Method, device, equipment and storage medium for adjusting coupling gain of parallel robots based on visual recognition

Abstract

The present application provides a method, device, equipment and storage medium for adjusting the coupling gain of a parallel robot based on visual recognition. It relates to the fields of machine vision, robotics and automation technology. The method comprises: obtaining the position of an object and the position of a workspace, and calculating the distance from the object to the center of the workspace according to the position of the object and the position of the workspace; calculating the inertia coupling index of each drive axis according to the load inertia of the drive axis itself and the coupling inertia between axes; setting the speed threshold value and acceleration threshold value of each joint of the parallel robot, and when the speed of the joint exceeds the speed threshold value or the acceleration exceeds the acceleration threshold value, determining the correction value according to the distance from the object to the center of the workspace and the inertia coupling index of the drive axis; and determining the fuzzy adjustment amount of the controller parameters of the parallel robot based on the correction value. The method of the present application can effectively reduce the influence of the coupling torque between joints on the tracking accuracy of the control system during high-speed motion.

Description

Technical Field

[0001] The present application relates to the fields of machine vision, robotics, and automation technology, and in particular to a method, apparatus, device, and storage medium for adjusting coupling gain of a parallel robot based on visual recognition. Background Art

[0002] PID controllers are commonly used in modern, new industrial robots. These controllers offer advantages such as simple structure, ease of calculation, and high reliability. However, fixed-gain controllers struggle to meet the high-speed, high-precision requirements of the entire industrial robot's workspace. Fuzzy PID, as a new controller, offers advantages such as high robustness and reliability, and can autonomously self-tune PID parameters. However, the tracking accuracy of high-speed parallel robots, which are affected by the joint forces acting on them during motion, is low, making it difficult to meet industrial requirements. Furthermore, the robot's inability to accurately locate the position of the moving object during motion results in a disordered coupling force between the joints. Therefore, a fuzzy coupling control method suitable for visual industrial robots is urgently needed. This method combines the pixel difference of the target object, a fuzzy algorithm, and a correction coefficient that takes coupling effects into account to improve the motion accuracy of industrial robots. Summary of the Invention

[0003] The present application provides a method, device, equipment and storage medium for adjusting the coupling gain of a parallel robot based on visual recognition, which can effectively reduce the impact of the disturbance generated by the coupling force between joints during high-speed movement of the parallel robot on the control system.

[0004] In a first aspect, the present application provides a method for adjusting coupling gain of a parallel robot based on visual recognition, comprising:

[0005] Obtaining the position of the object and the position of the workspace, and calculating the distance from the object to the center of the workspace based on the position of the object and the position of the workspace;

[0006] Calculate the inertia coupling index of each drive shaft based on the drive shaft's own load inertia and the inter-axis coupling inertia;

[0007] Setting a speed threshold and an acceleration threshold for each joint of the parallel robot, and determining a correction value based on the distance from the object to the center of the workspace and the inertia coupling index of the drive axis when the speed of the joint exceeds the speed threshold or the acceleration exceeds the acceleration threshold;

[0008] Based on the correction value, a fuzzy adjustment amount of a controller parameter of the parallel robot is determined.

[0009] In one possible design, the distance from the object to the center of the workspace is calculated based on the position of the object and the position of the workspace. h for:

[0010] ;

[0011] Where, x p 、 y p 、 z p are the position coordinates of the object, x o 、 y o 、 z o are the position coordinates of the workspace respectively.

[0012] In one possible design, the inertia coupling index of each drive shaft is calculated using the following formula based on the drive shaft's own load inertia and the inter-axis coupling inertia:

[0013] ;

[0014] Where, ICI i Indicates the inertia coupling index of the drive shaft, represents the coupling strength coefficient, e represents the base of natural logarithms, M g ( i , j ) represents a symmetric positive definite matrix, including the load inertia of the drive shaft itself and the coupling inertia between the shafts, i Indicates the charged joint, j represents the joints other than the controlled joints, and n represents the number of driven joints of the parallel robot.

[0015] In one possible design, the joint of the parallel robot includes a screw drive joint. When the speed of the screw drive joint exceeds the speed threshold or the acceleration exceeds the acceleration threshold, a first correction value is determined according to the distance between the object and the center of the workspace and the inertia coupling index of the drive shaft using the following formula:

[0016] ;

[0017] Where, represents the first correction value, and They represent the velocity threshold and acceleration threshold of the corresponding joints of the parallel robot, and are the joint velocities and accelerations of the parallel robot, h Indicates the distance from the object to the center of the workspace, Represents the first correction factor.

[0018] In one possible design, based on the first correction value, the fuzzy adjustment amount of the controller parameter of the screw transmission joint of the parallel robot is determined by the following formula:

[0019] ;

[0020] ;

[0021] ;

[0022] Where, Indicates the initial value of the proportional parameter adjustment output by the fuzzy controller, Indicates the initial value of the integral parameter adjustment output by the fuzzy controller, Indicates the initial value of the differential parameter adjustment output by the fuzzy controller, 、 and They represent the correction values ​​of the proportional parameter adjustment amount, the integral parameter adjustment amount, and the differential parameter adjustment amount respectively.

[0023] In one possible design, the joints of the parallel robot include branched drive joints. When the speed of the branched drive joint exceeds the speed threshold or the acceleration exceeds the acceleration threshold, a second correction value is determined according to the distance between the object and the center of the workspace and the inertia coupling index of the drive axis using the following formula:

[0024] ;

[0025] Where, represents the second correction value, and They represent the velocity threshold and acceleration threshold of the corresponding joints of the parallel robot, and are the joint velocities and accelerations of the parallel robot, h Indicates the distance from the object to the center of the workspace, Represents the second correction factor.

[0026] In one possible design, based on the second correction value, the fuzzy adjustment amount of the controller parameter of the branched drive joint of the parallel robot is determined by the following formula:

[0027] ;

[0028] ;

[0029] ;

[0030] Where, Indicates the initial value of the proportional parameter adjustment output by the fuzzy controller, Indicates the initial value of the integral parameter adjustment output by the fuzzy controller, Indicates the initial value of the differential parameter adjustment output by the fuzzy controller, 、 and They represent the correction values ​​of the proportional parameter adjustment amount, the integral parameter adjustment amount, and the differential parameter adjustment amount respectively.

[0031] In a second aspect, the present application provides a parallel robot coupling gain adjustment device based on visual recognition, the device comprising a controller, the controller being configured to:

[0032] a visual processing module configured to obtain a position of the object and a position of the workspace, and calculate a distance from the object to a center of the workspace based on the position of the object and the position of the workspace;

[0033] a coupling index calculation module configured to calculate an inertia coupling index of each drive shaft based on the load inertia of the drive shaft itself and the coupling inertia between the shafts;

[0034] a correction value calculation module configured to set a speed threshold value and an acceleration threshold value for each joint of the parallel robot, and determine a correction value based on a distance from the object to the center of the workspace and an inertia coupling index of the drive axis when the speed of the joint exceeds the speed threshold value or the acceleration exceeds the acceleration threshold value;

[0035] The adjustment amount calculation module is configured to determine the fuzzy adjustment amount of the controller parameter of the parallel robot based on the correction value.

[0036] In a third aspect, an embodiment of the present application provides an electronic device comprising: at least one processor and a memory; the memory stores computer-executable instructions; the at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor executes the parallel robot coupling gain adjustment method based on visual recognition as described in the first aspect and various possible designs of the first aspect.

[0037] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer execution instructions are stored. When a processor executes the computer execution instructions, the parallel robot coupling gain adjustment method based on visual recognition as described in the first aspect and various possible designs of the first aspect is implemented.

[0038] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the parallel robot coupling gain adjustment method based on visual recognition as described in the first aspect and various possible designs of the first aspect.

[0039] The method, device, equipment, and storage medium for adjusting the coupling gain of a parallel robot based on visual recognition provided in this application have at least the following beneficial effects:

[0040] This application proposes a correction coefficient (correction value) based on object pixel differences. By collecting, judging, and calculating the velocity and acceleration between robot joints, as well as the camera's recognition of the pixel differences of the target object, fuzzy adjustment of the controlled joint controller parameters is achieved. The advantage of this invention is that when performing fuzzy correction, there is no need to accurately identify the mathematical model of the controlled object. While taking into account the coupling between joints and the pixel differences of the target object, the calculation method is simple, consumes less hardware resources, and is easy to implement. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0042] Figure 1 This is a diagram of an application scenario of a method for adjusting coupling gain of a parallel robot based on visual recognition provided in an embodiment of the present application;

[0043] Figure 2 A flowchart of a method for adjusting coupling gains of a parallel robot based on visual recognition provided in an embodiment of the present application;

[0044] Figure 3 A control flow chart of a method for adjusting coupling gains of a parallel robot based on visual recognition according to an embodiment of the present application;

[0045] Figure 4 This is a structural diagram of the parallel robot coupling gain adjustment device based on visual recognition provided in an embodiment of the present application.

[0046] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0047] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0048] In the technical solution of this application, the collection, storage, use, processing, transmission, provision and disclosure of information such as financial data or user data involved comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0049] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned. They should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0050] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0051] The embodiment of the present application provides a method for adjusting the coupling gain of a parallel robot based on visual recognition. Figure 1 As shown in FIG, it is an application scenario diagram of a method for adjusting the coupling gain of a parallel robot based on visual recognition provided by an embodiment of the present application. Figure 1 As shown in the figure, the traditional control method of a parallel robot is implemented using a PID controller and a fuzzy controller. The basic principle is to add a fuzzy supervision layer to the PID control to dynamically correct the PID output. In the traditional control method, the fuzzy controller uses the error and its rate of change as input variables. After fuzzification and fuzzy inference, the fuzzy controller generates outputs that are used to adjust the fuzzy corrections of the proportional, integral, and differential parameters of each driving joint controller of the robot. The error and its rate of change can be determined by the PID controller. The generated fuzzy corrections are directly fed back to the PID controller, which outputs the control parameters to the controlled servo system, thereby controlling the parallel robot.

[0052] This application adds a visual system and correction coefficient to the traditional control method, and adjusts the fuzzy correction amount of the controller parameters by considering the coupling effect between joints. Figure 1 In the figure, j = 1, 2 represent the two lead screws of the parallel robot, j = 3, 4 represent the two branch chains of the parallel robot, h represents the distance from the object to the workspace, and are the joint velocities and accelerations of the parallel robot, Indicates the initial value of the controller parameter, represents the controller parameters adjusted by the regulation algorithm, Indicates the initial value of the proportional parameter adjustment output by the fuzzy controller, Indicates the initial value of the integral parameter adjustment output by the fuzzy controller, Indicates the initial value of the differential parameter adjustment output by the fuzzy controller, ICI i The inertia coupling index of each drive axis in the joint space of the parallel robot is represented. The visual system is achieved by adding a high-precision industrial camera to determine the distance from the object to the center of the workspace through camera recognition. h , and fed to the fuzzy controller, output 、 and , and add an adjustment algorithm to use the joint speed and acceleration of the parallel robot and the distance to the target object to adjust the controller parameters of the controlled joint. This method can effectively reduce the influence of the coupling torque between joints on the tracking accuracy of the control system during high-speed motion. Compared with the traditional method, the advantage of the present invention is that when performing coupling adjustment, there is no need to accurately identify the mathematical model of the controlled object, while taking into account the coupling effect between joints and the pixel difference of the target object. The algorithm structure is simple, occupies less hardware resources, and is easy to implement. For example, the parallel robot coupling gain adjustment method based on visual recognition proposed in this application can be directly configured in a PID controller or a fuzzy controller to achieve the adjustment of controller parameters.

[0053] Specifically, if Figure 2 As shown, it is a flow chart of the parallel robot coupling gain adjustment method based on visual recognition provided in an embodiment of the present application. The parallel robot coupling gain adjustment method based on visual recognition includes the following steps S10-S40.

[0054] S10: Obtain the position of the object and the position of the workspace, and calculate the distance from the object to the center of the workspace based on the position of the object and the position of the workspace.

[0055] In this embodiment, a camera can be set up to obtain the position of the object and the position of the workspace. For example, the camera first collects the position of the object （x p ,y p ,z p ) and the location of the workspace (x o ,y o ,z o )， Calculate the distance from the object to the workspace based on the collected information h for And return the result to the calculation process of the correction value to improve the control accuracy.

[0056] S20: Calculate the inertia coupling index of each drive shaft according to the load inertia of the drive shaft itself and the inter-axis coupling inertia.

[0057] In this embodiment, the inertia coupling index of each drive axis in the joint space of the parallel robot is ICI i , the inertia coupling index can be ICI i The index value is defined between (0, 1), which directly reflects the strength of the mechanism inertia coupling. A larger index value indicates a stronger coupling, and vice versa. The drive axis in the parallel robot joint space can be a drive joint of the parallel robot. For example, the drive axis includes a screw drive joint or a branch chain drive joint of the parallel robot.

[0058] In some embodiments, the inertia coupling index of each drive shaft is calculated according to the load inertia of the drive shaft itself and the inter-axis coupling inertia using the following formula:

[0059] ;

[0060] Where, ICI i Indicates the inertia coupling index of the drive shaft, represents the coupling strength coefficient, e represents the base of natural logarithms, M g ( i , j ) represents a symmetric positive definite matrix, including the load inertia of the drive shaft itself and the coupling inertia between the shafts, i Indicates the charged joint, j represents the joints other than the controlled joints, and n represents the number of driven joints of the parallel robot.

[0061] In this embodiment, the symmetric positive definite matrix M g Expressed as:

[0062] ;

[0063] Where, J mm is the load inertia of the drive shaft itself, and J mk is the inter-axis coupling inertia, m and k These are the serial numbers of the drive shafts. m=1, 2, 3, 4; k=1, 2, 3, 4; and m≠k .

[0064] Parallel robots often operate on trajectories with high speeds and accelerations, where the kinematic and force coupling between their joints is particularly pronounced. The resulting disturbances can significantly impact control performance and therefore cannot be ignored. To address this, the initial values ​​of the fuzzy adjustment variables for the controlled joints can be adjusted in real time based on the robot's motion state, using steps S30 and S40 below, thereby achieving interference mitigation and improving control accuracy.

[0065] S30: Set the speed threshold and acceleration threshold of each joint of the parallel robot. When the speed of the joint exceeds the speed threshold or the acceleration exceeds the acceleration threshold, determine the correction value based on the distance from the object to the center of the workspace and the inertia coupling index of the drive axis.

[0066] For example, a parallel robot may include two screw-driven joints and two branch-driven joints, wherein the speed thresholds and acceleration thresholds set for each joint may be partially the same or completely different depending on the actual situation. The speed and acceleration of each joint are compared with the speed threshold and acceleration threshold set for the corresponding joint, so that when the speed of the joint exceeds the speed threshold or the acceleration exceeds the acceleration threshold, the calculation of the correction value is initiated.

[0067] In some embodiments, for the controller parameters of the screw transmission joint, it is determined that when and When the blur adjustment amount is not corrected; when or When , the following formula is used to determine the correction value of the fuzzy adjustment amount:

[0068] ;

[0069] Where, represents the first correction value, and They represent the velocity threshold and acceleration threshold of the corresponding joints of the parallel robot, and are the joint velocities and accelerations of the parallel robot, h Indicates the distance from the object to the center of the workspace, represents the first correction factor, Take separately , , .

[0070] In some embodiments, for the controller parameters of the two chain drive joints, it is determined that when and When the blur adjustment amount is not corrected; when or When , the following formula is used to determine the correction value of the fuzzy adjustment amount:

[0071] ;

[0072] Where, represents the second correction value, and They represent the velocity threshold and acceleration threshold of the corresponding joints of the parallel robot, and are the joint velocities and accelerations of the parallel robot, h Indicates the distance from the object to the center of the workspace, represents the second correction factor, Take separately , , .

[0073] S40: Determine the fuzzy adjustment amount of the controller parameter of the parallel robot based on the correction value.

[0074] In some embodiments, based on the first correction value, the fuzzy adjustment amount of the controller parameter of the screw transmission joint of the parallel robot is determined by the following formula:

[0075] ;

[0076] ;

[0077] ;

[0078] Where, Indicates the initial value of the proportional parameter adjustment output by the fuzzy controller, Indicates the initial value of the integral parameter adjustment output by the fuzzy controller, Indicates the initial value of the differential parameter adjustment output by the fuzzy controller, 、 and They represent the correction values ​​of the proportional parameter adjustment amount, the integral parameter adjustment amount, and the differential parameter adjustment amount respectively.

[0079] In some embodiments, based on the second correction value, the fuzzy adjustment amount of the controller parameter of the branched chain driving joint of the parallel robot is determined by the following formula:

[0080] ;

[0081] ;

[0082] ;

[0083] Where, Indicates the initial value of the proportional parameter adjustment output by the fuzzy controller, Indicates the initial value of the integral parameter adjustment output by the fuzzy controller, Indicates the initial value of the differential parameter adjustment output by the fuzzy controller, 、 and They represent the correction values ​​of the proportional parameter adjustment amount, the integral parameter adjustment amount, and the differential parameter adjustment amount respectively.

[0084] Figure 3 This is a control flow chart of a method for adjusting the coupling gain of a parallel robot based on visual recognition provided in an embodiment of the present application. Figure 3 As shown, the rotation angle of each joint The interpolation module is used as the input value of the trajectory and performs preliminary interpolation. The interpolation result is used to perform the position inverse solution of the high-speed parallel robot through the position inverse solution, thereby obtaining the target position instructions of each joint. These instructions are used as the input of the servo control algorithm. ICIi As the input of the correction coefficient (including the correction coefficients of the two branches and the correction coefficients of the two screws). At the same time, the following error collected in real time by the control system of each joint et and error rate of change ec As the input of the controller, the fuzzy adjustment amount is calculated 、 、 . Determine the speed of each joint by threshold , acceleration , decides whether to call the correction value calculated in step S30. If so, S40 is executed to calculate the final fuzzy adjustment value. Finally, the servo control algorithm updates the controller parameters based on the fuzzy adjustment value and generates the final actual angle output.

[0085] In this embodiment, the following error e t , error change rate e c , the distance from the object to the workspace h , fuzzy adjustment amount 、 、 , each joint speed , acceleration Threshold value and All are defined as global variables to enable high-speed reading and writing of these data.

[0086] In summary, the present application proposes a method for adjusting the coupling gain of a parallel robot based on visual recognition. This method is based on the traditional fuzzy feedback control strategy, adds a visual system and correction coefficient, and adjusts the fuzzy correction amount of the controller parameters by considering the coupling effect between joints. The advantage of this application is that when performing fuzzy correction, there is no need to accurately identify the mathematical model of the controlled object. While taking into account the coupling effect between joints and the pixel difference of the target object, the algorithm has a simple structure, occupies less hardware resources, and is easy to implement.

[0087] The embodiment of the present application also provides a parallel robot coupling gain adjustment device based on visual recognition, such as Figure 4 As shown, the parallel robot coupling gain adjustment device based on visual recognition includes:

[0088] A visual processing module 401 is configured to obtain a position of an object and a position of a workspace, and calculate a distance from the object to a center of the workspace based on the position of the object and the position of the workspace;

[0089] A coupling index calculation module 402 is configured to calculate the inertia coupling index of each drive shaft based on the load inertia of the drive shaft itself and the inter-axis coupling inertia;

[0090] a correction value calculation module 403 configured to set a speed threshold and an acceleration threshold for each joint of the parallel robot, and to determine a correction value based on the distance between the object and the center of the workspace and the inertia coupling index of the drive axis when the speed of the joint exceeds the speed threshold or the acceleration exceeds the acceleration threshold;

[0091] The adjustment amount calculation module 404 is configured to determine the fuzzy adjustment amount of the controller parameter of the parallel robot based on the correction value.

[0092] In some embodiments, the visual processing module is further configured to calculate the distance from the object to the center of the workspace based on the position of the object and the position of the workspace. h for:

[0093] ;

[0094] Where, x p 、 y p 、 z p are the position coordinates of the object, x o 、 y o 、 z o are the position coordinates of the workspace respectively.

[0095] In some embodiments, the coupling index calculation module is further configured to calculate the inertia coupling index of each drive shaft according to the load inertia of the drive shaft itself and the inter-axis coupling inertia using the following formula:

[0096] ;

[0097] In Chinese, ICI i Indicates the inertia coupling index of the drive shaft, represents the coupling strength coefficient, e represents the base of natural logarithms, M g ( i , j ) represents a symmetric positive definite matrix, including the load inertia of the drive shaft itself and the coupling inertia between the shafts, i Indicates the charged joint, j represents the joints other than the controlled joints, and n represents the number of driven joints of the parallel robot.

[0098] In some embodiments, the joint of the parallel robot includes a screw transmission joint, and the correction value calculation module is further configured to determine the first correction value according to the distance between the object and the center of the workspace and the inertia coupling index of the drive shaft using the following formula when the speed of the screw transmission joint exceeds the speed threshold or the acceleration exceeds the acceleration threshold:

[0099] ;

[0100] Where, represents the first correction value, and They represent the velocity threshold and acceleration threshold of the corresponding joints of the parallel robot, and are the joint velocities and accelerations of the parallel robot, h Indicates the distance from the object to the center of the workspace, Represents the first correction factor.

[0101] In some embodiments, the adjustment amount calculation module is further configured to determine the fuzzy adjustment amount of the controller parameter of the screw transmission joint of the parallel robot based on the first correction value by the following formula:

[0102] ;

[0103] ;

[0104] ;

[0105] Where, Indicates the initial value of the proportional parameter adjustment output by the fuzzy controller, Indicates the initial value of the integral parameter adjustment output by the fuzzy controller, Indicates the initial value of the differential parameter adjustment output by the fuzzy controller, 、 and They represent the correction values ​​of the proportional parameter adjustment amount, the integral parameter adjustment amount, and the differential parameter adjustment amount respectively.

[0106] In some embodiments, the joints of the parallel robot include branched drive joints, and the correction value calculation module is further configured to determine the second correction value according to the distance from the object to the center of the workspace and the inertia coupling index of the drive axis using the following formula when the speed of the branched drive joint exceeds the speed threshold or the acceleration exceeds the acceleration threshold:

[0107] ;

[0108] Where, represents the second correction value, and They represent the velocity threshold and acceleration threshold of the corresponding joints of the parallel robot, and are the joint velocities and accelerations of the parallel robot, h Indicates the distance from the object to the center of the workspace, Represents the second correction factor.

[0109] In some embodiments, the adjustment amount calculation module is further configured to determine the fuzzy adjustment amount of the controller parameter of the branched chain drive joint of the parallel robot based on the second correction value by the following formula:

[0110] ;

[0111] ;

[0112] ;

[0113] Where, Indicates the initial value of the proportional parameter adjustment output by the fuzzy controller, Indicates the initial value of the integral parameter adjustment output by the fuzzy controller, Indicates the initial value of the differential parameter adjustment output by the fuzzy controller, 、 and They represent the correction values ​​of the proportional parameter adjustment amount, the integral parameter adjustment amount, and the differential parameter adjustment amount respectively.

[0114] An embodiment of the present application provides an electronic device, which may include a processor and a memory, wherein the processor and the memory can communicate with each other; illustratively, the processor and the memory communicate with each other via a communication bus.

[0115] The processor executes the computer-executable instructions stored in the memory, so that the processor implements the solutions in the above embodiments. The processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0116] The communication bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. System buses can be categorized as address buses, data buses, and control buses. Transceivers facilitate communication between the database access device and other computers (e.g., clients, read-write libraries, and read-only libraries). Memory may include random access memory (RAM) or non-volatile memory.

[0117] The electronic device provided in the embodiment of the present application may be the terminal device of the above embodiment.

[0118] An embodiment of the present application also provides a computer-readable storage medium, which stores computer instructions. When the computer instructions are executed on a computer, the computer executes the technical solution of the parallel robot coupling gain adjustment method based on visual recognition in the above embodiment.

[0119] An embodiment of the present application also provides a computer program product, which includes a computer program stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium. When at least one processor executes the computer program, it can implement the technical solution of the parallel robot coupling gain adjustment method based on visual recognition in the above embodiment.

[0120] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of modules is only a logical function division. In actual implementation, there may be other division methods, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or module, which can be electrical, mechanical or other forms.

[0121] Modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of these modules may be selected to implement the solution of this embodiment based on actual needs.

[0122] In addition, the functional modules in the various embodiments of the present application may be integrated into a single processing unit, or each module may exist physically separately, or two or more modules may be integrated into a single unit. The above-mentioned modules may be implemented in the form of hardware or hardware plus software functional units.

[0123] The integrated modules implemented in the form of software function modules can be stored in a computer-readable storage medium. The software function modules stored in a storage medium include a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute some of the steps of the methods of various embodiments of the present application.

[0124] It should be understood that the processor described above may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly executed by a hardware processor or by a combination of hardware and software modules within the processor.

[0125] The memory may include a high-speed RAM memory, and may also include non-volatile storage NVM, such as at least one disk memory, and may also be a USB flash drive, a mobile hard disk, a read-only memory, a magnetic disk or an optical disk.

[0126] A bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be divided into address buses, data buses, and control buses.

[0127] The storage medium may be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The storage medium may be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0128] An exemplary storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and the storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the storage medium can also exist as discrete components in an electronic control unit or a main control device.

[0129] Those skilled in the art will appreciate that all or part of the steps in the above-described method embodiments can be implemented using hardware associated with program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0130] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for adjusting coupling gain of a parallel robot based on visual recognition, characterized in that: The method comprises: Obtaining the position of the object and the position of the workspace, and calculating the distance from the object to the center of the workspace based on the position of the object and the position of the workspace; Calculate the inertia coupling index of each drive shaft based on the drive shaft's own load inertia and the inter-axis coupling inertia; Setting a speed threshold and an acceleration threshold for each joint of the parallel robot, and determining a correction value based on the distance from the object to the center of the workspace and the inertia coupling index of the drive axis when the speed of the joint exceeds the speed threshold or the acceleration exceeds the acceleration threshold; determining a fuzzy adjustment amount of a controller parameter of the parallel robot based on the correction value; Based on the load inertia of the drive shaft itself and the coupling inertia between the shafts, the inertia coupling index of each drive shaft is calculated using the following formula: ； Where, ICI i Indicates the inertia coupling index of the drive shaft, represents the coupling strength coefficient, e represents the base of natural logarithms, M g ( i , j ) represents a symmetric positive definite matrix, including the load inertia of the drive shaft itself and the coupling inertia between the shafts, i Indicates the charged joint, j represents the joints other than the controlled joints, and n represents the number of driven joints of the parallel robot; Symmetric positive definite matrix M g Expressed as: ； Where, J mm is the load inertia of the drive shaft itself, and J mk is the inter-axis coupling inertia, m and k These are the serial numbers of the drive shafts. m=1， 2，3，4；k=1，2，3，4； and m≠k .

2. The method for adjusting the coupling gain of a parallel robot based on visual recognition according to claim 1, characterized in that: Calculate the distance from the object to the center of the workspace based on the object position and the workspace position h for: ； Where, x p 、 y p 、 z p are the position coordinates of the object, x o 、 y o 、 z o are the position coordinates of the workspace respectively.

3. The method for adjusting coupling gain of a parallel robot based on visual recognition according to claim 1, characterized in that: The joint of the parallel robot includes a screw transmission joint. When the speed of the screw transmission joint exceeds the speed threshold or the acceleration exceeds the acceleration threshold, a first correction value is determined according to the distance between the object and the center of the workspace and the inertia coupling index of the drive shaft using the following formula: ； Where, represents the first correction value, and They represent the velocity threshold and acceleration threshold of the corresponding joints of the parallel robot, and are the joint velocities and accelerations of the parallel robot, h Indicates the distance from the object to the center of the workspace, Represents the first correction factor.

4. The method for adjusting coupling gain of a parallel robot based on visual recognition according to claim 3, characterized in that: Based on the first correction value, the fuzzy adjustment amount of the controller parameter of the screw transmission joint of the parallel robot is determined by the following formula: ； ； ； Where, Indicates the initial value of the proportional parameter adjustment output by the fuzzy controller, Indicates the initial value of the integral parameter adjustment output by the fuzzy controller, Indicates the initial value of the differential parameter adjustment output by the fuzzy controller, 、 and They represent the correction values ​​of the proportional parameter adjustment amount, the integral parameter adjustment amount, and the differential parameter adjustment amount respectively.

5. The method for adjusting the coupling gain of a parallel robot based on visual recognition according to claim 4, characterized in that: The joints of the parallel robot include branched drive joints. When the speed of the branched drive joint exceeds the speed threshold or the acceleration exceeds the acceleration threshold, a second correction value is determined according to the distance from the object to the center of the workspace and the inertia coupling index of the drive axis using the following formula: ； Where, represents the second correction value, and They represent the velocity threshold and acceleration threshold of the corresponding joints of the parallel robot, and are the joint velocities and accelerations of the parallel robot, h Indicates the distance from the object to the center of the workspace, Represents the second correction factor.

6. The method for adjusting coupling gain of a parallel robot based on visual recognition according to claim 5, characterized in that: Based on the second correction value, the fuzzy adjustment amount of the controller parameters of the branched drive joint of the parallel robot is determined by the following formula: ； ； ； Where, Indicates the initial value of the proportional parameter adjustment output by the fuzzy controller, Indicates the initial value of the integral parameter adjustment output by the fuzzy controller, Indicates the initial value of the differential parameter adjustment output by the fuzzy controller, 、 and They represent the correction values ​​of the proportional parameter adjustment amount, the integral parameter adjustment amount, and the differential parameter adjustment amount respectively.

7. A parallel robot coupling gain adjustment device based on visual recognition, characterized in that: The device comprises: a visual processing module configured to obtain a position of the object and a position of the workspace, and calculate a distance from the object to a center of the workspace based on the position of the object and the position of the workspace; a coupling index calculation module configured to calculate an inertia coupling index of each drive shaft based on the load inertia of the drive shaft itself and the coupling inertia between the shafts; a correction value calculation module configured to set a speed threshold value and an acceleration threshold value for each joint of the parallel robot, and determine a correction value based on a distance from the object to the center of the workspace and an inertia coupling index of the drive axis when the speed of the joint exceeds the speed threshold value or the acceleration exceeds the acceleration threshold value; an adjustment amount calculation module, configured to determine a fuzzy adjustment amount of a controller parameter of the parallel robot based on the correction value; The coupling index calculation module is further configured to calculate the inertia coupling index of each drive shaft according to the load inertia of the drive shaft itself and the inter-axis coupling inertia using the following formula: ； Where, ICI i Indicates the inertia coupling index of the drive shaft, represents the coupling strength coefficient, e represents the base of natural logarithms, M g ( i , j ) represents a symmetric positive definite matrix, including the load inertia of the drive shaft itself and the coupling inertia between the shafts, i Indicates the charged joint, j represents the joints other than the controlled joints, and n represents the number of driven joints of the parallel robot; Symmetric positive definite matrix M g Expressed as: ； Where, J mm is the load inertia of the drive shaft itself, and J mk is the inter-axis coupling inertia, m and k These are the serial numbers of the drive shafts. m=1， 2，3，4；k=1，2，3，4； and m≠k .

8. An electronic device, characterized in that: include: a processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the parallel robot coupling gain adjustment method based on visual recognition according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the parallel robot coupling gain adjustment method based on visual recognition according to any one of claims 1 to 6.

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