A robot friction torque online prediction method and device, and a computer readable storage medium

Abstract

The application discloses a robot friction torque online prediction method and device and a computer readable storage medium. For a joint type serial industrial robot to be identified, a dynamics model of the robot considering friction is established by using a Newton-Euler method, the dynamics model is linearized, five-term finite Fourier series are used to design an excitation trajectory, joint motion data of the robot under the excitation trajectory are collected to establish a regression equation set, the regression equation set is solved to obtain overall dynamics parameters to be identified, and the friction torque of the robot is calculated online according to the overall dynamics parameters to be identified, so that the friction torque of the robot can be predicted online.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of robot technology, and in particular to a robot friction torque online prediction method and device, and a computer readable storage medium. BACKGROUND

[0002] With the development of science and technology, industrial robots have been widely used in many industries, greatly improving productivity. In the fields of 3C manufacturing, man-machine cooperation, service and medical treatment, the application of mechanical arms is becoming more and more widespread and in-depth. At the same time, people's requirements for the performance of industrial robots are also getting higher and higher, requiring them to achieve more complex functions, which makes the requirements for the control precision, collision detection and other capabilities of the mechanical arm continue to rise. An accurate dynamics model is the cornerstone of high-precision trajectory control and external force perception of robots. Generally, the dynamics parameters of a robot include the friction parameters of the joints of the robot. However, the joint friction of the robot has nonlinear characteristics, and it is difficult to accurately identify the friction torque. SUMMARY

[0003] The technical problem to be solved by the present application is to provide a robot friction torque online prediction method and device, and a computer readable storage medium, which can realize online prediction of the friction torque of a robot.

[0004] To solve the above technical problems, the present application discloses a robot friction torque online prediction method in the first aspect, which is applied to a joint type serial industrial robot. The method comprises the following steps:

[0005] S1. A dynamics model of a robot considering friction is established by Newton-Euler method:

[0006]

[0007] In the formula: q、 are the position, velocity and acceleration vectors of the joints of the robot, M(q) is the inertia matrix of the robot arm, is a velocity term matrix related to centrifugal force and Coriolis force, g(q) is a gravity term, τ is a joint torque vector, represents the friction force / torque;

[0008] S2. The dynamics model is linearized to obtain the following dynamics expression form:

[0009]

[0010] wherein, is a regression matrix, β b is a minimum inertia parameter set of the dynamics parameters to be identified; is is a regression matrix related to the inertia base parameters, Regression matrix related to friction / torque; to-be-identified parameter set β b is divided into two subsets, one related to inertia-based parameters, denoted as β ib , and the other related to friction / torque, denoted as β f , β f = [f ci f vi b i ] T ;

[0011] S3. Design an excitation trajectory using Fourier series, and the excitation trajectory is defined as:

[0012]

[0013]

[0014]

[0015] where q i (t)、 and represent the position, angular velocity and angular acceleration of joint i at time t, q i0 is the initial joint angle; ω f is the base angular frequency of the Fourier series excitation trajectory, a k and b k are the coefficients of the Fourier series excitation trajectory;

[0016] S4. Control the robot to run according to the excitation trajectory, collect the motion data of each joint of the robot, and combine the linear regression equation set:

[0017]

[0018]

[0019]

[0020] where n is the number of dynamic data sets of the joints of the robot obtained when the robot moves according to the excitation trajectory, and each dynamic data set contains the input torque of all joints of the robot, the angle of the joint, the angular velocity of the joint and the angular acceleration of the joint.

[0021] S5. Solve the linear regression equation set to determine the to-be-identified parameter set β b ;

[0022] S6. Calculate the friction torque τ f at each time point by using the identified parameter set β b .

[0023]

[0024] S7. The frictional torque τ f Decomposed into friction observation matrix Φ for each joint i fi With the corresponding friction parameter β fi product

[0025] τ fi =Φ fi β fi ;

[0026] S8. Collect the frictional torque τ of each joint i at the current time k. fi (k) and joint motion information q i (k) Perform online friction estimation:

[0027] S81. Calculate the gain coefficient K of joint i. i (k):

[0028]

[0029] Where Rv = 0.999 is the observation noise variance, and P is the covariance matrix;

[0030] S82. Calculate the covariance matrix P of joint i. i (k):

[0031] P i (k)=(1-K i (k)Φ fi (k))P i (k-1)+R w ;

[0032] in, Let be the covariance matrix of the process noise;

[0033] S83. Calculate the friction parameters of joint i:

[0034] β fi (k)=β fi (k-1)+K i (k)(τ fi (k)-Φ fi (k)β fi (k-1))

[0035] S84. Use the updated friction parameters to obtain the friction torque of joint i at this moment:

[0036] τ fi (k)=Φ fi(k) β fi .

[0037] As an optional way, the solving the linear regression equation set determines the to-be-identified parameter set β b , comprising:

[0038] The solving the linear regression equation set determines the to-be-identified parameter set β b .

[0039] The second aspect of the present application discloses a robot friction torque online prediction device, comprising a memory, a processor and a computer program stored on the memory, and the processor executes the computer program to realize the steps in the method disclosed in the first aspect of the present application.

[0040] The third aspect of the present application discloses a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the steps in the method disclosed in the first aspect of the present application.

[0041] Compared with the prior art, the embodiment of the present application has the following beneficial effects:

[0042] Compared with the prior art, for the to-be-identified joint type serial industrial robot, the embodiment of the present application establishes the dynamics model of the robot considering friction by Newton-Euler method, then linearizes the dynamics model, uses five-term finite Fourier series to design an excitation trajectory, collects joint motion data of the robot under the excitation trajectory to establish a regression equation set, solves the regression equation set to obtain the overall to-be-identified dynamics parameters, and calculates the friction torque of the robot online according to the overall to-be-identified dynamics parameters, so that the friction torque of the robot can be predicted online. BRIEF DESCRIPTION OF DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0044] Figure 1 is a flowchart of a robot friction torque online prediction method disclosed by the embodiment of the present application;

[0045] Figure 2 is another flowchart of a robot friction torque online prediction method disclosed by the embodiment of the present application;

[0046] Figure 3is a structural schematic diagram of a robot friction torque online prediction device disclosed by an embodiment of the present application. DETAILED DESCRIPTION

[0047] In order to enable persons skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by persons skilled in the art without creative work fall within the protection scope of the present application.

[0048] Embodiment one

[0049] Referring to Figures 1-2 , the embodiment of the present application discloses a robot friction torque online prediction method, comprising the following steps:

[0050] S1. A dynamics model of a robot considering friction is established by Newton-Euler method:

[0051]

[0052] In the formula, q, are position, velocity and acceleration vectors of joints of the robot, M(q) is an inertia matrix of a robot arm, is a velocity term matrix related to centrifugal force and Coriolis force, g(q) is a gravity term, τ is a joint torque vector, represents friction force / torque.

[0053] S2. The dynamics model is linearized to obtain a dynamics expression as follows:

[0054]

[0055] In the formula, β is a regression matrix, β b is a minimum inertia parameter set containing dynamics parameters to be identified, such as basic inertia parameters of each link and friction parameters.

[0056] may be expressed as is a regression matrix related to inertia basic parameters, is a regression matrix related to friction force / torque, the parameter set β b may be divided into two subsets, one related to inertia basic parameters, which can be denoted as β ib , and the other related to friction force / torque, which can be denoted as β f , that is In the formula, β f= [f ci f vi b i ] T , f c is the Coulomb friction coefficient, f v is the viscous friction coefficient, b is the friction bias; β ib including the center of mass position, motor inertia, mass, link length, joint coordinate system offset and inertia moment.

[0057] S3. Design the excitation trajectory using Fourier series, the excitation trajectory is defined as:

[0058]

[0059]

[0060]

[0061] where q i (t)、 and represent the position, angular velocity and angular acceleration of the i-th joint at time t, q i0 is the initial joint angle, also known as the initial bias of the joint. ω f is the base angular frequency of the Fourier series excitation trajectory, a k and b k are the coefficients of the Fourier series excitation trajectory, which are to be determined parameters.

[0062] The role of the excitation trajectory is to drive the robot to move and generate as much data as possible to help accurately identify the dynamic model parameters. Fourier series is an ideal choice for excitation trajectory design, especially suitable for periodic motion robot parameter identification. Using Fourier series helps to generate smooth, low-noise, dynamic characteristics rich trajectories, while allowing parameters to be adjusted through optimization to meet specific needs.

[0063] S4. Control the series robot to run according to the excitation trajectory and collect joint motion data. Assuming that n sets of joint motion data are collected, the combined linear regression equation set is:

[0064]

[0065]

[0066]

[0067] Wherein, n is the number of the dynamic data groups of the joints of the robot obtained when the robot moves along the excitation trajectory, each of the dynamic data groups contains the input torque of all joints of the robot, the angle of the joint, the angular velocity of the joint and the angular acceleration of the joint.

[0068] S5. Solve the linear regression equation set to determine the to-be-identified parameter set β b ; the to-be-identified parameter set β b is used as the fixed parameter of the robot for subsequent position control.

[0069] S6. Calculate the to-be-identified parameter set β b by calculation. f

[0070]

[0071] S7. Split the friction torque τ f into the friction observation matrix Φ fi of each joint i and the product of the corresponding friction parameter β fi

[0072] τ fi = Φ fi β fi ;

[0073] S8. Collect the friction torque τ fi (k) and the joint motion information q i (k) of each joint i at the current k moment, perform online estimation of the friction:

[0074] S81. Calculate the gain coefficient K i (k) of the joint i:

[0075]

[0076] Wherein, Rv=0.999 is the variance of observation noise, and P is the covariance matrix.

[0077] S82. Calculate the covariance matrix P i (k) of the joint i:

[0078] P i (k) = (1-K i (k) Φ fi (k)) P i (k-1) + R w ;

[0079] Wherein, is the covariance matrix of process noise. ​​

[0080] S83. Calculate the friction parameter of joint i:

[0081] β fi (k)=β fi (k-1)+K i (k)(τ fi (k)-Φ fi (k)β fi (k-1))

[0082] S84. Obtain the friction torque of joint i at this moment using the updated friction parameter:

[0083] τ fi (k)=Φ fi (k)β fi .

[0084] Alternatively, a least square identification method under physical consistency constraint can be used for solving. After solving the to-be-identified parameter set β b , the friction parameter β f can be separated out, which is brought into the dynamics model for subsequent calculation of friction resistance of the motion trajectory, and subsequent dynamic control is realized.

[0085] Compared with the prior art, for the joint type serial industrial robot to be identified, the embodiment of the application establishes a dynamics model of the robot considering friction by Newton-Euler method, linearizes the dynamics model, uses five-term finite Fourier series to design an excitation trajectory, collects joint motion data of the robot under the excitation trajectory to establish a regression equation set, solves the regression equation set to obtain the overall to-be-identified dynamics parameter, and calculates the friction torque of the robot online according to the overall to-be-identified dynamics parameter, so that the friction torque of the robot can be predicted online.

[0086] Embodiment Two

[0087] Please refer to Figure 3 , Figure 3 is a structural schematic diagram of a robot friction torque online prediction device disclosed by the embodiment of the application, which comprises a memory 201, a processor 202 and a computer program stored on the memory, and the processor executes the computer program to realize the steps in the method disclosed in Embodiment One.

[0088] Embodiment Three

[0089] The embodiment of the application discloses a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the steps in the method disclosed in Embodiment One.

[0090] The embodiments disclosed in the content of the present application are only the preferred embodiments of the present application, and are only used to illustrate the technical solutions of the present application, but not limit the present application; although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that; the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced equivalently; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A robot friction torque online prediction method, characterized by, The method comprises the following steps: S1. A dynamic model of the robot considering friction is established by Newton-Euler method: wherein: q, are the position, velocity, acceleration vectors of the robot joint, respectively, M(q) is the robot arm inertia matrix, is the velocity term matrix related to centrifugal and Coriolis forces, g(q) is the gravity term, τ is the joint torque vector, represents the friction force / torque; S2. The dynamic model is linearized to obtain a dynamic expression form as follows: wherein, is a regression matrix, β b is a minimum inertia parameter set to be identified for the dynamics; is a regression matrix related to the inertia base parameters, is a regression matrix related to the inertia base parameters, is a regression matrix related to the friction / torque, b is divided into two subsets, one related to the inertia base parameters, denoted β ib and the other related to the friction / torque, denoted β f , β f = [f ci f vi b i ] T ; S3. An excitation trajectory is designed using Fourier series, and the excitation trajectory is defined as: where q i (t) is the position of the i th joint at time t, q (t) is the angular velocity of the i th joint at time t, and q (t) is the angular acceleration of the i th joint at time t, q i0 is the initial joint angle, q f is the base angular frequency of the Fourier series excitation trajectory, q k and q k are the coefficients of the Fourier series excitation trajectory. S4. The robot is controlled to run according to the excitation trajectory, motion data of each joint of the robot is collected, and a linear regression equation set is composed: Wherein, n is the number of dynamic data sets of the joints of the robot obtained when the robot moves according to the excitation trajectory, each dynamic data set contains input torque of all joints of the robot, angle of the joint, angular velocity of the joint and angular acceleration of the joint; S5. Solving the linear regression equations determines the set of parameters β to be identified b ; S6. The set of recognition parameters β is calculated b , the friction torque τ at each time is calculated f : S7. The friction torque τ f is split into a friction observation matrix Φ for each joint i fi and the product of the corresponding friction parameter β fi i: τ fi = Φ fi β fi ; S8. Collect friction torque τ of each joint i at current time k fi (k) and joint motion information q i (k), Perform friction online estimation, wherein: S81. Calculate the gain coefficient K of joint i i (k): Wherein, Rv=0.999 is an observation noise variance, and P is a covariance matrix; S82. Calculate the covariance matrix P of joint i i (k): P i (k) = (1 - K i (k) Φ fi (k)) P i (k - 1) + R w ; wherein is the covariance matrix of the process noise; S83. The friction parameter of joint i is calculated: β fi (k) = β fi (k - 1) + K i (k)(τ fi (k) - Φ fi (k)β fi (k - 1)) S84. The friction torque of joint i at this moment is obtained by using the updated friction parameter: τ fi (k) = Φ fi (k) β fi .

2. The robot friction torque online prediction method according to claim 1, characterized in that, Solving the linear regression equations determines the set of parameters β to be identified b comprising: The linear regression equation set is solved by using a least square identification method under a physical consistency constraint to determine the parameter set β to be identified b . 3.A robot friction torque online prediction device, comprising a memory, a processor and a computer program stored on the memory, characterized in that, The processor executes the computer program to realize the steps of the method in claim 1.

4. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the method in claim 1.

Images