the structural light vision sensor to acquire the spatial characteristic information of the welding seam, combines the arc sensor to feed back the position of the welding gun in real time to form a spatial fusion sensing system, and realizes the accurate positioning of the welding seam and the real-time correction of the position of the welding gun. The technical scheme of the application is beneficial to providing accurate welding gun position estimation, improving the precision and stability of the welding process, is suitable for complex pipeline all-position welding operation, and solves the problems of insufficient precision and real-time feedback lag in the traditional method.
More specifically, 1. Improve the tracking accuracy of the welding seam, through the fusion of laser and electric arc tracking, can accurately catch the characteristic of welding seam, especially when the groove of welding seam is complicated or irregular, the recognition of welding seam is more accurate.
2. And feeding back the position of the welding gun in real time, wherein the arc tracking provides real-time feedback in the welding process, and the spatial position of the welding gun can be effectively corrected by combining the information prediction of the laser tracking.
3. The welding environment adaptability is enhanced, namely the signals of laser tracking and arc tracking are uniformly converted into the same space coordinate system for fusion by utilizing known calibration parameters, a Kalman filtering algorithm is widely applied in the process to optimize the position estimation of the welding gun, the stable and accurate welding gun track is obtained by combining the laser geometric information and the real-time feedback of the arc, and the fusion algorithm can treat interference factors such as splashing, strong light and the like in the welding process, so that the stability and reliability of the weld tracking are ensured.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a schematic diagram showing a fusion method step based on laser tracking and arc tracking, according to an exemplary embodiment;
FIG. 2 is a schematic diagram illustrating a fusion method weld tracking based on laser tracking and arc tracking, according to an example embodiment;
FIG. 3 is a diagram illustrating a fused x-system composition based on laser tracking and arc tracking, according to an exemplary embodiment;
FIG. 4 is a schematic diagram illustrating a fusion device composition based on laser tracking and arc tracking, according to an example embodiment;
FIG. 5 is a flowchart illustrating laser tracking of a weld centerline, according to an example embodiment.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the invention. Rather, they are merely examples of apparatus and methods consistent with aspects of the invention as detailed in the accompanying claims.
Example 1
Referring to fig. 1 and fig. 2, fig. 1 is a schematic diagram illustrating a fusion method based on laser tracking and arc tracking according to an exemplary embodiment, the method includes:
s1, acquiring laser tracking data and arc tracking data;
S2, obtaining first picture information by utilizing the laser tracking data;
s3, extracting a weld joint center line by a preset method by utilizing the first picture information, and extracting weld joint feature points based on a preset weld joint geometric model;
S4, detecting a local extremum in the arc tracking data, estimating the position deviation direction of the welding gun by using the local extremum, calculating an extremum difference value in the arc tracking data by using a preset integration method, and estimating the offset of the welding gun by using the extremum difference value;
s5, fusing the laser tracking data and the arc tracking data, and determining a welding gun track by utilizing the welding seam characteristic points extracted by the preset welding seam geometric model, the position deviation direction of the welding gun and the offset of the welding gun for welding guidance.
In specific implementation, as described in step S1, acquiring laser tracking data includes:
projecting laser stripes of a specific mode to the surface of a measured object by using a preset structure light sensor to obtain stripe images;
Capturing the reflected fringe image by using a preset high-speed camera.
In the implementation, the structured light sensor projects laser stripes with known modes to the surface of the welding seam, and the spacing, width and angle of the stripes are accurately adjusted according to the shape of the welding workpiece, so that the laser stripes can be clearly projected and reflected.
The high-speed camera captures the reflected fringe image, and the relative positions of the camera and the structured light projector are calibrated and optimized, so that the fringe reflected image is accurately matched with the surface of the workpiece.
Further, the obtaining the first picture information by using the laser tracking data includes:
removing the influence of ambient light and stray light in the stripe image by adopting a median filtering method and a Gaussian filtering method;
And enhancing the stripe contrast in the stripe image and the image definition of the stripe image by using a histogram equalization image enhancement algorithm to obtain first picture information.
More specifically, the influence of ambient light and stray light is removed by adopting a denoising method such as median filtering, gaussian filtering and the like.
And the image enhancement algorithms such as histogram equalization and the like are applied to enhance the contrast of stripes, improve the definition of images and ensure that the geometric information of the welding seam can be accurately extracted in a complex environment.
Further, extracting the weld center line by using the first picture information through a preset method includes:
Identifying the position and the outline of the laser stripe in the first picture information by utilizing an edge detection algorithm, and distinguishing a welding line from a background area by utilizing area segmentation;
And determining a central line by utilizing a least square curve fitting extracted laser stripe contour, and optimizing the continuity of the central line by utilizing morphological operation.
In specific implementation, edge detection is carried out on the image to identify the positions of laser stripes, and the welding line and the background area are distinguished by area segmentation.
And (3) determining a central line of the extracted stripe profile by using a least square curve fitting method, and ensuring the accuracy by adopting a self-adaptive curve fitting method for welding seams (such as V-shaped grooves and U-shaped grooves) of different shapes.
The center line continuity is optimized by morphological operation (expansion and corrosion) so that the center line continuity is smooth and meets the actual welding requirement, and geometric characteristics such as the width of a welding line, the angle of a groove and the like are calculated based on the center line continuity, so that a reference is provided for welding gun control.
In specific implementation, referring to fig. 2, a welding seam center line can be obtained through each frame of image, so that a welding gun position can be obtained, and a pre-scanning mode is adopted to obtain position information of the whole welding seam, so that a preset track of the whole welding gun operation can be obtained.
Further, the extracting weld feature points based on the preset weld geometric model comprises the following steps:
constructing a welding seam geometric model by using prior knowledge of a welding process, and describing the type, width and angle parameters of the groove by using a polynomial or elliptic mathematical mode;
and extracting key feature points of the welding seam by using the constructed geometric model of the welding seam through a feature extraction algorithm.
It should be noted that, the weld geometry model is constructed according to the prior knowledge of the welding process, the parameters such as the groove type, width, angle and the like are described in a polynomial or elliptic mathematical mode, and the model parameters are dynamically adjusted along with the welding working condition.
The key characteristic points (such as a groove starting point, a groove end point, a welding line center point and the like) of the welding line are extracted through a characteristic extraction algorithm, and accurate characteristic point extraction is beneficial to real-time monitoring and adjustment of the position of a welding gun, so that high welding precision is ensured.
In practice, referring to fig. 5, the detection and positioning of a specific shape (e.g. groove) is mainly performed by the following steps:
1. acquiring an original image (streak image);
2. The original image is subjected to a gaussian blur operation to reduce image noise.
3. And (3) carrying out adaptive threshold binarization, namely carrying out adaptive threshold binarization processing on the Gaussian blurred image, and converting the image into a binary image.
4. And (3) carrying out skeleton refinement on the binarized image, and extracting a skeleton structure in the image.
5. And (3) detecting the Hough straight line, namely detecting the Hough straight line in the image with the thinned skeleton, and finding out the straight line in the image.
6, Classifying according to the slope (K) of the straight line, and if the absolute value of the slope of the straight line is smaller than tan (5), performing next step of 'fitting of the horizontal line outside the groove';
if the slope of the line is greater than or less than tan (60), then a "bevel edge fit" is performed.
7. And (3) solving the intersection point to obtain two ends of the groove, namely solving the intersection point through the fitted straight line, and further obtaining the two ends of the groove.
8. And calculating the position of the center of the groove according to the positions of the two ends of the groove.
In a specific implementation, the acquiring arc tracking data includes:
collecting welding current signals by using a Hall current sensor or a current transformer, and collecting arc voltage signals by using a voltage transformer or a Hall voltage sensor;
and respectively converting the welding current signal and the arc voltage signal into digital forms by using a preset digital acquisition card to obtain a first welding current digital signal and a first arc voltage digital signal.
Further, the method further comprises:
And respectively removing high-frequency noise in the first welding current digital signal and the first arc voltage digital signal by using a low-pass filter, and respectively removing pulse interference in the first welding current digital signal and the first arc voltage digital signal by using median filtering.
In particular implementations, a hall sensor or current transformer is used to collect welding current signals, and monitoring current changes may aid in determining gun position because current fluctuations are closely related to gun offset. Meanwhile, the change of the arc voltage signal can also provide support for the position adjustment of the welding gun. And then, the collected current and voltage signals are converted into digital forms by using a data acquisition card, so that the subsequent processing and analysis are convenient. Further, a low-pass filter is adopted to remove high-frequency noise, and a median filter is adopted to remove pulse interference, so that smoothness and effectiveness of signals are guaranteed, accuracy of arc signals is guaranteed, and a reliable data base is provided for follow-up arc tracking.
In the specific implementation, as described in step S4, the deviation direction between the welding gun and the welding seam is estimated by detecting the local extremum point in the arc signal and analyzing the change rule between the extremum points. For example, when an abnormal change occurs in the extreme point of the current signal, it may mean that the welding gun is deviated from the center of the weld, and by analyzing these changes, it may be judged whether the welding gun is deviated leftward or rightward, or the like.
Further, the extreme value difference in the arc signal is calculated by using an integration method, so that the offset of the welding gun is estimated more accurately. The accurate offset information is important for the welding robot to adjust the position of the welding gun in real time, so that the welding gun can be aligned with the welding seam more accurately, and the welding quality is ensured. The welding robot can correct the position of the welding gun in time through the welding gun position feedback obtained by the arc tracking feature extraction, and the stability and the accuracy of the welding process are ensured.
Further, the fusing the laser tracking data and the arc tracking data, and determining a welding gun track by using the welding seam feature points extracted by the preset welding seam geometric model, the position deviation direction of the welding gun and the offset of the welding gun, which is used for welding guidance, includes:
Uniformly converting the laser tracking data and the arc tracking data into the same space coordinate system by using known calibration parameters to perform data fusion, and optimizing the position of a welding gun by using a Kalman filtering algorithm;
And determining a welding gun track by utilizing real-time feedback of the position deviation direction of the welding seam characteristic points extracted by the preset welding seam geometric model and the welding gun and the offset of the welding gun, and using the welding gun track for welding guidance.
The method is characterized in that the known calibration parameters are utilized to uniformly convert the signals of laser tracking and arc tracking into the same space coordinate system for fusion, a Kalman filtering algorithm is widely applied to optimize the position estimation of the welding gun in the process, and the stable and accurate welding gun track is obtained by combining the laser geometric information and the real-time feedback of the arc.
The real-time feedback system transmits welding gun deviation information to the welding robot control system, and the PID control algorithm is adopted for automatic adjustment, so that the welding gun is ensured to always follow the center of the welding seam in the whole welding process, and the stability and the accuracy of welding quality are ensured.
In the concrete implementation, the accuracy and stability of the welding process are improved by providing accurate welding gun position estimation, the method is suitable for complex pipeline all-position welding operation, and the problems of insufficient accuracy and real-time feedback hysteresis in the traditional method are solved.
It should be noted that, in addition to the combination of laser tracking and arc tracking, alternatives include the introduction of other sensors, such as monitoring the temperature distribution of the weld bead by a thermal imaging sensor, or acquiring the position information of the welding gun by using the arc sound generated by welding, and fusing the position information with the existing laser and arc tracking information to realize further optimization of the weld bead tracking.
Referring to fig. 3, fig. 3 is a schematic diagram illustrating a system composition based on a fusion of laser tracking and arc tracking, the system comprising:
an acquisition module 30 for acquiring laser tracking data and arc tracking data;
A first processing module 31, configured to obtain first picture information by using the laser tracking data;
a second processing module 32, configured to extract a weld centerline by a preset method using the first picture information, and extract a weld feature point based on a preset weld geometric model;
The third processing module 33 is configured to detect a local extremum in the arc tracking data, estimate a position deviation direction of the welding gun using the local extremum, calculate an extremum difference value in the arc tracking data using a preset integration method, and estimate an offset of the welding gun using the extremum difference value;
And a fourth processing module 34, configured to fuse the laser tracking data and the arc tracking data, and determine a welding gun track for welding guidance by using the welding seam feature points extracted by the preset welding seam geometric model, the position deviation direction of the welding gun, and the offset of the welding gun.
Referring to fig. 4, fig. 4 is a schematic diagram illustrating a laser tracking and arc tracking based fusion device according to an exemplary embodiment, the device includes:
a memory 41 on which an executable program is stored;
A processor 42 for executing the executable program in the memory 41 to implement the steps of the method as described in any one of the above.
It is to be understood that the same or similar parts in the above embodiments may be referred to each other, and that in some embodiments, the same or similar parts in other embodiments may be referred to.
It should be noted that in the description of the present invention, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Furthermore, in the description of the present invention, unless otherwise indicated, the meaning of "plurality" means at least two.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of techniques known in the art, discrete logic circuits with logic gates for implementing logic functions on data signals, application specific integrated circuits with appropriate combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (10)
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN120742780A (en) * | 2025-08-22 | 2025-10-03 | 长沙立诚机械有限公司 | Video double-gun welding path planning control method based on synchronous visual recognition |
| CN121223317A (en) * | 2025-12-04 | 2025-12-30 | 江苏智享海工机器人有限公司 | Welding process dynamic deviation rectifying method and system based on multi-source data analysis |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN120742780A (en) * | 2025-08-22 | 2025-10-03 | 长沙立诚机械有限公司 | Video double-gun welding path planning control method based on synchronous visual recognition |
| CN121223317A (en) * | 2025-12-04 | 2025-12-30 | 江苏智享海工机器人有限公司 | Welding process dynamic deviation rectifying method and system based on multi-source data analysis |
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