| 英文摘要 | As an important manufacturing process, welding is widely used in industrial
production. At present, most welding robots belong to teach-and-playback and
off-line programming robots. They have an obvious shortcoming. They can only
move along the recorded path and cannot adjust the welding torch position to adapt to
the change of the position of the workpiece caused by machining error, assembly error
and welding thermal deformation, which reduces welding quality. In order to make
the welding robot autonomously perceive the welding environment and realize
intelligent welding, it is necessary to install welding sensors for the welding robot.
Due to the advantages of non-contact, abundant information, high precision and
strong anti-interference ability, the structured light vision sensor is favored by
researchers. Currently, although some structured light vision sensors have been used
in robot welding, there exist a series of shortcomings. For example, the automatic
recognition of weld seam type is not realized. The seam feature extraction methods
lack flexibility and computational efficiency. Moreover, these sensors are not well
suited for initial point guiding and seam tracking of micro gap weld seam. Therefore,
it has significant theoretical meaning and engineering value to study seam recognition
and tracking technology based on structured light vision.
In this thesis, the seam recognition and tracking technology based on structured
light vision is studied including the design and calibration of the structured light
vision sensor, weld seam type recognition, seam feature point extraction, initial point
guiding and seam tracking of micro-gap weld. The main work of this thesis is as
follows:
Aiming at the need of robot intelligent welding, a seam tracking sensor based on
structured light vision is designed and a simple and practical calibration method of
structured light plane parameters is presented. The developed sensor has two main
characteristics: the multi-layer drawer type dimming filter system is designed to
improve the environmental adaptability of the sensor, and the modular structure
design method is adopted to facilitate functional expansion and maintenance. By
controlling the constrained movement of the robot's end at one time, two non collinear
laser stripes are generated on the calibration target to complete the calibration of the
structural light plane parameters. This method does not need high-precision 3D calibration targets or manual measurement, and the calibration process is simple and
practical.
Aiming at the shortcomings of huge computation cost and low recognition rate of
the existing weld seam type recognition method, a weld seam type recognition method
based on support vector machine (SVM) is presented, which could achieve weld seam
type recognition quickly and accurately. Firstly, the captured structured light images
are processed to get the input feature vector of the classification model. Then, taking
the feature vector as input and the weld seam type as output, the SVM weld seam type
recognition model is established through training. Finally, aiming at the shortcoming
of the conventional seam feature point extraction method based on geometric features,
which requires a large amount of calculation and has poor flexibility in the process of
welding seam tracking, a seam feature point extraction method based on efficient
convolution algorithm is proposed. The method could be applied to different weld
types, such as butt groove weld and lap weld. Even under the interference of welding
noise, the method could extract the seam feature points quickly and robustly.
Aiming at the problem that traditional structured light vision sensor is not
applicable to initial point guiding of micro-gap weld and initial point guiding method
based on passive vision sensor has disadvantages of poor robustness and low guiding
accuracy, a dual-source structured light vision sensor is developed and initial point
guiding methods of micro-gap weld are proposed. The dual-source structured light
vision sensor is used to capture the high SNR image including laser stripe and
micro-gap weld seam, and achieve the recognition and measurement of micro-gap
weld. On this basis, initial point guiding methods of planar micro-gap welds based on
two stages and initial point guiding methods of cruve micro-gap welds based on
structured light scanning are presented. The initial point guiding method of planar
micro-gap weld has the characteristics of high precision and fast guiding speed. The
initial point guiding method of curved surface micro-gap weld has the characteristics
of wide application.
Based on the designed dual source structured light vision sensor, a seam tracking
method suitable for planar and curved micro-gap weld is proposed, which overcomes
the problems that traditional seam tracking method based on single source structured
light vision is not suitable for micro-gap weld and seam tracking method based on
passive vision has disadvantages of complex image processing and poor robustness.
This method could obtain the welding torch deviations in both horizontal and vertical
deviations, and independently design the decoupling controller in both directions. The
error filtering, Fuzzy-PID controller and output pulse verification are used to realize
the accurate tracking of micro-gap weld seam in both horizontal and vertical
directions.
Finally, the research work of this thesis is summarized and the future research
plan is proposed
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