CN117400260A - Multi-station robot control system, control method and multi-station robot - Google Patents
Multi-station robot control system, control method and multi-station robot Download PDFInfo
- Publication number
- CN117400260A CN117400260A CN202311609830.XA CN202311609830A CN117400260A CN 117400260 A CN117400260 A CN 117400260A CN 202311609830 A CN202311609830 A CN 202311609830A CN 117400260 A CN117400260 A CN 117400260A
- Authority
- CN
- China
- Prior art keywords
- robot
- intelligent robot
- data
- operation information
- station
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 25
- 238000004891 communication Methods 0.000 claims abstract description 40
- 238000012545 processing Methods 0.000 claims abstract description 12
- 230000009471 action Effects 0.000 claims abstract description 9
- 238000003745 diagnosis Methods 0.000 claims description 39
- 238000012544 monitoring process Methods 0.000 claims description 36
- 238000004458 analytical method Methods 0.000 claims description 25
- 230000007246 mechanism Effects 0.000 claims description 15
- 230000008569 process Effects 0.000 claims description 10
- 238000013500 data storage Methods 0.000 claims description 9
- 238000001514 detection method Methods 0.000 claims description 9
- 230000001133 acceleration Effects 0.000 claims description 6
- 238000005065 mining Methods 0.000 claims description 4
- 230000006399 behavior Effects 0.000 claims description 3
- 238000004519 manufacturing process Methods 0.000 abstract description 7
- 238000010586 diagram Methods 0.000 description 6
- 230000002159 abnormal effect Effects 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 238000012423 maintenance Methods 0.000 description 3
- 238000007726 management method Methods 0.000 description 3
- 238000012795 verification Methods 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
Classifications
-
- 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
- B25J9/1602—Programme controls characterised by the control system, structure, architecture
-
- 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
- B25J9/1602—Programme controls characterised by the control system, structure, architecture
- B25J9/161—Hardware, e.g. neural networks, fuzzy logic, interfaces, processor
-
- 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]
Landscapes
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Robotics (AREA)
- Mechanical Engineering (AREA)
- Physics & Mathematics (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- Fuzzy Systems (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Manipulator (AREA)
Abstract
The invention relates to the technical field of intelligent robots, in particular to a multi-station robot control system, which comprises: a multi-station robot control system comprising: the central processing unit further comprises: the intelligent robot group, the communication control network and the cloud computing server side; the intelligent robot group is connected with the cloud computing server through the communication control network. The multi-station robot control system, the control method and the multi-station robot are high in computing capacity. The cloud computing server is adopted for centralized processing control and feedback of data information, so that the computing capability is high, and the requirements on the data processing capability of the robot are reduced and the computing speed is high, so that the manufacturing and running cost of the robot is reduced under the condition that complex actions of robots at various stations are involved and the working procedures and parameters are numerous.
Description
Technical Field
The invention relates to the technical field of intelligent robots, in particular to a multi-station robot control system, a control method and a multi-station robot.
Background
With the development of industry 4.0, the robot industry is rapidly developed, and particularly, the development in the processing and manufacturing industries is abnormal and rapid, so how to realize the rapid and efficient use of robots has become a focus of attention of related field personnel. In order to meet the requirements of flexibility, accuracy, informatization, automation and high efficiency of the use environment, robots have evolved from the prior simple material handling and stacking to the today's comprehensive technology integrating mechanical design, computer science, communication technology, automation technology and management science. Among them, the robot vision device has been receiving a great deal of attention, and is rapidly applied to many places such as robot work positioning, range control, and the like. Generally, a robot is required to be installed at a corresponding station for fixing the robot in a manner of handling a workpiece at a fixed station, i.e., to be operated in a fixed manner and in a fixed posture after the robot is fixed. This mode of operation requires a robot to be specifically fitted with a fixed station.
The intelligent robot system is a basic unit for realizing intelligent production. Robots have long been and are widely used and developed in industrial production, and in mature production line arrangements, robots are generally organized in the form of a robot family or group of robots. The robot group is generally formed by organizing robots with different functions of continuous stations, and the realization of the coordinated operation of robots of each station in the robot group is an important subject for realizing stable and efficient production.
Through retrieval, chinese patent publication No. CN107685329A discloses a robot positioning control system and method. The control system comprises a robot control device for calculating the offset of a workpiece and adjusting the operation parameters of the robot workpiece, a vision device for acquiring images of the workpiece, a movement mechanism for driving the robot to perform station switching, a main controller and a station proximity sensor. The main controller controls the movement mechanism to drive the robot to move, and when the station proximity sensor judges that the robot approaches the station, the main controller is fed back to stop conveying the robot; the vision device is controlled by the robot control device to acquire the workpiece image, the workpiece offset is calculated, and then the working parameters are adjusted according to the offset, so that the workpiece is positioned after one machine of the robot is switched, and the problem of inaccurate workpiece processing caused by workpiece position change after station switching is solved.
The above patent suffers from the following disadvantages: the execution and adjustment of the action by the self computing capability of the intelligent robot obviously increases the manufacturing cost of the intelligent robot; as stations increase, the coordination becomes difficult and unstable; the efficiency of single channel internal communication is extremely low.
In view of this, we propose a multi-station robot control system, a control method and a multi-station robot.
Disclosure of Invention
The invention aims to provide a multi-station robot control system, a control method and a multi-station robot, so as to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a multi-station robot control system comprising: a multi-station robot control system comprising: the central processing unit further comprises:
the intelligent robot group, the communication control network and the cloud computing server side; the intelligent robot group is connected with the cloud computing server through the communication control network;
the main controller is used for driving the movement mechanism and the station proximity sensor of the robot for station switching;
the robot comprises a robot control device for calculating the offset of a workpiece and adjusting the operation parameters of the workpiece of the robot and a vision device for acquiring images of the workpiece; the main controller is in control connection with the movement mechanism, input samples of the main controller are connected with the station proximity sensor, and the main controller is connected with the robot control device; the robot control device inputs and samples and is connected with the vision device;
the fault diagnosis system is used for analyzing timely and accurate faults of the intelligent robot;
and the monitoring and early warning system is used for monitoring key operation data of the robot.
Preferably, the main controller is a PLC controller.
Preferably, the intelligent robot group is a multi-station intelligent robot set and at least comprises a first driving intelligent robot and a first driven intelligent robot; the first active intelligent robot transmits initial operation information and real-time operation information to a cloud computing server; the first slave intelligent robot transmits actual operation information to a cloud computing server; the cloud computing server plans and corrects the slave operation information of the first slave intelligent robot according to the initial operation information and the real-time operation information of the first master intelligent robot, transmits the slave operation information to the first slave intelligent robot through a communication control network, and controls and adjusts the action of the first slave intelligent robot; in order to facilitate the setting of initial parameters, a monitoring end is provided as a main control platform to define and send out instructions for system behaviors; the monitoring end is connected with the cloud computing server and used for setting, starting, interrupting or stopping initial operation information of the first active intelligent robot.
Preferably, the communication control network comprises a wired and/or wireless network.
Preferably, the diagnostic system comprises: the dynamic detection unit is used for calculating the operation data of the intelligent robot to obtain a fault point and a fault parameter of the intelligent robot, and obtaining the dumping time of the intelligent robot based on the fault parameter, wherein the dumping time is the fault clearing time in the operation process of the intelligent robot;
the fault parameters are sent to a diagnosis processor, and the diagnosis processor performs diagnosis analysis;
and sending the fault clearing time to the terminal equipment, so that a technician can complete fault diagnosis within the fault clearing time.
Preferably, the dynamic detection unit is arranged on the motion mechanism, the dynamic detection unit comprises four pressure sensors, height sensors, a first acceleration sensor and a second acceleration sensor, the four pressure sensors are respectively arranged on the bottom surfaces of four machine feet of the intelligent robot, a first speed sensor is arranged on the left front foot of the intelligent robot, a second speed sensor is arranged on the left rear foot of the intelligent robot, the two height sensors are arranged, one height sensor is arranged between the two front machine feet of the intelligent robot, and the other height sensor is arranged between the two rear machine feet of the intelligent robot.
Preferably, the monitoring and early warning system comprises: the system comprises a user local platform, a data acquisition controller and a remote monitoring diagnosis module, wherein: the remote monitoring diagnosis module specifically comprises a remote communication module, a data acquisition module, a data storage library and a data diagnosis analysis module, wherein the remote communication module transmits the data of the user local platform to the data storage library, and the data acquisition module acquires the data of the data acquisition controller and transmits the acquired data of the data acquisition controller to the data storage library; the data diagnosis and analysis module performs modeling analysis on the fault mechanism of each type of historical robot, and performs mining analysis on the historical fault information to obtain the type of the historical fault and corresponding data; the data repository and the data diagnostic analysis interact data with a server.
Preferably, the remote monitoring and diagnosing module performs fault diagnosis and analysis on the robot based on identification of key operation data, alarm data and auxiliary diagnosis data of the on-site operation robot.
The multi-station robot comprises the multi-station robot control system.
The control method of the multi-station robot comprises the following steps:
step one, setting a first active intelligent robot and initial operation information;
step two, the first active intelligent robot transmits initial operation information to a cloud computing server through a communication control network;
step three, the cloud computing server plans the slave operation information of the first slave intelligent robot according to the initial operation information of the first master intelligent robot, transmits the slave operation information to the first slave intelligent robot through a communication control network, and controls the first slave intelligent robot to act;
step four, the cloud computing server acquires real-time operation information of the first driving intelligent robot and actual operation information of the first driven intelligent robot, calculates, corrects the driven operation information of the first driven intelligent robot, transmits the corrected driven operation information to the first driven intelligent robot through a communication control network, and adjusts the action of the first driven intelligent robot; the cloud computing server judges that the real-time operation information of the first active intelligent robot exceeds a threshold value preset by standard operation information, and resets the initial operation information;
and fifthly, repeating the fourth step until the first active intelligent robot receives an interrupt or termination instruction. Compared with the prior art, the invention has the beneficial effects that:
1. the multi-station robot control system, the control method and the multi-station robot are high in computing capacity. The cloud computing server is adopted for centralized processing control and feedback of data information, so that the computing capability is high, and the requirements on the data processing capability of the robot are reduced and the computing speed is high, so that the manufacturing and running cost of the robot is reduced under the condition that complex actions of robots at various stations are involved and the working procedures and parameters are numerous.
2. According to the multi-station robot control system, the control method and the multi-station robot, dynamic data of the intelligent robot in the operation process are monitored in real time, pressure values of the intelligent robot in the operation process are processed through pre-learning, advancing states of the intelligent robot are researched and judged, difference processing is synchronously carried out on speeds of front feet and rear feet of the intelligent robot in the operation process, fault points of the intelligent robot are obtained, and through processing of differences in adjacent walking periods of the intelligent robot, dumping time of the intelligent robot is calculated, so that maintenance personnel can conduct fault elimination on the intelligent robot before the intelligent robot is dumped.
3. According to the multi-station robot control system, the control method and the multi-station robot, abnormal data occurring in the operation process of the intelligent robot are uploaded to the remote monitoring diagnosis module through the data acquisition controllers arranged at the key positions, so that the abnormal data are diagnosed and analyzed to finally realize the fault early warning of the robot, and the fault reasons are obtained through the analysis of the abnormal data, so that the operation, maintenance and maintenance of the robot are facilitated.
Drawings
FIG. 1 is an overall system block diagram of the present invention;
FIG. 2 is a diagram of the intelligent robot assembly of the present invention;
FIG. 3 is a diagram of the communication control network according to the present invention;
FIG. 4 is a system block diagram of a fault diagnosis system of the present invention;
FIG. 5 is a system block diagram of a monitoring and early warning system according to the present invention;
FIG. 6 is a system block diagram of a remote monitoring diagnostic module according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. 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.
As shown in fig. 1, the multi-station robot control system includes: a multi-station robot control system comprising: the central processing unit further comprises:
the intelligent robot group, the communication control network and the cloud computing server side; the intelligent robot group is connected with the cloud computing server through a communication control network;
the main controller is used for driving the movement mechanism and the station proximity sensor of the robot for station switching;
the robot comprises a robot control device for calculating the offset of the workpiece and adjusting the operation parameters of the workpiece of the robot and a vision device for acquiring the image of the workpiece; the main controller is controlled to be connected with the motion mechanism, input samples of the main controller are connected with the station proximity sensor, and the main controller is connected with the robot control device; the robot control device inputs samples and is connected with the vision device;
the fault diagnosis system is used for analyzing timely and accurate faults of the intelligent robot;
and the monitoring and early warning system is used for monitoring key operation data of the robot.
The main controller is a PLC controller.
As shown in fig. 2, the intelligent robot group is a multi-station intelligent robot set, and at least comprises a first driving intelligent robot and a first driven intelligent robot; the first active intelligent robot transmits initial operation information and real-time operation information to a cloud computing server; the first slave intelligent robot transmits actual operation information to the cloud computing server; the cloud computing server plans and corrects the slave operation information of the first slave intelligent robot according to the initial operation information and the real-time operation information of the first master intelligent robot, transmits the slave operation information to the first slave intelligent robot through a communication control network, and controls and adjusts the action of the first slave intelligent robot; in order to facilitate the setting of initial parameters, a monitoring end is provided as a main control platform to define and send out instructions for system behaviors; the monitoring end is connected with the cloud computing server and used for setting, starting, interrupting or terminating initial operation information of the first active intelligent robot.
As shown in fig. 3, the communication control network includes a wired and/or wireless network.
As shown in fig. 4, the diagnostic system includes: the dynamic detection unit is used for calculating the operation data of the intelligent robot to obtain a fault point and a fault parameter of the intelligent robot, and obtaining the dumping time of the intelligent robot based on the fault parameter, wherein the dumping time is the fault clearing time in the operation process of the intelligent robot;
the fault parameters are sent to a diagnosis processor, and the diagnosis processor performs diagnosis analysis;
and sending the fault clearing time to the terminal equipment, so that a technician can complete fault diagnosis within the fault clearing time.
As shown in fig. 4, the dynamic detection unit is disposed on the motion mechanism, the dynamic detection unit includes pressure sensors, height sensors, a first acceleration sensor and a second acceleration sensor, the pressure sensors are four, the four pressure sensors are disposed on four robot feet bottom surfaces of the intelligent robot respectively, and a first speed sensor is disposed on a left front foot of the intelligent robot, a second speed sensor is disposed on a left rear foot of the intelligent robot, the height sensors are two, one of the height sensors is disposed between two front robot feet of the intelligent robot, and the other of the height sensors is disposed between two rear robot feet of the intelligent robot.
The dynamic monitoring unit specifically detects the intelligent robot operation data as follows:
step one: the pressure sensor is used for monitoring the pressure information of the intelligent robot in real time to obtain pressure information values obtained by the four machines of the intelligent robot in the same walking period, deleting the maximum value and/or the minimum value in the group of data, calculating to obtain the average pressure value of the rest data in the group of data, and marking the average pressure value as Fi, i=1.
Step two: comparing the average pressure value in the running process of the intelligent robot in the first step with a preset pressure value uploaded by the terminal equipment, and calculating the difference value to obtain a pressure difference Pi, and judging that the intelligent robot normally moves in the same period when Pi < Ki, ki is a preset pressure threshold value;
if Pi is more than or equal to Ki, judging the running state of the intelligent robot;
step three: when judging the running state of the intelligent robot, acquiring a first speed of the left front foot of the intelligent robot in real time through a first speed sensor, marking the first speed as Vi1, acquiring a second speed of the left rear foot of the intelligent robot in real time through a second speed sensor, marking the second speed as Vi2, and comparing the difference between the first speed Vi1 and the second speed Vi 2;
if Vi1-Vi2 is not equal to 0, judging that the intelligent robot has a traveling fault and has a tilting risk;
step four: when judging that the intelligent robot fails, in the same walking period, acquiring a front end height value in real time through a height sensor between two front machine feet of the intelligent robot and marking the front end height value as Hi1, acquiring a rear end height value in real time through a height sensor between two rear machine feet of the intelligent robot and marking the rear end height value as Hi2, and comparing the difference between the front end height value Hi1 and the rear end height value Hi 2;
if Hi1-Hi2 are more than 0, the rear robot foot of the intelligent robot fails;
if Hi1-Hi2 are less than 0, the front robot foot of the intelligent robot fails;
step five: calculating the difference value between the front end height value H11 and the rear end height value H12 in the same walking period of the intelligent robot, namely Z1= -H11-H12|;
taking the difference between the front end height value H21 and the rear end height value H22 in the adjacent walking period, namely Z2= |H221-H2|;
comparing the difference value of Z1 and Z2, and calculating according to a formula Ch= -Z1-Z2|toobtain a height difference Ch in the walking process of two adjacent intelligent robots;
step six: setting the difference value between the critical height of the intelligent robot toppling in the walking process and the height of the intelligent robot in the normal state as D, and according to the formulaCalculating and obtaining the dumping time of the intelligent robot
As shown in fig. 5, the monitoring and early warning system includes: the system comprises a user local platform, a data acquisition controller and a remote monitoring diagnosis module, wherein: the remote monitoring diagnosis module specifically comprises a remote communication module, a data acquisition module, a data storage library and a data diagnosis analysis module, wherein the remote communication module transmits data of a user local platform to the data storage library, and the data acquisition module acquires data of a data acquisition controller and transmits the acquired data of the data acquisition controller to the data storage library; the data diagnosis analysis module is used for carrying out modeling analysis on the fault mechanism of each type of historical robot and carrying out mining analysis on the historical fault information to obtain the type of the historical fault and corresponding data; the data store and data diagnostic analysis interact data with the server.
In the embodiment of the application, a specific communication flow of the data acquisition controller is as follows:
(1) Firstly, a data acquisition controller initiates a TCP connection request to a remote monitoring diagnosis service platform, and after connection is established, the service platform sends a random sequence to the data acquisition controller.
(2) The data acquisition controller combines the received random sequence and a locally stored authentication key (configured by a local monitoring platform and stored in a communication point table) into a connection string, calculates an MD5 value of the connection string and sends the MD5 value to the data center.
(3) And the service platform compares the received MD5 value with a local calculation result, if the received MD5 value is consistent with the local calculation result, the verification is successful, the next operation can be performed, and if the received MD5 value is not consistent with the local calculation result, the verification is considered to be failed, and the connection is disconnected. The authentication key is configured by the local monitoring platform and stored in the communication point table. The service platform can update the configuration of the local platform through the network, so that the authentication key of the data acquisition controller is updated.
(4) After the authentication is successful, the service platform sends heartbeat packet data to the data acquisition controller at regular time, the validity of connection is maintained, and the data acquisition controller responds. And if the data acquisition controller is not responded after the timeout, the data acquisition controller is taken as a disconnection state, and the service platform is disconnected.
(5) After the authentication is successful, the service platform can perform data access on the data acquisition controller in real time, including reading the state parameters of the corresponding robots and remotely regulating the corresponding robots.
(6) The data acquisition controller needs to actively send information such as robot state parameters to the service platform at regular time. If network failure occurs, the heartbeat packet is disconnected, and the data acquisition controller needs to store data to be uploaded and wait for the network to recover to be normal and then upload again. Before the authentication is successful, the data communication adopts plaintext communication. After verification is successful, AES encryption communication is adopted for data communication, and the encryption and decryption key is an authentication key locally stored in the data acquisition controller. The communication data packet is transmitted in text form by using XML format.
Further, in a preferred embodiment of the present application, the remote monitoring diagnostic module performs fault diagnosis and analysis on the robot based on identification of critical operational data, alarm data and auxiliary diagnostic data of the robot operating in the field.
Further, in a preferred embodiment of the present application, the intelligent robot running state monitoring and early warning system further includes a wireless auxiliary sensor, a video module and a switch, and the data acquisition module is connected with the wireless auxiliary sensor, the data acquisition controller, the video module and the switch and acquires robot data through the wireless auxiliary sensor, the data acquisition controller and the video module.
Further, in a preferred embodiment of the present application, the wireless auxiliary sensor and the data acquisition controller are installed at each key part of the robot, and are used for acquiring operation data of the robot, adjusting and measuring the robot, and transmitting local and remote data through the switch.
Further, in a preferred embodiment of the present application, the remote monitoring and diagnosing module performs modeling analysis on the failure mechanism of each type of robot in the history, and performs mining analysis on the history failure information to obtain the history failure type and the corresponding data.
Further, in a preferred embodiment of the present application, the data acquisition controller is connected to the robot, the user local platform and the remote monitoring and diagnosis module, and the data acquisition controller includes a rights management unit, and the rights management unit limits data access guests of the data acquisition controller.
As shown in fig. 6, the remote monitoring diagnosis module performs fault diagnosis and analysis on the robot based on identification of key operation data, alarm data, and auxiliary diagnosis data of the on-site operation robot.
The multi-station robot comprises the multi-station robot control system.
The control method of the multi-station robot comprises the following steps:
step one, setting a first active intelligent robot and initial operation information;
step two, the first active intelligent robot transmits initial operation information to a cloud computing server through a communication control network;
step three, the cloud computing server plans the slave operation information of the first slave intelligent robot according to the initial operation information of the first master intelligent robot, transmits the slave operation information to the first slave intelligent robot through a communication control network, and controls the first slave intelligent robot to act;
step four, the cloud computing server acquires real-time operation information of the first driving intelligent robot and actual operation information of the first driven intelligent robot, calculates, corrects the driven operation information of the first driven intelligent robot, transmits the corrected driven operation information to the first driven intelligent robot through a communication control network, and adjusts the action of the first driven intelligent robot; the cloud computing server judges that the real-time operation information of the first active intelligent robot exceeds a threshold value preset by standard operation information, and resets the initial operation information;
and fifthly, repeating the fourth step until the first active intelligent robot receives an interrupt or termination instruction.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the above-described embodiments, and that the above-described embodiments and descriptions are only preferred embodiments of the present invention, and are not intended to limit the invention, and that various changes and modifications may be made therein without departing from the spirit and scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (10)
1. A multi-station robot control system comprising: the central processing unit, its characterized in that still includes:
the intelligent robot group, the communication control network and the cloud computing server side; the intelligent robot group is connected with the cloud computing server through the communication control network;
the main controller is used for driving the movement mechanism and the station proximity sensor of the robot for station switching;
the robot comprises a robot control device for calculating the offset of a workpiece and adjusting the operation parameters of the workpiece of the robot and a vision device for acquiring images of the workpiece; the main controller is in control connection with the movement mechanism, input samples of the main controller are connected with the station proximity sensor, and the main controller is connected with the robot control device; the robot control device inputs and samples and is connected with the vision device;
the fault diagnosis system is used for analyzing timely and accurate faults of the intelligent robot;
and the monitoring and early warning system is used for monitoring key operation data of the robot.
2. The multi-station robotic control system of claim 1, wherein: the main controller is a PLC controller.
3. The multi-station robotic control system of claim 2, wherein: the intelligent robot group is a multi-station intelligent robot set and at least comprises a first driving intelligent robot and a first driven intelligent robot; the first active intelligent robot transmits initial operation information and real-time operation information to a cloud computing server; the first slave intelligent robot transmits actual operation information to a cloud computing server; the cloud computing server plans and corrects the slave operation information of the first slave intelligent robot according to the initial operation information and the real-time operation information of the first master intelligent robot, transmits the slave operation information to the first slave intelligent robot through a communication control network, and controls and adjusts the action of the first slave intelligent robot; in order to facilitate the setting of initial parameters, a monitoring end is provided as a main control platform to define and send out instructions for system behaviors; the monitoring end is connected with the cloud computing server and used for setting, starting, interrupting or stopping initial operation information of the first active intelligent robot.
4. A multi-station robotic control system as claimed in claim 3, wherein: the communication control network comprises a wired and/or wireless network.
5. The multi-station robotic control system of claim 4, wherein: the diagnostic system includes: the dynamic detection unit is used for calculating the operation data of the intelligent robot to obtain a fault point and a fault parameter of the intelligent robot, and obtaining the dumping time of the intelligent robot based on the fault parameter, wherein the dumping time is the fault clearing time in the operation process of the intelligent robot;
the fault parameters are sent to a diagnosis processor, and the diagnosis processor performs diagnosis analysis;
and sending the fault clearing time to the terminal equipment, so that a technician can complete fault diagnosis within the fault clearing time.
6. The multi-station robotic control system of claim 5, wherein: the dynamic detection unit is arranged on the motion mechanism, the dynamic detection unit comprises four pressure sensors, two height sensors, a first acceleration sensor and a second acceleration sensor, the four pressure sensors are respectively arranged on the bottom surfaces of four robot feet of the intelligent robot, a first speed sensor is arranged on the left front foot of the intelligent robot, a second speed sensor is arranged on the left rear foot of the intelligent robot, the two height sensors are arranged, one height sensor is arranged between the two front robot feet of the intelligent robot, and the other height sensor is arranged between the two rear robot feet of the intelligent robot.
7. The multi-station robotic control system of claim 6, wherein: the monitoring and early warning system comprises: the system comprises a user local platform, a data acquisition controller and a remote monitoring diagnosis module, wherein: the remote monitoring diagnosis module specifically comprises a remote communication module, a data acquisition module, a data storage library and a data diagnosis analysis module, wherein the remote communication module transmits the data of the user local platform to the data storage library, and the data acquisition module acquires the data of the data acquisition controller and transmits the acquired data of the data acquisition controller to the data storage library; the data diagnosis and analysis module performs modeling analysis on the fault mechanism of each type of historical robot, and performs mining analysis on the historical fault information to obtain the type of the historical fault and corresponding data; the data repository and the data diagnostic analysis interact data with a server.
8. The multi-station robotic control system of claim 7, wherein: the remote monitoring and diagnosing module performs fault diagnosis and analysis on the robot based on identification of key operation data, alarm data and auxiliary diagnosis data of the on-site operation robot.
9. A multi-station robot comprising a multi-station robot control system according to any of the preceding claims 1-8.
10. The control method of a multi-station robot according to claim 9, comprising the steps of:
step one, setting a first active intelligent robot and initial operation information;
step two, the first active intelligent robot transmits initial operation information to a cloud computing server through a communication control network;
step three, the cloud computing server plans the slave operation information of the first slave intelligent robot according to the initial operation information of the first master intelligent robot, transmits the slave operation information to the first slave intelligent robot through a communication control network, and controls the first slave intelligent robot to act;
step four, the cloud computing server acquires real-time operation information of the first driving intelligent robot and actual operation information of the first driven intelligent robot, calculates, corrects the driven operation information of the first driven intelligent robot, transmits the corrected driven operation information to the first driven intelligent robot through a communication control network, and adjusts the action of the first driven intelligent robot; the cloud computing server judges that the real-time operation information of the first active intelligent robot exceeds a threshold value preset by standard operation information, and resets the initial operation information;
and fifthly, repeating the fourth step until the first active intelligent robot receives an interrupt or termination instruction.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311609830.XA CN117400260A (en) | 2023-11-29 | 2023-11-29 | Multi-station robot control system, control method and multi-station robot |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311609830.XA CN117400260A (en) | 2023-11-29 | 2023-11-29 | Multi-station robot control system, control method and multi-station robot |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117400260A true CN117400260A (en) | 2024-01-16 |
Family
ID=89498124
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311609830.XA Pending CN117400260A (en) | 2023-11-29 | 2023-11-29 | Multi-station robot control system, control method and multi-station robot |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117400260A (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE202006008591U1 (en) * | 2006-05-29 | 2006-11-02 | 4 Jet Sales + Service Gmbh | Automatic process for laser printing of information onto the surface of road vehicle tyres uses a handling robot and multi station table |
| WO2013075297A1 (en) * | 2011-11-23 | 2013-05-30 | 湖南深拓智能设备股份有限公司 | Remote real-time monitoring system based on cloud computing |
| CN105119985A (en) * | 2015-08-10 | 2015-12-02 | 唐思钊 | Multi-station intelligent robot and control method thereof |
| CN107685329A (en) * | 2017-10-16 | 2018-02-13 | 河南森源电气股份有限公司 | A kind of robot workpiece positioning control system and method |
| CN112775975A (en) * | 2021-02-01 | 2021-05-11 | 重庆皮克索自动化科技有限公司 | Vision-guided multi-station robot welding deviation correcting device and method |
| CN114851195A (en) * | 2022-04-24 | 2022-08-05 | 重庆创御智能装备有限公司 | Control method of visual welding process system |
| CN116319253A (en) * | 2022-09-09 | 2023-06-23 | 北京西普阳光科技股份有限公司 | Industrial robot fault analysis system based on internet |
| CN116483054A (en) * | 2023-04-19 | 2023-07-25 | 广州市阳普机电工程有限公司 | Industrial robot running state monitoring and early warning system and method |
-
2023
- 2023-11-29 CN CN202311609830.XA patent/CN117400260A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE202006008591U1 (en) * | 2006-05-29 | 2006-11-02 | 4 Jet Sales + Service Gmbh | Automatic process for laser printing of information onto the surface of road vehicle tyres uses a handling robot and multi station table |
| WO2013075297A1 (en) * | 2011-11-23 | 2013-05-30 | 湖南深拓智能设备股份有限公司 | Remote real-time monitoring system based on cloud computing |
| CN105119985A (en) * | 2015-08-10 | 2015-12-02 | 唐思钊 | Multi-station intelligent robot and control method thereof |
| CN107685329A (en) * | 2017-10-16 | 2018-02-13 | 河南森源电气股份有限公司 | A kind of robot workpiece positioning control system and method |
| CN112775975A (en) * | 2021-02-01 | 2021-05-11 | 重庆皮克索自动化科技有限公司 | Vision-guided multi-station robot welding deviation correcting device and method |
| CN114851195A (en) * | 2022-04-24 | 2022-08-05 | 重庆创御智能装备有限公司 | Control method of visual welding process system |
| CN116319253A (en) * | 2022-09-09 | 2023-06-23 | 北京西普阳光科技股份有限公司 | Industrial robot fault analysis system based on internet |
| CN116483054A (en) * | 2023-04-19 | 2023-07-25 | 广州市阳普机电工程有限公司 | Industrial robot running state monitoring and early warning system and method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110394688B (en) | Machine tool state monitoring method based on edge calculation | |
| KR102025100B1 (en) | System and method for monitoring robot motion | |
| CN109144014A (en) | The detection system and method for industrial equipment operation conditions | |
| CN116841262A (en) | Intelligent factory production on-line monitoring analysis system based on machine vision | |
| EP3076256A1 (en) | Systems and methods for assessing a facility based on audio/visual delta analysis | |
| EP2942680B1 (en) | Process control system and process control method | |
| US20200262074A1 (en) | Remote robot monitoring system and method | |
| US11782431B2 (en) | Control device and non-transitory computer-readable recording medium recording program | |
| CN112276976B (en) | Cloud control platform-based functional robot control system | |
| CN107172128B (en) | Cloud-assisted manufacturing equipment big data acquisition method | |
| CN110221581B (en) | Industrial control network monitoring device and method | |
| KR101814108B1 (en) | Remote server | |
| CN103412492B (en) | Multimachine electric system distributed intelligence sync control device and method | |
| CN111948994A (en) | Industrial production line closed-loop automatic quality control method based on data integration and correlation analysis | |
| CN109919505A (en) | A kind of mode of operation judgment method, device, system, equipment and storage medium | |
| CN111970155B (en) | Intelligent early warning system for fault state of crane equipment | |
| CN111283731A (en) | Industrial robot operation fault determination method and system | |
| CN116872206A (en) | Robot fault detection method and system based on industrial Internet | |
| KR102418965B1 (en) | Apparatus for Interfacing, Semiconductor Manufacturing System and Management Method | |
| CN116902536A (en) | Intelligent deviation rectifying system of belt conveyor | |
| CN117400260A (en) | Multi-station robot control system, control method and multi-station robot | |
| CN113572260A (en) | Distributed energy station intelligent operation and maintenance system based on digital twin technology | |
| CN117318249A (en) | Battery charging cloud monitoring method and system | |
| CN111431954B (en) | Remote fault solving system and method for production equipment | |
| CN117348528A (en) | Numerical control machine tool and self-adaptive control system based on deep learning |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |