CN107838922B - Robot repetition-free teaching method - Google Patents
Robot repetition-free teaching method Download PDFInfo
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- CN107838922B CN107838922B CN201711005740.4A CN201711005740A CN107838922B CN 107838922 B CN107838922 B CN 107838922B CN 201711005740 A CN201711005740 A CN 201711005740A CN 107838922 B CN107838922 B CN 107838922B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/0081—Programme-controlled manipulators with master teach-in means
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
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- H04L67/10—Protocols in which an application is distributed across nodes in the network
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Abstract
The invention relates to a robot repetition-free teaching method, which is used for semantically modeling environmental equipment information, robot information and teaching information and writing the semantically modeled environment equipment information, robot information and teaching information into a node of an OPC UA server address space; the method comprises the steps that an OPC UA client accesses a node of an address space of an OPC UA server, analyzes semantic modeling information, converts the semantic modeling information into an XML file and sends the XML file to a cloud server; the cloud server analyzes the XML file, and according to the robot body model information base, robot behavior resolving is carried out to obtain a robot planning instruction, and a motion track point configuration file is formed; and the OPC UA client receives the configuration file of the motion track points and transmits the configuration file to one or more corresponding robot controllers through the OPC UA communication stack, and the robot executes operation according to the obtained motion track points. The invention adopts semantization to package the equipment model information and the work description information, realizes intercommunication and interconnection among equipment, quickly completes configuration, and greatly improves the application efficiency because equipment replacement and function update are synchronized through OPC UA.
Description
Technical Field
The invention relates to a robot repetition-free teaching method. The invention is suitable for application scenes in which the industrial robots need to repeatedly teach, and is particularly suitable for the field of intelligent factories in which a plurality of industrial robots finish the same task.
Background
When a robot is used instead of a human to perform a work, it is necessary to give an instruction to the robot in advance and to specify a specific content of an operation and a work to be performed by the robot. This process is referred to as teaching the robot or programming the robot. In order to make the robot realize the desired action, the robot needs to be given various information, firstly, the information of the action sequence of the robot and the coordination information of the external equipment; secondly, additional condition information when the robot works; again the position and attitude information of the robot. The first two aspects are largely related to the work to be performed by the robot and the associated process requirements, and the teaching of position and attitude is often the focus of the robot teaching.
At present, robot teaching modes are roughly divided into two modes, namely teaching of a teaching device and off-line programming. Teaching of a teaching device refers to what is commonly known as hand-grip teaching, a person directly carries an arm of a robot to complete a series of operations, a robot controller records and stores information of each motion point, and the robot can automatically repeat the task according to instructions. The off-line programming is that a three-dimensional virtual environment of the whole working scene is reconstructed in a computer through software, then the software can automatically generate a motion track of the robot, namely a control instruction, according to the size, the shape and the material of a part to be processed and simultaneously matched with some operations of a software operator, then the track is simulated and adjusted in the software, and finally a robot program is generated and transmitted to the robot. The off-line programming makes full use of the research results of computer graphics to establish a robot and an environment object model thereof, and then uses a computer Visual programming language Visual C + + (or Visual Basic) to perform operation off-line planning and simulation, but the operation description cannot be simple and direct, and a user needs to have certain knowledge of robot programming. The demonstrator has low teaching programming threshold, is simple and convenient, and does not need an environment model; when teaching an actual robot, errors due to a mechanical structure can be corrected. The teaching demonstrator is widely applied to the field of robot control due to the advantages of simplicity and directness.
According to the traditional robot demonstrator mode, when the position of the robot is changed, a base coordinate system and a user coordinate system are changed, the robot which is well taught cannot learn changed information, the previous teaching is completely abandoned, and the robot needs to be taught again. The teaching method and the teaching process are not changed, so that a large number of workers are needed to carry out repeated work, and meanwhile, the teaching devices of all the robots are eight-door, different in operation and different in programming instructions. The robot is stopped during teaching, which brings a large amount of work.
Therefore, aiming at the problems, the teaching method breaks through the problem of complicated and repeated teaching of the traditional robot, researches the teaching method based on OPC UA semantization, realizes data multiplexing and improves working efficiency.
Disclosure of Invention
Aiming at the problem of teaching of the existing demonstrator, the invention provides a robot repetition-free teaching method, which has the core idea that semantic modeling is respectively carried out on robot environment equipment information, relevant basic configuration information of a robot body, motion parameters of a robot and data information generated by direct teaching of the demonstrator, and the data information is uploaded to a cloud server through OPC UA service; the cloud performs robot behavior calculation based on the size information base through semantic analysis, and transmits a planning result to the robot controller to complete robot programming; if other robots need to finish the same work, the same work is directly downloaded to the local from the cloud server through the OPC UA, data configuration is automatically finished, and complicated and repeated teaching processes are omitted.
The technical scheme adopted by the invention for solving the technical problems is as follows: a robot repetition-free teaching method comprises the following steps:
step 1: performing semantic modeling on the environmental equipment information, the robot information and the teaching information, and writing the semantic modeling into a node of an OPC UA server address space;
step 2: the method comprises the steps that an OPC UA client accesses a node of an OPC UA server address space and obtains semantic modeling information; analyzing the semantic modeling information, converting the semantic modeling information into an XML file and sending the XML file to a cloud server;
and step 3: the cloud server analyzes the XML file to obtain environmental equipment information, robot information and teaching information, and according to the robot body model information base, robot behavior resolving is carried out to obtain a robot planning instruction, and a motion track point configuration file is formed;
and 4, step 4: the method comprises the following steps that an OPC UA client receives a motion track point configuration file transmitted by a cloud server, the motion track point configuration file is transmitted to one or more corresponding robot controllers through an OPC UA communication stack, and the robot executes operation according to obtained motion track points;
and 5: and inquiring the OPC UA server end node through the OPC UA client, if the node information is changed, returning to the step S2, otherwise, continuously executing the local task, and repeating the step 5.
The node information is obtained by the following steps: and modifying one or more of the environmental equipment information, the robot information and the teaching information, then performing semantic modeling on the environmental equipment information, the robot information and the teaching information, and writing the semantic modeling into a node of an address space of an OPC UA server.
The semantic modeling of the environmental equipment information, the robot information and the teaching information and the writing of the nodes into the address space of the OPC UA server comprises the following steps:
constructing environment equipment information, robot information and teaching information into a basic information layer, and writing the basic information layer into an OPC UA service end node;
constructing semantically described environmental equipment information, robot information and teaching information into a semantic information layer, and writing the semantic information layer into a child node of an OPC UA service end node;
and combining the basic information layer and the semantic information layer to construct a semantic model layer, and writing the semantic model layer into the next child node of the child node.
And the OPC UA server and the OPC UA client communicate by adopting an OPC UA communication protocol, and the OPC UA client and the cloud communicate by adopting an HTTP protocol.
The robot information comprises model information of a robot body and motion parameters of the robot.
The environmental device information includes model information of a workpiece and a jig used in a robot application.
The teaching information is the action task description information recorded in the manual teaching process of the robot.
The invention has the following beneficial effects and advantages:
1. the robot controller supporting the cloud service platform is adopted for application development, the robot controller is suitable for the characteristic that the application environment of the robot is complex and changeable, the problem of repeated teaching of the robot is avoided, and a large amount of manpower and material resources are saved.
2. The OPC UA communication interface is adopted, the equipment information accessed to the application environment can be automatically identified, and the robot can dynamically adjust the robot application according to the work description file, so that the flexibility and the adaptability of the robot application are improved.
3. Equipment model information and work description information are packaged in a semantic mode, intercommunication and interconnection among equipment are achieved, configuration is completed quickly, equipment replacement and function updating are synchronized through OPC UA, and application efficiency is improved greatly.
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FIG. 1 is a block diagram of the components of an embodiment of the present invention;
fig. 2 is a flow chart of the operation of an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to examples.
In a multi-robot working scene shown in fig. 1, semantic modeling is performed on relevant information, motion parameters, teaching information and the like of a workpiece, a clamp and a robot body of a robot a, and semantic data is written into a node of a server-side address space of OPC UA. And the OPC UA client accesses the server, analyzes all information according to the semantic model, and transmits the information to the cloud server in an XML form through an HTTP protocol. The cloud server analyzes the XML file information, robot behavior calculation is carried out based on the robot body model information base, a robot planning instruction is obtained through the robot behavior calculation module, and a configuration file of the controller is formed. When other robots such as the robot B need to perform the same operation as the robot A or environmental equipment changes, manual repeated teaching is not needed, the request is sent to the cloud server through OPC UA synchronous updating, and is transmitted to other robot controllers through OPC UA clients to complete automatic configuration, so that automatic teaching can be realized.
The semantic modeling is model design of the semantic modeling, and semantics are loaded into a data model and comprise a basic model layer, a semantic information layer and a semantic model layer. The basic model layer represents a basic component element set of the equipment, the semantic information layer describes basic component element attributes by means of semantics, and the semantic model layer combines data of the following two layers to form a data model with semantics.
As shown in fig. 2, it performs a process workflow:
step S1: performing semantic modeling on environmental equipment information such as a robot workpiece and a clamp, related information of a robot body, motion parameters and teaching information of the robot, and writing semantic information into nodes of an OPC UA address space. For example: taking an example of an object A needing to be clamped by the robot, constructing a basic information layer by using basic information of the object A, such as size, shape and relative robot tail end position, and writing the basic information layer into an OPC UA node; constructing a semantic information layer by using semantic description information such as bigsmalls, shape and position, and writing the semantic information layer into an OPC UA child node; and combining the basic information layer and the semantic information layer to construct a semantic model layer, wherein the semantic model layer is written into the next child node of the OPC UA if the correspondence is clear, the big attribute is represented by the big and small, the shape attribute is represented by the shape, and the position represents the position information.
Step S2: and executing a motion control main program and simultaneously triggering and executing an OPC UA communication thread.
Step S3: the OPC UA client accesses the server through the communication stack, identifies the server equipment, and accesses the node of the address space to obtain semantic modeling information; and analyzing to obtain environment equipment information, relevant information of the robot body, motion parameters and teaching information of the robot, converting the environment equipment information, the relevant information of the robot body into an XML file form, and sending the XML file form to the cloud server.
Step S4: and the cloud server analyzes the XML file to obtain robot environment equipment information, robot local related information, robot motion parameters and demonstrator information. For example, for an object a that needs to be clamped to the robot, size, shape, and relative robot position information are obtained. And based on the robot body model information base, a robot planning instruction is obtained through a robot behavior resolving module to form a motion track point configuration file.
Step S5: and the OPC UA client receives the motion track point configuration file transmitted by the cloud server, transmits the motion track point configuration file to one or more corresponding robot controllers through the OPC UA communication stack, and the controllers analyze the configuration file, install the motion track point motion and update the robot operation.
Step S6: and accessing the service end node through the OPC UA client, polling and checking the node information, executing the step S3 if the node information changes, continuing to execute the local task if the node information does not change, and repeating the judgment of the step S6.
The robot controller is a general computer platform based on an X86 architecture and runs a QNX real-time operating system;
the communication protocol adopts an OPC UA communication protocol between the OPC UA server and the client, and an HTTP protocol is adopted between the OPC UA client and the cloud platform;
the environment equipment comprises equipment such as a workpiece, a clamp and a robot body which can be used in robot application, and the equipment model information can be transmitted to the robot controller through a UA (user interface);
the teaching information is a teaching information file recorded by the robot in the manual teaching process, is a series of action task description files and is transmitted to the robot controller through a UA (user interface);
the robot behavior settlement module is a robot control instruction obtained by combining a robot forward and backward solution algorithm, applying tasks and physical size information of equipment in the environment, and can be used for guiding the operation of the robot.
The invention uses 3 application scenario cases.
1. When multiple robots of the same type need to complete the same action. For example, a robot is used for screwing a bottle cap, only manual teaching needs to be performed on one robot, and body configuration parameters of the robot and action information obtained through teaching are packaged into XML and uploaded to a cloud server. And other robots download from the cloud server and automatically configure, so that the same action can be completed without teaching.
2. When the pose and user coordinates of the robot are known, but the device to be operated is moved in a known direction and distance. For example, bottle cap screwing robots have been taught to be able to unscrew bottle caps placed on tables. But at the moment, the table is uniformly moved forward by 5cm, all robots do not need to be taught repeatedly, the moving direction and the moving distance only need to be modified on the semantic modeling, the configuration file is sent to the robots again, the configuration is carried out again, the actions can be updated on line, and a large amount of manpower and material resources are saved.
3. When the pose of the robot and the user coordinates are unknown, but all robots are modified identically. For example, a table for placing bottles moves a certain distance in a certain direction, and the distance and the direction are unknown. The robot teaching system has the advantages that one robot is taught manually, used configuration and action information are recorded, the configuration and action information is packaged into XML and uploaded to a cloud server, and other robots can complete corresponding operation only by downloading and reconfiguring.
In conclusion, the invention has good expandability and applicability, and the robot controller supporting the cloud service platform is used, so that the deployment cycle of the robot application can be greatly shortened, and the implementation cost of the robot application is reduced. Meanwhile, semantic description and OPC UA communication service can enable the robot controller to be suitable for various industrial production scenes.
Claims (7)
1. A robot repetition-free teaching method is characterized by comprising the following steps:
step 1: performing semantic modeling on the environmental equipment information, the robot information and the teaching information, and writing the semantic modeling into a node of an OPC UA server address space;
step 2: the method comprises the steps that an OPC UA client accesses a node of an OPC UA server address space and obtains semantic modeling information; analyzing the semantic modeling information, converting the semantic modeling information into an XML file and sending the XML file to a cloud server;
and step 3: the cloud server analyzes the XML file to obtain environmental equipment information, robot information and teaching information, and according to the robot body model information base, robot behavior resolving is carried out to obtain a robot planning instruction, and a motion track point configuration file is formed;
and 4, step 4: the method comprises the following steps that an OPC UA client receives a motion track point configuration file transmitted by a cloud server, the motion track point configuration file is transmitted to one or more corresponding robot controllers through an OPC UA communication stack, and the robot executes operation according to obtained motion track points;
and 5: and inquiring the OPC UA server end node through the OPC UA client, if the node information is changed, returning to the step S2, otherwise, continuously executing the local task, and repeating the step 5.
2. The robot repetition-free teaching method according to claim 1, characterized in that: the node information is obtained by the following steps: and modifying one or more of the environmental equipment information, the robot information and the teaching information, then performing semantic modeling on the environmental equipment information, the robot information and the teaching information, and writing the semantic modeling into a node of an address space of an OPC UA server.
3. The method for teaching robot without repetition according to claim 1 or 2, wherein the semantically modeling the environmental device information, the robot information and the teaching information and writing the semantically modeled environment device information, robot information and teaching information into the node of the address space of the OPC UA server comprises the following steps:
constructing environment equipment information, robot information and teaching information into a basic information layer, and writing the basic information layer into an OPC UA service end node;
constructing environment equipment information, robot information and teaching information which are described semantically into a semantic information layer, and writing the semantic information layer into a child node of the affiliated OPCUA service end node;
and combining the basic information layer and the semantic information layer to construct a semantic model layer, and writing the semantic model layer into the next child node of the child node.
4. The robot repetition-free teaching method according to claim 1, characterized in that: and the OPC UA server and the OPC UA client communicate by adopting an OPC UA communication protocol, and the OPC UA client and the cloud server communicate by adopting an HTTP protocol.
5. The method according to claim 1, wherein the robot information includes model information of a robot body and motion parameters of the robot.
6. The robot repetition-free teaching method according to claim 4, wherein: the environmental device information includes model information of a workpiece to be machined and a fixture used in robot application.
7. The robot repetition-free teaching method according to claim 1, characterized in that: the teaching information is the action task description information recorded in the manual teaching process of the robot.
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| CN108828995A (en) * | 2018-05-31 | 2018-11-16 | 苏州浪潮智能软件有限公司 | Study of Intelligent Robot Control system and control method based on cloud platform |
| EP3806422A4 (en) | 2018-08-01 | 2022-01-26 | Siemens Aktiengesellschaft | Interconnection device, communication method and system comprising robot |
| JP6839160B2 (en) | 2018-11-21 | 2021-03-03 | 本田技研工業株式会社 | Robot devices, robot systems, robot control methods, and programs |
| EP3723346A1 (en) * | 2019-04-10 | 2020-10-14 | ABB Schweiz AG | Selective address space aggregation |
| CN111142487A (en) * | 2019-12-30 | 2020-05-12 | 浪潮通用软件有限公司 | Equipment data acquisition system based on OPC UA unified architecture protocol |
| CN113836702A (en) * | 2021-09-03 | 2021-12-24 | 深圳市如本科技有限公司 | Robot teaching programming method and robot teaching programming device |
| CN114571471B (en) * | 2022-05-07 | 2022-10-14 | 广东隆崎机器人有限公司 | Method and system for centralized control of multiple SCARA robots |
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| JPS59160209A (en) * | 1983-03-02 | 1984-09-10 | Amada Co Ltd | Controlling method of industrial robot |
| CN101770710A (en) * | 2009-12-31 | 2010-07-07 | 哈尔滨工业大学 | Laser-vision sensing assisted remote teaching method for remote welding |
| EP3002921B1 (en) * | 2014-09-30 | 2018-10-31 | Siemens Aktiengesellschaft | Appliance device for an automation system |
| CN104360844B (en) * | 2014-10-24 | 2018-02-09 | 交控科技股份有限公司 | Protocol conversion server and ATS systems based on OPC UA standards |
| CN205485380U (en) * | 2015-12-29 | 2016-08-17 | 普奥云信息科技(北京)有限公司 | Industrial data collection system |
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