CN113910245A - Industrial robot control method based on big data clustering - Google Patents
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Abstract
The invention provides an industrial robot control method based on big data clustering, which relates to the technical field of robot control and comprises the following steps: controlling connection, establishing a data set, fitting signals, planning a running track, integrating a control system, designing a teaching interface and designing a reproduction module; the invention debugs the initial value and noise by utilizing the uniform traversal characteristic of big data clustering on mass data, effectively improves the clustering performance of the data, has the advantages of small calculated amount and high real-time performance, and improves the control capability of the industrial robot.
Description
Technical Field
The invention relates to the technical field of robot control, in particular to an industrial robot control method based on big data clustering.
Background
Currently, the emergence of industrial robots has promoted the development of modernization in the industrial field and gradually became important equipment that cannot be replaced in industrial control, the social demand has been continuously expanded, so that the productivity has been rapidly developed, the demand for labor has been gradually increased, wherein the component of repeated labor is particularly prominent, and the emergence of robots perfectly solves the problem;
in the current industrial production field, robots are used in a certain number and scale, but the autonomous research and development capacity and application level of the industrial robots still do not reach the expected standards, the control precision of the robots is not high enough, and the deviation of running tracks is large, so that the invention provides an industrial robot control method based on big data clustering to solve the problems in the prior art.
Disclosure of Invention
Aiming at the problems, the invention provides the industrial robot control method based on the big data clustering, which debugs the initial value and the noise by utilizing the uniform traversal characteristic of the big data clustering on the mass data, effectively improves the clustering performance of the data, has the advantages of small calculated amount and high real-time performance, and improves the control capability of the industrial robot.
In order to realize the purpose of the invention, the invention is realized by the following technical scheme: the industrial robot control method based on big data clustering comprises the following steps:
the method comprises the following steps: control connection
An industrial control computer is used as an upper computer structure of the system, a GMM06EEA01 robot six-axis motion controller is selected to connect the industrial control computer with a servo motor and a driver, and the servo motor is matched with a transmission structure to control a robot body;
step two: building data sets
Fitting linear frequency modulation control signals based on big data clustering, firstly establishing a big data distributed structure model in a control system, researching big data fuzzy control in the control system according to the concept of fuzzy control, and establishing a limited data set of sea level control data in the control system;
step three: fitting of signals
Fitting linear frequency modulation signals of large data information streams in the control system by using the fitting idea of a fuzzy control and control system, and calculating large data information characteristic points of the control system by combining a large data clustering algorithm to complete fitting of the linear frequency modulation control signals in the control system;
step four: trajectory planning
Performing overall track planning on an industrial control computer, coordinating and controlling the motion of each axis point of the industrial robot on a GMM06EEA01 robot six-axis motion controller by using a contour mode, and finishing the operation track planning of the industrial robot;
step five: control system integration
The whole process of industrial robot production operation is completed in a teaching mode, an industrial robot production operation file is generated in the process, combination of teaching reproduction and trajectory planning is achieved, and a control system is integrated;
step six: teaching interface design
Selecting a cross-platform C graphical user interface application program development frame developed by a Qt Company for a teaching interface, adding an industrial robot axis operation button, a speed control button and a teaching coordinate space selection button on the interface, and adding an industrial robot operation instruction signal set into the interface;
step seven: reproduction module design
And the reproduction module selects another cross-platform C + + application program development framework developed by a Qt Company, calls a teaching task file generated through a teaching process in a dialog box, starts the operation of the industrial robot after setting all the operation time parameters and reproduces the task of the robot.
The further improvement lies in that: in the first step, the GMM06EEA01 robot six-axis motion controller is connected with an industrial control computer through Ethernet, the GMM06EEA01 robot six-axis motion controller communicates with servo high speed through EtherCAT to realize accurate pose control of the robot, and the GMM06EEA01 robot six-axis motion controller communicates with an industrial PC, a demonstrator and HMI industrial field equipment through an Ethernet bus to realize teaching and monitoring of the robot.
The further improvement lies in that: in the second step, the limited data set is as follows:
wherein Y represents a finite set of data; n represents the number of samples contained in a big data distributed structure model in the control system; r represents the big data cluster vector space at arbitrary norm.
The further improvement lies in that: in the second step, when the clustering channel fitting factor of the finite data set Y is 0, the formula (1) is satisfied:
in the formula, rho represents a clustering channel fitting factor; h represents the iteration number of the big data cluster; sgn denotes a sign function.
The further improvement lies in that: in the third step, the formula of calculation is as follows:
X(a,b)＝∑[(ai,bi)-(ai+Δa,bi+Δb)]2 (2)
wherein X (a, b) represents a characteristic point function; Δ a and Δ b represent two-dimensional characteristic displacement of the control system big data information flow; (a)i,bi) Expressing the characteristics of linear frequency modulation, and finishing the fitting of the linear frequency modulation control signal in the control system according to a formula (2).
The further improvement lies in that: in the fourth step, the specific planning process is as follows:
s1: constructing a track mathematical equation by using an interpolation track algorithm, and calculating to obtain a specific position coordinate of a next interpolation point;
s2: using kinematics to reversely solve to obtain the rotation angle of each joint on the interpolation point coordinates, and obtaining the position of the industrial robot joint interpolation point and the angle deviation between the position and the previous interpolation point;
s3: calculating incremental values of all joints of the industrial robot, and writing the incremental values into a file in a dms format;
s4: and judging whether the interpolation is finished or not, returning to the step again when the corresponding interpolation operation is not finished, and loading the dms-format file into a GMM06EEA01 robot six-axis motion controller and executing a corresponding command control signal in the dms-format file to finish the operation track planning of the industrial robot when the interpolation operation is finished.
The further improvement lies in that: in the fifth step, the operation file is control statements taking a robot control programming language and control data as cores, each control statement comprises a signal set of each operation instruction and corresponding parameter information, and the selected industrial robot programming languages comprise three types: decision class, coordination class, and execution class.
The further improvement lies in that: in the fifth step, a signal set of the operation instruction comprises a motion instruction signal, an input instruction signal, an output instruction signal, a calculation instruction signal and a control instruction signal for the industrial robot, wherein the motion instruction signal comprises forward, backward, rotary and translational motion; calculating the instruction signal comprises adding, subtracting, multiplying and dividing; the control instruction signals comprise waiting, calling and jumping, wherein the completion of the motion instruction signals relates to straight line, arc line and track planning, so that the combination of teaching reproduction and track planning is realized, a control system is integrated, and the system performance is improved.
The further improvement lies in that: in the sixth step, in the teaching interface, when the user selects the instruction signal set, the relevant interface window is automatically hidden or displayed.
The further improvement lies in that: in the seventh step, when the teaching task file generated through the teaching process is called, the teaching task file is decoded, so that the teaching task file is suitable for the relevant sentences in the six-axis motion controller of the GMM06EEA01 robot.
The invention has the beneficial effects that:
1. the method and the system have the advantages of effectively improving the clustering performance of data, having small calculated amount and high real-time performance, improving the control capability of the industrial robot, having more accurate control capability of the industrial robot control system based on the big data clustering, and matching with the operation track planning, so that compared with the traditional system, the method and the system have smaller deviation of the operation track when the industrial robot is controlled, are more in line with the requirements of industrial production, and have higher economic value.
2. The invention adopts a teaching and reproducing mode to realize the operation control of the control system on the robot and generate an industrial robot production operation file in the process, wherein the operation file is a control statement taking a robot control programming language and control data as a core and relates to the planning of straight lines, arc lines and tracks, thereby realizing the combination of teaching and reproducing and track planning, integrating the control system and improving the system performance.
3. The invention adopts a two-stage computing structure to complete the control of the robot, uses the industrial control computer as the upper computer structure of the system, has the capability of storing and rapidly computing mass data, has more reliable and abundant interfaces, only needs to connect the required lower computer according to a proper interface when the lower computer needs to be replaced, and uses the GMM06EEA01 robot six-axis motion controller as the lower computer, connects the industrial control computer with a servo motor driver, supports various high-speed buses, realizes the accurate pose control of the robot and the teaching and monitoring of the robot.
Drawings
Fig. 1 is a flow chart of robot control according to the present invention.
Detailed Description
In order to further understand the present invention, the following detailed description will be made with reference to the following examples, which are only used for explaining the present invention and are not to be construed as limiting the scope of the present invention.
Example one
According to fig. 1, the present embodiment proposes an industrial robot control method based on big data clustering, which includes the following steps:
the method comprises the following steps: control connection
An industrial control computer is used as an upper computer structure of the system, a GMM06EEA01 robot six-axis motion controller is selected to connect the industrial control computer with a servo motor and a driver, the servo motor is matched with a transmission structure to control a robot body, the GMM06EEA01 robot six-axis motion controller is connected with the industrial control computer through Ethernet, the GMM06EEA01 robot six-axis motion controller is communicated with servo high-speed through EtherCAT to realize accurate pose control of the robot, the GMM06EEA01 robot six-axis motion controller is communicated with an industrial PC, a demonstrator and HMI industrial field equipment through an Ethernet bus to realize teaching and monitoring of the robot; the robot control system has the advantages that a two-stage computing structure is selected to complete control over the robot, an industrial control computer is used as an upper computer structure of the system, the system has the capability of storing and rapidly calculating mass data, and has more reliable and abundant interfaces, when a lower computer needs to be replaced, only the required lower computer needs to be connected according to a proper interface, and a GMM06EEA01 robot six-axis motion controller is used as the lower computer, the industrial control computer is connected with a servo motor driver, various high-speed buses are supported, and accurate pose control over the robot and teaching and monitoring over the robot are achieved;
step two: building data sets
The method comprises the following steps of performing linear frequency modulation control signal fitting based on big data clustering, firstly establishing a big data distributed structure model in a control system, researching big data fuzzy control in the control system according to the concept of fuzzy control, and establishing a limited data set of sea volume control data in the control system:
wherein Y represents a finite set of data; n represents the number of samples contained in a big data distributed structure model in the control system; r represents a big data clustering vector space under any norm, and when the clustering channel fitting factor of the limited data set Y is 0, the formula (1) is satisfied:
in the formula, rho represents a clustering channel fitting factor; h represents the iteration number of the big data cluster; sgn denotes a sign function;
step three: fitting of signals
Fitting the linear frequency modulation signal of the big data information flow in the control system by using the fitting thought of the fuzzy control and control system, and calculating the big data information characteristic points of the control system by combining a big data clustering algorithm, wherein the formula of the calculation is as follows:
X(a,b)＝∑[(ai,bi)-(ai+Δa,bi+Δb)]2 (2)
wherein X (a, b) represents a characteristic point function; Δ a and Δ b represent two-dimensional characteristic displacement of the control system big data information flow; (a)i,bi) Expressing the linear frequency modulation characteristics, and finishing the fitting of a linear frequency modulation control signal in the control system according to a formula (2); initial values and noise are debugged by utilizing the uniform traversal characteristic of the big data clustering on the mass data, the clustering performance of the data is effectively improved, the advantages of small calculated amount and high real-time performance are achieved, and the control capability of the industrial robot is improved;
step four: trajectory planning
Performing overall track planning on an industrial control computer, coordinating and controlling the motion of each axis point of the industrial robot on a GMM06EEA01 robot six-axis motion controller by using a contour mode, and finishing the operation track planning of the industrial robot; the industrial robot control system based on big data clustering has more accurate control capability and is matched with the operation track planning, so that compared with the traditional system, the deviation of the operation track is smaller when the industrial robot is controlled;
step five: control system integration
Utilize the teaching mode to accomplish industrial robot production operation's whole process to industrial robot production operation file is generated in the process, the operation file is the control statement with robot control programming language and control data as the core, all contains the signal set and the corresponding parameter information of each item job instruction in every control statement, wherein, the industrial robot programming language of chooseing for use includes three kinds: the system comprises a decision-making class, a coordination class and an execution class, wherein a signal set of a working instruction comprises a motion instruction signal, an input instruction signal, an output instruction signal, a calculation instruction signal and a control instruction signal for the industrial robot, wherein the motion instruction signal comprises forward, backward, rotary and translational motion; calculating the instruction signal comprises adding, subtracting, multiplying and dividing; the control instruction signals comprise waiting, calling and jumping, wherein the completion of the motion instruction signals relates to straight line, arc line and track planning, so that the combination of teaching reproduction and track planning is realized, a control system is integrated, and the system performance is improved; the operation control of the control system on the robot is realized by adopting a teaching and reproducing mode, an industrial robot production operation file is generated in the process, the operation file is a control statement taking a robot control programming language and control data as a core, and relates to the planning of straight lines, arc lines and tracks, so that the combination of teaching and reproducing and the planning of the tracks is realized, the control system is integrated, and the system performance is improved;
step six: teaching interface design
The method comprises the following steps that a teaching interface selects a cross-platform C graphical user interface application program development frame developed by a Qt Company, an industrial robot axis operation button, a speed control button and a teaching coordinate space selection button are added on the interface, an industrial robot operation instruction signal set is added in the interface, and in the teaching interface, when a user selects the instruction signal set, a related interface window is automatically hidden or displayed;
step seven: reproduction module design
The reproduction module selects another cross-platform C + + application development framework developed by Qt Company, calls a teaching task file generated through a teaching process in a dialog box, decodes the teaching task file, so that the teaching task file is suitable for relevant sentences in the GMM06EEA01 robot six-axis motion controller, starts the operation of the industrial robot after all operation time parameters are set, and reproduces the task of the robot.
Example two
The embodiment provides an industrial robot control method based on big data clustering, overall track planning is performed on an industrial control computer, the motion of each axis point of an industrial robot is coordinated and controlled by using a contour mode on a GMM06EEA01 robot six-axis motion controller, and the specific planning flow is as follows:
s1: constructing a track mathematical equation by using an interpolation track algorithm, and calculating to obtain a specific position coordinate of a next interpolation point;
s2: using kinematics to reversely solve to obtain the rotation angle of each joint on the interpolation point coordinates, and obtaining the position of the industrial robot joint interpolation point and the angle deviation between the position and the previous interpolation point;
s3: calculating incremental values of all joints of the industrial robot, and writing the incremental values into a file in a dms format;
s4: judging whether the interpolation is finished or not, returning to the step again when the corresponding interpolation operation is not finished, and loading the dms-format file into a GMM06EEA01 robot six-axis motion controller and executing a corresponding instruction control signal in the dms-format file to finish the operation track planning of the industrial robot when the interpolation operation is finished; the industrial robot control system based on big data clustering has more accurate control capability, and is matched with the operation track planning, so that compared with the traditional system, the deviation of the operation track is smaller when the industrial robot is controlled.
The method and the system have the advantages of effectively improving the clustering performance of data, having small calculated amount and high real-time performance, improving the control capability of the industrial robot, having more accurate control capability of the industrial robot control system based on the big data clustering, and matching with the operation track planning, so that compared with the traditional system, the method and the system have smaller deviation of the operation track when the industrial robot is controlled, are more in line with the requirements of industrial production, and have higher economic value. The invention adopts a teaching and reproducing mode to realize the operation control of the control system on the robot and generate an industrial robot production operation file in the process, wherein the operation file is a control statement taking a robot control programming language and control data as a core and relates to the planning of straight lines, arc lines and tracks, thereby realizing the combination of teaching and reproducing and track planning, integrating the control system and improving the system performance. Meanwhile, the invention adopts a two-stage computing structure to complete the control of the robot, an industrial control computer is used as an upper computer structure of the system, the invention has the capability of storing and rapidly calculating mass data, and has more reliable and abundant interfaces, when a lower computer needs to be replaced, only the required lower computer needs to be connected according to a proper interface, and a GMM06EEA01 robot six-axis motion controller is used as the lower computer, the industrial control computer is connected with a servo motor driver, and various high-speed buses are supported, so that the accurate pose control of the robot and the teaching and monitoring of the robot are realized.
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (10)
1. The industrial robot control method based on big data clustering is characterized by comprising the following steps:
the method comprises the following steps: control connection
An industrial control computer is used as an upper computer structure of the system, a GMM06EEA01 robot six-axis motion controller is selected to connect the industrial control computer with a servo motor and a driver, and the servo motor is matched with a transmission structure to control a robot body;
step two: building data sets
Fitting linear frequency modulation control signals based on big data clustering, firstly establishing a big data distributed structure model in a control system, researching big data fuzzy control in the control system according to the concept of fuzzy control, and establishing a limited data set of sea level control data in the control system;
step three: fitting of signals
Fitting linear frequency modulation signals of large data information streams in the control system by using the fitting idea of a fuzzy control and control system, and calculating large data information characteristic points of the control system by combining a large data clustering algorithm to complete fitting of the linear frequency modulation control signals in the control system;
step four: trajectory planning
Performing overall track planning on an industrial control computer, coordinating and controlling the motion of each axis point of the industrial robot on a GMM06EEA01 robot six-axis motion controller by using a contour mode, and finishing the operation track planning of the industrial robot;
step five: control system integration
The whole process of industrial robot production operation is completed in a teaching mode, an industrial robot production operation file is generated in the process, combination of teaching reproduction and trajectory planning is achieved, and a control system is integrated;
step six: teaching interface design
Selecting a cross-platform C graphical user interface application program development frame developed by a Qt Company for a teaching interface, adding an industrial robot axis operation button, a speed control button and a teaching coordinate space selection button on the interface, and adding an industrial robot operation instruction signal set into the interface;
step seven: reproduction module design
And the reproduction module selects another cross-platform C + + application program development framework developed by a Qt Company, calls a teaching task file generated through a teaching process in a dialog box, starts the operation of the industrial robot after setting all the operation time parameters and reproduces the task of the robot.
2. The industrial robot control method based on big data clustering according to claim 1, characterized in that: in the first step, the GMM06EEA01 robot six-axis motion controller is connected with an industrial control computer through Ethernet, the GMM06EEA01 robot six-axis motion controller communicates with servo high speed through EtherCAT to realize accurate pose control of the robot, and the GMM06EEA01 robot six-axis motion controller communicates with an industrial PC, a demonstrator and HMI industrial field equipment through an Ethernet bus to realize teaching and monitoring of the robot.
3. The industrial robot control method based on big data clustering according to claim 2, characterized in that: in the second step, the limited data set is as follows:
wherein Y represents a finite set of data; n represents the number of samples contained in a big data distributed structure model in the control system; r represents the big data cluster vector space at arbitrary norm.
4. The industrial robot control method based on big data clustering according to claim 3, characterized in that: in the second step, when the clustering channel fitting factor of the finite data set Y is 0, the formula (1) is satisfied:
in the formula, rho represents a clustering channel fitting factor; h represents the iteration number of the big data cluster; sgn denotes a sign function.
5. The industrial robot control method based on big data clustering according to claim 4, characterized in that: in the third step, the formula of calculation is as follows:
X(a,b)＝∑[(ai,bi)-(ai+Δa,bi+Δb)]2 (2)
wherein X (a, b) represents a characteristic point function; Δ a and Δ b represent two-dimensional characteristic displacement of the control system big data information flow; (a)i,bi) Expressing the characteristics of linear frequency modulation, and finishing the fitting of the linear frequency modulation control signal in the control system according to a formula (2).
6. The industrial robot control method based on big data clustering according to claim 5, characterized in that: in the fourth step, the specific planning process is as follows:
s1: constructing a track mathematical equation by using an interpolation track algorithm, and calculating to obtain a specific position coordinate of a next interpolation point;
s2: using kinematics to reversely solve to obtain the rotation angle of each joint on the interpolation point coordinates, and obtaining the position of the industrial robot joint interpolation point and the angle deviation between the position and the previous interpolation point;
s3: calculating incremental values of all joints of the industrial robot, and writing the incremental values into a file in a dms format;
s4: and judging whether the interpolation is finished or not, returning to the step again when the corresponding interpolation operation is not finished, and loading the dms-format file into a GMM06EEA01 robot six-axis motion controller and executing a corresponding command control signal in the dms-format file to finish the operation track planning of the industrial robot when the interpolation operation is finished.
7. The industrial robot control method based on big data clustering according to claim 6, characterized in that: in the fifth step, the operation file is control statements taking a robot control programming language and control data as cores, each control statement comprises a signal set of each operation instruction and corresponding parameter information, and the selected industrial robot programming languages comprise three types: decision class, coordination class, and execution class.
8. The industrial robot control method based on big data clustering according to claim 7, characterized in that: in the fifth step, a signal set of the operation instruction comprises a motion instruction signal, an input instruction signal, an output instruction signal, a calculation instruction signal and a control instruction signal for the industrial robot, wherein the motion instruction signal comprises forward, backward, rotary and translational motion; calculating the instruction signal comprises adding, subtracting, multiplying and dividing; the control instruction signals comprise waiting, calling and jumping, wherein the completion of the motion instruction signals relates to straight line, arc line and track planning, so that the combination of teaching reproduction and track planning is realized, a control system is integrated, and the system performance is improved.
9. The industrial robot control method based on big data clustering according to claim 8, characterized in that: in the sixth step, in the teaching interface, when the user selects the instruction signal set, the relevant interface window is automatically hidden or displayed.
10. The industrial robot control method based on big data clustering according to claim 9, characterized in that: in the seventh step, when the teaching task file generated through the teaching process is called, the teaching task file is decoded, so that the teaching task file is suitable for the relevant sentences in the six-axis motion controller of the GMM06EEA01 robot.
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Application publication date: 20220111 |