An online identification method for dynamic parameters of articulated serial robots

Abstract

The present invention discloses a method for online identification of the dynamic parameters of an articulated serial robot, comprising the following steps: S1. For the articulated serial industrial robot to be identified, a robot dynamic model that takes friction into account is established; S2. The dynamic model of the articulated serial industrial robot is linearized; S3. An optimal excitation trajectory is designed for the identification experiment, using a five-term finite Fourier series to obtain the excitation trajectory by solving a constrained nonlinear optimization problem; S4. The serial robot runs the excitation trajectory while simultaneously collecting the position information of each joint of the robot and the driving current of each joint at each moment, and online calculating the parameters to be identified at each moment. The present invention can perform online identification of robot dynamic parameters, improves the efficiency of robot dynamic parameter identification, and can be used in industrial scenarios such as robot mobile handling, polishing and grinding, and heavy-load hydraulics.

Description

Technical Field

[0001] The invention belongs to the technical field of robots, and in particular relates to an online identification method for dynamic parameters of an articulated serial robot. Background Art

[0002] With the development of industrial automation and robotics, many application scenarios are becoming increasingly complex, and the requirements for robot control accuracy are also increasing. To achieve high-precision robot control, it is necessary to identify the robot's dynamic parameters. Traditional offline parameter identification methods usually require multiple experiments to collect sufficient data, which consumes a lot of time and resources. The results are highly dependent on the quality of the experimental data. If the data is noisy or incomplete, it may lead to inaccurate identification results. In addition, because environmental and operating conditions often change dynamically, traditional offline identification methods cannot adapt to these changes. With the advancement of computing technology, real-time data processing has become increasingly feasible. The popularity of modern sensors and computing platforms has enabled the implementation of online identification algorithms in practical applications. Summary of the Invention

[0003] The main purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art and to propose an online identification method for the dynamic parameters of an articulated serial robot.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions:

[0005] An online identification method for dynamic parameters of an articulated serial robot comprises the following steps:

[0006] S1. For the articulated serial industrial robot to be identified, establish a robot dynamics model that takes friction into account;

[0007] S2. Linearize the dynamic model of the articulated serial industrial robot;

[0008] S3. Design the optimal excitation trajectory for identification by solving a constrained nonlinear optimization problem using a five-term finite Fourier series to obtain the excitation trajectory.

[0009] S4. The robot runs the excitation trajectory, and at the same time collects the position information of each joint of the robot and the driving current of each joint at each moment, and calculates the parameters to be identified online at each moment.

[0010] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0011] 1. The present invention can perform online identification of robot dynamic parameters, improving the efficiency of robot dynamic parameter identification, and can be used in industrial scenarios such as robot mobile handling, polishing and grinding, and heavy-load hydraulics; online identification has real-time adaptability and can update parameters in real time, enabling the robot to quickly adapt to environmental changes and dynamic loads.

[0012] 2. Improve control accuracy. The online identification method of the present invention can continuously adjust parameters through real-time feedback, thereby improving the accuracy and response speed of the control system.

[0013] 3. Reduce experimental requirements. The method of the present invention does not require a large amount of offline experimental data and can perform parameter identification during normal operation, saving time and resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is an overall flow chart of the method of the present invention;

[0015] Figure 2 This is a flow chart of online calculation of parameters to be identified at each moment in the method of the present invention. DETAILED DESCRIPTION

[0016] The present invention will be described in further detail below with reference to the embodiments and drawings, but the embodiments of the present invention are not limited thereto.

[0017] Example

[0018] like Figure 1 As shown, the present invention provides an online identification method for dynamic parameters of an articulated serial robot, comprising the following steps:

[0019] S1. For the articulated serial industrial robot to be identified, establish a robot dynamics model that takes friction into account. In this embodiment, specifically:

[0020] For the articulated serial industrial robot to be identified, the robot dynamics model considering friction is established using the Newton-Euler method, which is expressed as:

[0021]

[0022] Among them, q, are the position, velocity and acceleration vectors of the robot joints, M(q) is the inertia matrix of the robot arm, is the velocity term matrix related to centrifugal force and Coriolis force, g(q) is the gravity term, τ is the joint torque vector, represents friction;

[0023] For articulated serial robots, the friction at the joints needs to be considered. In the established robot dynamics model, for joint i, the Coulomb viscous friction model is used to model the joint friction, which is expressed as:

[0024]

[0025] Among them, f c is the Coulomb friction coefficient, f v is the viscous friction coefficient, b is the friction bias, and sign is the sign function.

[0026] S2. Linearize the dynamic model of the articulated serial industrial robot. In this embodiment, specifically:

[0027] The established dynamic model of the articulated serial industrial robot is linearized, and the full-rank minimum regression matrix expression and the corresponding minimum inertia parameter set are obtained by using methods such as QR decomposition and parameter recombination. Finally, the following dynamic expression is obtained:

[0028]

[0029] in, is the observation matrix, β b It is the minimum inertia parameter set that includes the basic inertia parameters and friction parameters of each connecting rod waiting for the identification of dynamic parameters.

[0030] S3. Design an optimal excitation trajectory for identification. Use five finite Fourier series to obtain the excitation trajectory by solving a constrained nonlinear optimization problem. In this embodiment, the excitation trajectory is defined as:

[0031]

[0032]

[0033]

[0034] Among them, q i (t), and They represent the position, angular velocity and angular acceleration of joint i at time t, respectively, and q i0 is the joint angle at the initial moment; ω f is the fundamental frequency of the Fourier series excitation trajectory, a k and b k are the coefficients of the Fourier series excitation trajectory and are parameters to be determined.

[0035] Design the optimal excitation trajectory for identification, use the minimization of the observation matrix condition number as the optimization index of the excitation trajectory, and design the optimization objective function of the excitation trajectory as:

[0036]

[0037] st:

[0038] q min ≤q≤q max

[0039]

[0040]

[0041]

[0042]

[0043] Among them, cond is the observation matrix condition number, q min and q max are the lower and upper limits of the rotational positions of each joint of the robot, and are the lower and upper limits of the robot's joint speeds, and are the lower and upper limits of the acceleration of each joint of the robot; are the robot joint velocity and acceleration at the initial moment, are the velocity and acceleration of each joint of the robot at the termination moment respectively.

[0044] S4, the robot runs the excitation trajectory, and at the same time collects the position information of each joint of the robot and the driving current of each joint at each moment, and calculates the parameters to be identified online at each moment; in this embodiment, Figure 2 As shown, specifically including:

[0045] S41, the robot runs the excitation trajectory, at the initial sampling moment, set the inertia parameter to be identified β b (0) and the initial value of the covariance matrix P(0);

[0046] The second and subsequent moments include the following steps:

[0047] S42, processing the position information q and the sampled current through a first-order low-pass filter, obtaining the angular velocity and angular acceleration respectively through the second-order differentiation of the position information q, and calculating the sampled torque of each other joint through the sampled current;

[0048] S43, based on the trajectory information q(k) of the robot joint at the current moment (recorded as moment k), To calculate the observation matrix at the current moment Note Φ b (k)

[0049]

[0050] S44, according to the Φ calculated at the last moment b (k-1) and P(k-1) calculate the current moment gain matrix K(k), the formula is as follows:

[0051]

[0052] Where I is the unit diagonal matrix, is Φ b The transposed matrix of

[0053] S45, according to the inertia parameter β to be identified at the previous moment b (k-1) is used to calculate the estimation error ε, the formula is:

[0054] ε=τ(k)-Φ b (k)β b (k-1)

[0055] Among them, τ(k) is the sampling torque at the current moment;

[0056] S46, the inertia parameter β to be identified at the current moment b (k) is updated, the formula is:

[0057] β b (k) = β b (k-1)+K(k)ε

[0058] S47. Calculate the covariance matrix P at the current moment and reserve it for calculation at the next moment. The formula is:

[0059] P(k)=(IK(k)Φ b (k))P(k-1)

[0060] S48, the robot executes the excitation trajectory, and performs recursive calculations until the trajectory ends, and finally obtains the inertial parameter identification result β b .

[0061] The method of this embodiment can perform online identification of robot dynamic parameters, improve the efficiency of robot dynamic parameter identification, and can be used in industrial scenarios such as robot mobile handling, polishing and grinding, and heavy-load hydraulics.

[0062] It should also be noted that, in this specification, terms such as "comprises", "includes" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or apparatus comprising the element.

[0063] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An online identification method for dynamic parameters of an articulated serial robot, characterized in that: The following steps are involved: S1. For the articulated serial industrial robot to be identified, establish a robot dynamics model that takes friction into account; specifically: For the articulated serial industrial robot to be identified, the robot dynamics model considering friction is established using the Newton-Euler method, which is expressed as: Among them, q, are the position, velocity and acceleration vectors of the robot joints, M(q) is the inertia matrix of the robot arm, is the velocity term matrix related to centrifugal force and Coriolis force, g(q) is the gravity term, τ is the joint torque vector, represents friction; For articulated serial robots, the friction at the joints needs to be considered. In the established robot dynamics model, for joint i, the Coulomb viscous friction model is used to model the joint friction, which is expressed as: Among them, f c is the Coulomb friction coefficient, f v is the viscous friction coefficient, b is the friction bias, and sign is the sign function; S2. Linearize the dynamic model of the articulated serial industrial robot; specifically: The established dynamic model of the articulated serial industrial robot is linearized, and the full-rank minimum regression matrix expression and the corresponding minimum inertia parameter set are obtained using the QR decomposition parameter recombination method. Finally, the following dynamic expression is obtained: in, is the observation matrix, β b is the minimum inertia parameter set of the dynamic parameters to be identified, and the dynamic parameters to be identified include but are not limited to the basic inertia parameters and friction parameters of each connecting rod; S3. Design the optimal excitation trajectory for identification by solving a constrained nonlinear optimization problem using a five-term finite Fourier series to obtain the excitation trajectory. S4: The robot runs the excitation trajectory, and at the same time collects the position information of each joint of the robot and the driving current of each joint at each moment, and calculates the parameters to be identified online at each moment, including: S41, the robot runs the excitation trajectory, at the initial sampling moment, set the inertia parameter to be identified β b (0) and the initial value of the covariance matrix P(0); The second and subsequent moments include the following steps: S42, processing the position information q and the sampled current through a first-order low-pass filter, obtaining the angular velocity and angular acceleration respectively through the second-order differentiation of the position information q, and calculating the sampled torque of each other joint through the sampled current; S43, according to the current moment, that is, the trajectory information q(k) of the robot joint at moment k, To calculate the observation matrix at the current moment Note Φ b (k) S44, according to the Φ calculated at the last moment b (k-1) and P(k-1) calculate the current moment gain matrix K(k), the formula is as follows: Where I is the unit diagonal matrix, is Φ b The transposed matrix of S45, according to the inertia parameter β to be identified at the previous moment b (k-1) is used to calculate the estimation error ε, the formula is: ε=τ(k)-Φ b (k)b b (k-1) Among them, τ(k) is the sampling torque at the current moment; S46, the inertia parameter β to be identified at the current moment b (k) is updated, the formula is: b b (k)=β b (k-1)+K(k)e S47. Calculate the covariance matrix P at the current moment and reserve it for calculation at the next moment. The formula is: P(k)=(I-K(k)Φ b (k))P(k-1) S48, the robot executes the excitation trajectory, and performs recursive calculations until the trajectory ends, and finally obtains the inertial parameter identification result β b .

2. The online identification method of dynamic parameters of an articulated serial robot according to claim 1, characterized in that: In step S3, the excitation trajectory is defined as: Among them, q i (t), and They represent the position, angular velocity and angular acceleration of joint i at time t, respectively, and q i0 is the joint angle at the initial moment; ω f is the fundamental frequency of the Fourier series excitation trajectory, a k and b k are the coefficients of the Fourier series excitation trajectory and are parameters to be determined.

3. The online identification method of dynamic parameters of an articulated serial robot according to claim 2, characterized in that: In step S3, the optimal excitation trajectory for identification is designed, and the optimization index of the excitation trajectory is minimized by the observation matrix condition number. The optimization objective function of the excitation trajectory is designed as: st: q min ≤q≤q max Among them, cond is the observation matrix condition number, q min and q max are the lower and upper limits of the rotational positions of each joint of the robot, and are the lower and upper limits of the robot's joint speeds, and are the lower and upper limits of the acceleration of each joint of the robot; are the robot joint velocity and acceleration at the initial moment, are the velocity and acceleration of each joint of the robot at the termination moment respectively.

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