CN114161048A - Iron tower foot parametric welding method and device based on 3D vision - Google Patents

Iron tower foot parametric welding method and device based on 3D vision Download PDF

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CN114161048A
CN114161048A CN202111653960.4A CN202111653960A CN114161048A CN 114161048 A CN114161048 A CN 114161048A CN 202111653960 A CN202111653960 A CN 202111653960A CN 114161048 A CN114161048 A CN 114161048A
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welding
module
vision
tower
side plate
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CN114161048B (en
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龚烨飞
程艳花
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Changshu Institute of Technology
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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
    • B23K37/0211—Carriages for supporting the welding or cutting element travelling on a guide member, e.g. rail, track
    • 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
    • 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
    • B23K37/0247—Driving means
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J11/00—Manipulators not otherwise provided for
    • B25J11/005—Manipulators for mechanical processing tasks
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1628—Programme controls characterised by the control loop
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
    • B25J9/1664—Programme controls characterised by programming, planning systems for manipulators characterised by motion, path, trajectory planning
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1679—Programme controls characterised by the tasks executed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1694—Programme controls characterised by use of sensors other than normal servo-feedback from position, speed or acceleration sensors, perception control, multi-sensor controlled systems, sensor fusion
    • B25J9/1697—Vision controlled systems

Abstract

The invention discloses a 3D vision-based iron tower foot parametric welding method, which comprises the steps of establishing a foot model ideal model according to foot modeling parameters, carrying out trajectory planning by the foot model ideal model to obtain welding seam sequence template data and a motion trajectory sequence, carrying out actual welding seam scanning recognition by a 3D vision sensor according to the welding seam sequence template data and the motion trajectory sequence, generating a welding trajectory sequence according to a scanning result, sending the welding trajectory sequence to a robot control execution module, controlling a mechanical arm to move by the robot control execution module through a welding robot controller, and carrying a welding gun by the mechanical arm to complete welding seams. The invention also discloses a parameterized welding device for the tower foot of the iron tower based on the 3D vision. The invention can be self-adaptive to non-standard tower legs and ensure the welding quality.

Description

Iron tower foot parametric welding method and device based on 3D vision
Technical Field
The invention relates to a tower foot welding method and device, in particular to a parameterized welding method and device for an iron tower foot based on 3D vision.
Background
The iron tower is an important infrastructure and is an upstream important carrier for extra-high voltage power grid and 5G communication. The tower foot of transmission line iron tower is the support component of whole transmission line iron tower structure, and the structural stability of its structure directly determines the structural stability and the life of transmission line iron tower. Generally, the welding of the tower foot of the iron tower is completed manually, so the technical level and the working attitude of a welder directly influence the quality, the production efficiency and the manufacturing cost of an iron tower product. At present, some automatic welding systems are used for welding tower legs, such as welding robots disclosed in chinese patents with publication numbers CN108746938A, CN107855703A, and so on. These devices can improve the efficiency of batch standardized welding work, but the tower foot as a foundation must be adapted to the environment, geographical conditions and power supply parameter requirements of the place where the tower foot is located, so that the tower foot is a non-standard and non-fixed parameter component in nature, and therefore, manufacturers always encounter the problem that the robot system may not be adapted to the change of the currently required processed components when the robot system is applied. These systems therefore require, in particular applications, a dedicated operator for each piece of equipment to adapt to this change in a taught manner, which results in the need for a professional operation of the operator for each batch, which essentially entails the problem of not reducing the basic requirements of the operator, and of requiring a large number of dedicated robot operators to cooperate in order to be able to put the system into the welding production of new machined parts, which also creates new problems for the enterprise in terms of costs and input cycles.
Chinese patent publication nos. CN112658520A and CN112589303A disclose a method of calculating a weld by inputting tower foot parameters and then performing welding, which solves the problem that batch tower feet need to be continuously taught to change welding parameters to adapt to product changes. However, the tower foot is used as a large thick plate component, the precision of previous processing and assembly is poor, so that for the same batch, the welding seam of each workpiece has more or less errors, and therefore, the errors are difficult to be individually adjusted in the technical scheme so as to ensure the final welding quality of welding. And due to the nonstandard characteristic of the tower foot, the tower foot has the characteristic of large difference of structure sizes with different batches, so that the final detection method fails due to the fact that some sensing schemes aiming at error adjustment have problems in detection accessibility.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a parameterized welding method for a tower foot of an iron tower based on 3D vision, aiming at solving the problem of automatic adaptation to error change of a tower foot workpiece and improving the welding quality. The invention further aims to provide a parameterized iron tower foot welding device based on 3D vision.
The technical scheme of the invention is as follows: a parameterized welding method for tower feet of an iron tower based on 3D vision comprises the following steps:
step 1, inputting tower foot modeling parameters into a tower foot modeling module, and constructing an ideal tower foot model by the tower foot modeling module according to the tower foot modeling parameters;
step 2, the tower foot modeling module sends the tower foot ideal model to a tower foot welding seam module, the tower foot welding seam module converts the data structure of the tower foot ideal model and sends the converted data structure to a visual detection track planning module, and the visual detection track planning module completes detection track planning according to the converted data structure of the tower foot ideal model to respectively obtain welding seam sequence template data and a motion track sequence;
step 3, the welding seam sequence template data are sent to a 3D vision sensor, the motion trail sequence is sent to a robot control execution module, the robot control execution module controls the mechanical arm to move through a welding robot controller according to the motion trail sequence, and the 3D vision sensor is connected to the mechanical arm and controls a scanning path through the welding seam sequence template data;
step 4, the welding seam surface data obtained in the step 3 are sent to a welding seam identification and three-dimensional reconstruction module, and the welding seam identification and three-dimensional reconstruction module calculates welding seam characteristic data and sends the welding seam characteristic data to a robot welding track planning module;
and 5, generating a welding track sequence by the robot welding track planning module according to the welding track characteristic data, sending the welding track sequence to a robot control execution module, controlling the movement of a mechanical arm by a welding robot controller according to the welding track sequence by the robot control execution module, and carrying a welding gun by the mechanical arm to complete welding of the welding track.
Further, the step 1 includes inputting welding process parameters to the tower foot welding seam module, the tower foot welding seam module sends the welding process parameters to the welding track planning module in the step 5, and the robot welding track planning module generates the welding track sequence together according to the welding seam characteristic data and the welding process parameters.
Further, the ideal tower foot model is characterized in that a main side plate is crossed with a first side plate and a second side plate, and the main side plate, the first side plate and the second side plate are supported by a bottom plate, and the first side plate and the second side plate are respectively positioned at two sides of the main side plate; the tower foot modeling parameters comprise respective length, width and height data of the main side plate, the first side plate and the second side plate, the length and width data of the bottom plate, the positions of the cross points of the main side plate, the first side plate and the second side plate on the bottom plate, and the included angles of the main side plate, the first side plate and the second side plate and the bottom plate respectively.
Further, the weld sequence template data is obtained by the following steps: establishing a light plane model scanned by laser through linear motion, obtaining a fillet weld biplane model from the tower foot ideal model, obtaining 1 sensor detection weld template by intersection calculation of the light plane model and the fillet weld biplane model, and arranging all the sensor detection weld templates needing to be detected in the whole tower foot according to the scanning sequence of the sensors to obtain a weld sequence template.
Further, the motion trail sequence is obtained by the following steps: establishing a sensor viewpoint coordinate system based on the irradiation direction of the 3D vision sensor, obtaining a fillet weld biplane model by the tower foot weld module, and obtaining a weld by intersecting two planes in the fillet weld biplane model
Figure BDA0003445385880000021
Figure BDA0003445385880000022
In the direction of the weld, piThe normal directions of the two planes are added to obtain the normal direction of the welding seam for any point on the welding seam
Figure BDA0003445385880000031
For any one detection point on the welding seampivPose of the 3D vision sensor is
Figure BDA0003445385880000032
All poses form a motion track sequence.
The other technical scheme of the invention is as follows: a iron tower foot parameterization welding device based on 3D vision comprises a tower foot modeling module, a tower foot welding seam module, a vision detection track module, a 3D vision sensor, a welding seam identification and three-dimensional reconstruction module, a robot welding track planning module, a robot control execution module, a welding robot controller, an mechanical arm and a welding gun, wherein the 3D vision sensor and the welding gun are fixedly connected to the mechanical arm, the tower foot welding seam module is respectively connected with the tower foot modeling module, the vision detection track module, the welding seam identification and three-dimensional reconstruction module and the robot welding track planning module, the 3D vision sensor is respectively connected with the vision detection track module and the welding seam identification and three-dimensional reconstruction module, the robot control execution module is respectively connected with the vision detection track module and the robot welding track planning module, the robot control execution module is connected with the welding robot controller and used for controlling the movement of the mechanical arm, and the welding device executes the iron tower foot parametric welding method based on the 3D vision.
The technical scheme provided by the invention has the advantages that:
the method does not need to manually carry out independent modeling design on welding path parameters aiming at each tower foot workpiece, the built model can adapt to the welding of tower feet in similar batches, the problem of nonstandard multiple error pain points of the welding of the tower feet is solved, the welding quality is ensured, and the production efficiency is improved.
Drawings
Fig. 1 is a system block diagram of a parameterized iron tower foot welding device based on 3D vision according to an embodiment.
Fig. 2 is a schematic flow chart of a parameterized welding method for tower legs of an iron tower based on 3D vision according to an embodiment.
Fig. 3 is a schematic view of an ideal model of a tower foot.
FIG. 4 is a schematic diagram of a sensor detection model simulating a real-world sensor beam.
FIG. 5 is a schematic diagram of a sensor model detecting the attitude of a weld model.
FIG. 6 is a schematic view of the weld detection points.
Fig. 7 is a schematic diagram of calculation of the detected position on the weld path.
FIG. 8 is a schematic view of a weld profile feature identification process.
Fig. 9 is a schematic diagram showing the relative position of the welding torch and the workpiece to be welded.
Detailed Description
The present invention is further described in the following examples, which are intended to be illustrative only and not to be limiting as to the scope of the invention, which is to be given the full breadth of the appended claims and any and all equivalent modifications within the scope of the following claims.
Please refer to fig. 1, the iron tower leg parametric welding device based on 3D vision in this embodiment includes a tower leg modeling module 1, a tower leg welding seam module 2, a vision detection track module 3, a 3D vision sensor 4, a welding seam identification and three-dimensional reconstruction module 5, a robot welding track planning module 6, a robot control execution module 7, a welding robot controller 8, a mechanical arm 9, and a welding gun, wherein the 3D vision sensor 4 and the welding gun are both fixedly connected to the mechanical arm 9. The robot control execution module is connected with the visual detection track module and the robot welding track planning module respectively, and is connected with the welding robot controller and used for controlling the movement of the mechanical arm. The specific process of the modules for processing and transmitting data is described in detail below for the welding method.
Referring to fig. 2, based on the welding device, the parameterized welding method for the tower foot of the iron tower based on the 3D vision in this embodiment is as follows:
step 1, a user of the welding device establishes an ideal tower foot model for tower foot welding through interactive operation by combining known prior information of the tower feet, namely tower foot modeling parameters, through a system software interface in an off-line state. "offline" herein refers to a period of time outside of where the system is not initially started up in the field, so its operation can be operated in the office and downloaded to the field "online controller" via a network or a removable storage medium. In addition, the system user generally comprises two types, namely iron tower welding operation personnel capable of reading design drawing information, and related simple tower foot modeling parameters are input according to the drawing information and interface prompts of a tower foot modeling module interface, so that preliminary modeling of an ideal tower foot model is completed.
The ideal model of the tower foot is shown in fig. 3, and the ideal model of the tower foot is formed by crossed main side plates and first side plates and second side plates and supported by a bottom plate, wherein the parameters of the tower foot modeling comprise respective length, width and height data of the main side plates, the first side plates and the second side plates and length and width data of the bottom plate, the cross points of the main side plates and the first side plates and the second side plates are positioned at the bottom plate, the included angles of the main side plates, the first side plates and the second side plates and the bottom plate are respectively positioned at the bottom plate, the plane where the bottom plate is positioned is an XY plane, and the included angles of the main side plates, the first side plates, the second side plates and the bottom plate are positioned at positive angles towards the positive direction of an XY axis. The ideal model of the tower foot can be completely defined through the parameters. The user then specifies the relevant weld and matches it with the relevant existing process from the weld welding process library. The other part is a technician who grasps the welding of the iron tower automation equipment, and the current known process information is mainly recorded into the welding seam welding process library 10, although in some cases, the two kinds of skills may be grasped by the same person.
And 2, the tower foot modeling module sends the ideal tower foot model to the tower foot welding seam module, and the sending mode can be stored through a network port processing program or a mobile storage medium and executed by a software interface in the 'online controller'. And the tower foot welding seam module converts the data structure of the tower foot ideal model and then sends the converted data structure to the visual detection track planning module, and the visual detection track planning module finishes detection track planning according to the tower foot ideal model after the data structure is converted to respectively obtain welding seam sequence template data and a motion track sequence.
The acquisition of the weld sequence template data is such that: a sensor detection model simulating a real sensor light beam is established in the visual detection trajectory planning module, as shown in fig. 4, wherein a dotted line part is a light plane model (mathematically described By adopting a plane n: Ax + By + Cz + D ═ 0) obtained By scanning simulated single-point laser through linear motion, in addition, after the tower foot welding line module is subjected to data structure conversion, the fillet welding line of each tower foot can be simulated into a structure described By two planes according to tower foot modeling parameters input By a user, and then, By calculating the plane intersection calculation between the light plane model and the fillet welding line biplane model, a segmented linear profile l of a scanning signal of the model sensor can be obtained1And l2Single segmented profile combination mi＝(li1，li2) The method is characterized in that 1 sensor detection weld template is used, all weld templates needing to be detected in the whole tower foot are arranged according to the scanning sequence of the sensors, and weld sequence template data M for sensor detection are obtainedi＝(miN), the model data is template data according to which the weld joint is identified as signal data obtained by actually scanning the weld joint by the sensor.
The acquisition of the sequence of motion trajectories is such that: as shown in fig. 5, the 3D vision sensor itself is composed of dual range point lasers, and its view center will establish a "sensor viewpoint coordinate system", where a point on a light beam when the sensor reading is at the middle value of the range is taken as a sensor reference point, a midpoint position of a connecting line between two light sensor reference points is set as a coordinate system origin (i.e., viewpoint), a Z axis is defined toward the sensor along the sensor irradiation direction, a direction of a connecting line of the other two sensor readings, both of which are at the middle value, is a pseudo X' axis, a Y axis of the sensor can be obtained by a right-hand criterion, a formal X axis of the coordinate system is further normalized by the right-hand criterion, and the above information will completely define the "sensor viewpoint coordinate system".
Through the tower footObtaining a fillet weld biplane model join by a weld modulei＝(Пj,Пk) Description data, i.e. bi-plane description IIjHe-PikFrom which the corresponding normal to its plane can be derived i.e.
Figure BDA0003445385880000051
And
Figure BDA0003445385880000052
the fillet normal can be calculated by adding and normalizing the two normal
Figure BDA0003445385880000053
In addition, through the combination of modelsjHe-PikThe linear path characteristics of the welding line can be obtained by intersection calculation
Figure BDA0003445385880000054
Setting a point p on the straight line characteristic of the weldivDetecting the position of the detection point for the welding line, and finally aiming at the teamiThe sensor detection pose at a certain position is calculated as
Figure BDA0003445385880000055
Wherein
Figure BDA0003445385880000056
And forming a motion track sequence by all the detection poses for vector cross multiplication.
Step 3, sending the welding seam sequence template data to a 3D vision sensor, sending the motion trail sequence to a robot control execution module, controlling the motion of a mechanical arm by a welding robot controller according to the motion trail sequence by the robot control execution module, connecting the 3D vision sensor to the mechanical arm and controlling a scanning path by the welding seam sequence template data;
and 4, the welding seam surface data obtained in the step 3 are sent to a welding seam identification and three-dimensional reconstruction module, and the welding seam identification and three-dimensional reconstruction module calculates welding seam characteristic data and sends the welding seam characteristic data to a robot welding track planning module.
As shown in fig. 6, a tower foot weld is a typical fillet weld, and the scanning will sample key feature points (generally 2N) on the weld, generally if the weld is short, N is 1 sampling, and if the weld is long, the system will automatically plan to sample it N >1 sampling.
Referring to fig. 7, let the distance between the detection points of the weld seam detection be D, D = ω · D, where ω ≧ 1 is a proportionality coefficient, which is mainly set by the user according to the compromise between efficiency and accuracy.
From the fillet weld straight line path characteristic team in the step 2iThe length l thereof can be obtainediDivide the whole weld into
Figure BDA0003445385880000061
Segments, thus for the straight weld each segment distance is
Figure BDA0003445385880000062
Calculating the position of the detected point on the linear path characteristic of the welding line
Figure BDA0003445385880000063
Wherein p is1Is the starting point position of the welding seam path.
And the welding line identification and three-dimensional reconstruction module calculates the welding line characteristic points according to a welding line outline characteristic identification method in a welding line tracking characteristic extraction method based on structured light vision, and the data is transmitted back to the tower foot welding line module to be used as actual data of the ideal model welding line and update the relevant state. And the tower foot welding seam module sends the determined welding process parameters and the welding seam characteristic point data to the welding track planning module.
The main idea of calculating the weld feature points is shown in fig. 8, and mainly includes that each time the sensor scans the fillet weld to obtain continuous distance measurement data, the continuous distance measurement data is spread on a time axis, for the data, "least square segmentation straight line segment fitting based on double threshold value" in a weld tracking feature extraction method based on structured light vision "is adopted to complete segmentation straight line fitting, and further, the detected fillet weld joint is obtained according to element definition of the segmentation straight lineOutlining the symbolic description, and then combining the symbolic description with the weld sequence template data M from the foregoingi＝(miN) m 1iMatching the corresponding outline symbol description, if the matching is successful, obtaining the final recognition result, and extracting miThe defined welding seam process positions are consistent and defined as final characteristic positions, namely welding seam characteristic points are calculated through the internal angle geometric relation.
And 5, generating a welding track sequence by the robot welding track planning module according to the weld characteristic data and sending the welding track sequence to the robot control execution module, wherein the generation of the welding track sequence is as follows:
the pose of the welding gun can be obtained by rigid body pose transformation with respect to the weld feature coordinate system (SeamCoord), i.e.
Figure BDA0003445385880000064
Wherein
Figure BDA0003445385880000065
And
Figure BDA0003445385880000066
respectively, the position of the welding gun and the welding line in the robot base coordinate system, respectively
Figure BDA0003445385880000067
Then the weld defines a generic homogeneous transformation matrix for the weld gun. However, it is not customary to describe this relationship in a matrix in the welding process, but rather to use a more simple 6-dimensional quantity (i.e. a representation of the position of the robot tool coordinate system and the euler angle) expressed in (x, y, z, α, β, γ), where x, y, z are the positions of the welding torch, and the position o of the weld coordinate system is taken directlyJAlpha is a welding walking angle, beta is a welding working angle, and gamma is a welding spinning angle. As shown in FIG. 9, the welding travel angle α is the welding gun xHAxis-to-weld coordinate system xoyJProjection on plane and xJThe included angle therebetween. In the definition, | alpha | ≦ 90 °, the actual value should be smaller, generally smaller than 45 °. And forward inclination takes a positive value and backward inclination takes a negative value. The working angle of welding beta is the welding gun xHAxis and weld coordinate system xoyJThe angle between the planes. In the definition, | beta | ≦ 90 °, the actual value should be smaller, generally smaller than 45 °. The welding spin angle gamma is a welding redundancy freedom degree, and | alpha | is less than or equal to 180 degrees.
The pose relation between the welding gun and the welding line during the robot welding is
Figure BDA0003445385880000071
Wherein
Figure BDA0003445385880000072
Thus ultimately for each join in the tower foot weld moduleiActual data obtained through sensor detection are obtained, and robot welding track points are obtained through secondary calculation to be generated
Figure BDA0003445385880000073
And all the welding seam tracks are serially connected in the execution order to be used as the final welding track sequence. And the robot control execution module controls the mechanical arm to move through the welding robot controller according to the welding track sequence, and the mechanical arm carries a welding gun to complete welding of the welding seam.

Claims (6)

1. A parameterized welding method for tower feet of an iron tower based on 3D vision is characterized by comprising the following steps:
step 1, inputting tower foot modeling parameters to a tower foot welding seam module, and constructing a tower foot ideal model by the tower foot welding seam module according to the tower foot modeling parameters;
step 2, the tower foot welding seam module sends the tower foot ideal model to a visual detection track planning module, and the visual detection track planning module finishes detection track planning according to the tower foot ideal model to respectively obtain welding seam sequence template data and a motion track sequence;
step 3, the welding seam sequence template data are sent to a 3D vision sensor, the motion trail sequence is sent to a robot control execution module, the robot control execution module controls the mechanical arm to move through a welding robot controller according to the motion trail sequence, and the 3D vision sensor is connected to the mechanical arm and controls a scanning path through the welding seam sequence template data;
step 4, the welding seam surface data obtained in the step 3 are sent to a welding seam identification and three-dimensional reconstruction module, and the welding seam identification and three-dimensional reconstruction module calculates welding seam characteristic data and sends the welding seam characteristic data to a robot welding track planning module;
and 5, generating a welding track sequence by the robot welding track planning module according to the welding track characteristic data, sending the welding track sequence to a robot control execution module, controlling the movement of a mechanical arm by a welding robot controller according to the welding track sequence by the robot control execution module, and carrying a welding gun by the mechanical arm to complete welding of the welding track.
2. The iron tower foot parametric welding method based on the 3D vision is characterized in that the step 1 includes inputting welding process parameters to the foot welding module, the foot welding module sends the welding process parameters to the welding track planning module in the step 5, and the robot welding track planning module generates the welding track sequence according to the welding track characteristic data and the welding process parameters.
3. The iron tower foot parametric welding method based on the 3D vision is characterized in that the ideal tower foot model is formed by crossing a main side plate with a first side plate and a second side plate, and supporting the main side plate, the first side plate and the second side plate by a bottom plate, wherein the first side plate and the second side plate are respectively positioned on two sides of the main side plate; the tower foot modeling parameters comprise respective length, width and height data of the main side plate, the first side plate and the second side plate, the length and width data of the bottom plate, the positions of the cross points of the main side plate, the first side plate and the second side plate on the bottom plate, and the included angles of the main side plate, the first side plate and the second side plate and the bottom plate respectively.
4. The iron tower foot parametric welding method based on 3D vision is characterized in that the welding seam sequence template data are obtained by the following steps: establishing a light plane model scanned by laser through linear motion, obtaining a fillet weld biplane model from the tower foot ideal model, obtaining 1 sensor detection weld template by intersection calculation of the light plane model and the fillet weld biplane model, and arranging all the sensor detection weld templates needing to be detected in the whole tower foot according to the scanning sequence of the sensors to obtain a weld sequence template.
5. The iron tower foot parametric welding method based on 3D vision is characterized in that the motion track sequence is obtained by the following steps: establishing a sensor viewpoint coordinate system based on the irradiation direction of the 3D vision sensor, obtaining a fillet weld biplane model by the tower foot weld module, and obtaining a weld by intersecting two planes in the fillet weld biplane model
Figure FDA0003445385870000021
Figure FDA0003445385870000022
In the direction of the weld, piThe normal directions of the two planes are added to obtain the normal direction of the welding seam for any point on the welding seam
Figure FDA0003445385870000023
For any one detection point p on the welding seamivPose of the 3D vision sensor is
Figure FDA0003445385870000024
All poses form a motion track sequence.
6. A parameterization welding device for a tower leg of an iron tower based on 3D vision is characterized by comprising a tower leg modeling module, a tower leg welding seam module, a vision detection track module, a 3D vision sensor, a welding seam identification and three-dimensional reconstruction module, a robot welding track planning module, a robot control execution module, a welding robot controller, a mechanical arm and a welding gun, wherein the 3D vision sensor and the welding gun are fixedly connected to the mechanical arm, the tower leg welding seam module is respectively connected with the tower leg modeling module, the vision detection track module, the welding seam identification and three-dimensional reconstruction module and the robot welding track planning module, the 3D vision sensor is respectively connected with the vision detection track module and the welding seam identification and three-dimensional reconstruction module, the robot control execution module is respectively connected with the vision detection track module and the robot welding track planning module, the robot control execution module is connected with a welding robot controller for controlling the movement of the mechanical arm, and the welding device executes the iron tower foot parametric welding method based on the 3D vision in any one of claims 1 to 5.
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