CN116619394B - Industrial robot simulation method, device, equipment and storage medium - Google Patents
Industrial robot simulation method, device, equipment and storage medium Download PDFInfo
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- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
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- B25J9/1602—Programme controls characterised by the control system, structure, architecture
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- B—PERFORMING OPERATIONS; TRANSPORTING
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Abstract
The application relates to the technical field of digital twin simulation, and discloses an industrial robot simulation method, an industrial robot simulation device, an industrial robot simulation equipment and a storage medium. The method comprises the following steps: acquiring the latest sampling data of the industrial robot in the running state; when the latest sampling data is received, generating a first instruction by taking the gesture corresponding to the latest sampling data as a simulation target; generating latest predicted data when the last simulation action of the virtual robot is finished and the latest sampling data is not obtained yet, and generating a second instruction by taking the gesture corresponding to the latest predicted data as a simulation target; calculating the latest simulation speed; and controlling the tail end joint of the virtual robot to complete the simulation action corresponding to the first instruction or the second instruction at the latest simulation speed, and outputting simulation data. The embodiment of the application can overcome the defect of blocking or frame jumping during digital twin simulation motion.
Description
Technical Field
The application relates to the technical field of digital twin simulation, in particular to an industrial robot simulation method, an industrial robot simulation device, an industrial robot simulation equipment and a storage medium.
Background
Along with the development of technology, a digital twin technology is introduced in the process of controlling the industrial robot, and through real-time simulation of the state of the industrial robot, a worker can conveniently monitor the running state of the industrial robot remotely and discover the abnormal state of the industrial robot in time, so that the industrial robot is controlled correspondingly.
In the prior art, the attitude data of an industrial robot is generally obtained through a sensor arranged on the industrial robot, and then the attitude of a digital twin model corresponding to the industrial robot is changed according to the obtained attitude data, so that the digital twin simulation of the industrial robot is realized. However, the analog mode has higher requirement on sampling equipment and needs a large amount of sampling data, otherwise, the problem that the industrial robot is blocked or jumped in movement due to insufficient sampling data easily affects the industrial production effect.
Disclosure of Invention
The application aims to provide an industrial robot simulation method, an industrial robot simulation device, an industrial robot simulation equipment and a storage medium, and aims to solve the technical problems that a large amount of sampling data is required during digital twin simulation of an industrial robot, and clamping or frame skipping occurs during simulation motion of a virtual robot due to insufficient or missing sampling data.
The embodiment of the application provides an industrial robot simulation method, which comprises the following steps:
acquiring the latest sampling data of the industrial robot in the running state, wherein the sampling data comprises angle values of all joints of the industrial robot at the sampling moment;
when the latest sampling data is received, generating a first instruction by taking the gesture corresponding to the latest sampling data as a simulation target;
generating latest prediction data when the last simulation action of the virtual robot is finished and the latest sampling data is not obtained, and generating a second instruction by taking the gesture corresponding to the latest prediction data as a simulation target, wherein the virtual robot is a digital twin body of the industrial robot, and the prediction data comprises angle values for predicting all joints of the industrial robot at the sampling moment;
calculating the latest simulation speed, wherein the latest simulation speed is larger than the simulation speed of the last simulation action when generating the first instruction, and is smaller than the simulation speed of the last simulation action when generating the second instruction;
and controlling the tail end joint of the virtual robot to complete the simulation action corresponding to the first instruction or the second instruction at the latest simulation speed, outputting simulation data, and returning to the step of acquiring the latest sampling data of the industrial robot in the running state.
Further, the generating a first instruction with the pose corresponding to the latest sampling data as the simulation target includes:
calculating the position of the tail end joint of the industrial robot by using the latest sampling data to obtain a first calculation result;
determining a displacement path of the tail end joint of the virtual robot according to the first calculation result and the current position of the tail end joint of the virtual robot, and obtaining a first simulation path;
a first instruction is generated to drive a displacement of an end joint of the virtual robot along a first simulation path.
Further, the generating the latest prediction data includes:
generating the latest sampling time of the predicted data according to the sampling time analog calculation of the historical sampling data to obtain the predicted sampling time;
according to the predicted sampling time and the simulation speed of the last simulation action, simulating and calculating the displacement length of the tail end joint of the industrial robot to obtain a predicted displacement length;
and simulating and calculating the position of the industrial robot at the latest sampling moment according to the predicted displacement length and the sampling data of the last simulation movement or the predicted data to obtain the latest predicted data.
Further, the generating the second instruction with the gesture corresponding to the latest prediction data as the simulation target includes:
Calculating the position of the tail end joint of the industrial robot by using the generated prediction data to obtain a second calculation result;
determining a displacement path of the tail end joint of the virtual robot according to the second calculation result and the position of the tail end joint of the virtual robot to obtain a second simulation path;
a second instruction is generated that drives the end joint of the virtual robot to displace along a second simulation path.
Further, the calculating the latest simulation speed includes:
when a first instruction is generated, calculating the displacement speed of the tail end joint of the industrial robot according to the latest sampling data and sampling time to obtain the latest real speed, taking the product of the latest regulating coefficient and the latest real speed as the latest simulation speed, wherein the latest regulating coefficient is increased in proportion to the regulating coefficient of the last simulation action;
when the second instruction is generated, calculating the displacement speed of the tail end joint of the industrial robot according to the latest prediction data and the sampling time to obtain the latest prediction speed, taking the product of the latest adjustment coefficient and the latest prediction speed as the latest simulation speed, wherein the latest adjustment coefficient is reduced in proportion to the adjustment coefficient of the last simulation action.
Further, the controlling the terminal joint of the virtual robot to complete the simulation action corresponding to the first instruction or the second instruction at the latest simulation speed, and outputting simulation data includes:
creating a driving script of each joint of the virtual robot according to the first instruction or the second instruction, and configuring the driving script of each joint of the virtual robot;
and carrying out real-time simulation on each joint of the virtual robot based on the driving script, so that the tail end joint of the virtual robot is displaced at the latest simulation speed, and simulation data are output.
Further, the industrial robot simulation method further comprises the following steps:
and when the latest sampling data is received, the virtual robot does not complete the last simulation movement, and the last simulation movement is terminated.
The embodiment of the application also provides an industrial robot simulation device, which comprises:
the first module is used for acquiring the latest sampling data of the industrial robot in the running state, wherein the sampling data comprises angle values of all joints of the industrial robot at the sampling moment;
the second module is used for generating a first instruction by taking the gesture corresponding to the latest sampling data as a simulation target when the latest sampling data is received;
The third module is used for generating latest prediction data when the last simulation action of the virtual robot is finished and the latest sampling data is not obtained, and generating a second instruction by taking the gesture corresponding to the latest prediction data as a simulation target, wherein the virtual robot is a digital twin body of the industrial robot, and the prediction data comprises angle values for predicting all joints of the industrial robot at the sampling moment;
a fourth module, configured to calculate a latest simulation speed, where the latest simulation speed is greater than a simulation speed of a last simulation action when generating the first instruction, and the latest simulation speed is less than the simulation speed of the last simulation action when generating the second instruction;
and a fifth module, configured to control the end joint of the virtual robot to complete the simulation action corresponding to the first instruction or the second instruction at the latest simulation speed, and output simulation data.
The embodiment of the application also provides electronic equipment, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the industrial robot simulation method when executing the computer program.
Embodiments of the present application also provide a computer-readable storage medium storing a computer program which, when executed by a processor, implements an industrial robot simulation method as described above.
The application has the beneficial effects that: when the latest sampling data is obtained, digital twin simulation is carried out based on the sampling data, prediction data is generated to carry out digital twin simulation when the latest sampling data is not received when the last simulation action is completed, the simulation action is completed at the simulation speed which is greater than the simulation speed of the last simulation action when the digital twin simulation is carried out by the sampling data, the simulation action is completed at the simulation speed which is less than the simulation speed of the last simulation action when the digital twin simulation is carried out by the prediction data, the dependence on the sampling data is reduced, the defect that the virtual robot is blocked or jumped when the simulation action is caused by the insufficient or missing sampling data is overcome, and the simulation precision is improved.
Drawings
Fig. 1 is a flowchart of an industrial robot simulation method provided in an embodiment of the present application.
Fig. 2 is a flowchart of step S102 in fig. 1.
Fig. 3 is a flowchart of step S103 in fig. 1.
Fig. 4 is a flowchart of step S104 in fig. 1.
Fig. 5 is a flowchart of step S106 in fig. 1.
Fig. 6 is a schematic structural diagram of an industrial robot simulation device according to an embodiment of the present application.
Fig. 7 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present application.
Fig. 8 is a schematic diagram of a path for completing a simulation action by an end joint of a virtual robot according to an embodiment of the present application.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments can be embodied in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the disclosed aspects may be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the disclosure.
The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.
It will be understood that, although the terms first, second, third, etc. may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one element from another element. Accordingly, a first component discussed below could be termed a second component without departing from the teachings of the concepts of the present disclosure. As used herein, the term "and/or" includes any one of the associated listed items and all combinations of one or more.
References herein to "a plurality" means two or more. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship.
Those skilled in the art will appreciate that the drawings are schematic representations of example embodiments and that the modules or flows in the drawings are not necessarily required to practice the present disclosure, and therefore, should not be taken to limit the scope of the present disclosure.
Digital twinning (digital twinning) is a simulation process integrating multidisciplinary, multiscale and multiscale by fully utilizing data such as physical models, sensor updating and operation histories, and mainly constructs a uniform entity in the digital world by digitally simulating events (objects) in the physical world, thereby realizing the processes of understanding, analyzing and optimizing the physical entity. In the design stage of the product, digital twin is utilized to improve the accuracy of the design and verify the performance of the product in a real environment.
In the related art, a digital twin system for monitoring the production line process mainly drives a virtual model to move through data collected by sampling equipment in the monitoring condition of the production line-oriented production process. However, the real events in the real world have uncertainty, and network fluctuation and network delay often exist, so that the motion data acquisition of the industrial robot is discontinuous, and the phenomena of clamping, frame skipping and the like in the motion process of the virtual robot in the three-dimensional digital twin scene are caused, so that very bad monitoring experience is brought to people. The reason is that if the virtual scene is required to be in a real state, the requirement on sampling equipment is high, a large amount of sampling data is required, otherwise, the virtual robot is easy to be blocked or jumped when moving due to insufficient sampling data, and the industrial production effect is influenced.
Based on the above, the embodiments of the present application provide an industrial robot simulation method, apparatus, device, and storage medium, which perform digital twin simulation based on actual sampling data or generating prediction data when sampling data is not received, so that motion postures of a virtual robot and an industrial robot are dynamically adjusted, and the technical problem that when digital twin simulation is performed on the industrial robot, a large amount of sampling data is required, and a problem that a jam or frame skip occurs when the virtual robot simulates motion due to insufficient or missing sampling data is easily caused is solved.
Fig. 1 is a flowchart of an industrial robot simulation method according to an embodiment of the present application, and the method in fig. 1 may include, but is not limited to, steps S101 to S106.
Step S101, the latest sampling data of the industrial robot in the running state are obtained.
The sampled data contains the angle values of each joint of the industrial robot at the sampling time. Illustratively, taking a six-axis industrial robot as an example, the sampled data sampled at a sampling time t (n) is p (n), p (n) = [ J (n)1 ,J (n)2 ,J (n)3 ,J (n)4 ,J (n)5 ,J (n)6 ]，J (n)1 To J (n)6 The angle values of the joints of the industrial robot at the sampling time t (n) are respectively shown.
In specific implementation, the industrial robot, the sampling equipment and the digital twin simulation platform perform data transmission based on TCP, the sampling equipment samples angles of all joints of the industrial robot during operation to obtain sampling data, and the sampling equipment transmits the sampling data obtained by real-time sampling to the digital twin simulation platform so as to obtain the latest sampling data of the industrial robot. The sampling device collects sampling data at sampling time intervals, when receiving the latest sampling data, the step S102 is executed, and when the last simulation action of the virtual robot is finished and the latest sampling data is not obtained yet, the step S103 is executed.
It should be noted that an industrial robot is a multi-joint manipulator or a multi-degree-of-freedom robot widely used in the industrial field, and is a multi-axis industrial robot that performs functions such as material handling (e.g., moving or stacking product items), palletizing of products, processing of stock materials (e.g., using a processing tool articulated by a robot arm), product scanning, or other such functions in an operating state.
Before acquiring the sampling data, firstly, a virtual robot is constructed on a digital twin simulation platform, the virtual robot is a digital twin body of an industrial robot, structural data (comprising component names, component sizes, component materials, a connecting mode between two connected components and component movement amplitude) of the industrial robot can be acquired based on TCP (Transmission Control Protocol ), and the obtained virtual robot can be subjected to light weight treatment so as to improve the operation speed of the virtual robot. The virtual robot can accelerate data transmission and driving based on a coroutine mechanism and a JSON format. The obtained simulation data can be subjected to simulation analysis of kinematics and dynamics through automatic analysis of mechanical system dynamics so as to complete simulation of structural data and obtain the virtual robot. The state and data of the virtual robot can be reflected on the three-dimensional animation engine, and the virtual robot can be observed more intuitively.
In some embodiments, the virtual robot does not complete the last simulation movement when the latest sampling data is received, and terminates the last simulation movement.
Step S102, a first instruction is generated by taking the gesture corresponding to the latest sampling data as a simulation target.
It can be understood that the sampled data includes angle values of each joint of the industrial robot, more specifically, rotation angle information of each joint of the industrial robot, and when the latest sampled data is received, an optimal estimation result of each joint angle value at the sampling moment can be obtained by performing data fusion on the latest sampled data, and then a control instruction for controlling each joint of the virtual robot can be generated by resolving the posture of each joint of the industrial robot, so as to obtain a first instruction, and step S105 is executed.
It will be appreciated that the first instruction may be generated by fusing the latest sample data by a virtual controller, which is built based on simulation software, and may be written in a robotic operating platform, such as the ROS2 platform, based on the python language. In addition, the virtual controller can be connected with the digital twin simulation platform through a real-time communication interface, so that the latest sampling data can be obtained.
Step S103, generating the latest prediction data.
The prediction data includes angle values for predicting each joint of the industrial robot at the sampling time. Illustratively, taking a six-axis industrial robot as an example, the sampled data sampled at the predicted sampling time t '(n) is p' (n), p '(n) = [ J') (n)1 ,J´ (n)2 ,J´ (n)3 ,J´ (n)4 ,J´ (n)5 ,J´ (n)6 ]，J´ (n)1 To J (n)6 The angle values predicted for the joints of the industrial robot at the predicted sampling time t (n) are respectively obtained.
In a specific implementation, for various reasons, when the digital twin simulation platform still does not acquire the latest sampling data sent by the sampling device after the virtual robot completes the last simulation action, the digital twin simulation platform predicts the angle of each joint in the latest sampling data according to the angle value and the sampling time interval of each joint corresponding to the last simulation action, so as to obtain the latest prediction data, and step S104 is executed.
More specifically, if the last simulation action is completed according to the sampling data, the digital twin simulation platform generates the latest prediction data according to the last sampling data, and if the last simulation action is completed according to the prediction data, the digital twin simulation platform generates the latest prediction data according to the last prediction data, and the logic for generating the prediction data is to enable the tail end joint of the virtual robot to move along the same direction as the last simulation action.
Step S104, generating a second instruction by taking the gesture corresponding to the latest prediction data as a simulation target.
In a specific implementation, when the latest prediction data is received, the latest prediction data is subjected to data fusion, so that an optimal estimation result of the angle value of each joint at the predicted sampling time can be obtained, then the gesture of each joint of the industrial robot is resolved, a control instruction for controlling each joint of the virtual robot can be generated, a second instruction is obtained, and step S105 is executed.
It can be appreciated that the control instructions may be generated by fusing the latest predicted data through a virtual controller, which is built based on simulation software, and may be written in a robot operating platform, such as the ROS2 platform, based on the python language. In addition, the virtual controller can be connected with the digital twin simulation platform through a real-time communication interface, so that the latest prediction data can be obtained.
Step S105, the latest simulation speed is calculated.
It can be understood that the simulation speed is a displacement speed of the virtual robot for completing the simulation action corresponding to the first instruction or the second instruction, and when the digital twin simulation platform updates the three-dimensional animation of the virtual robot, the virtual robot performs the posture change at the calculated simulation speed.
In specific implementation, the simulation speed is preset as a speed initial value at the beginning of simulation, when the latest control instruction is the first instruction, the virtual robot completes the current simulation action based on the latest sampling data, takes the speed which is greater than the simulation speed of the last simulation action as the latest simulation speed, because the simulation process has hysteresis, the virtual robot needs to catch up with the gesture change speed of the industrial robot at the faster displacement speed, when the latest control instruction is the second instruction, the virtual robot completes the current simulation action based on the latest prediction data, takes the speed which is less than the simulation speed of the last simulation action as the latest simulation speed, because the prediction data has errors, the virtual robot completes the simulation action with errors at the slower displacement speed, and completes the high-precision simulation action at the faster displacement speed when the latest sampling data is acquired, thereby reducing the simulation errors on the basis of maintaining the smooth simulation image.
And S106, controlling the tail end joint of the virtual robot to complete the simulation action corresponding to the first instruction or the second instruction at the latest simulation speed, and outputting simulation data. Returning to step S101.
In specific implementation, the digital twin simulation platform controls the posture of each joint of the virtual robot to be changed to the posture corresponding to the angle value of each joint in the latest sampling data or the latest prediction data according to the generated first instruction or the second instruction, and through the posture change coordination of each joint, the terminal joint of the virtual robot completes the simulation action corresponding to the first instruction or the second instruction at the latest simulation speed, the terminal executing mechanism positioned at the terminal joint is shifted to the target point, and the simulation data is output.
As shown in fig. 8, exemplary, the sampling device sends sampled data obtained by sampling in real time, where the sampled data p (n) is obtained at time t (n), the corresponding end joint target point is b (n), the sampled data p (n+1) is obtained at time t (n+1), the corresponding end joint target point is b (n+1), and the sampled data p (n+2) is obtained at time t (n+2), and the corresponding end joint target point is b (n+2).
As shown in fig. 8 (2), when the sampling data p (n+1) is acquired at time n+1, a first command is generated from the sampling data p (n+1), and the virtual robot executes the first command to complete the corresponding simulation operation, and the distal joint is displaced from the joint target point b (n) to the joint target point b (n+1).
As shown in fig. 8 (1), when the sampling data p (n+2) is acquired at time n+2 or in a time interval of (n+1, n+2), a first instruction is generated according to the sampling data p (n+2), and the virtual robot executes the first instruction to complete a corresponding simulation operation, and the end joint of the virtual robot is displaced from the joint position between the current positions (b (n) and b (n+1) or the joint target point b (n+1)) to the joint target point b (n+2).
As shown in fig. 8 (3), when the sampling data p (n+2) is not acquired at time n+2 or within the time interval of (n+1, n+2), the predicted data p 'n+2 is generated, the corresponding end joint target point is b' n+2, the second instruction is generated from the predicted data p 'n+2, the virtual robot executes the second instruction, and the corresponding simulation operation is completed, and the end joint is shifted from b (n+1) to b' n+2. In this process, if the sampling data p (n+3) is received, the corresponding end joint target point is b (n+3), the virtual robot interrupts the current simulation action, and generates a first instruction according to the sampling data p (n+3), and the end joint of the first instruction is displaced from the current position (b (n+1) and the joint position between b '(n+2)) or the joint target point b' (n+2)) to the joint target point b (n+3).
In the steps S101 to S106 shown in the embodiment of the present application, digital twin simulation is performed based on the sampled data when the latest sampled data is obtained, prediction data is generated to perform digital twin simulation when the latest sampled data is not received when the last simulation operation is completed, the simulation operation is completed at a simulation speed which is greater than the simulation speed of the last simulation operation when the sampled data is subjected to digital twin simulation, the simulation operation is completed at a simulation speed which is less than the simulation speed of the last simulation operation when the sampled data is subjected to digital twin simulation, the dependence on the sampled data is reduced, the defect that the virtual robot is jammed or jumped when the simulation operation occurs due to the shortage or the lack of the sampled data is overcome, and the simulation precision is improved.
Referring to fig. 2, in some embodiments, step S102 may include, but is not limited to, steps S201 to S203.
Step S201, calculating the position of the tail end joint of the industrial robot by using the latest sampling data to obtain a first calculation result.
Step S202, determining a displacement path of the tail end joint of the virtual robot according to the first calculation result and the current position of the tail end joint of the virtual robot, and obtaining a first simulation path.
In step S203, a first instruction for driving the end joint of the virtual robot to displace along the first simulation path is generated.
In step S201, the angle value of each joint in the latest sampling data is extracted, the posture of each joint of the industrial robot is calculated from the angle value of each joint of the industrial robot, and the position of the end joint of the industrial robot is determined as the first calculation result.
In step S202, a displacement path, in which the virtual robot is to complete the current simulation operation, is generated as a first simulation path, starting from the current position of the end joint of the virtual robot and ending from the position of the end joint of the industrial robot in the first calculation result.
In step S203, a first instruction is generated with the objective of driving the end joints of the virtual robot to displace along the first simulation path, where the first instruction is an instruction set, and the first instruction includes a plurality of instructions for performing attitude control on the joints of the virtual robot, and when the virtual robot executes the first instruction, the end joints of the virtual robot displace along the first simulation path.
As shown in fig. 8, when sampling data p (n+1) is obtained at time n+1, the position of the end joint of the industrial robot is determined by calculating according to the angle value of each joint of the sampling data p (n+1), a first calculation result b (n+1) is obtained, the current position of the end joint of the virtual robot is b (n), a first simulation path from b (n) to b (n+1) is generated, meanwhile, a first instruction is generated by the array twin simulation platform, the virtual robot completes the simulation action corresponding to the first instruction generated currently, and the end joint of the virtual robot is displaced from b (n) to b (n+1).
Referring to fig. 3, in some embodiments, step S103 may include, but is not limited to, steps S301 to S303.
Step S301, generating the latest sampling time of the predicted data according to the sampling time analog calculation of the historical sampling data, and obtaining the predicted sampling time.
Step S302, the displacement length of the tail end joint of the industrial robot is calculated in a simulation mode according to the predicted sampling time and the simulation speed of the last simulation action, and the predicted displacement length is obtained.
Step S303, the position of the industrial robot at the latest sampling moment is calculated in a simulation mode according to the predicted displacement length and the sampling data or the predicted data of the last simulation movement, and the latest predicted data is obtained.
In step S301, the difference in sampling time between the last two times of sampling data is used to calculate the difference in sampling time between the last two times of sampling data, the calculated difference in sampling time is used as the difference in sampling time between the last time of sampling data and the predicted data, and the calculated difference in sampling time is added to the last time of sampling data to obtain the predicted sampling time.
In step S302, a sampling time difference between the last sampled data and the predicted data is calculated from the predicted sampling time, and a product of a simulation speed of the last simulation action and the sampling time difference between the last sampled data and the predicted data is used as a predicted displacement length, and the last simulation action includes a simulation action generated based on the sampled data or the predicted data.
In step S303, the calculated predicted displacement length is used to perform angle value conversion on the last sampled data or predicted data, so that the angle value of each joint in the last sampled data or predicted data is converted into the angle value after the end joint is displaced to the end point of the predicted displacement length, and the latest predicted data is obtained, wherein the last simulation motion is performed based on the sampled data, the last sampled data is subjected to angle value conversion, and the last simulation motion is performed based on the predicted data, and the last predicted data is subjected to angle value conversion.
Referring to fig. 4, in some embodiments, step S104 may include, but is not limited to, including steps S401 to S403.
Step S401, calculating the position of the end joint of the industrial robot by using the generated prediction data, and obtaining a second calculation result.
Step S402, determining a displacement path of the tail end joint of the virtual robot according to the second calculation result and the position of the tail end joint of the virtual robot, and obtaining a second simulation path.
Step S403, generating a second instruction for driving the end joint of the virtual robot to displace along the second simulation path.
In step S401, the angle value of each joint in the newly generated prediction data is extracted, the posture of each joint of the industrial robot is calculated from the angle value of each joint of the industrial robot, and the position of the end joint of the industrial robot is determined as the second calculation result.
In step S402, a displacement path, in which the virtual robot is to complete the current simulation operation, is generated as a second simulation path, starting from the current position of the end joint of the virtual robot and ending from the position of the end joint of the industrial robot in the second calculation result.
In step S403, a second instruction is generated with the objective of driving the end joints of the virtual robot to displace along the second simulation path, where the second instruction is an instruction set, and the second instruction includes a plurality of instructions for performing attitude control on the joints of the virtual robot.
As shown in fig. 8, when the sampling data p (n+2) is not acquired at the time of n+2, the position of the end joint of the industrial robot is calculated according to the angle value of each joint of the generated prediction data p '(n+2), a second calculation result b' (n+2) is obtained, the current position of the end joint of the virtual robot is b (n+1), a second simulation path from b (n+1) to b (n+2) is generated, the array twin simulation platform generates a second instruction, the virtual robot completes the simulation motion corresponding to the second instruction currently generated, the end joint of the virtual robot moves from b (n+1) to b (n+2), in this process, if the sampling data p (n+3) is acquired, the execution of the second instruction is terminated, the position of the end joint of the industrial robot is calculated according to the angle value of each joint of the sampling data p (n+3), the first calculation result b (n+3) is obtained, the first simulation path from the current position of the end joint of the virtual robot to b (n+3) is generated, and the virtual robot is simultaneously generated, the virtual robot generates a first simulation path from the current position of the end joint of the virtual robot to the first twin robot (n+3), and the virtual robot generates the virtual robot has the virtual motion corresponding to the first simulation path of the end joint of the virtual robot.
In some embodiments, when generating the first instruction, calculating the most recent simulation speed includes: and calculating the displacement speed of the tail end joint of the industrial robot according to the latest sampling data and the sampling time to obtain the latest real speed, taking the product of the latest regulating coefficient and the latest real speed as the latest simulation speed, and increasing the latest regulating coefficient in proportion to the regulating coefficient of the last simulation action.
Specifically, when the digital twin simulation platform acquires sampling data, the sampling moments of the sampling data are received together, based on the sampling moment of the latest sampling data and the sampling moment of sampling data or prediction data of the last simulation action, the time length between the two sampling moments can be calculated, the latest sampling data and the sampling data or prediction data of the last simulation action can be calculated, the positions of the tail end joints of the industrial robot at the two sampling moments can be obtained, the movement paths of the tail end joints of the industrial robot are calculated, and the latest real speed is obtained by using the movement paths of the tail end joints of the industrial robot and the time length calculation speed between the two sampling moments. Because the simulation motion has hysteresis, the simulation speed of the virtual robot is larger than the running speed of the industrial robot, the latest adjustment coefficient is multiplied by the latest real speed, the latest simulation speed is calculated, the adjustment coefficient is adjusted in the process of each simulation action, and if the current simulation motion is executed based on the first instruction, the latest adjustment coefficient is larger than the adjustment coefficient of the last simulation action, more particularly, the adjustment coefficient is increased in proportion.
Illustratively, the (n+1) th sampling instant is taken as the latest sampling instant.
The latest calculation formula of the real speed is as follows:
v (n+1) = (b (n+1) -b (n))/(t (n+1) -t (n)), or
v(n+1)=(b(n+1)-b´(n))/(t(n+1)-t(n))；
The latest calculation formula of the adjustment coefficient is as follows:
m=m´+k·m´；
the latest calculation formula of the simulation speed is as follows:
V(n+1)=m·v(n+1)；
wherein V (n+1) is the true speed corresponding to the n+1th sampling moment, b (n+1) is the true position of the end joint of the industrial robot at the n+1th sampling moment, b (n) is the position of the end joint of the industrial robot at the n-th sampling moment, b '(n) is the predicted position of the end joint of the industrial robot at the n-th sampling moment, t (n+1) is the n+1th sampling moment, t (n) is the n-th sampling moment, m is the latest adjustment coefficient, m' is the adjustment coefficient of the last simulation action, k is the proportionality coefficient, and V (n+1) is the simulation speed generated according to the first instruction at the n+1th sampling moment.
In some embodiments, when generating the second instruction, calculating the most recent simulation speed includes: and calculating the displacement speed of the tail end joint of the industrial robot according to the latest prediction data and the sampling time to obtain the latest prediction speed, taking the product of the latest adjustment coefficient and the latest prediction speed as the latest simulation speed, wherein the latest adjustment coefficient is reduced in proportion to the adjustment coefficient of the last simulation action.
Specifically, when the digital twin simulation platform generates the prediction data, the sampling time of the prediction data is generated together, based on the sampling time of the latest prediction data and the sampling time of the sampling data or the prediction data of the last simulation action, the time length between the two sampling times can be calculated, the latest prediction data and the sampling data or the prediction data of the last simulation action can be calculated, the positions of the tail end joints of the industrial robot at the two sampling times can be obtained, the movement path of the tail end joints of the industrial robot is calculated, and the latest prediction speed is obtained by using the movement path of the tail end joints of the industrial robot and the time length calculation speed between the two sampling times. Because the prediction data has errors, the simulation speed of the virtual robot is smaller than the prediction speed of the industrial robot, the latest adjustment coefficient is multiplied by the latest real speed, the latest simulation speed is calculated, the adjustment coefficient is adjusted in the process of each simulation action, and if the current simulation action is executed based on the second instruction, the latest adjustment coefficient is smaller than the adjustment coefficient of the last simulation action, more particularly, the adjustment coefficient is increased in proportion.
Illustratively, the (n+1) th sampling instant is taken as the latest sampling instant.
The latest calculation formula of the prediction speed is as follows:
v '(n+1) = (b' (n+1) -b (n))/(t (n+1) -t (n)), or
v´(n+1)=(b´(n+1)-b´(n))/(t(n+1)-t(n))；
The latest calculation formula of the adjustment coefficient is as follows:
m=m´-k·m´；
the latest calculation formula of the simulation speed is as follows:
V´(n+1)=m·v´(n+1)；
wherein V ' (n+1) is a predicted speed corresponding to the (n+1) th sampling time, b ' (n+1) is a predicted position of the end joint of the industrial robot at the (n+1) th sampling time, and V ' (n+1) is a simulation speed generated according to the second instruction at the (n+1) th sampling time.
Referring to fig. 5, in some embodiments, step S106 may include, but is not limited to, including steps S501 to S502.
Step S501, a driving script of each joint of the virtual robot is created according to the first instruction or the second instruction, and the driving script of each joint of the virtual robot is configured.
Step S502, performing real-time simulation on each joint of the virtual robot based on the driving script, enabling the tail end joint of the virtual robot to displace at the latest simulation speed, and outputting simulation data.
The driving script of each joint of the virtual robot is written based on the first instruction or the second instruction in the digital twin simulation platform, and the configuration of the driving script of each joint of the virtual robot by the virtual controller is completed in the digital twin simulation platform, so that the physical world and the information world can be interacted conveniently, and the visualization of the digital twin simulation production process is realized.
Specifically, the virtual controller can drive each joint of the virtual robot in the digital twin simulation platform to simulate in real time by transmitting each joint control instruction in real time, and output simulation data. The simulation data comprise angle data of each joint and position coordinates of a tail end joint of the virtual robot.
Referring to fig. 6, an embodiment of the present application further provides an industrial robot simulation apparatus, which may implement the above industrial robot simulation method, where the apparatus includes:
a first module 601, configured to obtain latest sampling data of the industrial robot in an operation state;
a second module 602, configured to generate, when receiving the latest sampling data, a first instruction with a posture corresponding to the latest sampling data as a simulation target;
a third module 603, configured to generate latest prediction data when the last simulation action of the virtual robot is completed and the latest sampling data is not yet acquired, and generate a second instruction by taking a gesture corresponding to the latest prediction data as a simulation target;
a fourth module 604, configured to calculate a latest simulation speed;
and a fifth module 605, configured to control the end joint of the virtual robot to complete the simulation action corresponding to the first instruction or the second instruction at the latest simulation speed, and output simulation data.
The specific implementation of the industrial robot simulation device is basically the same as the specific embodiment of the industrial robot simulation method, and will not be described herein.
Fig. 7 is a block diagram of an electronic device, according to an example embodiment.
An electronic device 700 according to such an embodiment of the present disclosure is described below with reference to fig. 7. The electronic device 700 shown in fig. 7 is merely an example and should not be construed to limit the functionality and scope of use of embodiments of the present disclosure in any way.
As shown in fig. 7, the electronic device 700 is embodied in the form of a general purpose computing device. Components of electronic device 700 may include, but are not limited to: at least one processing unit 710, at least one memory unit 720, a bus 730 connecting the different system components (including the memory unit 720 and the processing unit 710), a display unit 740, and the like.
Wherein the storage unit stores program code that is executable by the processing unit 710 such that the processing unit 710 performs steps according to various exemplary embodiments of the present disclosure described in the above-mentioned industrial robot simulation method section of the present specification. For example, the processing unit 710 may perform the steps as shown in fig. 1, 2, 3, and 4.
The memory unit 720 may include readable media in the form of volatile memory units, such as Random Access Memory (RAM) 7201 and/or cache memory 7202, and may further include Read Only Memory (ROM) 7203.
The storage unit 720 may also include a program/utility 7204 having a set (at least one) of program modules 7205, such program modules 7205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
Bus 730 may be a bus representing one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.
The electronic device 700 may also communicate with one or more external devices 700' (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 700, and/or any device (e.g., router, modem, etc.) that enables the electronic device 700 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 750. Also, electronic device 700 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet, through network adapter 760. Network adapter 760 may communicate with other modules of electronic device 700 via bus 730. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 700, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program which realizes the industrial robot simulation method when being executed by a processor.
According to the industrial robot simulation method, device, equipment and storage medium provided by the embodiment of the application, when the latest sampling data is obtained, digital twin simulation is performed based on the sampling data, when the latest sampling data is not received when the last simulation action is completed, predictive data is generated to perform digital twin simulation, the simulation action is completed at the simulation speed which is greater than the simulation speed of the last simulation action when the sampling data is subjected to digital twin simulation, the simulation action is completed at the simulation speed which is less than the simulation speed of the last simulation action when the predictive data is subjected to digital twin simulation, the dependence on the sampling data is reduced, the defect that the virtual robot is jammed or jumped when the simulation action is caused by the shortage or the lack of the sampling data is overcome, and the simulation precision is improved.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (may be a personal computer, a server, or a network device, etc.) to perform the above-described method according to the embodiments of the present disclosure.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable storage medium may also be any readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
Those skilled in the art will appreciate that the modules may be distributed throughout several devices as described in the embodiments, and that corresponding variations may be implemented in one or more devices that are unique to the embodiments. The modules of the above embodiments may be combined into one module, or may be further split into a plurality of sub-modules.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or in combination with the necessary hardware. Thus, the technical solutions according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, and include several instructions to cause a computing device (may be a personal computer, a server, a mobile terminal, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
Exemplary embodiments of the present disclosure are specifically illustrated and described above. It is to be understood that this disclosure is not limited to the particular arrangements, instrumentalities and methods of implementation described herein; on the contrary, the disclosure is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (10)
1. An industrial robot simulation method, comprising:
acquiring the latest sampling data of the industrial robot in the running state, wherein the sampling data comprises angle values of all joints of the industrial robot at the sampling moment;
When the latest sampling data is received, generating a first instruction by taking the gesture corresponding to the latest sampling data as a simulation target;
generating latest prediction data when the last simulation action of the virtual robot is finished and the latest sampling data is not obtained, and generating a second instruction by taking the gesture corresponding to the latest prediction data as a simulation target, wherein the virtual robot is a digital twin body of the industrial robot, and the prediction data comprises angle values for predicting all joints of the industrial robot at the sampling moment;
calculating the latest simulation speed, wherein the latest simulation speed is larger than the simulation speed of the last simulation action when generating the first instruction, and is smaller than the simulation speed of the last simulation action when generating the second instruction;
and controlling the tail end joint of the virtual robot to complete the simulation action corresponding to the first instruction or the second instruction at the latest simulation speed, outputting simulation data, and returning to the step of acquiring the latest sampling data of the industrial robot in the running state.
2. The method for simulating an industrial robot according to claim 1, wherein generating the first command for simulating the target with the pose corresponding to the latest sampling data comprises:
Calculating the position of the tail end joint of the industrial robot by using the latest sampling data to obtain a first calculation result;
determining a displacement path of the tail end joint of the virtual robot according to the first calculation result and the current position of the tail end joint of the virtual robot, and obtaining a first simulation path;
a first instruction is generated to drive a displacement of an end joint of the virtual robot along a first simulation path.
3. The industrial robot simulation method of claim 1, wherein generating the latest prediction data comprises:
generating the latest sampling time of the predicted data according to the sampling time analog calculation of the historical sampling data to obtain the predicted sampling time;
according to the predicted sampling time and the simulation speed of the last simulation action, simulating and calculating the displacement length of the tail end joint of the industrial robot to obtain a predicted displacement length;
and simulating and calculating the position of the industrial robot at the latest sampling moment according to the predicted displacement length and the sampling data of the last simulation action or the predicted data to obtain the latest predicted data.
4. The method for simulating an industrial robot according to claim 1, wherein generating the second instruction for simulating the object with the pose corresponding to the latest predicted data comprises:
Calculating the position of the tail end joint of the industrial robot by using the generated prediction data to obtain a second calculation result;
determining a displacement path of the tail end joint of the virtual robot according to the second calculation result and the position of the tail end joint of the virtual robot to obtain a second simulation path;
a second instruction is generated that drives the end joint of the virtual robot to displace along a second simulation path.
5. The industrial robot simulation method according to claim 1, wherein the calculating of the latest simulation speed includes:
when a first instruction is generated, calculating the displacement speed of the tail end joint of the industrial robot according to the latest sampling data and sampling time to obtain the latest real speed, taking the product of the latest regulating coefficient and the latest real speed as the latest simulation speed, wherein the latest regulating coefficient is increased in proportion to the regulating coefficient of the last simulation action;
when the second instruction is generated, calculating the displacement speed of the tail end joint of the industrial robot according to the latest prediction data and the sampling time to obtain the latest prediction speed, taking the product of the latest adjustment coefficient and the latest prediction speed as the latest simulation speed, wherein the latest adjustment coefficient is reduced in proportion to the adjustment coefficient of the last simulation action.
6. The simulation method of an industrial robot according to claim 1, wherein controlling the end joint of the virtual robot to complete the simulation action corresponding to the first instruction or the second instruction at the latest simulation speed, and outputting the simulation data, comprises:
creating a driving script of each joint of the virtual robot according to the first instruction or the second instruction, and configuring the driving script of each joint of the virtual robot;
and carrying out real-time simulation on each joint of the virtual robot based on the driving script, so that the tail end joint of the virtual robot is displaced at the latest simulation speed, and simulation data are output.
7. The industrial robot simulation method according to claim 1, further comprising:
and when the latest sampling data is received, the virtual robot does not complete the last simulation action, and the last simulation action is terminated.
8. An industrial robot simulation apparatus, comprising:
the first module is used for acquiring the latest sampling data of the industrial robot in the running state, wherein the sampling data comprises angle values of all joints of the industrial robot at the sampling moment;
the second module is used for generating a first instruction by taking the gesture corresponding to the latest sampling data as a simulation target when the latest sampling data is received;
The third module is used for generating latest prediction data when the last simulation action of the virtual robot is finished and the latest sampling data is not obtained, and generating a second instruction by taking the gesture corresponding to the latest prediction data as a simulation target, wherein the virtual robot is a digital twin body of the industrial robot, and the prediction data comprises angle values for predicting all joints of the industrial robot at the sampling moment;
a fourth module, configured to calculate a latest simulation speed, where the latest simulation speed is greater than a simulation speed of a last simulation action when generating the first instruction, and the latest simulation speed is less than the simulation speed of the last simulation action when generating the second instruction;
and a fifth module, configured to control the end joint of the virtual robot to complete the simulation action corresponding to the first instruction or the second instruction at the latest simulation speed, and output simulation data.
9. An electronic device, characterized in that it comprises a memory storing a computer program and a processor implementing the industrial robot simulation method according to any one of claims 1 to 7 when executing the computer program.
10. A computer readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the industrial robot simulation method of any one of claims 1 to 7.
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