CN116256991A - Remote monitoring method of collaborative robot based on data twinning - Google Patents
Remote monitoring method of collaborative robot based on data twinning Download PDFInfo
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- CN116256991A CN116256991A CN202210599383.3A CN202210599383A CN116256991A CN 116256991 A CN116256991 A CN 116256991A CN 202210599383 A CN202210599383 A CN 202210599383A CN 116256991 A CN116256991 A CN 116256991A
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Abstract
The invention discloses a cooperative robot remote monitoring method based on data twinning, and relates to the technical field of remote monitoring. The invention comprises the following steps: step1: creating a digital twin model; step2: creating a virtual motion platform; step3: data acquisition and transmission; step4: establishing a reference point; step5: establishing a motion virtual model; step6: and (5) functional evaluation. The invention establishes a twin data model in real time communication with the cooperative robot in the information platform, is used for monitoring the motion state of the cooperative robot in real time, simulates a motion virtual model in the future time of the cooperative robot in advance through calculation, compares the twin data model with the motion virtual model in the same time, achieves the purposes of avoiding abnormality in advance and improving monitoring accuracy, and judges whether the cooperative robot perfectly executes the current action or not and verifies the accuracy of real-time synchronization of the digital twin model by establishing a plurality of reference points.
Description
Technical Field
The invention relates to the technical field of remote monitoring, in particular to a data twinning-based collaborative robot remote monitoring method.
Background
A collaborative robot is a robot that can interact closely with humans in a common workspace. In short, a cooperative robot is a robot capable of safely directly interacting/contacting with a human being in consideration of reducing risk of injury from the beginning of design. When the traditional industrial robot works, the traditional industrial robot is limited by technical or historical reasons, and certain measures are required to be taken to ensure safety so as to exclude human beings from a working area, for example, procedures such as automobile welding, paint spraying and the like do not need human participation at all, so that the traditional industrial robot is enclosed by a safety fence. However, this has drawbacks in that many tasks requiring human intervention cannot be automated to a high degree by robots. Collaborative robots are designed to combine the repetitive performance of robots (precision) with the unique skills and capabilities of humans, who are good at solving the problem of inaccuracy/ambiguity, while robots are superior in precision, strength and durability.
Digital twins, also known as "digital twins," are virtual worlds that map the structure, state, behavior, functionality, and performance of complex physical systems, such as industrial products, manufacturing systems, cities, etc., to digitization. Physical systems are characterized, predicted and controlled through real-time sensing, connection mapping, accurate analysis and immersion interaction, virtual-real fusion of a complex system is realized, and the full elements, the full process and the full value chain of the system are optimized in a closed loop to the greatest extent. The digital twin body is used as a data storage platform, various original data are collected and then are subjected to fusion processing, and dynamic operation of each part of the simulation model is driven, so that each business flow is effectively reflected.
The prior patent (application number 202111415248.0) discloses a digital twinning-based collaborative robot remote monitoring system and a digital twinning-based collaborative robot remote monitoring method, wherein the monitoring system is based on a digital twinning five-dimensional model concept, and the digital twinning remote monitoring system is constructed by taking the collaborative robot as an example. Compared with the traditional remote monitoring system, the invention has the following characteristics: real-time property, data are derived from the sensor, and the state of the monitored object can be reflected more accurately; the persistence, the data can be finally persisted into a database, so that the data can be conveniently analyzed and processed; the virtual model is simplified with low time delay, so that the real-time rendering resource and time consumption of the system are reduced, and the real-time performance is enhanced; the invention adopts a fuzzy comprehensive evaluation method to evaluate the health degree of the robot in real time, does not need a large amount of historical data training models, and has quick evaluation response. The invention can be applied to remote monitoring of man-machine co-fusion scene of complex equipment, can grasp the equipment state in real time, and can early warn in time, thereby reducing the occurrence of safety accidents.
The above patent uses digital twinning to realize remote monitoring of the cooperative robot, but the following drawbacks still exist: firstly, lacking in advance, because the digital twin model of the remote robot and the entity cooperative robot adopt synchronous communication, if the entity cooperative robot does not complete the current task due to factors such as damage, accidents and the like, the digital twin model synchronous with the entity cooperative robot cannot make advance judgment, and related problems cannot be avoided or loss is reduced; secondly, reference contrast is lacking, a plurality of reference points are not arranged for judging whether the cooperative robot perfectly executes the current action or not and verifying the accuracy of real-time synchronization of the digital twin model, therefore, the remote monitoring method of the cooperative robot based on data twin is provided, a twin data model is built in an information platform for real-time monitoring of the motion state of the cooperative robot, a motion virtual model of the cooperative robot in future time is simulated in advance through calculation, and then the twin data model and the motion virtual model in the same time are compared, so that the purposes of avoiding abnormality in advance and improving monitoring accuracy are achieved.
Disclosure of Invention
The invention aims to provide a remote monitoring method of a collaborative robot based on data twinning, which aims to solve the problems in the background.
In order to solve the technical problems, the invention is realized by the following technical scheme:
the invention discloses a data twinning-based remote monitoring method for a cooperative robot, which comprises the following steps:
step1: creating a digital twin model, and building the collaborative robot 1 on an information platform according to the collaborative robot: 1, simplifying the digital twin model by using a simplified edge folding algorithm of a secondary error matrix on the premise of retaining physical characteristics of the cooperative robot, reducing rendering consumption and reducing system time delay;
step2: creating a virtual motion platform, and building a real scene 1 according to a working assembly line of the cooperative robot by utilizing a virtual reality technology: 1, fusing a digital twin model with the virtual motion platform, and simulating the motion state of the cooperative robot;
step3: the method comprises the steps of data acquisition and transmission, wherein a sensor is arranged on the cooperative robot or a sensor is additionally arranged at a later stage, so that dynamic data such as real-time rotation angle, voltage, current, temperature, speed and the like in the movement process of the cooperative robot are acquired, the acquired related data are transmitted to an information platform for analysis, processing and storage, and then the related data are fused with a digital twin model to monitor the related data of the cooperative robot in real time;
step4: establishing reference points, namely establishing a plurality of key reference points in a motion path of the cooperative robot by utilizing a position sensor, and sending a normal operation instruction to an information platform by the position sensor after the cooperative robot moves to the reference points after a specified time; otherwise, the position sensor sends an abnormal operation instruction to the information platform;
step5: establishing a motion virtual model, simulating the motion virtual model in the future time (such as after 10s and after 20 s) of the twin data model in advance by the information platform through calculation, comparing the twin data model with the motion virtual model in the same time, and sending a simulation normal instruction to the information platform if the motion virtual model and the twin data model are completely overlapped in the same time; otherwise, sending an abnormality simulation instruction to the information platform;
step6: the information platform carries out health state assessment on the cooperative robot according to the normal/abnormal running instructions and the normal/abnormal simulating instructions, carries out visual presentation on various index data of the cooperative robot, and further carries out visual presentation on data according to charts such as different selection line diagrams, cake-shaped diagrams, instrument panels and the like of the characteristics of the various index data;
through the steps, a twin data model communicated with the cooperative robot in real time is established in the information platform, so that the motion state of the cooperative robot is monitored in real time, then a motion virtual model in future time of the cooperative robot is simulated in advance through calculation, and then the twin data model and the motion virtual model in the same time are compared, so that the purposes of avoiding abnormality in advance and improving monitoring accuracy are achieved.
Preferably, the collaborative robot is a six degree of freedom collaborative robot.
Preferably, the virtual motion platform is simplified by using a simplified edge folding algorithm of a secondary error matrix, so that the consumption of computing power resources of the information platform in the real-time rendering process is reduced, the display efficiency is accelerated, and the monitoring efficiency is improved.
Preferably, the information platform utilizes a multi-stage fuzzy comprehensive evaluation method to evaluate the overall health state of the cooperative robot, and the evaluation result is classified into three grades of fault, moderate and good.
The invention has the following beneficial effects:
the invention establishes a twin data model in real time communication with the cooperative robot in the information platform, is used for monitoring the motion state of the cooperative robot in real time, simulates a motion virtual model in the future time of the cooperative robot in advance through calculation, and compares the twin data model with the motion virtual model in the same time so as to achieve the purposes of avoiding abnormality in advance and improving monitoring accuracy.
The invention is used for judging whether the cooperative robot perfectly executes the current action or not by establishing a plurality of reference points and is used for verifying the accuracy of real-time synchronization of the digital twin model.
Of course, it is not necessary for any one product to practice the invention to achieve all of the advantages set forth above at the same time.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an operation method of a remote monitoring method of a collaborative robot based on data twinning of 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.
Example 1
Please refer to fig. 1: the invention discloses a data twinning-based remote monitoring method for a cooperative robot, which comprises the following steps:
step1: creating a digital twin model, and building the collaborative robot 1 on an information platform according to the collaborative robot: 1, simplifying the digital twin model by using a simplified edge folding algorithm of a secondary error matrix on the premise of retaining physical characteristics of the cooperative robot, reducing rendering consumption and reducing system time delay;
step2: creating a virtual motion platform, and building a real scene 1 according to a working assembly line of the cooperative robot by utilizing a virtual reality technology: 1, fusing a digital twin model with the virtual motion platform, and simulating the motion state of the cooperative robot;
step3: the method comprises the steps of data acquisition and transmission, wherein a sensor is arranged on the cooperative robot or a sensor is additionally arranged at a later stage, so that dynamic data such as real-time rotation angle, voltage, current, temperature, speed and the like in the movement process of the cooperative robot are acquired, the acquired related data are transmitted to an information platform for analysis, processing and storage, and then the related data are fused with a digital twin model to monitor the related data of the cooperative robot in real time;
step4: establishing reference points, namely establishing a plurality of key reference points in a motion path of the cooperative robot by utilizing a position sensor, and sending a normal operation instruction to an information platform by the position sensor after the cooperative robot moves to the reference points after a specified time; otherwise, the position sensor sends an abnormal operation instruction to the information platform;
step5: establishing a motion virtual model, simulating the motion virtual model in the future time (such as after 10s and after 20 s) of the twin data model in advance by the information platform through calculation, comparing the twin data model with the motion virtual model in the same time, and sending a simulation normal instruction to the information platform if the motion virtual model and the twin data model are completely overlapped in the same time; otherwise, sending an abnormality simulation instruction to the information platform;
step6: the information platform carries out health state assessment on the cooperative robot according to the normal/abnormal running instructions and the normal/abnormal simulating instructions, carries out visual presentation on various index data of the cooperative robot, and further carries out visual presentation on data according to charts such as different selection line diagrams, cake-shaped diagrams, instrument panels and the like of the characteristics of the various index data;
through the steps, a twin data model communicated with the cooperative robot in real time is established in the information platform, so that the motion state of the cooperative robot is monitored in real time, then a motion virtual model in future time of the cooperative robot is simulated in advance through calculation, and then the twin data model and the motion virtual model in the same time are compared, so that the purposes of avoiding abnormality in advance and improving monitoring accuracy are achieved.
The collaborative robot is a six degree of freedom collaborative robot.
The virtual motion platform is simplified by using a simplified edge folding algorithm of a secondary error matrix, so that the consumption of computing power resources of the information platform in the real-time rendering process is reduced, the display efficiency is accelerated, and the monitoring efficiency is improved.
The information platform utilizes a multi-stage fuzzy comprehensive evaluation method to evaluate the overall health state of the cooperative robot, and the evaluation result is classified into three grades of fault, moderate and good.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (4)
1. The remote monitoring method of the collaborative robot based on data twinning is characterized by comprising the following steps of: the method comprises the following steps:
step1: creating a digital twin model, and building the collaborative robot 1 on an information platform according to the collaborative robot: 1, simplifying the digital twin model by using a simplified edge folding algorithm of a secondary error matrix on the premise of retaining physical characteristics of the cooperative robot, reducing rendering consumption and reducing system time delay;
step2: creating a virtual motion platform, and building a real scene 1 according to a working assembly line of the cooperative robot by utilizing a virtual reality technology: 1, fusing a digital twin model with the virtual motion platform, and simulating the motion state of the cooperative robot;
step3: the method comprises the steps of data acquisition and transmission, wherein a sensor is arranged on the cooperative robot or a sensor is additionally arranged at a later stage, so that dynamic data such as real-time rotation angle, voltage, current, temperature, speed and the like in the movement process of the cooperative robot are acquired, the acquired related data are transmitted to an information platform for analysis, processing and storage, and then the related data are fused with a digital twin model to monitor the related data of the cooperative robot in real time;
step4: establishing reference points, namely establishing a plurality of key reference points in a motion path of the cooperative robot by utilizing a position sensor, and sending a normal operation instruction to an information platform by the position sensor after the cooperative robot moves to the reference points after a specified time; otherwise, the position sensor sends an abnormal operation instruction to the information platform;
step5: establishing a motion virtual model, simulating the motion virtual model in the future time (such as after 10s and after 20 s) of the twin data model in advance by the information platform through calculation, comparing the twin data model with the motion virtual model in the same time, and sending a simulation normal instruction to the information platform if the motion virtual model and the twin data model are completely overlapped in the same time; otherwise, sending an abnormality simulation instruction to the information platform;
step6: the information platform carries out health state assessment on the cooperative robot according to the normal/abnormal running instructions and the normal/abnormal simulating instructions, carries out visual presentation on various index data of the cooperative robot, and further carries out visual presentation on data according to charts such as different selection line diagrams, cake-shaped diagrams and instrument panels of the characteristics of the various index data.
Through the steps, a twin data model communicated with the cooperative robot in real time is established in the information platform, so that the motion state of the cooperative robot is monitored in real time, then a motion virtual model in future time of the cooperative robot is simulated in advance through calculation, and then the twin data model and the motion virtual model in the same time are compared, so that the purposes of avoiding abnormality in advance and improving monitoring accuracy are achieved.
2. The data twinning-based collaborative robot remote monitoring method of claim 1, wherein the collaborative robot is a six degree of freedom collaborative robot.
3. The data twinning-based collaborative robot remote monitoring method according to claim 1, wherein the virtual motion platform is simplified by a simplified edge folding algorithm of a secondary error matrix, so that consumption of computing power resources of an information platform in a real-time rendering process is reduced, display efficiency is improved, and monitoring efficiency is improved.
4. The remote monitoring method of the collaborative robot based on the data twinning of claim 1, wherein the information platform utilizes a multi-stage fuzzy comprehensive evaluation method to evaluate the overall health state of the collaborative robot, and the evaluation result is classified into three grades of fault, moderate and good.
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| CN117454488A (en) * | 2023-11-08 | 2024-01-26 | 河北建工集团有限责任公司 | Multi-device integration method and system based on digital twin sensor |
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| CN117454488A (en) * | 2023-11-08 | 2024-01-26 | 河北建工集团有限责任公司 | Multi-device integration method and system based on digital twin sensor |
| CN117454488B (en) * | 2023-11-08 | 2024-03-26 | 河北建工集团有限责任公司 | Multi-device integration method and system based on digital twin sensor |
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