CN108527319B - Robot teaching method and system based on vision system - Google Patents
Robot teaching method and system based on vision system Download PDFInfo
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- CN108527319B CN108527319B CN201810264284.3A CN201810264284A CN108527319B CN 108527319 B CN108527319 B CN 108527319B CN 201810264284 A CN201810264284 A CN 201810264284A CN 108527319 B CN108527319 B CN 108527319B
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- robot
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- vision system
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- 238000000034 method Methods 0.000 title claims abstract description 9
- 238000012545 processing Methods 0.000 claims abstract description 21
- 238000012937 correction Methods 0.000 claims abstract description 16
- 238000009434 installation Methods 0.000 claims abstract description 7
- 238000005457 optimization Methods 0.000 claims abstract description 6
- 230000000007 visual effect Effects 0.000 claims description 11
- 238000004519 manufacturing process Methods 0.000 claims description 7
- 238000005094 computer simulation Methods 0.000 claims description 3
- 238000013461 design Methods 0.000 claims description 3
- 238000003754 machining Methods 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
Classifications
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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 leader teach-in means
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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
- B25J9/16—Programme controls
- B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
- B25J9/1671—Programme controls characterised by programming, planning systems for manipulators characterised by simulation, either to verify existing program or to create and verify new program, CAD/CAM oriented, graphic oriented programming systems
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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
- B25J9/16—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
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
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- Engineering & Computer Science (AREA)
- Robotics (AREA)
- Mechanical Engineering (AREA)
- Numerical Control (AREA)
Abstract
The invention discloses a robot teaching method and a system based on a vision system, which are used for eliminating equipment installation errors in the robot offline program teaching by means of acquisition analysis of the vision system and matching optimization of a data platform, and firstly controlling the robot to run according to a track provided by an offline program; the vision system moves along the rough track of the robot, and images are acquired; analyzing the track offset relation between the robot and the workpiece according to the image; the data platform analyzes and determines the optimal track and the track correction parameters by matching the processing data stored in the database. The teaching system comprises a robot processing system, a vision system and a data platform. The invention applies the vision system to the teaching of the robot processing system, solves the problem that the error is eliminated by manually optimizing the track by the aid of the demonstrator after the offline program is imported in the past, and the robot acquires the track by the vision system, analyzes and judges the track by the aid of big data, and autonomously selects and optimizes the teaching track, so that the teaching efficiency is high, and the track optimization is accurate.
Description
Technical Field
The invention relates to the field of automobile manufacturing, in particular to a robot teaching method and system based on a vision system.
Background
The industrial robot is used for realizing automatic production, replaces workers to engage in complex and repeated labor, and is widely applied to the field of automobile manufacturing. Industrial robots require teaching prior to use.
The robot teaching is commonly realized by combining an offline program with a demonstrator, and the motion trail of the robot reaches the processing requirement under manual intervention. However, the use of the demonstrator to optimize the track has high requirements on the skill level of the operator, the time and effort spent by manual teaching are high, and the manual teaching is only carried out by naked eye observation and experience, so that the teaching precision is low.
Disclosure of Invention
The invention aims to provide a robot teaching method and a robot teaching system based on a vision system.
In order to achieve the above purpose, the technical scheme of the invention is as follows: a robot teaching method based on a vision system eliminates equipment installation errors in the robot offline program teaching by means of acquisition and analysis of the vision system and matching optimization of a data platform, and specifically comprises the following steps:
step one, offline programming, namely generating a robot offline program based on 3D digital-analog dynamic simulation of a robot processing system;
installing equipment, namely installing the robot and the clamp installation design data in place; the offline program is imported into the robot controller;
step three, the coarse track is operated, and the robot is controlled to operate according to the track provided by the off-line program;
step four, image acquisition, wherein a vision system moves along a rough track of a robot, and depth images of workpieces mounted on a clamp are acquired;
fifthly, calculating the track offset, wherein the visual system analyzes the spatial position relation between the robot and the workpiece according to the depth image, and calculates the track offset of the robot through the visual system;
step six, matching the optimal track, wherein the visual system feeds back the track offset of the robot and the workpiece to a data platform, and the data platform analyzes and determines the optimal track by matching the processing data stored in the database;
step seven, track correction parameters are calculated, the data platform compares the space deviation of the optimal track and the coarse track, and track correction parameters are determined;
and step eight, track correction, wherein the data platform feeds back the track correction parameters to the robot controller, and the robot is controlled by the data platform to automatically optimize the track correction, so that teaching is completed.
Further, the data platform is connected with a factory data center and is used for storing or calling existing parameters of the mass production vehicle type, such as workpiece parameters, machining parameters, material forming parameters and the like, so that enough reference can be provided for robot teaching.
The robot teaching system based on the vision system comprises a robot processing system, a vision system and a data platform, wherein the robot processing system comprises a robot controller, a robot, a clamp and a teaching workpiece arranged on the clamp; the visual system comprises an image acquisition device and an image processing industrial personal computer; the image acquisition device is arranged at the tail end of the robot and moves along with the robot; a spatial position analysis module is arranged in the image processing industrial personal computer; the image processing industrial personal computer is connected with the data platform, the data platform is connected with the robot controller, and the data platform stores the existing parameters of the vehicle model.
The beneficial effects of the invention are as follows:
the invention applies the vision system to the teaching of the robot processing system, solves the problem that the error is eliminated by manually optimizing the track by the aid of the demonstrator after the offline program is imported in the past, and the robot acquires the track by the vision system, analyzes and judges the track by the aid of big data, and autonomously selects and optimizes the teaching track, so that the teaching efficiency is high, and the track optimization is accurate.
Drawings
FIG. 1 is a schematic diagram of the structure of the present invention.
Detailed Description
The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.
A robot teaching method based on a vision system eliminates equipment installation errors in the robot offline program teaching by means of acquisition and analysis of the vision system and matching optimization of a data platform, and specifically comprises the following steps:
step one, offline programming, namely generating a robot offline program based on 3D digital-analog dynamic simulation of a robot processing system;
installing equipment, namely installing the robot and the clamp installation design data in place; the offline program is imported into the robot controller;
step three, the coarse track is operated, and the robot is controlled to operate according to the track provided by the off-line program;
step four, image acquisition, wherein a vision system moves along a rough track of a robot, and depth images of workpieces mounted on a clamp are acquired;
fifthly, calculating the track offset, wherein the visual system analyzes the spatial position relation between the robot and the workpiece according to the depth image, and calculates the track offset of the robot through the visual system;
step six, matching the optimal track, wherein the visual system feeds back the track offset of the robot and the workpiece to a data platform, and the data platform analyzes and determines the optimal track by matching the processing data stored in the database;
step seven, track correction parameters are calculated, the data platform compares the space deviation of the optimal track and the coarse track, and track correction parameters are determined;
and step eight, track correction, wherein the data platform feeds back the track correction parameters to the robot controller, and the robot is controlled by the data platform to automatically optimize the track correction, so that teaching is completed.
The data platform is connected with the factory data center and is used for storing or calling existing parameters of the mass production vehicle type, such as workpiece parameters, machining parameters, material forming parameters and the like, so that enough references for robot teaching can be provided.
As shown in fig. 1, a robot teaching system based on a vision system comprises a robot processing system, a vision system and a data platform, wherein the robot processing system comprises a robot controller, a robot, a clamp and a teaching workpiece arranged on the clamp; the visual system comprises an image acquisition device and an image processing industrial personal computer; the image acquisition device is arranged at the tail end of the robot and moves along with the robot; a spatial position analysis module is arranged in the image processing industrial personal computer; the image processing industrial personal computer is connected with the data platform, the data platform is connected with the robot controller, and the data platform stores the existing parameters of the vehicle model.
The data platform is built based on big data ideas, needs to have the functions of accessing factory-level and industry-level data platforms, has the autonomous learning capability of updating data periodically or irregularly, and has the capability of acquiring network data by connecting the Internet in real time, the capability of finishing and summarizing production practice data, and is suitable for the development of the Internet of things in the artificial intelligence direction.
The described embodiments are only some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Claims (2)
1. The robot teaching method based on the vision system is characterized by eliminating equipment installation errors in the robot offline program teaching by means of acquisition and analysis of the vision system and matching optimization of a data platform, and comprises the following specific steps:
step one, offline programming, namely generating a robot offline program based on 3D digital-analog dynamic simulation of a robot processing system;
installing equipment, namely installing the robot and the clamp installation design data in place; the offline program is imported into the robot controller;
step three, the coarse track is operated, and the robot is controlled to operate according to the track provided by the off-line program;
step four, image acquisition, wherein a vision system moves along a rough track of a robot, and depth images of workpieces mounted on a clamp are acquired;
fifthly, calculating the track offset, wherein the visual system analyzes the spatial position relation between the robot and the workpiece according to the depth image, and calculates the track offset of the robot through the visual system;
step six, matching the optimal track, wherein the visual system feeds back the track offset of the robot and the workpiece to a data platform, and the data platform analyzes and determines the optimal track by matching the processing data stored in the database;
step seven, track correction parameters are calculated, the data platform compares the space deviation of the optimal track and the coarse track, and track correction parameters are determined;
and step eight, track correction, wherein the data platform feeds back the track correction parameters to the robot controller, and the robot is controlled by the data platform to automatically optimize the track correction, so that teaching is completed.
2. The vision system-based robot teaching method according to claim 1, wherein the data platform is connected to a factory data center for storing or calling existing parameters of a model of a production vehicle, and providing a sufficient reference for robot teaching.
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| Application Number | Priority Date | Filing Date | Title |
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| CN201810264284.3A CN108527319B (en) | 2018-03-28 | 2018-03-28 | Robot teaching method and system based on vision system |
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| CN201810264284.3A CN108527319B (en) | 2018-03-28 | 2018-03-28 | Robot teaching method and system based on vision system |
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| CN108527319A CN108527319A (en) | 2018-09-14 |
| CN108527319B true CN108527319B (en) | 2024-02-13 |
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Families Citing this family (9)
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| CN109483532A (en) * | 2018-11-01 | 2019-03-19 | 东莞市中天自动化科技有限公司 | Lathe and its corase grinding robot control method |
| CN109530984A (en) * | 2018-11-19 | 2019-03-29 | 施努卡(苏州)智能装备有限公司 | Vision positioning welding and assembling method |
| CN109676583B (en) * | 2018-12-03 | 2021-08-24 | 深圳市越疆科技有限公司 | Deep learning visual acquisition method based on target posture, learning system and storage medium |
| CN111002317A (en) * | 2019-11-20 | 2020-04-14 | 希美埃（芜湖）机器人技术有限公司 | Novel spraying method and novel spraying device based on robot vision in door and window spraying industry |
| CN110977260B (en) * | 2019-12-17 | 2021-08-17 | 易思维（杭州）科技有限公司 | Intelligent repair welding system and follow-up repair welding method for body-in-white |
| CN111283685A (en) * | 2020-03-05 | 2020-06-16 | 广州市斯睿特智能科技有限公司 | Vision teaching method of robot based on vision system |
| CN113050424A (en) * | 2021-03-19 | 2021-06-29 | 安徽理工大学环境友好材料与职业健康研究院(芜湖) | Intelligent self-adaptive control method and system based on image recognition |
| CN114170314B (en) * | 2021-12-07 | 2023-05-26 | 群滨智造科技(苏州)有限公司 | Intelligent 3D vision processing-based 3D glasses process track execution method |
| CN114683283B (en) * | 2022-03-25 | 2024-04-16 | 中铁科工集团有限公司 | Teaching-free welding method and device for welding robot |
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