A method for identifying mechanical arm dynamics parameters

Abstract

The application provides a mechanical arm dynamics parameter identification method based on a mechanical arm, and belongs to the field of mechanical arm dynamics. Specifically, the following steps are included: according to the configuration and structural parameters of the mechanical arm, a parameter table of each connecting rod coordinate system of the mechanical arm is established through the Denavit-Hartenberg method; according to the parameter table of each connecting rod coordinate system of the mechanical arm, a mechanical arm dynamics model is established through Newton-Euler equation, the mechanical arm dynamics model parameters are linearized, and a minimum parameter set is obtained; the optimization of the mechanical arm excitation trajectory is carried out through fmincon in the matlab toolbox, a Fourier series-based trajectory is adopted, parameter identification is carried out based on the least square method, and parameter estimation is carried out on the parameter linearized mechanical arm dynamics model through the least square method. The identified dynamics parameters are more accurate, more universal and more operable.

Description

Technical Field

[0001] This invention relates to a method for identifying dynamic parameters of a robotic arm, belonging to the field of robotic arm dynamics technology. Background Technology

[0002] Currently, there are two main implementation methods for force control in robotic arms: torque sensors and current loop control. Torque sensors are expensive but more accurate. To reduce costs, current loop control can achieve the same functions in force control applications, including collision detection and drag teaching.

[0003] Identifying the dynamic parameters of robotic arms is a hot research topic both domestically and internationally, and many researchers have proposed corresponding solutions, mainly including disassembly measurement methods and model CAD acquisition methods. Disassembly measurement methods determine the dynamic parameters by disassembling the robotic arm's joints and conducting experiments. However, certain dynamic parameters and friction coefficients of the robotic arm are unavailable, and the disassembly process is time-consuming, labor-intensive, and inefficient. Model CAD methods can directly obtain dynamic parameter values, but there are corresponding errors compared to the actual dynamic parameter values ​​of the robotic arm, and the dynamic parameters also vary depending on manufacturing factors and the specific characteristics of each robotic arm. Summary of the Invention

[0004] The purpose of this invention is to provide a method for identifying the dynamic parameters of a robotic arm, which identifies more accurate, more universal, and more operable dynamic parameters.

[0005] To achieve the above objectives, the present invention employs the following technical solution:

[0006] Step 1: Based on the configuration and structural parameters of the robotic arm, establish a parameter table for the coordinate system of each link of the robotic arm using the Denavit-Hartenberg method;

[0007] Step 2: Based on the coordinate system parameter table of each link of the robotic arm, establish the dynamic model of the robotic arm through the Newton-Euler equations, linearize the parameters of the dynamic model of the robotic arm, and obtain the minimum parameter set;

[0008] The specific linearized robotic arm dynamics model is as follows:

[0009]

[0010] In the formula: Let X represent the set of dynamic parameters of the robotic arm, and q represent the joint position. Indicates joint velocity. Indicates joint acceleration;

[0011] Step 3: Optimize the excitation trajectory of the robot arm's linearized dynamic model using fmincon in the MATLAB toolbox, employing a trajectory based on Fourier series. The specific formula is as follows:

[0012]

[0013] Among them, q i (t) represents the position of the i-th joint at time t, w f Based on the frequency, q i0 Let a be the joint position constant. i ,b i Let i represent the amplitude of the sine and cosine, t represent time, and N represent the total order.

[0014] The identifiability of dynamic parameters is greatly related to the selected joint motion. If the selection is inappropriate, some parameters may not be identifiable. This paper adopts a trajectory based on Fourier series.

[0015] Step 4: Parameter identification based on the least squares method. The parameters of the linearized robotic arm dynamics model are estimated using the least squares method.

[0016] Preferably, the parameter linearization robotic arm dynamics model is as follows:

[0017]

[0018] In the formula: Let X represent the regression matrix of the robotic arm's dynamic parameters, and let X represent the set of the robotic arm's dynamic parameters.

[0019] Preferably, the set of dynamic parameters of the robotic arm is represented by 13 parameters:

[0020] X = [m i ,I ixx ,I iyy ,I izz ,I ixy ,I ixz ,I iyz ,m i p ix ,m i p iy ,m i p iz ,F s ,F v J m ];

[0021] Where, m i Indicates the mass of the connecting rod, I ixx ,I iyy ,Iizz ,I ixy ,I ixz ,I iyz p represents the moment of inertia of the link. ix ,p iy ,p iz Indicates the position of the center of mass, F s F represents the Coulomb coefficient of friction. v J represents the coefficient of viscous friction. m This represents the motor's inertia.

[0022] Preferably, the specific method for obtaining the minimum parameter set is as follows: The minimum dynamic parameter set is obtained by solving for the rank of the parameter regression matrix; when the regression matrix is ​​removed... After removing a column from the matrix, the rank of the matrix decreases by 1. These are the identifiable dynamic parameters, which are then grouped into the minimum parameter set. When a column of the regression matrix H(q,q,q) is removed, the rank of the matrix remains unchanged. These are the identifiable dynamic parameters, which are then deleted.

[0023] Preferably, the constraints for optimizing the robotic arm's excitation trajectory using fmincon in the MATLAB toolbox are as follows:

[0024] |q i (t)|≤q i,max

[0025]

[0026]

[0027] Where, q i,max This represents the maximum joint angle of the robotic arm. For the angular velocity of the robotic arm joints, This represents the maximum angular acceleration of the robotic arm joints.

[0028] Preferably, the parameter table of each link coordinate system of the robotic arm includes the number of links, link length, link twist, link offset, and joint angle.

[0029] Preferably, the parameter estimation of the linearized robotic arm dynamics model using the least squares method is specifically formulated as follows:

[0030]

[0031] The advantages of this invention are as follows: The proposed method for identifying dynamic parameters involves collecting torque values, positions, velocities, and accelerations of each joint during the actual process, given a pre-optimized excitation trajectory for the robotic arm. These measured values ​​are then input into the dynamic model, and the actual values ​​of the robotic arm's dynamic parameters can be calculated using the least squares method. Since the identification process is essentially consistent with actual experiments, the identified dynamic parameters are more accurate, more universal, and more operable. Attached Figure Description

[0032] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0033] Figure 1 This is a schematic diagram of the process structure of the present invention.

[0034] Figure 2 This is a schematic diagram of the excitation trajectory of the present invention.

[0035] Figure 3 This is a schematic diagram comparing the calculated values ​​with the actual values ​​of the present invention. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] A method for identifying the dynamic parameters of a robotic arm is proposed, which identifies more accurate, more universal, and more operable dynamic parameters.

[0038] To achieve the above objectives, the present invention employs the following technical solution:

[0039] Step 1: Based on the configuration and structural parameters of the robotic arm, establish a coordinate system parameter table for each link of the robotic arm using the Denavit-Hartenberg method; the coordinate system parameter table for each link of the robotic arm includes the number of links, link length, link twist, link offset, and joint angle.

[0040] Step 2: Based on the coordinate system parameter table of each link of the robotic arm, establish the dynamic model of the robotic arm through the Newton-Euler equation, linearize the parameters of the dynamic model of the robotic arm, and obtain the minimum parameter set.

[0041] The dynamic model of the robotic arm is as follows:

[0042]

[0043] The specific linearized robotic arm dynamics model is as follows:

[0044]

[0045] In the formula: Let X represent the set of dynamic parameters of the robotic arm, and q represent the joint position. Indicates joint velocity. This indicates joint acceleration.

[0046] Preferably, the set of dynamic parameters of the robotic arm is represented by 13 parameters:

[0047] X = [m i ,I ixx ,I iyy ,I izz ,I ixy ,I ixz ,I iyz ,m i p ix ,m i p iy ,m i p iz ,F s ,F v J m ];

[0048] Where, m i Indicates the mass of the connecting rod, I ixx ,I iyy ,I izz ,I ixy ,I ixz ,I iyz p represents the moment of inertia of the link. ix ,p iy ,p iz Indicates the position of the center of mass, F s F represents the Coulomb coefficient of friction. v J represents the coefficient of viscous friction. m This represents the motor's inertia.

[0049] Preferably, the specific method for obtaining the minimum parameter set is as follows: The minimum dynamic parameter set is obtained by solving for the rank of the parameter regression matrix; when the regression matrix is ​​removed... After deleting a certain column, the rank of the matrix decreases by 1, and the identifiable dynamic parameters are then summarized into the set of minimum parameters. This is achieved when the regression matrix is ​​removed. If the rank of the matrix remains unchanged after a certain column, then it is an unidentifiable dynamic parameter and should be deleted.

[0050] Step 3: Optimize the robot arm's excitation trajectory using fmincon in the MATLAB toolbox, employing a trajectory based on Fourier series. The specific formula is as follows:

[0051]

[0052] Among them, q i (t) represents the position of the i-th joint at time t, w f Based on the frequency, q i0 Let a be the joint position constant. i ,b i The amplitudes of the sine and cosine values ​​are given.

[0053] The specific constraints are as follows:

[0054] |q i (t)|≤q i,max

[0055]

[0056]

[0057] Where, q i,max This represents the maximum joint angle of the robotic arm. For the angular velocity of the robotic arm joints, This represents the maximum angular acceleration of the robotic arm joints.

[0058] Step 4: Parameter identification based on the least squares method. The parameters of the linearized robotic arm dynamics model are estimated using the least squares method; the specific formula is as follows:

[0059]

[0060] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for identifying the dynamic parameters of a robotic arm, characterized in that, Includes the following steps: Step 1: Based on the configuration and structural parameters of the robotic arm, establish a parameter table for the coordinate system of each link of the robotic arm using the Denavit-Hartenberg method; Step 2: Based on the coordinate system parameter table of each link of the robotic arm, establish the dynamic model of the robotic arm through the Newton-Euler equations, linearize the parameters of the dynamic model of the robotic arm, and obtain the minimum parameter set; The specific linearized robotic arm dynamics model is as follows: In the formula: Let X represent the set of dynamic parameters of the robotic arm, and q represent the joint position. Indicates joint velocity. Indicates joint acceleration; Step 3: Optimize the excitation trajectory of the robot arm's linearized dynamic model using fmincon in the MATLAB toolbox, employing a trajectory based on Fourier series. The specific formula is as follows: Where, q i (t) represents the position of the i-th joint at time t, w f Based on the frequency, q i0 Let a be the joint position constant. i ,b i Let i represent the amplitude of the sine and cosine, t represent the time, and N represent the total order. Step 4: Parameter identification based on the least squares method. The parameters of the linearized robotic arm dynamics model are estimated using the least squares method.

2. The method for identifying the dynamic parameters of a robotic arm according to claim 1, characterized in that, The set of dynamic parameters for the robotic arm is represented by 13 parameters: X=[m i ,I ixx ,I iyy ,I izz ,I ixy ,I ixz ,I iyz ,m i p ix ,m i p iy ,m i p iz ,F s ,F v ,J m ]; Where, m i Indicates the mass of the connecting rod, I ixx ,I iyy ,I izz ,I ixy ,I ixz ,I iyz p represents the moment of inertia of the link. ix ,p iy ,p iz Indicates the position of the center of mass, F s F represents the Coulomb coefficient of friction. v J represents the coefficient of viscous friction. m This represents the motor's inertia.

3. The method for identifying the dynamic parameters of a robotic arm according to claim 1, characterized in that, The specific method for obtaining the minimum parameter set is as follows: The minimum dynamic parameter set is obtained by solving for the rank of the parameter regression matrix. When the regression matrix is ​​removed... After deleting a certain column, the rank of the matrix decreases by 1, and the identifiable dynamic parameters are then summarized into the set of minimum parameters. This is achieved when the regression matrix is ​​removed. If the rank of the matrix remains unchanged after a certain column, then it is an unidentifiable dynamic parameter and should be deleted.

4. The method for identifying the dynamic parameters of a robotic arm according to claim 1, characterized in that, The specific constraints for optimizing the robotic arm's excitation trajectory using fmincon in the MATLAB toolbox are as follows: |q i (t)|≤q i,max Where, q i,max This represents the maximum joint angle of the robotic arm. For the angular velocity of the robotic arm joints, This represents the maximum angular acceleration of the robotic arm joints.

5. The method for identifying the dynamic parameters of a robotic arm according to claim 1, characterized in that, The parameter table for each link of the robotic arm includes the number of links, link length, link twist, link offset, and joint angle.

6. The method for identifying the dynamic parameters of a robotic arm according to claim 1, characterized in that, The parameter estimation formula for the linearized robotic arm dynamics model using the least squares method is as follows:

Images