Dynamic parameter identification method and device, robot and readable storage medium

Abstract

The invention provides a kinetic parameter identification method and device, a robot and a readable storage medium. The kinetic parameter identification method comprises the following steps: establishing a kinetic equation in a minimum parameter set form of a robot; constructing an optimal excitation track of the robot; controlling the robot to operate according to the optimal excitation track, and acquiring sensing data of the robot; determining noise information according to the sensing data and the kinetic equation; and iteratively updating the kinetic equation according to the noise information to identify kinetic parameters of the robot.

Description

Technical Field

[0001] The present invention relates to the field of robot technology, and in particular to a dynamic parameter identification method and device, a robot and a readable storage medium. Background Art

[0002] Dynamics analysis plays a vital role in the field of robot applications. For example, the collision detection and handheld teaching function, force control function, dynamics-based trajectory performance optimization, and optimization of electromechanical system design of collaborative robots all require the help of dynamics analysis. Among them, when performing dynamics analysis on a robot, identifying the dynamics parameters of the robot is an important way to obtain a dynamics model.

[0003] However, in the process of identifying the dynamic parameters of the robot, the robot's sensors are affected by environmental noise when collecting signals. In addition, there is velocity coupling between the joints of the robot when it moves, and the noise collected by the sensors on the joints will also affect each other. In this way, the results of the dynamic parameter identification of the robot will be affected by noise, reducing the accuracy of the dynamic parameter identification of the robot. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art or related art.

[0005] To this end, the first aspect of the present invention is to propose a kinetic parameter identification method.

[0006] The second aspect of the present invention is to provide a dynamic parameter identification device.

[0007] The third aspect of the present invention is to provide another dynamic parameter identification device.

[0008] A fourth aspect of the present invention is to provide a robot.

[0009] A fifth aspect of the present invention is to provide a readable storage medium.

[0010] In view of this, according to one aspect of the present invention, a method for dynamic parameter identification is proposed, which includes: establishing a dynamic equation in the form of a minimum parameter set of the robot; constructing an optimal excitation trajectory of the robot; controlling the robot to run according to the optimal excitation trajectory and obtaining sensor data of the robot; determining noise information based on the sensor data and the dynamic equation; iteratively updating the dynamic equation based on the noise information to identify the dynamic parameters of the robot.

[0011] The dynamic parameter identification method provided by the present invention is used to identify the dynamic parameters of a robot. In actual application, the robot may be a serial robot, which is not specifically limited here.

[0012] Specifically, in the dynamic parameter identification method provided by the present invention, in the process of identifying the dynamic parameters of the robot, the dynamic model of the robot is established, and then based on the dynamic model of the robot, the dynamic equation of the robot is constructed. Among them, the dynamic equation corresponds to the minimum parameter set of the dynamic model of the robot, and the above dynamic equation is specifically the least squares equation in the form of the minimum parameter set of the robot. Further, based on the dynamic equation of the robot, i.e., the least squares equation, the excitation trajectory of the robot is constructed and optimized to obtain the optimal excitation trajectory of the robot. Further, the optimal excitation trajectory of the robot is used as the expected trajectory of the robot, and the robot is controlled to run according to the optimal excitation trajectory. At the same time, in the process of controlling the robot to run according to the optimal excitation trajectory, the sensor data of each sensor installed on the robot is obtained. Further, according to the dynamic equation of the robot and the sensor data, the noise information of the robot during the working process is determined, and the noise information can be specifically used to characterize the environmental noise of the robot during the working process, and the coupling noise between the joints of the robot. On this basis, based on the noise information of the robot, the dynamic equation of the robot, i.e., the least squares equation, is repeatedly iterated and updated to identify the dynamic parameters of the robot.

[0013] In this way, in the process of identifying the dynamic parameters of the robot, the dynamic equation of the robot, i.e., the least squares equation, is iteratively updated repeatedly based on the noise information of the robot, which can reduce the coupling between the noise information of the robot during work and optimize the coupling between the noise information, thereby reducing the influence of the noise information on the dynamic parameter identification results of the robot and improving the accuracy of the dynamic parameter identification of the robot.

[0014] The above-mentioned kinetic parameter identification method according to the present invention may also have the following additional technical features:

[0015] In some technical schemes, optionally, the dynamic equations are iteratively updated according to the noise information, including: initializing the covariance matrix of the noise information; updating the dynamic equations according to the covariance matrix; determining the residual matrix according to the updated dynamic equations and sensor data; updating the covariance matrix according to the residual matrix, and iteratively updating the dynamic equations according to the updated covariance matrix until the covariance matrix converges.

[0016] In the technical scheme, in the process of repeatedly iterating and updating the dynamic equation of the robot, i.e., the least square equation, based on the noise information of the robot, to identify the dynamic parameters of the robot, after obtaining the noise information of the robot during the working process, the initial covariance matrix of the noise information is calculated. On this basis, based on the covariance matrix of the noise information of the robot, the dynamic equation of the robot, i.e., the least square equation, is updated, and then the residual matrix of the robot is determined according to the sensor data of the robot and the updated dynamic equation. Further, according to the determined residual matrix, the covariance matrix of the noise information of the robot is updated. On this basis, it is judged whether the covariance matrix of the noise information of the robot is in a convergence state. When the covariance matrix of the noise information of the robot is in a convergence state, the iterative update of the covariance matrix and the dynamic equation is terminated to obtain the identification result of the dynamic parameters of the robot. When the covariance matrix of the robot's noise information is in an unconverged state, the robot's dynamic equation is updated again according to the updated covariance matrix, and then the robot's new residual matrix is ​​determined according to the robot's sensor data and the updated dynamic equation. According to the determined new residual matrix, the covariance matrix of the robot's noise information is updated again, and the above judgment process is repeated.

[0017] This cycle is repeated, and the robot's dynamic equation, i.e., the least squares equation, is iteratively updated based on the covariance matrix of the robot's noise information until the covariance matrix of the robot's noise information converges, and the iterative update of the covariance matrix and the dynamic equation is terminated to obtain the identification result of the robot's dynamic parameters. In this way, in the process of identifying the robot's dynamic parameters, the robot's dynamic equation, i.e., the least squares equation, is iteratively updated repeatedly based on the covariance matrix of the robot's noise information, which can reduce the coupling between the robot's noise information during the working process, optimize the coupling between the noise information, so that the covariance matrix of the robot's noise information eventually tends to a diagonal matrix, reducing the influence of the noise information on the robot's dynamic parameter identification results, and improving the accuracy of the robot's dynamic parameter identification.

[0018] In some technical solutions, optionally, after iteratively updating the dynamic equation according to the noise information, the method also includes: initializing a weighted matrix of the sensor data; updating the sensor data according to the weighted matrix; updating the weighted matrix according to the residual matrix finally determined in the process of updating the covariance matrix, and iteratively updating the sensor data according to the updated weighted matrix until the weighted matrix converges.

[0019] In the technical solution, after the dynamic equation is iteratively updated according to the noise information, the above-mentioned dynamic parameter identification method may also specifically include: initializing the weighted matrix of the robot's sensor data, and then weighting the sensor data of the robot according to the weighted matrix to update the sensor data. Further, in combination with the threshold function, the weighted matrix is ​​updated according to the residual matrix finally determined in the above-mentioned updating covariance matrix process. Among them, the residual data, i.e., abnormal data, in the robot's sensor data can be effectively separated by the threshold function. Further, it is determined whether the weighted matrix of the robot's sensor data is in a convergence state. In the case that the weighted matrix of the robot's sensor data is in a convergence state, the iterative update of the weighted matrix and the sensor data is terminated. In the case that the weighted matrix of the robot's sensor data is in an unconverged state, the sensor data of the robot is updated again according to the updated weighted matrix, and then the residual matrix of the robot is determined according to the robot's sensor data and the dynamic equation, and the weighted matrix is ​​updated again according to the residual matrix, and the above-mentioned judgment process is repeated.

[0020] This cycle is repeated, and the sensor data and dynamic equations of the robot are iteratively updated based on the threshold function and the weighted matrix of the sensor data of the robot, until the weighted matrix of the sensor data of the robot converges, and the iterative update of the sensor data and the dynamic equations is terminated. In this way, in the process of identifying the dynamic parameters of the robot, the sensor data and dynamic equations of the robot are iteratively updated based on the threshold function and the weighted matrix of the sensor data of the robot, and the residual data, i.e., abnormal data, in the sensor data of the robot can be identified and eliminated, thereby reducing the influence of abnormal data on the dynamic parameter identification results of the robot and improving the accuracy of dynamic parameter identification of the robot.

[0021] In some technical solutions, optionally, after updating the weighting matrix, the method further includes: if the weighting matrix has not converged, iteratively updating the dynamic equation again according to the noise information of the updated sensor data.

[0022] In this technical solution, after updating the weighting matrix, if the weighting matrix has not converged, the updated weighting matrix will be used as the existing weighting matrix, and the above-mentioned step of iteratively updating the dynamic equation according to the noise information will be returned to. According to the noise information of the updated sensor data, the above-mentioned dynamic equation will be repeatedly iteratively updated again, and after the covariance matrix of the noise information reaches a convergence state again, the weighting matrix and the sensor data will be iteratively updated again until the weighting matrix converges. In this way, a two-layer iterative architecture can be formed, that is, two layers of iterative loops are designed for the least squares equation. In the process of identifying the dynamic parameters of the robot based on the least squares equation, the influence of noise information and abnormal data on the identification results can be reduced respectively through the two layers of iterative loops, and by repeatedly solving the least squares equation, the convergence of the iterative results of the least squares equation is guaranteed, the convergence and accuracy of the final dynamic parameters are guaranteed, and the accuracy of dynamic parameter identification is improved.

[0023] In some technical solutions, optionally, a dynamic equation of the robot in the form of a minimum parameter set is established, including: establishing a dynamic model of the robot; determining a basic dynamic equation based on a basic parameter set of the dynamic model; and determining the dynamic equation in the form of a minimum parameter set based on the minimum parameter set of the dynamic model and the basic dynamic equation.

[0024] In this technical solution, in the process of establishing the dynamic equation of the robot in the form of the minimum parameter set, the dynamic model of the robot is established, and then the basic dynamic equation of the robot is constructed according to the basic parameter set of the dynamic model. On this basis, the dynamic equation of the robot in the form of the minimum parameter set, i.e., the least squares equation, is constructed according to the basic dynamic equation of the robot and the minimum parameter set of the dynamic model. In this way, the accuracy of the constructed dynamic equation is guaranteed, and then the accuracy of the identification result of the subsequent dynamic parameter identification based on the dynamic equation is guaranteed.

[0025] In some technical solutions, optionally, constructing the optimal excitation trajectory of the robot includes: constructing the excitation trajectory according to the regression matrix of the robot; determining the excitation trajectory model according to the excitation trajectory; optimizing the trajectory parameters of the excitation trajectory model according to the parameter constraints of the excitation trajectory model to obtain the optimal excitation trajectory.

[0026] In this technical solution, in the process of constructing and optimizing the robot's excitation trajectory based on the robot's dynamic equation, i.e., the least squares equation, and obtaining the robot's optimal excitation trajectory, the robot's excitation trajectory is constructed according to the robot's regression matrix, and the experimental data, i.e., the sensor data, of the robot during operation according to the excitation trajectory are collected, and the optimization criteria of the excitation trajectory are constructed based on the collected experimental data, i.e., the sensor data. Further, based on the constructed excitation trajectory, a periodic excitation trajectory model composed of a finite Fourier series is determined. Further, based on the optimization criteria of the excitation trajectory and the parameter constraints of the excitation trajectory model, the trajectory parameters of the excitation trajectory model are optimized to obtain the robot's optimal excitation trajectory. In this way, the robot's excitation trajectory is optimized to obtain the robot's optimal excitation trajectory, which ensures the accuracy of the subsequent identification of the robot's dynamic parameters based on the optimal excitation trajectory and improves the accuracy of the robot's dynamic parameter identification results.

[0027] According to the second aspect of the present invention, a dynamic parameter identification device is proposed, which includes: a processing unit, which is used to establish a dynamic equation in the form of a minimum parameter set of the robot; the processing unit is also used to construct an optimal excitation trajectory of the robot; the processing unit is also used to control the robot to run according to the optimal excitation trajectory and obtain the sensor data of the robot; the processing unit is also used to determine noise information based on the sensor data and the dynamic equation; the processing unit is also used to iteratively update the dynamic equation based on the noise information to identify the dynamic parameters of the robot.

[0028] The dynamic parameter identification device provided by the present invention is used to identify the dynamic parameters of a robot. In actual application, the robot may be a serial robot, which is not specifically limited here.

[0029] Specifically, the dynamic parameter identification device provided by the present invention includes a processing unit. In the process of identifying the dynamic parameters of the robot, the dynamic model of the robot is established by the processing unit, and then the dynamic equation of the robot is constructed based on the dynamic model of the robot. Among them, the dynamic equation corresponds to the minimum parameter set of the dynamic model of the robot, and the above dynamic equation is specifically a least squares equation in the form of the minimum parameter set of the robot. Further, the processing unit constructs the excitation trajectory of the robot based on the dynamic equation of the robot, that is, the least squares equation, and optimizes it to obtain the optimal excitation trajectory of the robot. Further, the processing unit uses the optimal excitation trajectory of the robot as the expected trajectory of the robot, controls the robot to run according to the optimal excitation trajectory, and at the same time, in the process of controlling the robot to run according to the optimal excitation trajectory, the processing unit obtains the sensor data of each sensor installed on the robot. Further, the processing unit determines the noise information of the robot during the working process according to the dynamic equation and sensor data of the robot, and the noise information can be specifically used to characterize the environmental noise of the robot during the working process, as well as the coupling noise between the joints of the robot. On this basis, the processing unit repeatedly iteratively updates the robot's dynamic equation, namely the least squares equation, based on the robot's noise information to identify the robot's dynamic parameters.

[0030] In this way, in the process of identifying the dynamic parameters of the robot, the dynamic equation of the robot, i.e., the least squares equation, is iteratively updated repeatedly based on the noise information of the robot, which can reduce the coupling between the noise information of the robot during work and optimize the coupling between the noise information, thereby reducing the influence of the noise information on the dynamic parameter identification results of the robot and improving the accuracy of the dynamic parameter identification of the robot.

[0031] According to the third aspect of the present invention, another kinetic parameter identification device is proposed, comprising: a memory storing a program or instruction; a processor, and the processor implements the steps of the kinetic parameter identification method in any of the above technical solutions when executing the program or instruction. Therefore, the kinetic parameter identification device proposed in the third aspect of the present invention has all the beneficial effects of the kinetic parameter identification method in any of the above technical solutions in the first aspect, and will not be repeated here.

[0032] According to the fourth aspect of the present invention, a robot is proposed, comprising: the dynamic parameter identification device in the technical solution of the third aspect. The robot proposed in the fourth aspect of the present invention comprises the dynamic parameter identification device in the technical solution of the third aspect. Therefore, the robot proposed in the fourth aspect of the present invention has all the beneficial effects of the dynamic parameter identification device in the technical solution of the third aspect, which will not be repeated here.

[0033] According to the fifth aspect of the present invention, a readable storage medium is provided, on which a program or instruction is stored, and when the program or instruction is executed by a processor, a kinetic parameter identification method as in any of the above technical solutions is implemented. Therefore, the readable storage medium provided in the fifth aspect of the present invention has all the beneficial effects of the kinetic parameter identification method in any of the technical solutions in the first aspect, and no further description is given here.

[0034] Additional aspects and advantages of the present invention will become apparent from the following description or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0036] Figure 1 One of the flow charts of the method for identifying kinetic parameters according to an embodiment of the present invention is shown;

[0037] Figure 2 The second schematic flow chart of the method for identifying dynamic parameters according to the embodiment of the present invention is shown;

[0038] Figure 3 The third flowchart of the method for identifying dynamic parameters according to the embodiment of the present invention is shown;

[0039] Figure 4 A fourth flow chart of the method for identifying dynamic parameters according to an embodiment of the present invention is shown;

[0040] Figure 5 FIG5 shows a fifth flow chart of a method for identifying dynamic parameters according to an embodiment of the present invention;

[0041] Figure 6 FIG6 shows a sixth flow chart of a method for identifying dynamic parameters according to an embodiment of the present invention;

[0042] Figure 7 FIG7 shows a flow chart of a method for identifying dynamic parameters according to an embodiment of the present invention;

[0043] Figure 8 FIG8 is a flow chart showing a method for identifying dynamic parameters according to an embodiment of the present invention;

[0044] Fig. 9 One of the structural block diagrams of the kinetic parameter identification device according to an embodiment of the present invention is shown;

[0045] Fig.10 The second structural block diagram of the dynamic parameter identification device according to the embodiment of the present invention is shown;

[0046] Fig.11A structural block diagram of a robot according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0047] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

[0048] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0049] Combine the following Figures 1 to 11 , the dynamic parameter identification method and device, robot and readable storage medium provided in the embodiments of the present application are described in detail through specific embodiments and their application scenarios.

[0050] In one embodiment of the present invention, Figure 1 As shown, the kinetic parameter identification method may specifically include the following steps 102 to 110:

[0051] Step 102, establishing the dynamic equation of the robot in the form of a minimum parameter set;

[0052] Step 104, constructing the optimal excitation trajectory of the robot;

[0053] Step 106, controlling the robot to run according to the optimal excitation trajectory and acquiring sensor data of the robot;

[0054] Step 108, determining noise information according to the sensor data and the dynamic equation;

[0055] Step 110, iteratively updating the dynamic equation according to the noise information to identify the dynamic parameters of the robot.

[0056] The dynamic parameter identification method provided by the present invention is used to identify the dynamic parameters of a robot. In actual application, the robot may be a serial robot, which is not specifically limited here.

[0057] Specifically, in the dynamic parameter identification method provided by the present invention, in the process of identifying the dynamic parameters of the robot, the dynamic model of the robot is established, and then based on the dynamic model of the robot, the dynamic equation of the robot is constructed. Among them, the dynamic equation corresponds to the minimum parameter set of the dynamic model of the robot, and the above dynamic equation is specifically the least squares equation in the form of the minimum parameter set of the robot. Further, based on the dynamic equation of the robot, i.e., the least squares equation, the excitation trajectory of the robot is constructed and optimized to obtain the optimal excitation trajectory of the robot. Further, the optimal excitation trajectory of the robot is used as the expected trajectory of the robot, and the robot is controlled to run according to the optimal excitation trajectory. At the same time, in the process of controlling the robot to run according to the optimal excitation trajectory, the sensor data of each sensor installed on the robot is obtained. Further, according to the dynamic equation of the robot and the sensor data, the noise information of the robot during the working process is determined, and the noise information can be specifically used to characterize the environmental noise of the robot during the working process, and the coupling noise between the joints of the robot. On this basis, based on the noise information of the robot, the dynamic equation of the robot, i.e., the least squares equation, is repeatedly iterated and updated to identify the dynamic parameters of the robot.

[0058] In this way, in the process of identifying the dynamic parameters of the robot, the dynamic equation of the robot, i.e., the least squares equation, is iteratively updated repeatedly based on the noise information of the robot, which can reduce the coupling between the noise information of the robot during work and optimize the coupling between the noise information, thereby reducing the influence of the noise information on the dynamic parameter identification results of the robot and improving the accuracy of the dynamic parameter identification of the robot.

[0059] In some embodiments of the present invention, optionally, Figure 4 As shown, the above step 102 may specifically include the following steps 102a to 102c:

[0060] Step 102a, establishing a dynamic model of the robot;

[0061] Step 102b, determining a basic kinetic equation according to a basic parameter set of the kinetic model;

[0062] Step 102c, determining the kinetic equation in the form of the minimum parameter set according to the minimum parameter set of the kinetic model and the basic kinetic equation.

[0063] In this embodiment, in the process of establishing the dynamic equation of the robot in the form of the minimum parameter set, the dynamic model of the robot is established, and then the basic dynamic equation of the robot is constructed according to the basic parameter set of the dynamic model. On this basis, the dynamic equation of the robot in the form of the minimum parameter set, i.e., the least squares equation, is constructed according to the basic dynamic equation of the robot and the minimum parameter set of the dynamic model. In this way, the accuracy of the constructed dynamic equation is guaranteed, and then the accuracy of the identification result of the subsequent dynamic parameter identification based on the dynamic equation is guaranteed.

[0064] In the process of establishing the dynamic model of the robot, the Newton-Euler method is used to model the dynamics of the serial robot to obtain the dynamic model of the robot. The dynamic model in the state space form can be specifically expressed by the following formula (1):

[0065]

[0066] Among them, q is the angle vector of each joint of the robot, is the angular velocity of each joint of the robot, is the angular acceleration vector of each joint of the robot, M(q) is the symmetric positive definite inertia matrix, is the n×1 centrifugal force and Coriolis force matrix. The centrifugal force term is the combination of all squared joint velocity terms. The Coriolis force term is a combination of the product of any different joint velocities. G(q) is the gravity term. G(q) contains all terms related to the gravitational acceleration g. is the nonlinear friction matrix.

[0067] Furthermore, in the process of constructing the basic dynamics equation of the robot according to the basic parameter set of the dynamics model, the torque of each joint in the robot is regarded as a linear function of the dynamics parameters of the robot, and the following formula (2) is obtained:

[0068]

[0069] in, It is about q, and The nonlinear matrix, δ i represents the basic dynamic parameter set of the i-th connecting rod, δ i Contains 12 basic dynamic parameters including inertia tensor, mass, center of mass position and friction factor.

[0070] That is, δ i =[m i ,I ixx ,I ixy ,I ixz ,I iyy ,Iiyz ,I izz ,m i p ix ,m i p iy ,m i p iz ,f ic ,f iv ] T Among them, m i represents the mass of connecting rod i, I ixx ,I ixy ,I ixz ,I iyy ,I iyz and I izz represents the inertia tensor coefficient of the connecting rod i in the coordinate system, p ix 、p iy and p iz represents the center of mass position of connecting rod i, f ic represents the Coulomb friction factor, f iv represents the viscous friction factor.

[0071] On this basis, for a serial robot, there are a total of 12×n basic dynamic parameters. Based on this, the basic dynamic equation of the serial robot can be determined by the following formula (3):

[0072] τ=H full δ full , (3)

[0073] Where τ=[τ 1 ,…,τ n ] T , τ is the joint torque vector, H full is a matrix of size n×12n, δ full It is a matrix of size 12n×1.

[0074] Furthermore, in general, for a serial robot, not all of its dynamic parameters will affect the joint torque. On this basis, after obtaining the basic dynamic equation of the robot, the basic dynamic equation of the robot is transformed according to the minimum parameter set of the robot to obtain the dynamic equation of the robot in the form of the minimum parameter set, i.e., the least squares equation. Among them, the dynamic equation of the robot in the form of the minimum parameter set, i.e., the least squares equation, can be specifically determined by the following formula (4):

[0075] τ=H - β, (4)

[0076] Among them, H- is the full rank of the column, and β is the minimum parameter set of the robot.

[0077] In some embodiments of the present invention, optionally, Figure 5 As shown, the above step 104 may specifically include the following steps 104a to 104c:

[0078] Step 104a, constructing an excitation trajectory according to the robot's regression matrix;

[0079] Step 104b, determining an excitation trajectory model according to the excitation trajectory;

[0080] Step 104c, optimizing the trajectory parameters of the excitation trajectory model according to the parameter constraints of the excitation trajectory model to obtain the optimal excitation trajectory.

[0081] In this embodiment, in the process of constructing and optimizing the robot's excitation trajectory based on the robot's dynamic equation, i.e., the least squares equation, and obtaining the robot's optimal excitation trajectory, the robot's excitation trajectory is constructed according to the robot's regression matrix, and the experimental data, i.e., the sensor data, of the robot during operation according to the excitation trajectory are collected, and the optimization criteria of the excitation trajectory are constructed based on the collected experimental data, i.e., the sensor data. Further, based on the constructed excitation trajectory, a periodic excitation trajectory model composed of a finite Fourier series is determined. Further, based on the optimization criteria of the excitation trajectory and the parameter constraints of the excitation trajectory model, the trajectory parameters of the excitation trajectory model are optimized to obtain the robot's optimal excitation trajectory. In this way, the robot's excitation trajectory is optimized to obtain the robot's optimal excitation trajectory, which ensures the accuracy of the subsequent identification of the robot's dynamic parameters based on the optimal excitation trajectory and improves the accuracy of the robot's dynamic parameter identification results.

[0082] In the process of collecting experimental data, i.e., sensor data, during the operation of the robot according to the excitation trajectory, m experimental data are collected, and the collected experimental data H = [H - 1 T ,H - 2 T ,…,H - m T ] T and T = [τ^ 1 T ,τ^ 2 T ,…,τ^ m T ] T Among them, τ^ m T represents the measured torque τ^ of the robot collected for the mth time m The transposed matrix, H - mT represents the sensor data H of the robot collected for the mth time - m The transposed matrix of . That is, T = Hβ.

[0083] On this basis, in the process of constructing the optimization criterion of the excitation trajectory according to the collected experimental data, that is, the sensor data, the optimization criterion can be specifically determined by the following formula (5):

[0084] J=cond(H)=||H||·||H -1 ||, (5)

[0085] Where J represents the optimization criterion of the excitation trajectory.

[0086] Furthermore, in the process of determining a periodic excitation trajectory model composed of a finite Fourier series, the excitation trajectory corresponding to the excitation trajectory model can be specifically expressed by the following equation group (6):

[0087]

[0088] Where t represents time, q i0 、a l,i and b l,i is the parameter to be optimized in the excitation trajectory. i0 is the constant term of the i-th joint position, a l,i and b l,i is the amplitude of the sine and cosine functions of joint i. Further, w f =2πf, f is the operation period of the excitation trajectory. Further, N represents the order of the Fourier series. When N=5, the excitation trajectory is in the form of a quintic Fourier series.

[0089] Furthermore, in order to ensure the safety and periodicity of the robot during motion, the designed excitation trajectory should meet the physical limitations of the robot. That is, the designed excitation trajectory should meet the robot's angle limit, angular velocity limit, and angular acceleration limit. Furthermore, the angle, angular velocity, and angular acceleration at the start and end of the designed excitation trajectory are all zero.

[0090] On this basis, in the process of optimizing the trajectory parameters of the excitation trajectory model based on the optimization criteria of the excitation trajectory and the parameter constraints of the excitation trajectory model, the parameter constraints of the excitation trajectory model can be specifically expressed by the following equation group (7):

[0091]

[0092] Among them, q i,min and q i,max is the angle limit value of the robot, and is the robot’s angular velocity limit value, and is the robot’s angular acceleration limit, q i (t) and q i (t f ) are the angles of the start and end time of the excitation trajectory, and are the angular velocities at the start and end of the excitation trajectory, and are the angular accelerations at the start and end of the excitation trajectory, respectively.

[0093] In this way, by combining the above equations (6) and (7), the parameter constraints of the excitation trajectory model can be solved to obtain the parameter coefficient a to be optimized l,i and b l,i and q l,i .

[0094] In some embodiments of the present invention, optionally, Figure 2 As shown, the above step 110 may specifically include the following steps 110a to 110d:

[0095] Step 110a, initializing the covariance matrix of noise information;

[0096] Step 110b, updating the dynamic equation according to the covariance matrix;

[0097] Step 110c, determining a residual matrix according to the updated dynamic equation and the sensor data;

[0098] Step 110d, updating the covariance matrix according to the residual matrix, and iteratively updating the dynamic equation according to the updated covariance matrix until the covariance matrix converges.

[0099] In this embodiment, in the process of repeatedly iterating and updating the dynamic equation of the robot, i.e., the least square equation, based on the noise information of the robot, to identify the dynamic parameters of the robot, after obtaining the noise information of the robot during the working process, the initial covariance matrix of the noise information is calculated. On this basis, based on the covariance matrix of the noise information of the robot, the dynamic equation of the robot, i.e., the least square equation, is updated, and then the residual matrix of the robot is determined according to the sensor data of the robot and the updated dynamic equation. Further, according to the determined residual matrix, the covariance matrix of the noise information of the robot is updated. On this basis, it is determined whether the covariance matrix of the noise information of the robot is in a convergence state. When the covariance matrix of the noise information of the robot is in a convergence state, the iterative update of the covariance matrix and the dynamic equation is terminated to obtain the identification result of the dynamic parameters of the robot. When the covariance matrix of the robot's noise information is in an unconverged state, the robot's dynamic equation is updated again according to the updated covariance matrix, and then the robot's new residual matrix is ​​determined according to the robot's sensor data and the updated dynamic equation. According to the determined new residual matrix, the covariance matrix of the robot's noise information is updated again, and the above judgment process is repeated.

[0100] This cycle is repeated, and the robot's dynamic equation, i.e., the least squares equation, is iteratively updated based on the covariance matrix of the robot's noise information until the covariance matrix of the robot's noise information converges, and the iterative update of the covariance matrix and the dynamic equation is terminated to obtain the identification result of the robot's dynamic parameters. In this way, in the process of identifying the robot's dynamic parameters, the robot's dynamic equation, i.e., the least squares equation, is iteratively updated repeatedly based on the covariance matrix of the robot's noise information, which can reduce the coupling between the robot's noise information during the working process, optimize the coupling between the noise information, so that the covariance matrix of the robot's noise information eventually tends to a diagonal matrix, reducing the influence of the noise information on the robot's dynamic parameter identification results, and improving the accuracy of the robot's dynamic parameter identification.

[0101] As shown in the following formula (8), the noise information of the robot during operation can be specifically expressed by the error ε between the measured torque τ^ and the estimated torque τ of the robot:

[0102] ε=τ^-τ=τ^-H - β, (8)

[0103] Where τ^ represents the measured torque of the robot, τ represents the estimated torque of the robot, and ε represents the error between the measured torque τ^ and the estimated torque τ.

[0104] On this basis, in the process of calculating the initial covariance matrix of the noise information, the covariance matrix Ω of the noise information can be specifically determined by the following formula (9):

[0105] Ω=E(ε,ε T )=RR T / (mp), (9)

[0106] Among them, p is the dimension of the robot's minimum dynamic parameter set, m is the number of sampling times of experimental data, i.e. sensor data, and R is the robot's residual matrix, which can be specifically the residual vector R res =T-Hβ is the n×m dimensional matrix after joint reconstruction. Where β represents the minimum dynamic parameter set of the robot, which can be determined by the following formula (10):

[0107] β=(H T Ω -1 H) -1 H T Ω -1 T, (10)

[0108] Among them, H and T are the experimental data, i.e., sensor data, collected when the robot is running along the excitation trajectory, and H = [H - 1 T ,H - 2 T ,…,H - m T] T , T = [τ^ 1 T ,τ^ 2 T ,…,τ^ m T ] T .

[0109] Furthermore, in the process of updating the robot's dynamic equation, i.e., the least squares equation, based on the covariance matrix of the robot's noise information, the robot's dynamic equation, i.e., the least squares equation, can be updated specifically by the following formula (11):

[0110] τ new =Ω -1 / 2 τ old 、H- new =Ω -1 / 2 H- old , (11)

[0111] Among them, Ω represents the covariance matrix of noise information, τ new represents the updated dynamic equation, i.e., the least squares equation, τold represents the dynamic equation before updating, i.e., the least squares equation, H- new represents the updated experimental data, i.e., sensor data, H- old It represents the experimental data before updating, i.e., the sensor data.

[0112] On this basis, according to the robot's sensor data and the updated dynamic equation, the residual matrix R of the robot is determined, and then according to the following formula (12), the covariance matrix of the robot's noise information is updated according to the determined residual matrix:

[0113] Ω new =Ω old -1 / 2 ·R·R T ·Ω old -1 / 2 / (mp), (12)

[0114] Among them, Ω new represents the updated covariance matrix, Ω old represents the covariance matrix before updating, p is the dimension of the minimum dynamic parameter set of the robot, m is the number of sampling times of the experimental data, i.e., the sensor data, R is the residual matrix of the robot, R T is the transposed matrix of R.

[0115] Further, it is determined whether the updated covariance matrix is ​​in a convergence state, that is, whether the updated covariance matrix meets the convergence condition. The convergence condition is that each change of the covariance matrix is ​​lower than the set threshold threshold, that is, it is determined whether ||Ω new -Ω old ||≤threshold holds true. On this basis, in ||Ω new -Ω old When ||≤threshold holds, the covariance matrix is ​​determined to be in a convergence state, and the iterative update of the covariance matrix and the dynamic equation is terminated to obtain the identification result of the robot's dynamic parameters. new -Ω old When ||≤threshold does not hold, it is determined that the covariance matrix is ​​not in a converged state, and the above formulas (9) and (11) are iteratively cycled to repeatedly iterate and update the dynamic equation based on the covariance matrix until the covariance matrix converges. Before each iterative cycle, the covariance matrix must be updated by the above formula (12), and based on the covariance matrices before and after the update, it is determined whether the covariance matrix converges.

[0116] In actual application, the above value can be 1e-6, and no specific limitation is given here.

[0117] In some embodiments of the present invention, optionally, Figure 3 As shown, after the above step 110, the above kinetic parameter identification method may further include the following steps 112 and 116:

[0118] Step 112, initializing a weighted matrix of sensor data;

[0119] Step 114, updating the sensor data according to the weighted matrix;

[0120] Step 116, updating the weighting matrix according to the residual matrix finally determined in the process of updating the covariance matrix, and iteratively updating the sensing data according to the updated weighting matrix until the weighting matrix converges.

[0121] In this embodiment, after the dynamic equation is iteratively updated according to the noise information, the above-mentioned dynamic parameter identification method may further specifically include: initializing the weighted matrix of the sensor data of the robot, and then weighting the sensor data of the robot according to the weighted matrix to update the sensor data. Further, in combination with the threshold function, the weighted matrix is ​​updated according to the residual matrix finally determined in the above-mentioned updating covariance matrix process. Among them, the residual data, i.e., abnormal data, in the sensor data of the robot can be effectively separated by the threshold function. Further, it is determined whether the weighted matrix of the sensor data of the robot is in a convergence state. In the case that the weighted matrix of the sensor data of the robot is in a convergence state, the iterative update of the weighted matrix and the sensor data is terminated. In the case that the weighted matrix of the sensor data of the robot is in an unconvergent state, the sensor data of the robot is updated again according to the updated weighted matrix, and then the residual matrix of the robot is determined according to the sensor data and the dynamic equation of the robot, and the weighted matrix is ​​updated again according to the residual matrix, and the above-mentioned judgment process is repeated.

[0122] This cycle is repeated, and the sensor data and dynamic equations of the robot are iteratively updated based on the threshold function and the weighted matrix of the sensor data of the robot, until the weighted matrix of the sensor data of the robot converges, and the iterative update of the sensor data and the dynamic equations is terminated. In this way, in the process of identifying the dynamic parameters of the robot, the sensor data and dynamic equations of the robot are iteratively updated based on the threshold function and the weighted matrix of the sensor data of the robot, and the residual data, i.e., abnormal data, in the sensor data of the robot can be identified and eliminated, thereby reducing the influence of abnormal data on the dynamic parameter identification results of the robot and improving the accuracy of dynamic parameter identification of the robot.

[0123] In the process of initializing the weighted matrix of the robot's sensor data, the weighted matrix W of the sensor data is an all-1 matrix.

[0124] Furthermore, in the process of weighting the sensor data of the robot according to the weighting matrix to update the sensor data, the sensor data T can be updated specifically by the following formula (13):

[0125] T new =W×T old , (13)

[0126] Among them, T new represents the updated sensor data, T old represents the sensor data before updating, W is a weighting matrix of size (mn×1), in which the line corresponding to the abnormal data is 0, and the line corresponding to the normal data is 1.

[0127] At the same time, the column vector of the weight matrix W is expanded by columns, and p columns are expanded to obtain a column-expanded weight matrix W of size (mn×p) e , and according to the following formula (14), based on the column expansion weight matrix W e , update the sensor data H:

[0128] H new =W e ×H old , (14)

[0129] Among them, H new represents the updated sensor data, H old represents the sensor data before updating, W e The column expanded weight matrix W is obtained after the column vector is expanded.

[0130] Furthermore, in combination with the threshold function, according to the residual matrix finally determined in the above-mentioned process of updating the covariance matrix, in the process of updating the weighting matrix, the weighting matrix can be updated specifically by the following formula (15):

[0131] W new =min(W old ,ψ(R res )), (15)

[0132] Among them, W old represents the weight matrix before update, W newrepresents the updated weighted matrix, ψ() is the threshold function, and ψ() is specifically a T-type hard weight function. When the element in ψ() is greater than the set threshold, the output of ψ() is 0, and when the element in ψ() is less than or equal to the set threshold, the output of ψ() is 1. Under the action of the threshold function ψ(), the residual data in the sensor data that is greater than the set threshold, i.e., the abnormal data, can be effectively separated. Furthermore, the min() function returns the minimum value of its input element by element, thereby ensuring that the previous abnormal data will no longer be treated as normal data.

[0133] Further, it is determined whether the updated weighted matrix is ​​in a convergence state, that is, whether the updated weighted matrix meets the convergence condition. The convergence condition is that each change of the covariance matrix is ​​lower than the set threshold threshold, that is, it is determined whether ||W new -W old ||≤threshold holds true. On this basis, in ||W new -W old When ||≤threshold holds, the weighting matrix is ​​determined to be in a convergence state, and the iterative update of the weighting matrix, sensor data, and dynamic equations ends. new -W old When ||≤threshold does not hold, it is determined that the weighting matrix is ​​not in a converged state, and the above formulas (13) to (15) are iteratively cycled to repeatedly iterate and update the sensor data and the dynamic equation based on the weighting matrix until the weighting matrix converges.

[0134] It should be noted that, when the weighting matrix is ​​not in a converged state, after updating the weighting matrix, the updated weighting matrix will be used as the existing weighting matrix, that is, W old =W new , and then return to the step of iteratively updating the dynamic equation according to the noise information, and iteratively update the dynamic equation again based on the noise information of the updated sensor data, and after the covariance matrix of the noise information reaches a convergence state again, based on the above formulas (13) to (15), update the weighting matrix and the sensor data again, and repeat the above judgment process until the weighting matrix converges.

[0135] In some embodiments of the present invention, optionally, after updating the weighting matrix, the above-mentioned kinetic parameter identification method may further include the following step 118:

[0136] Step 118: When the weighted matrix has not converged, the dynamic equation is iteratively updated again according to the noise information of the updated sensor data.

[0137] In this embodiment, after updating the weighting matrix, if the weighting matrix has not converged, the updated weighting matrix will be used as the existing weighting matrix, and the above-mentioned step of iteratively updating the dynamic equation according to the noise information will be returned to, and the above-mentioned dynamic equation will be repeatedly iteratively updated according to the noise information of the updated sensor data, and after the covariance matrix of the noise information reaches a convergence state again, the weighting matrix and the sensor data will be iteratively updated again until the weighting matrix converges. In this way, a two-layer iterative architecture can be formed, that is, two layers of iterative loops are designed for the least squares equation. In the process of identifying the dynamic parameters of the robot based on the least squares equation, the influence of noise information and abnormal data on the identification results can be reduced respectively through the two layers of iterative loops, and by repeatedly solving the least squares equation, the convergence of the iterative results of the least squares equation is guaranteed, the convergence and accuracy of the final dynamic parameters are guaranteed, and the accuracy of the dynamic parameter identification is improved.

[0138] In summary, if Figure 6 As shown, the kinetic parameter identification provided by the embodiment of the present invention may specifically include the following steps 202 to 216:

[0139] Step 202, establishing a dynamic model of the serial robot;

[0140] Step 204, establishing a least squares equation in the form of a minimum parameter set;

[0141] Step 206, optimizing the excitation trajectory using a finite Fourier series;

[0142] Step 208, controlling the serial robot to run along the excitation trajectory and collecting sensor data during the trajectory;

[0143] Step 210, noise-weighted least squares inner iteration;

[0144] Step 212, determine: the covariance matrix Ω converges, if so, execute step 214, if not, execute step 210;

[0145] Step 214, least squares outer iteration to eliminate abnormal data;

[0146] Step 216, determine whether the weighted matrix W converges, if so, end the process, if not, execute step 210.

[0147] Among them, Figure 7 As shown, the above step 210 may specifically include the following steps 302 to 314:

[0148] Step 302, collecting experimental data H- and T during the excitation trajectory;

[0149] Step 304, initializing the noise covariance matrix Ω;

[0150] Step 306: weight the experimental data new =Ω -1 / 2 τ old , H- new =Ω -1 / 2 H- old ;

[0151] Step 308, calculate the minimum parameter set β = (H T Ω -1 H) -1 H T Ω -1 T;

[0152] Step 310, calculating the residual matrix R;

[0153] Step 312, update the covariance matrix Ω new =RR T / (mp);

[0154] Step 314, determine: ||Ω new -Ω old ||≤threshold, if yes, end the process, if no, execute step 306.

[0155] Furthermore, if Figure 8 As shown, the above step 214 may specifically include the following steps 402 to 412:

[0156] Step 402, collecting experimental data H- and T during the excitation trajectory;

[0157] Step 404, initializing the noise weighting matrix W;

[0158] Step 406, calculate the residual vector R res ;

[0159] Step 408, update the weight matrix W new =min(W old ,ψ(R res ));

[0160] Step 410, determine: ||W new -W old ||≤threshold, if yes, end the process, if no, execute step 412;

[0161] Step 412, update the weight matrix W old =W new .

[0162] Among them, Figure 8As shown, after executing step 412, the process returns to step 210 to execute the step of the inner layer iteration of the least squares based on noise weighting to realize the two-layer iteration loop of the least squares equation.

[0163] Thus, the dynamic parameter identification method proposed in the present invention designs two layers of inner and outer iterative loops for the least squares equation in the process of identifying the dynamic parameters of the robot based on the least squares equation. Among them, for the inner iterative loop, considering the coupling between the noises when the robot is working, the covariance matrix based on the noise information is continuously weighted to the measured data, and the least squares equation is repeatedly iterated so that the covariance matrix of the noise eventually tends to a diagonal matrix, thereby reducing the coupling between the noises and reducing the influence of the noise on the measured data. For the outer iterative loop, considering the abnormal data collected by the robot's sensor, the least squares equation and the measured data are iterated based on the threshold function, thereby eliminating the abnormal data and reducing the influence of the abnormal data on the identification result, thereby achieving the best fitting effect of the dynamic parameters and avoiding the influence of environmental interference on the measured data. Moreover, in the process of identifying the dynamic parameters of the robot based on the least squares equation, the least squares equation is repeatedly solved to ensure the convergence and accuracy of the final dynamic parameters and improve the accuracy of the dynamic parameter identification.

[0164] In one embodiment of the present invention, a dynamic parameter identification device is also provided. Fig. 9 As shown, Fig. 9 FIG. 5 is a block diagram showing a structure of a kinetic parameter identification device 500 according to an embodiment of the present invention. The kinetic parameter identification device 500 may specifically include the following processing unit 502:

[0165] The processing unit 502 is used to establish the dynamic equations of the robot in the form of a minimum parameter set;

[0166] The processing unit 502 is also used to construct the optimal excitation trajectory of the robot;

[0167] The processing unit 502 is also used to control the robot to run according to the optimal excitation trajectory and obtain the sensor data of the robot;

[0168] The processing unit 502 is further used to determine noise information based on the sensor data and the dynamic equation;

[0169] The processing unit 502 is further used to iteratively update the dynamic equation according to the noise information to identify the dynamic parameters of the robot.

[0170] The dynamic parameter identification device 500 provided in the embodiment of the present invention is used to identify the dynamic parameters of a robot. In actual application, the robot may be a serial robot, which is not specifically limited here.

[0171] Specifically, the dynamic parameter identification device 500 provided by the present invention includes a processing unit 502. In the process of identifying the dynamic parameters of the robot, the dynamic model of the robot is established by the processing unit 502, and then the dynamic equation of the robot is constructed based on the dynamic model of the robot. Among them, the dynamic equation corresponds to the minimum parameter set of the dynamic model of the robot, and the above dynamic equation is specifically a least squares equation in the form of the minimum parameter set of the robot. Further, the processing unit 502 constructs and optimizes the excitation trajectory of the robot based on the dynamic equation of the robot, that is, the least squares equation, to obtain the optimal excitation trajectory of the robot. Further, the processing unit 502 uses the optimal excitation trajectory of the robot as the expected trajectory of the robot, controls the robot to run according to the optimal excitation trajectory, and at the same time, in the process of controlling the robot to run according to the optimal excitation trajectory, the processing unit 502 obtains the sensor data of each sensor installed on the robot. Further, the processing unit 502 determines the noise information of the robot during the working process according to the dynamic equation and sensor data of the robot, and the noise information can be specifically used to characterize the environmental noise of the robot during the working process, as well as the coupling noise between the joints of the robot. On this basis, the processing unit 502 repeatedly iteratively updates the robot's dynamic equation, ie, the least square equation, based on the robot's noise information, so as to identify the robot's dynamic parameters.

[0172] In this way, in the process of identifying the dynamic parameters of the robot, the dynamic equation of the robot, i.e., the least squares equation, is iteratively updated repeatedly based on the noise information of the robot, which can reduce the coupling between the noise information of the robot during work and optimize the coupling between the noise information, thereby reducing the influence of the noise information on the dynamic parameter identification results of the robot and improving the accuracy of the dynamic parameter identification of the robot.

[0173] In some embodiments of the present invention, optionally, the processing unit 502 is specifically used to: initialize the covariance matrix of the noise information; update the dynamic equation according to the covariance matrix; determine the residual matrix according to the updated dynamic equation and the sensor data; update the covariance matrix according to the residual matrix, and iteratively update the dynamic equation according to the updated covariance matrix until the covariance matrix converges.

[0174] In some embodiments of the present invention, optionally, the processing unit 502 is specifically used to: initialize the weighting matrix of the sensor data; update the sensor data according to the weighting matrix; update the weighting matrix according to the residual matrix finally determined in the process of updating the covariance matrix, and iteratively update the sensor data according to the updated weighting matrix until the weighting matrix converges.

[0175] In some embodiments of the present invention, optionally, after updating the weighting matrix, the processing unit 502 is further configured to iteratively update the dynamic equation again according to the noise information of the updated sensor data when the weighting matrix has not converged.

[0176] In some embodiments of the present invention, optionally, the processing unit 502 is specifically used to: establish a dynamic model of the robot; determine the basic dynamic equation based on the basic parameter set of the dynamic model; determine the dynamic equation in the form of a minimum parameter set based on the minimum parameter set of the dynamic model and the basic dynamic equation.

[0177] In some embodiments of the present invention, optionally, the processing unit 502 is specifically used to: construct an excitation trajectory according to the regression matrix of the robot; determine an excitation trajectory model according to the excitation trajectory; optimize the trajectory parameters of the excitation trajectory model according to the parameter constraints of the excitation trajectory model to obtain the optimal excitation trajectory.

[0178] In one embodiment of the present invention, another kinetic parameter identification device is also provided. Fig.10 As shown, Fig.10 FIG. 6 is a block diagram of a kinetic parameter identification device 600 provided in an embodiment of the present invention. The kinetic parameter identification device 600 includes:

[0179] A memory 602, where programs or instructions are stored;

[0180] Processor 604, when executing the above program or instruction, implements the steps of the kinetic parameter identification method in any of the above embodiments.

[0181] The kinetic parameter identification device 600 provided in this embodiment includes a memory 602 and a processor 604. When the program or instructions in the memory 602 are executed by the processor 604, the steps of the kinetic parameter identification method in any of the above embodiments are implemented. Therefore, the kinetic parameter identification device 600 has all the beneficial effects of the kinetic parameter identification method in any of the above embodiments, which will not be repeated here.

[0182] Specifically, the memory 602 and the processor 604 may be connected via a bus or other means. The processor 604 may include one or more processing units, and the processor 604 may be a central processing unit (CPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), and other chips.

[0183] In one embodiment of the present invention, a robot is also provided. Fig.11 As shown, Fig.11 The structure block diagram of the robot 700 provided by the embodiment of the present invention is shown. The robot 700 has the dynamic parameter identification device 600 in the above embodiment. Therefore, the robot 700 has all the technical effects of the dynamic parameter identification device 600 in the above embodiment, which will not be described in detail here.

[0184] In actual application, the robot 700 may be a serial robot, which is not specifically limited herein.

[0185] The fifth aspect of the present invention provides a readable storage medium on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps of the kinetic parameter identification method in any of the above embodiments are implemented.

[0186] The readable storage medium provided in the embodiment of the present invention can implement the steps of the kinetic parameter identification method in any of the above embodiments when the program or instruction stored in the medium is executed by the processor. Therefore, the readable storage medium has all the beneficial effects of the kinetic parameter identification method in any of the above embodiments, which will not be described in detail here.

[0187] Specifically, the above-mentioned readable storage medium may include any medium capable of storing or transmitting information. Examples of readable storage media include electronic circuits, semiconductor memory devices, read-only memory (ROM), random access memory (RAM), compact disc read-only memory (CD-ROM), flash memory, erasable ROM (EROM), magnetic tape, floppy disk, optical disk, hard disk, optical fiber medium, radio frequency (RF) link, optical data storage device, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0188] In the description of this specification, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance unless otherwise clearly specified and limited; the terms "connection", "installation", "fixation" and the like should be understood in a broad sense, for example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be directly connected or indirectly connected through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0189] In the description of this specification, the description of the terms "one embodiment", "some embodiments", "specific embodiments", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0190] In addition, the technical solutions between the various embodiments of the present invention can be combined with each other, but it must be based on the fact that ordinary technicians in the field can implement it. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0191] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for identifying kinetic parameters, characterized in that: The method comprises: Establish the robot's dynamic equations in the form of a minimum parameter set; constructing an optimal excitation trajectory for the robot; Controlling the robot to run according to the optimal excitation trajectory and acquiring sensor data of the robot; Determining noise information based on the sensor data and the dynamic equation; The dynamic equation is iteratively updated according to the noise information to identify the dynamic parameters of the robot.

2. The method for identifying kinetic parameters according to claim 1, characterized in that: The iterative updating of the dynamic equation according to the noise information comprises: Initializing the covariance matrix of the noise information; updating the dynamic equation according to the covariance matrix; Determine a residual matrix according to the updated dynamic equation and the sensor data; The covariance matrix is ​​updated according to the residual matrix, and the dynamic equation is iteratively updated according to the updated covariance matrix until the covariance matrix converges.

3. The method for identifying kinetic parameters according to claim 2, characterized in that: After iteratively updating the dynamic equation according to the noise information, the method further includes: Initializing a weighting matrix of the sensor data; updating the sensor data according to the weighting matrix; The weighting matrix is ​​updated according to the residual matrix finally determined in the process of updating the covariance matrix, and the sensing data is iteratively updated according to the updated weighting matrix until the weighting matrix converges.

4. The method for identifying kinetic parameters according to claim 3, characterized in that: After updating the weighting matrix, the method further comprises: In the case that the weighting matrix has not converged, the dynamic equation is iteratively updated again according to the updated noise information of the sensor data.

5. The method for identifying kinetic parameters according to any one of claims 1 to 4, characterized in that: The dynamic equations of the robot in the form of a minimum parameter set are established, including: Establishing a dynamic model of the robot; Determining a basic kinetic equation based on a basic parameter set of the kinetic model; According to the minimum parameter set of the kinetic model and the basic kinetic equation, a kinetic equation in the form of a minimum parameter set is determined.

6. The method for identifying kinetic parameters according to any one of claims 1 to 4, characterized in that: The constructing of the optimal excitation trajectory of the robot comprises: constructing an incentive trajectory according to a regression matrix of the robot; determining an excitation trajectory model according to the excitation trajectory; According to the parameter constraints of the excitation trajectory model, the trajectory parameters of the excitation trajectory model are optimized to obtain the optimal excitation trajectory.

7. A kinetic parameter identification device, characterized in that: The device comprises: a processing unit for establishing the dynamic equations of the robot in the form of a minimum set of parameters; The processing unit is further used to construct an optimal excitation trajectory of the robot; The processing unit is further used to control the robot to run according to the optimal excitation trajectory and obtain sensor data of the robot; The processing unit is further used to determine noise information according to the sensor data and the dynamic equation; The processing unit is further used to iteratively update the dynamic equation according to the noise information to identify the dynamic parameters of the robot.

8. A kinetic parameter identification device, characterized in that: include: Memory, storing programs or instructions; A processor, wherein when executing the program or instruction, the processor implements the steps of the kinetic parameter identification method according to any one of claims 1 to 6.

9. A robot, characterized in that: include: The kinetic parameter identification device as claimed in claim 8.

10. A readable storage medium, characterized in that: The readable storage medium stores a program or instruction, and when the program or instruction is executed by the processor, the steps of the kinetic parameter identification method according to any one of claims 1 to 6 are implemented.

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