Robot adaptive impedance control method and device, computer equipment, readable storage medium and program product

Abstract

The invention relates to a robot adaptive impedance control method and device, computer equipment, a readable storage medium and a program product. The method comprises the following steps: a motion control step: controlling the motion of the motion robot according to teaching motion parameter information of the motion robot and preset spring damping constraint, and detecting actual motion parameter information of the motion robot; determining a motion parameter error formed between the teaching motion parameter information and the actual motion parameter information; according to a preset local kernel function and the motion parameter error, correcting a motion impedance influence parameter in preset spring damping constraint; updating the actual motion parameter information based on the teaching motion parameter information and the corrected motion impedance influence parameter, and returning to the motion control step until the motion parameter error is smaller than a preset error threshold value to obtain the target actual motion parameter information; and controlling the motion of the motion robot according to the target actual motion parameter information. By adopting the method, motion impedance control can be accurately carried out.

Description

Technical Field

[0001] The present application relates to the field of automatic control technology, and in particular to a robot adaptive impedance control method, device, computer equipment, computer-readable storage medium and computer program product. Background Art

[0002] The control of the motion impedance of a motion robot is largely affected by the dynamic parameters of the interaction environment. For example, changes in the stiffness of the environment will cause external force disturbances, which will affect the motion position and speed of the motion robot. Therefore, how to control the motion impedance of a motion robot is currently a key issue.

[0003] In traditional technology, the method of controlling the motion impedance of a motion robot is generally achieved by teaching the motion robot. Specifically, the motion robot is taught in advance by manual dragging to obtain the taught motion position and taught motion speed of the motion robot. Then, according to the taught motion position and taught motion speed of the motion robot, the actual motion position and actual motion speed of the motion robot are controlled respectively, so that the actual motion position and actual motion speed of the motion robot are close to the taught motion position and taught motion speed of the equipment, so as to achieve the control of the motion impedance of the motion robot.

[0004] However, the current robot impedance control method still has the problem of insufficient accuracy. Summary of the invention

[0005] Based on this, it is necessary to provide an accurate robot adaptive impedance control method, device, computer equipment, computer readable storage medium and computer program product to address the above technical problems.

[0006] In a first aspect, the present application provides a robot adaptive impedance control method, comprising:

[0007] Obtain the teaching motion parameter information of the motion robot;

[0008] Motion control step: controlling the motion of the motion robot according to the taught motion parameter information and the preset spring damping constraint, and detecting the actual motion parameter information of the motion robot, wherein the preset spring damping constraint is used to characterize the relative correlation parameter between the taught motion parameter information and the actual motion parameter information of the motion robot;

[0009] Determine the motion parameter error formed between the taught motion parameter information and the actual motion parameter information;

[0010] According to the preset local kernel function and the motion parameter error, the motion impedance influence parameter in the preset spring damping constraint is corrected, wherein the motion impedance influence parameter is used to adjust the relative correlation parameter between the teaching motion parameter information and the actual motion parameter information of the motion robot;

[0011] Based on the teaching motion parameter information and the corrected motion impedance influence parameter, the actual motion parameter information of the motion robot is updated, and the motion control step is returned until the motion parameter error is less than the preset error threshold, thereby obtaining the target actual motion parameter information;

[0012] The motion impedance of the motion robot is controlled according to the actual motion parameter information of the target.

[0013] In one embodiment, the teaching motion parameter information includes a teaching position parameter and a teaching speed parameter, and the actual motion parameter information includes an actual position parameter and an actual speed parameter; determining a motion parameter error formed between the teaching motion parameter information and the actual motion parameter information includes:

[0014] generating a position parameter error according to the taught position parameter and the actual position parameter, and generating a speed parameter error according to the taught speed parameter and the actual speed parameter;

[0015] The position parameter error and the velocity parameter error are combined to obtain the motion parameter error.

[0016] In one embodiment, the motion impedance influencing parameter in the preset spring damping constraint is corrected according to the preset local kernel function and the motion parameter error, including:

[0017] Obtaining a preset local kernel function and a preset total kernel function, wherein the preset local kernel function is used to characterize the kernel function corresponding to each motion parameter error, and the preset total kernel function is used to characterize the sum of the kernel functions corresponding to all motion parameter errors;

[0018] For any motion parameter error, the local proportion of the preset local kernel function in the preset total kernel function is obtained, and based on the local proportion and the motion parameter error, the motion impedance influencing parameter corresponding to the motion parameter error in the preset spring damping constraint is corrected.

[0019] In one embodiment, the motion impedance influencing parameters include feedforward parameters, stiffness parameters, and damping parameters; based on the local proportion and the motion parameter error, the motion impedance influencing parameters corresponding to the motion parameter error in the preset spring damping constraint are corrected, including:

[0020] Based on the position parameter error and the speed parameter error, a combined parameter error is obtained;

[0021] Based on the local proportion, combined parameter error and position parameter error, the stiffness parameters are corrected;

[0022] Based on the local proportion, combination parameter error and speed parameter error, the damping parameters are corrected;

[0023] Based on the local proportion and combination parameter errors, the feedforward parameters are corrected.

[0024] In one embodiment, a combined parameter error is obtained based on the position parameter error and the speed parameter error, including:

[0025] Obtain a preset position weight coefficient and a preset speed weight coefficient, wherein the preset position weight coefficient is used to characterize the weight of the position parameter error in all parameter errors, and the preset speed weight coefficient is used to characterize the weight of the speed parameter error in all parameter errors; all parameter errors are the sum of the position parameter error and the speed parameter error shown;

[0026] Based on a preset position weight coefficient, updating a position parameter error, and based on a preset speed weight coefficient, updating a speed parameter error;

[0027] The updated position parameter error is fused with the updated speed parameter error to obtain a combined parameter error.

[0028] In one embodiment, obtaining the teaching motion parameter information of the motion robot includes:

[0029] In the case of controlling the terminal motion of the motion robot, detecting the teaching joint motion parameter information of the motion robot in the joint coordinate system;

[0030] The coordinate system transformation is performed on the teaching joint motion parameter information to obtain the teaching motion parameter information of the motion robot in the terminal coordinate system.

[0031] In a second aspect, the present application also provides a robot adaptive impedance control device, comprising:

[0032] A teaching motion parameter acquisition module is used to obtain teaching motion parameter information of the motion robot;

[0033] An actual motion parameter acquisition module is used in the motion control step: controlling the motion of the motion robot according to the teaching motion parameter information and the preset spring damping constraint, and detecting the actual motion parameter information of the motion robot, wherein the preset spring damping constraint is used to characterize the relative correlation parameter between the teaching motion parameter information and the actual motion parameter information of the motion robot;

[0034] An error determination module, used to determine a motion parameter error formed between the teaching motion parameter information and the actual motion parameter information;

[0035] An influencing parameter correction module is used to correct the motion impedance influencing parameter in the preset spring damping constraint according to the preset local kernel function and the motion parameter error, wherein the motion impedance influencing parameter is used to adjust the relative correlation parameter between the teaching motion parameter information and the actual motion parameter information of the motion robot;

[0036] A motion parameter correction module is used to update the actual motion parameter information of the motion robot based on the teaching motion parameter information and the corrected motion impedance influence parameter, and return to the motion control step until the motion parameter error is less than a preset error threshold, thereby obtaining the target actual motion parameter information;

[0037] The impedance control module is used to control the motion impedance of the motion robot according to the actual motion parameter information of the target.

[0038] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0039] Obtain the teaching motion parameter information of the motion robot;

[0040] Motion control step: controlling the motion of the motion robot according to the taught motion parameter information and the preset spring damping constraint, and detecting the actual motion parameter information of the motion robot, wherein the preset spring damping constraint is used to characterize the relative correlation parameter between the taught motion parameter information and the actual motion parameter information of the motion robot;

[0041] Determine the motion parameter error formed between the taught motion parameter information and the actual motion parameter information;

[0042] According to the preset local kernel function and the motion parameter error, the motion impedance influence parameter in the preset spring damping constraint is corrected, wherein the motion impedance influence parameter is used to adjust the relative correlation parameter between the teaching motion parameter information and the actual motion parameter information of the motion robot;

[0043] Based on the teaching motion parameter information and the corrected motion impedance influence parameter, the actual motion parameter information of the motion robot is updated, and the motion control step is returned until the motion parameter error is less than the preset error threshold, thereby obtaining the target actual motion parameter information;

[0044] The motion impedance of the motion robot is controlled according to the actual motion parameter information of the target.

[0045] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the following steps are implemented:

[0046] Obtain the teaching motion parameter information of the motion robot;

[0047] Motion control step: controlling the motion of the motion robot according to the taught motion parameter information and the preset spring damping constraint, and detecting the actual motion parameter information of the motion robot, wherein the preset spring damping constraint is used to characterize the relative correlation parameter between the taught motion parameter information and the actual motion parameter information of the motion robot;

[0048] Determine the motion parameter error formed between the taught motion parameter information and the actual motion parameter information;

[0049] According to the preset local kernel function and the motion parameter error, the motion impedance influence parameter in the preset spring damping constraint is corrected, wherein the motion impedance influence parameter is used to adjust the relative correlation parameter between the teaching motion parameter information and the actual motion parameter information of the motion robot;

[0050] Based on the teaching motion parameter information and the corrected motion impedance influence parameter, the actual motion parameter information of the motion robot is updated, and the motion control step is returned until the motion parameter error is less than the preset error threshold, thereby obtaining the target actual motion parameter information;

[0051] The motion impedance of the motion robot is controlled according to the actual motion parameter information of the target.

[0052] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0053] Obtain the teaching motion parameter information of the motion robot;

[0054] Motion control step: controlling the motion of the motion robot according to the taught motion parameter information and the preset spring damping constraint, and detecting the actual motion parameter information of the motion robot, wherein the preset spring damping constraint is used to characterize the relative correlation parameter between the taught motion parameter information and the actual motion parameter information of the motion robot;

[0055] Determine the motion parameter error formed between the taught motion parameter information and the actual motion parameter information;

[0056] According to the preset local kernel function and the motion parameter error, the motion impedance influence parameter in the preset spring damping constraint is corrected, wherein the motion impedance influence parameter is used to adjust the relative correlation parameter between the teaching motion parameter information and the actual motion parameter information of the motion robot;

[0057] Based on the teaching motion parameter information and the corrected motion impedance influence parameter, the actual motion parameter information of the motion robot is updated, and the motion control step is returned until the motion parameter error is less than the preset error threshold, thereby obtaining the target actual motion parameter information;

[0058] The motion impedance of the motion robot is controlled according to the actual motion parameter information of the target.

[0059] The above-mentioned robot adaptive impedance control method, device, computer equipment, computer-readable storage medium and computer program product are different from simply obtaining actual motion parameter information according to the taught motion parameter information. The present application provides a more accurate robot adaptive impedance control method, that is, when controlling the motion of the motion robot according to the taught motion parameter information and the preset spring damping constraint, the actual motion parameter information of the motion robot is detected, and the motion impedance influencing parameters of the relative correlation parameters between the taught motion parameter information and the actual motion parameter information of the motion robot are corrected by the preset local kernel function and the motion parameter error formed between the taught motion parameter information and the actual motion parameter information. Then, based on the taught motion parameter information and the corrected motion impedance influencing parameters, the actual motion parameter information of the motion robot is iteratively updated to obtain the target actual motion parameter information. Since the target actual motion parameter information is the actual motion parameter information when the motion parameter error is less than the preset error threshold, the motion impedance control of the motion robot according to the target actual motion parameter information is more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments of the present application or the related technical descriptions will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0061] Figure 1 A diagram showing an application environment of a robot adaptive impedance control method in one embodiment;

[0062] Figure 2 A schematic diagram of a flow chart of a robot adaptive impedance control method in one embodiment;

[0063] Figure 3 A schematic diagram of a flow chart of a robot adaptive impedance control method in another embodiment;

[0064] Figure 4 A schematic diagram of an environmental dynamics system and a spring damping system in one embodiment;

[0065] Figure 5A schematic diagram of a function of a preset kernel function and a local ratio in one embodiment;

[0066] Figure 6 is a schematic diagram showing how the stiffness term, the damping term and the feedforward term of the controller change over time as the number of iterations increases in one embodiment;

[0067] Figure 7 is a schematic diagram of the change of position parameter error and speed parameter error over time as the number of iterations increases in one embodiment;

[0068] Figure 8 is a structural block diagram of a robot adaptive impedance control device in one embodiment;

[0069] Fig. 9 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0070] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are used to explain the present application and are not used to limit the present application.

[0071] In the related art, the motion robot is approximated as a flexible spring with adjustable stiffness, damping and feedforward, and the requirements of the robot arm's output force to the environment are met by the approximate spring force. The method of controlling the motion impedance of the motion robot is generally achieved by teaching the motion robot. Specifically, the motion robot is taught in advance by manual dragging to obtain the taught motion position and taught motion speed of the motion robot, and then the actual motion position and actual motion speed of the motion robot are controlled according to the taught motion position and taught motion speed of the motion robot, so that the actual motion position and actual motion speed of the motion robot are close to the taught motion position and taught motion speed of the device, so as to achieve the control of the motion impedance of the motion robot.

[0072] However, although simple teaching can control the motion impedance of the motion robot so that the actual motion position and actual motion speed of the motion robot are close to the taught motion position and taught motion speed of the device, in some special cases, it is impossible to accurately control the motion of the motion robot.

[0073] For example, when the motion robot is pushed hard, the actual motion position and actual motion speed of the motion robot will not be close to the taught motion position and taught motion speed of the device due to the influence of external force on the motion robot. The greater the external force disturbance, the greater the distance that the actual motion position deviates from the taught motion position. At this time, the impedance control of the robot motion is greatly affected by the environmental contact stiffness (i.e., external force). Therefore, it is necessary to provide a more accurate robot adaptive impedance control method to control the motion of the motion robot so that the motion error of the motion robot is smaller when it is affected by external force.

[0074] The robot adaptive impedance control method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown, the motion robot 102 communicates with the controller 104 via a network. The data storage system can store data that the controller 104 needs to process. The data storage system can be integrated on the controller 104, or placed on the cloud or other network servers.

[0075] The controller 104 obtains the taught motion parameter information of the motion robot 102 uploaded by the user, or obtains the taught motion parameter information of the motion robot 102 pre-stored in the controller 104 from the storage module to execute the motion control step: according to the taught motion parameter information and the preset spring damping constraint, the motion of the motion robot 102 is controlled, and the actual motion parameter information of the motion robot 102 is detected, wherein the preset spring damping constraint is used to characterize the relative correlation parameters between the taught motion parameter information and the actual motion parameter information of the motion robot 102.

[0076] Furthermore, the controller 104 determines the motion parameter error formed between the taught motion parameter information and the actual motion parameter information, and corrects the motion impedance influencing parameters in the preset spring damping constraint according to the preset local kernel function and the motion parameter error, wherein the motion impedance influencing parameters are used to adjust the relative correlation parameters between the taught motion parameter information and the actual motion parameter information of the motion robot 102, and updates the actual motion parameter information of the motion robot 102 based on the taught motion parameter information and the corrected motion impedance influencing parameters, and returns to the motion control step until the motion parameter error is less than the preset error threshold, and the target actual motion parameter information is obtained, and the motion impedance of the motion robot 102 is controlled according to the target actual motion parameter information.

[0077] In an exemplary embodiment, Figure 2 As shown, a robot adaptive impedance control method is provided, and the method is applied to Figure 1 The controller 104 in FIG. 1 is used as an example for explanation.

[0078] S100, obtaining teaching motion parameter information of the motion robot.

[0079] Among them, motion robot teaching is a basic programming method, which guides the motion robot and records a series of guiding operations and motion instructions as teaching motion parameter information, so that the motion robot can automatically move according to the teaching motion parameter information in the future.

[0080] The teaching motion parameter information can be obtained in real time or from a storage module of the controller. Acquisition from the storage module of the controller means storing the teaching motion parameter information in the storage module of the controller, and then extracting the teaching motion parameter information from the storage module so that the motion robot moves according to the teaching motion parameter information. In addition, the source of the teaching motion parameter information can be not only uploaded after manual teaching by the user, but also obtained from images captured by a shooting device.

[0081] In one embodiment, the teaching motion parameter information of the motion robot is obtained, including: the user manually guides the motion robot and records a series of guiding operations and motion instructions, and in the process of robot motion control, the motion parameter information of the motion robot at this time is used as the teaching motion parameter information, and the teaching motion parameter information is uploaded to the controller in real time to execute motion control of the motion robot.

[0082] In another embodiment, obtaining the teaching motion parameter information of the motion robot also includes: the user manually guides the motion robot and records a series of guiding operations and motion instructions, and uses the motion parameter information of the motion robot at this time as the teaching motion parameter information, which is stored in the storage module of the controller. When the controller subsequently responds to the robot motion control request, the controller extracts the teaching motion parameter information from the storage module to perform motion control of the motion robot based on the teaching motion parameter information.

[0083] In another embodiment, taking the shooting device as an optical camera as an example, obtaining the taught motion parameter information of the motion robot also includes: the user manually guides the motion robot, the optical camera captures the process image of the user manually guiding the motion robot, based on the process image, the motion parameter information of the motion robot is extracted frame by frame, and the motion parameter information of the motion robot is sent to the controller, so that the controller obtains the taught motion parameter information of the motion robot.

[0084] S200, motion control step: controlling the motion of the motion robot according to the taught motion parameter information and the preset spring damping constraint, and detecting the actual motion parameter information of the motion robot.

[0085] The preset spring damping constraint is used to characterize the relative correlation parameters between the taught motion parameter information and the actual motion parameter information of the motion robot.

[0086] Specifically, the motion robot is approximated as a flexible spring with adjustable parameters, and the approximate spring force is used to meet the robot arm's requirements for environmental output force. In other words, the motion robot is approximated as a spring-damper system, interacting with an unknown environment.

[0087] When a moving robot interacts with an unknown environment, two control systems can be established, including a spring-damper system and an environmental dynamics system. The spring-damper system can be regarded as the control system of the robot, and the environmental dynamics system describes the model of the environment, and the output is the environmental force.

[0088] The input of the spring damping system is the teaching motion parameter information, and the output is the actual motion parameter information. The spring damping system needs to satisfy the preset spring damping constraint. The preset spring damping constraint is a second-order dynamic system used to characterize the relative correlation parameters between the teaching motion parameter information and the actual motion parameter information of the motion robot. The actual motion parameter information of the motion robot is affected by the preset spring damping constraint. In addition, the preset spring damping constraint is also related to the force input to the system by the environment.

[0089] When the motion robot is acted upon by forces in the environment, even if the motion robot is controlled to move according to the taught motion parameter information and the preset spring damping constraint, the actual motion parameter information will deviate from the taught motion parameter information due to the force. Therefore, the actual motion parameter information needs to be corrected. The premise of the correction is to first obtain the actual motion parameter information of the motion robot at this time, that is, the uncorrected actual motion parameter information.

[0090] S300, determining a motion parameter error formed between the teaching motion parameter information and the actual motion parameter information.

[0091] Specifically, the motion parameter error formed between the taught motion parameter information and the actual motion parameter information is determined, that is, the difference between the taught motion parameter information and the actual motion parameter information is obtained, and the difference between the taught motion parameter information and the actual motion parameter information is determined as the motion parameter error.

[0092] Furthermore, the taught motion of the motion robot is a motion trajectory, not a point. Therefore, the taught motion parameter information is the motion parameter information corresponding to several points in a trajectory. It can be determined that there is more than one motion parameter error. As time changes, the taught motion parameter information and the actual motion parameter information of the motion robot change, and the corresponding motion parameter errors are also different.

[0093] S400, correcting the motion impedance influencing parameters in the preset spring damping constraint according to the preset local kernel function and the motion parameter error.

[0094] Among them, the motion impedance influence parameter is used to adjust the relative correlation parameter between the teaching motion parameter information and the actual motion parameter information of the motion robot. The preset local kernel function is a function composed of Gaussian functions, usually represented by w(s). The preset local kernel function is a local function whose independent variable s ranges from [0, 1], which can achieve dimensionality reduction of data, that is, compress the computational amount of trajectory learning through the Gaussian kernel function.

[0095] Specifically, the parameters in the preset spring damping constraint include not only the actual motion parameter information, the teaching motion parameter information and the environmental input force, but also the motion impedance influence parameter, which is the relative correlation parameter between the teaching motion parameter information and the actual motion parameter information used to adjust the motion robot. The purpose of this application is to adjust the motion impedance influence parameter so that the motion parameter error gradually converges and decreases, and the actual motion parameter information gradually fits the teaching motion parameter information.

[0096] The spring damping system is essentially an error-driven negative feedback system, that is, based on the motion parameter error obtained by the spring damping system and combined with the preset local kernel function, feedback correction is performed on the motion impedance influencing parameters in the preset spring damping constraint.

[0097] The purpose of feedback correction is to influence the motion parameter error so that the motion parameter error is within the accuracy error range of the motion robot, that is, when the motion impedance influence parameter is the optimal motion impedance influence parameter, the motion parameter error is less than the preset error threshold. Generally, the preset error threshold is set to 0.

[0098] S500, based on the teaching motion parameter information and the corrected motion impedance influence parameter, the actual motion parameter information of the motion robot is updated, and the motion control step is returned until the motion parameter error is less than the preset error threshold, thereby obtaining the target actual motion parameter information.

[0099] Specifically, the preset spring damping constraint includes motion impedance influencing parameters. Therefore, correcting the motion impedance influencing parameters means updating the preset spring damping constraint, and then based on the updated preset spring damping constraint, controlling the motion of the motion robot again to update the actual motion parameter information of the motion robot.

[0100] After updating the actual motion parameter information of the motion robot, the actual motion parameter information at this time does not necessarily approach the taught motion parameter information, and the motion parameter error between the actual motion parameter information and the taught motion parameter information may still be greater than the preset error threshold. Therefore, it is necessary to determine the motion parameter error between the updated actual motion parameter information and the taught motion parameter information, and compare the motion parameter error with the preset error threshold.

[0101] When the motion parameter error is less than the preset error threshold, the actual motion parameter information at this time is used as the target actual motion parameter information. When the motion parameter error is greater than or equal to the preset error threshold, the actual motion parameter information of the motion robot still needs to be corrected, that is, the motion impedance influencing parameters in the preset spring damping constraint are corrected according to the preset local kernel function and the motion parameter error, and the actual motion parameter information when the motion parameter error is less than the preset error threshold is obtained by repeated iterations, and the actual motion parameter information when the motion parameter error is less than the preset error threshold is determined as the target actual motion parameter information.

[0102] S600, controlling the motion impedance of the motion robot according to the target actual motion parameter information.

[0103] Among them, the kinematic impedance characterizes the resistance tendency of the robot when it moves. Depending on the characteristics of the environment, whether it is flexible or rigid, the manipulator is expected to perform different compliant movements. Specifically, if the manipulated object is a flexible object, a lower mechanical impedance is required so that the interaction force generated by contact can be dissipated; while if the manipulator is to manipulate an object with a rigid surface, a higher impedance should be specified so that the object maintains good stability during operation.

[0104] Specifically, the target actual motion parameter information is the motion parameter information when the motion parameter error is less than the preset error threshold, that is, the actual motion parameter information at this time is close to the taught motion parameter information. Therefore, the motion impedance of the motion robot can be controlled according to the target actual motion parameter information, so that even under the influence of environmental forces, the motion robot can keep the error between the actual motion parameter information and the taught motion parameter information within the normal threshold range, thereby improving the accuracy of the robot's motion impedance control.

[0105] In the above-mentioned robot adaptive impedance control method, different from simply obtaining the actual motion parameter information according to the taught motion parameter information, the present application provides a more accurate robot adaptive impedance control method, that is, when controlling the motion of the motion robot according to the taught motion parameter information and the preset spring damping constraint, the actual motion parameter information of the motion robot is detected, and the motion parameter error formed between the taught motion parameter information and the actual motion parameter information of the motion robot is corrected by the preset local kernel function and the motion parameter error formed between the taught motion parameter information and the actual motion parameter information, and then based on the taught motion parameter information and the corrected motion impedance influence parameters, the actual motion parameter information of the motion robot is iteratively updated to obtain the target actual motion parameter information. Since the target actual motion parameter information is the actual motion parameter information when the motion parameter error is less than the preset error threshold, the motion control of the motion robot according to the target actual motion parameter information is more accurate.

[0106] In an exemplary embodiment, Figure 3 As shown, the teaching motion parameter information includes the teaching position parameter and the teaching speed parameter, and the actual motion parameter information includes the actual position parameter and the actual speed parameter. S300 includes:

[0107] S320, generating a position parameter error according to the taught position parameter and the actual position parameter, and generating a speed parameter error according to the taught speed parameter and the actual speed parameter.

[0108] S340, combining the position parameter error and the speed parameter error to obtain a motion parameter error.

[0109] Specifically, the motion parameter information includes position parameters and speed parameters. Therefore, the motion parameter error includes position parameter error and speed parameter error.

[0110] The specific method of obtaining the two parameter errors includes: generating a position parameter error according to the teaching position parameter and the actual position parameter, and generating a speed parameter error according to the teaching speed parameter and the actual speed parameter. More specifically, the difference between the teaching position parameter and the actual position parameter is determined as the position parameter error, and the difference between the teaching speed parameter and the actual speed parameter is determined as the speed parameter error. Further, the position parameter error and the speed parameter error are combined to obtain the motion parameter error, that is, the motion parameter error includes the position parameter error and the speed parameter error.

[0111] In one embodiment, when actual motion parameter information is obtained in which the motion parameter error is less than a preset error threshold, feedback correction is performed on the actual motion parameter information, which is essentially to correct the actual position parameter and the actual speed parameter respectively, so that the position parameter error is less than the preset position error threshold, and the speed parameter error is less than the preset speed error threshold. Generally, the preset position error threshold and the preset speed error threshold are both set to 0.

[0112] In this embodiment, by setting the motion parameter information to include position parameters and speed parameters, the motion parameter information can be comprehensively corrected in terms of position and speed. This is different from teaching only position parameters or speed parameters. The motion of the motion robot can be controlled more accurately based on the position parameter error and the speed parameter error.

[0113] In an exemplary embodiment, the motion impedance influencing parameters in the preset spring damping constraint are corrected according to the preset local kernel function and the motion parameter error, including:

[0114] Obtain a preset local kernel function and a preset total kernel function, obtain the local proportion of the preset local kernel function in the preset total kernel function for any motion parameter error, and based on the local proportion and the motion parameter error, correct the motion impedance influencing parameters corresponding to the motion parameter error in the preset spring damping constraint.

[0115] The preset local kernel function is used to characterize the kernel function corresponding to each motion parameter error, which describes the local function composed of Gaussian functions. The preset total kernel function is used to characterize the sum of the kernel functions corresponding to all motion parameter errors.

[0116] Specifically, each actual position parameter corresponds to a local kernel function, and the preset total kernel function is the sum of the local kernel functions corresponding to all actual position parameters. For example, when the actual position parameter is s, the corresponding local kernel function is , where i refers to the i-th position in the motion trajectory of the moving robot. The preset total kernel function at this time is .

[0117] Since each actual position parameter corresponds to a motion parameter error, in order to simply describe the preset local kernel function, the preset local kernel function can be used as the kernel function corresponding to each motion parameter error, and the preset total kernel function can be used as the sum of the kernel functions corresponding to all motion parameter errors.

[0118] For any motion parameter error, the motion impedance influencing parameters corresponding to the motion parameter error in the preset spring damping constraint are corrected through a discrete learning method.

[0119] Specifically, the local proportion of the preset local kernel function in the preset total kernel function is obtained. Let the preset local kernel function be , the default total kernel function is , then the local proportion of the preset local kernel function in the preset total kernel function , where k is the kth iteration.

[0120] Furthermore, in the kth iteration process, based on the local proportion and motion parameter error corresponding to the kth iteration, the motion impedance influence parameter corresponding to the motion parameter error in the preset spring damping constraint is corrected, that is, based on the local proportion of the Gaussian kernel function in the motion parameter error, a finite number of discrete motion parameter errors are controlled, and then the motion impedance influence parameter corresponding to the motion parameter error in the preset spring damping constraint is learned. In other words, let the motion parameter error be E s，k , in the kth iteration process, the corrected motion impedance influence parameter = the motion impedance influence parameter before correction + g s，k f(E s，k ), where f(E s，k ) is a function expression related to the motion parameter error when the actual position parameter is s during the kth iteration, and the function expression includes at least one of the motion parameter error and the high-order motion parameter error.

[0121] Furthermore, the present application gradually reduces the motion parameter error by adjusting the motion impedance influencing parameter. However, the motion impedance influencing parameter is not a value, but a continuous function described by a kernel function, the purpose of which is to make the motion impedance influencing parameter change with time in a dynamic motion process. In order to achieve this goal, in each round of iteration, the random process is described by using a kernel function with a local scope, so that the function shape of the motion impedance influencing parameter changes with the actual motion parameter information. Furthermore, by controlling a finite number of discrete motion impedance influencing parameters, the final motion impedance influencing parameters in this round of iteration are learned.

[0122] That is to say, since the corrected motion impedance influence parameter is actually a discrete motion impedance influence parameter, further processing is required, that is, the motion impedance influence parameter corresponding to each motion parameter error in this round of iteration is multiplied by the local proportion, and then superimposed and updated to obtain the final motion impedance influence parameter. Specifically, the corrected motion impedance influence parameter is , then the expression can be the final motion impedance influence parameter = , where i refers to the i-th position in the motion trajectory of the motion robot, and N is the total number of positions. In fact, it is the local scale during the current iteration.

[0123] In one embodiment, the motion parameter error includes the position parameter error e s，k Error with speed parameters In the case of, the corrected motion impedance influence parameter = the motion impedance influence parameter before correction + g s，k f(e s，k , ).

[0124] In another embodiment, the motion impedance influence parameter corresponding to the motion parameter error in the preset spring damping constraint is corrected and also related to the learning rate Q in the iterative process. k Related, based on the learning rate Q in the iterative process k , local proportion and motion parameter errors, and correct the parameters affecting motion impedance. Among them, the learning rate Q k is a constant value used to control learning convergence. For example, let the motion parameter error be E s，k , in the kth iteration process, the corrected motion impedance influence parameter = the motion impedance influence parameter before correction + Q k g s，k f(E s，k ).

[0125] Furthermore, the motion parameter error includes the position parameter error e s，k Error with speed parameters In the case of, the corrected motion impedance influence parameter = the motion impedance influence parameter before correction + Q k g s，k f(e s，k , ).

[0126] In this embodiment, by presetting the local proportion of the local kernel function in the preset total kernel function, the discrete motion parameter error is controlled, and the motion impedance influencing parameters are corrected, so that the corrected result is more accurate.

[0127] In an exemplary embodiment, the motion impedance influencing parameters include feedforward parameters, stiffness parameters, and damping parameters; based on the local proportion and the motion parameter error, the motion impedance influencing parameters corresponding to the motion parameter error in the preset spring damping constraint are corrected, including:

[0128] Based on the position parameter error and the speed parameter error, the combined parameter error is obtained. Based on the local proportion, the combined parameter error and the position parameter error, the stiffness parameter is corrected. Based on the local proportion, the combined parameter error and the speed parameter error, the damping parameter is corrected. Based on the local proportion and the combined parameter error, the feedforward parameter is corrected.

[0129] Specifically, in the kth iteration process, the corrected motion impedance influence parameter = the motion impedance influence parameter before correction + Q k g s，k f(e s，k , ), where f(E s，k ) is a function expression related to the motion parameter error when the actual position parameter is s during the kth iteration, and the function expression includes at least one of the motion parameter error and the high-order motion parameter error.

[0130] The motion parameter error in this application includes not only position parameter error or velocity parameter error, but also position parameter error e s，k Error with speed parameters The combined parameter error of the two. In other words, the combined parameter error =E(e s，k , ).

[0131] The high-order motion parameter error refers to the product of two parameter errors, including but not limited to the product of the combined parameter error and the position parameter error and the product of the combined parameter error and the velocity parameter error.

[0132] Therefore, based on at least one of the position parameter error, the speed parameter error, the product of the combined parameter error and the position parameter error, and the product of the combined parameter error and the speed parameter error, the motion impedance influencing parameter is discretely corrected.

[0133] Furthermore, the motion impedance influencing parameters in the spring damping system include three basic parameters, namely the feedforward parameter v, the stiffness parameter And the damping parameters , so the preset spring-damper constraints of the spring-damper system include .

[0134] The following describes the method of discretely correcting each motion impedance influencing parameter. Let the discrete motion impedance influencing parameter be ,By controlling multiple discrete motion impedance influencing parameters, the final motion impedance influencing parameters in the current ,iterative process can be learned.

[0135] For discrete stiffness parameters Correction: Based on the local ratio g s，k , combined parameter error And the position parameter error , for the stiffness parameter More specifically, based on the combined parameter error Error with position parameters The product of the high-order motion parameter error is obtained, and then based on the local ratio g s，k and the errors of higher-order motion parameters, stiffness parameters Correction is performed to obtain the corrected discrete stiffness parameters .

[0136] For discrete damping parameters Correction: Based on the local ratio g s，k , combined parameter error And speed parameter error , for the damping parameter More specifically, based on the combined parameter error Error with speed parameters The product of the high-order motion parameter error is obtained, and then based on the local ratio g s，k and the error of higher-order motion parameters, the damping parameters Correction is performed to obtain the corrected discrete stiffness parameters .

[0137] For the feedforward parameters Correction: Based on the local ratio g s，k and combined parameter error , the feedforward parameter v is corrected to obtain the corrected discrete feedforward parameter .

[0138] Through the above correction means, each motion impedance influencing parameter is discretely corrected. If the motion parameter error is larger, the adjusted local kernel function can be increased.

[0139] In addition, in the iteration process, since the high-order motion parameter errors can be used to correct the stiffness parameters and the damping parameters, the stiffness parameters and the damping parameters can be preferentially used as the priority functions of the adaptation.

[0140] In this embodiment, based on motion parameter errors, high-order motion parameter errors, etc., various motion impedance influencing parameters are corrected accurately and effectively, and the motion impedance influencing parameters include feedforward parameters, stiffness parameters and damping parameters, which can comprehensively correct the actual motion parameter information of the motion robot, thereby improving the accuracy of the correction.

[0141] In an exemplary embodiment, a combined parameter error is obtained based on the position parameter error and the speed parameter error, including:

[0142] Obtain a preset position weight coefficient and a preset speed weight coefficient; update the position parameter error based on the preset position weight coefficient, and update the speed parameter error based on the preset speed weight coefficient; merge the updated position parameter error with the updated speed parameter error to obtain a combined parameter error.

[0143] The preset position weight coefficient is used to characterize the weight of the position parameter error in all parameter errors, and the preset speed weight coefficient is used to characterize the weight of the speed parameter error in all parameter errors. All parameter errors are the sum of the position parameter error and the speed parameter error shown.

[0144] Specifically, the combined parameter error , where e s，k is the position parameter error, is the speed parameter error.

[0145] Get the preset position weight coefficient and the preset speed weight coefficient. The preset position weight coefficient is used to characterize the weight of the position parameter error in all parameter errors, that is, the proportion of the position parameter error in the sum of the position parameter error and the speed parameter error. The preset speed weight coefficient is used to characterize the weight of the speed parameter error in all parameter errors, that is, the proportion of the speed parameter error in the sum of the position parameter error and the speed parameter error.

[0146] Based on the preset position weight coefficient, the position parameter error is updated, and based on the preset speed weight coefficient, the speed parameter error is updated, and the updated position parameter error and the updated speed parameter error are fused to obtain a combined parameter error.

[0147] The updated position parameter error and the updated speed parameter error are merged to obtain a combined parameter error, including: summing the updated position parameter error and the updated speed parameter error to obtain a combined parameter error.

[0148] For example, let the position parameter error e s，k The preset position weight coefficient is , speed parameter error The preset speed weight coefficient is , then based on the preset position weight coefficient , update the position parameter error, and get the updated position parameter error as , and based on the preset speed weight coefficient , update the speed parameter error, and get the updated speed parameter error as , then based on the updated position parameter error The error with the updated speed parameter , generating the combined parameter error Generally speaking, =1, that is, the combined parameter error .

[0149] In this embodiment, the position parameter error and the speed parameter error are accurately fused by weighted summation to obtain a combined parameter error, and a high-order motion parameter error is generated based on the combination of the combined parameter error and other motion parameter errors. The high-order motion parameter error is then used to more accurately correct the motion impedance influencing parameters of the motion robot.

[0150] In an exemplary embodiment, obtaining teaching motion parameter information of a motion robot includes:

[0151] When controlling the terminal motion of the motion robot, the teaching joint motion parameter information of the motion robot in the joint coordinate system is detected, and the coordinate system transformation is performed on the teaching joint motion parameter information to obtain the teaching motion parameter information of the motion robot in the terminal coordinate system.

[0152] Among them, the end of the motion robot is a force control unit equipped with a grinding head. This model makes the controller flexible, outputs force to the environment and can achieve active and flexible interaction with the environment.

[0153] Specifically, taking user teaching as an example, the user manually drags the end of the motion robot for teaching. The robot records the teaching joint motion parameter information of the joint in the joint coordinate system at the current time based on the joint encoder. The teaching joint motion parameter information includes the teaching joint position parameter and the teaching joint speed parameter, and a teaching joint motion parameter sequence is obtained, which is recorded as , is the teaching joint position parameter of the joint at the current time, is the teaching joint velocity parameter of the joint at the current time, t=1, 2, ..., .

[0154] Since what needs to be obtained is the teaching motion parameter information of the end of the motion robot, it is necessary to perform a coordinate system conversion to convert the teaching joint motion parameter information of the joint in the joint coordinate system into the teaching motion parameter information of the end of the motion robot in the end coordinate system.

[0155] In this embodiment, by detecting the joint encoder and recording the teaching joint motion parameter information of the joint in the joint coordinate system at the current time, and converting it into the teaching motion parameter information of the motion robot in the terminal coordinate system, accurate acquisition of the teaching motion parameter information is achieved, thereby improving the accuracy of subsequent robot motion control.

[0156] In an exemplary embodiment, Figure 4 As shown in Figure 1, the environmental dynamics part (the transmission relationship between position and force) in the environmental dynamics system is usually a complex nonlinear function, which is assumed to be:

[0157]

[0158] in, Characterize the location characteristics of the environment, that is, is the actual position parameter, is the actual speed parameter, represents the dynamic parameters in the environmental model, Derived from the output of the previous spring-damper system. It is the force output by the environmental dynamic system to the environment.

[0159] In other words, the spring-damper system and the environmental dynamics system are equivalent to a second-order dynamic system consisting of a spring block and a damping block, whose input is position and output is force.

[0160] For the spring damping system, it is affected by the motion impedance influencing parameters, which control its motion characteristics. The motion impedance influencing parameters include the feedforward parameter v and the stiffness parameter K. S And the damping parameter K d , which can be described as:

[0161]

[0162] in, is the force input to the module by the environment, is the position parameter error, is the speed parameter error. , , To teach position parameters, It is the teaching speed parameter.

[0163] The above formula can be simplified to:

[0164]

[0165] in, Characterize multiple motion impedance influencing parameters.

[0166] That is, the purpose of this application is to control , so that Approaching In the case of approaches 0, where Characterized as the optimal motion impedance influencing parameter.

[0167] The following is an explanation of the specific models of the three modules:

[0168] 1. Teaching model:

[0169] Taking user teaching as an example, the main function of teaching is to obtain teaching motion parameter information, such as teaching position parameters and teaching speed parameters. The user drags the end to move by manually dragging, and the robot arm records the teaching joint motion parameter information of the joint in the joint coordinate system based on the joint encoder, and finally obtains a trajectory sequence. Since what needs to be obtained is the teaching motion parameter information of the end of the moving robot, it is necessary to perform a coordinate system transformation to convert the teaching joint motion parameter information of the joint in the joint coordinate system into the teaching motion parameter information of the end of the moving robot in the end coordinate system.

[0170] 2. Admittance control model based on spring damping system:

[0171] The admittance control model is also called the preset spring damping constraint, which includes multiple motion impedance influencing parameters, including but not limited to the feedforward parameter v, the stiffness parameter And the damping parameters , the preset spring damping constraints are as follows:

[0172]

[0173] 3. Iterative correction model:

[0174] Multiple discrete motion impedance influencing parameters are learned separately through discrete learning methods. The learning method is as follows:

[0175]

[0176] Where k represents the kth iteration, is the learning rate, which is a constant value used to control learning convergence. is the local proportion of the preset local kernel function in the preset total kernel function , is a local kernel function composed of Gaussian functions. , the independent variable s is the actual position parameter after normalization, and the interval is [0, 1]. Let N = 20, such as Figure 5 The following is a preset kernel function in one embodiment: (Left) and local ratio g s，k (right) Schematic diagram.

[0177] It is the combined parameter error, that is, the weighted sum of the position parameter error and the speed parameter error. Generally, , is the position parameter error, is the speed parameter error.

[0178] Furthermore, by controlling a finite number of discrete motion impedance influence parameters, such as , as well as , the final motion impedance influencing parameters in the current iteration process can be learned.

[0179] For example, taking the stiffness parameter as an example, , through this , make the final feedback correction to the stiffness parameters in the preset spring damping constraint. Repeat this step to correct all the parameters affecting the motion impedance.

[0180] Then, based on the taught motion parameter information and the corrected motion impedance influence parameters, the actual motion parameter information of the motion robot is updated to achieve iteration until the motion parameter error is less than the preset error threshold, and the target actual motion parameter information is obtained.

[0181] The motion of the motion robot is controlled according to the actual motion parameter information of the target.

[0182] In an exemplary embodiment, the result of motion control of the motion robot is as follows:

[0183] 1. If Figure 6 As shown in Figure 2, as the number of iterations increases, the stiffness term, damping term, and feedforward term of the controller change over time.

[0184] 2. If Figure 7 As shown in the figure, with the increase of the number of iterations, the position parameter error and the speed parameter error gradually decrease, and the motion accuracy gradually improves.

[0185] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0186] Based on the same inventive concept, the embodiment of the present application also provides a robot adaptive impedance control device for implementing the robot adaptive impedance control method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more robot adaptive impedance control device embodiments provided below can refer to the limitations of the robot adaptive impedance control method above, and will not be repeated here.

[0187] In an exemplary embodiment, Figure 8 As shown, a robot adaptive impedance control device is provided, comprising: a teaching motion parameter acquisition module 100, an actual motion parameter acquisition module 200, an error determination module 300, an influence parameter correction module 400, a motion parameter correction module 500 and an impedance control module 600, wherein:

[0188] The teaching motion parameter acquisition module 100 is used to acquire the teaching motion parameter information of the motion robot;

[0189] The actual motion parameter acquisition module 200 is used in the motion control step: controlling the motion of the motion robot according to the teaching motion parameter information and the preset spring damping constraint, and detecting the actual motion parameter information of the motion robot, wherein the preset spring damping constraint is used to characterize the relative correlation parameter between the teaching motion parameter information and the actual motion parameter information of the motion robot;

[0190] An error determination module 300, used to determine a motion parameter error formed between the teaching motion parameter information and the actual motion parameter information;

[0191] An influencing parameter correction module 400 is used to correct the motion impedance influencing parameter in the preset spring damping constraint according to the preset local kernel function and the motion parameter error, wherein the motion impedance influencing parameter is used to adjust the relative correlation parameter between the teaching motion parameter information and the actual motion parameter information of the motion robot;

[0192] The motion parameter correction module 500 is used to update the actual motion parameter information of the motion robot based on the teaching motion parameter information and the corrected motion impedance influence parameter, and return to the motion control step until the motion parameter error is less than the preset error threshold, thereby obtaining the target actual motion parameter information;

[0193] The impedance control module 600 is used to control the motion impedance of the motion robot according to the target actual motion parameter information.

[0194] In one embodiment, the taught motion parameter information includes taught position parameters and taught speed parameters, and the actual motion parameter information includes actual position parameters and actual speed parameters; the error determination module 300 is also used to generate a position parameter error based on the taught position parameters and the actual position parameters, and to generate a speed parameter error based on the taught speed parameters and the actual speed parameters; the position parameter error and the speed parameter error are combined to obtain a motion parameter error.

[0195] In one embodiment, the influencing parameter correction module 400 is also used to obtain a preset local kernel function and a preset total kernel function, wherein the preset local kernel function is used to characterize the kernel function corresponding to each motion parameter error, and the preset total kernel function is used to characterize the sum of the kernel functions corresponding to all motion parameter errors; for any motion parameter error, the local proportion of the preset local kernel function in the preset total kernel function is obtained, and based on the local proportion and the motion parameter error, the motion impedance influencing parameter corresponding to the motion parameter error in the preset spring damping constraint is corrected.

[0196] In one embodiment, the motion impedance influencing parameters include feedforward parameters, stiffness parameters and damping parameters; the influencing parameter correction module 400 is also used to obtain a combined parameter error based on a position parameter error and a velocity parameter error; the stiffness parameter is corrected based on a local proportion, a combined parameter error and a position parameter error; the damping parameter is corrected based on a local proportion, a combined parameter error and a velocity parameter error; the feedforward parameter is corrected based on a local proportion and a combined parameter error.

[0197] In one embodiment, the influencing parameter correction module 400 is also used to obtain a preset position weight coefficient and a preset speed weight coefficient, wherein the preset position weight coefficient is used to characterize the weight of the position parameter error in all parameter errors, and the preset speed weight coefficient is used to characterize the weight of the speed parameter error in all parameter errors, and all parameter errors are the sum of the position parameter error and the speed parameter error shown; based on the preset position weight coefficient, the position parameter error is updated, and based on the preset speed weight coefficient, the speed parameter error is updated; the updated position parameter error is merged with the updated speed parameter error to obtain a combined parameter error.

[0198] In one of the embodiments, the teaching motion parameter acquisition module 100 is also used to detect the teaching joint motion parameter information of the motion robot in the joint coordinate system while controlling the terminal motion of the motion robot; and perform coordinate system transformation on the teaching joint motion parameter information to obtain the teaching motion parameter information of the motion robot in the terminal coordinate system.

[0199] Each module in the above-mentioned robot adaptive impedance control device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0200] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig. 9 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as teaching motion parameter information. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a robot adaptive impedance control method is implemented.

[0201] Those skilled in the art will understand that Fig. 9 The structure shown in the figure is a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0202] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0203] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0204] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0205] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0206] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0207] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A robot adaptive impedance control method, characterized in that: The method comprises: Obtain the teaching motion parameter information of the motion robot; Motion control step: controlling the motion of the motion robot according to the teaching motion parameter information and the preset spring damping constraint, and detecting the actual motion parameter information of the motion robot, wherein the preset spring damping constraint is used to characterize the relative correlation parameter between the teaching motion parameter information and the actual motion parameter information of the motion robot; Determining a motion parameter error formed between the teaching motion parameter information and the actual motion parameter information; According to a preset local kernel function and the motion parameter error, the motion impedance influence parameter in the preset spring damping constraint is corrected, wherein the motion impedance influence parameter is used to adjust the relative correlation parameter between the teaching motion parameter information and the actual motion parameter information of the motion robot; Based on the teaching motion parameter information and the corrected motion impedance influence parameter, the actual motion parameter information of the motion robot is updated, and the motion control step is returned until the motion parameter error is less than a preset error threshold, thereby obtaining the target actual motion parameter information; The motion impedance of the motion robot is controlled according to the target actual motion parameter information.

2. The method according to claim 1, characterized in that The teaching motion parameter information includes a teaching position parameter and a teaching speed parameter, and the actual motion parameter information includes an actual position parameter and an actual speed parameter; and determining a motion parameter error formed between the teaching motion parameter information and the actual motion parameter information includes: generating a position parameter error according to the teaching position parameter and the actual position parameter, and generating a speed parameter error according to the teaching speed parameter and the actual speed parameter; The position parameter error and the speed parameter error are combined to obtain a motion parameter error.

3. The method according to claim 2, characterized in that The step of correcting the motion impedance influencing parameter in the preset spring damping constraint according to the preset local kernel function and the motion parameter error includes: Obtaining a preset local kernel function and a preset total kernel function, wherein the preset local kernel function is used to characterize the kernel function corresponding to each of the motion parameter errors, and the preset total kernel function is used to characterize the sum of the kernel functions corresponding to all the motion parameter errors; For any of the motion parameter errors, a local proportion of the preset local kernel function in the preset total kernel function is obtained, and based on the local proportion and the motion parameter error, the motion impedance influencing parameter corresponding to the motion parameter error in the preset spring damping constraint is corrected.

4. The method according to claim 3, characterized in that The motion impedance influencing parameters include feedforward parameters, stiffness parameters and damping parameters; the motion impedance influencing parameters corresponding to the motion parameter error in the preset spring damping constraint are corrected based on the local proportion and the motion parameter error, including: Based on the position parameter error and the speed parameter error, a combined parameter error is obtained; Based on the local proportion, the combined parameter error and the position parameter error, the stiffness parameter is corrected; Based on the local proportion, the combined parameter error and the speed parameter error, modifying the damping parameter; The feedforward parameter is modified based on the local ratio and the combined parameter error.

5. The method according to claim 4, characterized in that The obtaining of a combined parameter error based on the position parameter error and the speed parameter error comprises: Obtaining a preset position weight coefficient and a preset speed weight coefficient, wherein the preset position weight coefficient is used to characterize the weight of the position parameter error in all parameter errors, and the preset speed weight coefficient is used to characterize the weight of the speed parameter error in all parameter errors, and the all parameter errors are the sum of the position parameter error and the speed parameter error shown; Based on the preset position weight coefficient, updating the position parameter error, and based on the preset speed weight coefficient, updating the speed parameter error; The updated position parameter error and the updated speed parameter error are merged to obtain a combined parameter error.

6. The method according to claim 1, characterized in that The step of obtaining the teaching motion parameter information of the motion robot includes: Under the condition of controlling the terminal motion of the motion robot, detecting the teaching joint motion parameter information of the motion robot in the joint coordinate system; The teaching joint motion parameter information is subjected to coordinate system transformation to obtain the teaching motion parameter information of the motion robot in the terminal coordinate system.

7. A robot adaptive impedance control device, characterized in that: The device comprises: A teaching motion parameter acquisition module is used to obtain teaching motion parameter information of the motion robot; An actual motion parameter acquisition module is used in the motion control step: controlling the motion of the motion robot according to the teaching motion parameter information and the preset spring damping constraint, and detecting the actual motion parameter information of the motion robot, wherein the preset spring damping constraint is used to characterize the relative correlation parameter between the teaching motion parameter information and the actual motion parameter information of the motion robot; An error determination module, used to determine a motion parameter error formed between the teaching motion parameter information and the actual motion parameter information; An influencing parameter correction module, used for correcting the motion impedance influencing parameter in the preset spring damping constraint according to a preset local kernel function and the motion parameter error, wherein the motion impedance influencing parameter is used for adjusting the relative correlation parameter between the teaching motion parameter information and the actual motion parameter information of the motion robot; A motion parameter correction module is used to update the actual motion parameter information of the motion robot based on the teaching motion parameter information and the corrected motion impedance influence parameter, and return to the motion control step until the motion parameter error is less than a preset error threshold, thereby obtaining the target actual motion parameter information; The impedance control module is used to control the motion impedance of the motion robot according to the target actual motion parameter information.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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