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CN-121989243-A - Dynamic simulation damping parameter solving method based on gravity compensation mechanism

CN121989243ACN 121989243 ACN121989243 ACN 121989243ACN-121989243-A

Abstract

The invention relates to the technical field of industrial robots, in particular to a dynamic simulation damping parameter solving method based on a gravity compensation mechanism, which comprises the following steps: acquiring a joint basic parameter set of an industrial robot to be tested, determining a test boundary condition according to the joint basic parameter set, and constructing a test task sequence based on the test boundary condition. According to the invention, the fusion weight of static and dynamic rigidity is intelligently adjusted according to the motion speed, acceleration or force control frequency band characteristics in specific application tasks, so that the limitation of a single parameter under complex and variable working conditions is avoided. The parameter fusion mechanism aiming at specific task characteristics enables the generated joint comprehensive stiffness parameters to truly reflect physical responses of the robot in different working stages, reduces prediction errors between a physical simulation environment and actual physical operation, and meets the severe requirements of high-precision scenes such as semiconductor packaging and the like on micron-level positioning precision and force control stability.

Inventors

  • Lin sida
  • Jia Chupei
  • GAO QIANHUI
  • Jia Chuyu

Assignees

  • 智擎体元(上海)科技有限公司

Dates

Publication Date
20260508
Application Date
20260225

Claims (10)

  1. 1. The dynamic simulation damping parameter solving method based on the gravity compensation mechanism is characterized by comprising the following steps of: Acquiring a joint basic parameter set of an industrial robot to be tested, determining a test boundary condition according to the joint basic parameter set, and constructing a test task sequence based on the test boundary condition; performing zero load attitude calibration on the industrial robot to be detected, monitoring tail end attitude change data, and determining a zero load deformation reference position according to the tail end attitude change data; Loading a hierarchical static load to the industrial robot to be tested based on the zero load deformation reference position, collecting a static response data sequence under the hierarchical static load, and processing the static response data sequence by using a weighted least square method to obtain a joint static stiffness coefficient; applying dynamic frequency domain excitation to the industrial robot to be detected, collecting dynamic time domain response signals of the industrial robot to be detected under the dynamic frequency domain excitation, and extracting joint dynamic stiffness characteristics corresponding to the dynamic time domain response signals through cross power spectral density analysis; acquiring task characteristic parameters of a target simulation scene, constructing a self-adaptive weighted fusion model according to the task characteristic parameters, and carrying out parameter fusion on the joint static stiffness coefficient and the joint dynamic stiffness characteristic by utilizing the self-adaptive weighted fusion model to obtain the joint comprehensive stiffness parameter for physical simulation.
  2. 2. The method for solving the dynamic simulation damping parameters based on the gravity compensation mechanism according to claim 1, wherein the step of performing zero load gesture calibration on the industrial robot to be tested, monitoring terminal pose change data, and determining a zero load deformation reference position according to the terminal pose change data comprises the following steps: Controlling the industrial robot to be tested to move to a preset initial test posture, and locking a joint brake of a non-test shaft; Applying a preset standard trace test load to the tail end of the industrial robot to be tested under the initial test gesture, and acquiring the gesture change data of the tail end in real time by using a displacement sensor; calculating displacement deviation in the terminal pose change data, and comparing the displacement deviation with a preset rigid body deformation threshold; If the displacement deviation is larger than the preset rigid body deformation threshold, adjusting the joint angle of the industrial robot to be tested, and repeating the steps of applying the standard trace test load and collecting the terminal pose change data until the displacement deviation is smaller than or equal to the preset rigid body deformation threshold; And determining the joint angle when the displacement deviation is smaller than or equal to the preset rigid body deformation threshold as the zero load deformation reference position, and recording the joint state at the moment as an anti-coupling interference reference state.
  3. 3. The method for solving the dynamic simulation damping parameters based on the gravity compensation mechanism according to claim 1, wherein the step of loading the industrial robot to be tested with a hierarchical static load based on the zero-load deformation reference position and collecting a static response data sequence under the hierarchical static load comprises the following steps: Setting a hierarchical loading step length and a maximum loading threshold according to rated load parameters in the joint basic parameter set; starting from the zero load deformation reference position, controlling a moment loading device to gradually increase the load moment according to the step loading step length until the maximum loading threshold value is reached; In the loading and maintaining stage of each stage of load moment, the data acquisition card is utilized to synchronously record the joint input moment data and the joint output angle data; Monitoring time domain fluctuation variance of the joint output angle data, and extracting a steady state moment value and a steady state angle value at the current moment when the time domain fluctuation variance is smaller than a preset steady state judgment index; and traversing all loading levels, and generating the static response data sequence containing a plurality of groups of steady-state moment values and steady-state angle values.
  4. 4. The method for solving the dynamic simulation damping parameters based on the gravity compensation mechanism according to claim 3, wherein the step of obtaining the joint static stiffness coefficient by processing the static response data sequence by using a weighted least square method depends on a static stiffness calculation formula containing stability weight, wherein the static stiffness calculation formula is as follows: , Wherein, the Representing the static stiffness coefficient of the joint, Representing the total number of data samples in the static response data sequence, An index representing the current data sample point, Represent the first The steady state torque values for the individual sampling points, Representing the average of all of the steady state torque values, Represent the first The steady state angle values of the individual sampling points, Represents the average of all of the steady state angle values, Represent the first Confidence weight coefficients of the sampling points; The confidence coefficient weight coefficient is calculated by the following formula: Wherein Expressed in natural constant As a function of the base of the exponentiation, Represent the first Residual values of the sampling points relative to a straight line obtained by performing linear regression fit on the static response data sequence based on a common least square method, Representing the inherent noise variance of the measuring sensor.
  5. 5. The method for solving the dynamic simulation damping parameters based on the gravity compensation mechanism according to claim 1, wherein the step of applying dynamic frequency domain excitation to the industrial robot to be tested, collecting dynamic time domain response signals of the industrial robot to be tested under the dynamic frequency domain excitation, and extracting joint dynamic stiffness characteristics corresponding to the dynamic time domain response signals through cross power spectral density analysis comprises the following steps: Generating a sinusoidal sweep frequency signal with the frequency coverage of 0.1Hz to 5Hz, and inputting the sinusoidal sweep frequency signal to a servo controller of the industrial robot to be tested as a moment instruction; Setting the signal sampling frequency to be at least 10 times of the highest frequency of the excitation signal, and synchronously collecting the dynamic time domain response signals comprising the dynamic moment time domain sequence and the dynamic angle time domain sequence; Windowing and cutting off the dynamic moment time domain sequence and the dynamic angle time domain sequence by utilizing a hanning window function, and inhibiting spectrum leakage; Calculating a cross power spectral density function and a self power spectral density function based on the windowed dynamic moment time domain sequence and the dynamic angle time domain sequence; And calculating a frequency response function according to the cross power spectral density function and the self power spectral density function, and extracting the dynamic stiffness characteristics of the joint corresponding to different frequency points from the frequency response function.
  6. 6. The method for solving the dynamic simulation damping parameters based on the gravity compensation mechanism according to claim 5, wherein the step of extracting the dynamic stiffness characteristic of the joint corresponding to the dynamic time domain response signal through cross power spectral density analysis depends on a dynamic stiffness frequency domain calculation formula, and the dynamic stiffness frequency domain calculation formula is: , Wherein, the Expressed at a specific frequency The dynamic stiffness characteristics of the joint described below, Representing an operation of taking the real part of the complex number, Representing cross-power spectral densities between the dynamic moment time domain sequence and the dynamic angle time domain sequence, A self-power spectral density representing the dynamic angular time domain sequence, Representing a dynamic hardening correction factor for compensating for nonlinear hardening effects at high frequencies, A logarithmic function with a base of 10 is shown, Representing the natural frequency reference value of the system for normalizing the frequency variable.
  7. 7. The method for solving the dynamic simulation damping parameters based on the gravity compensation mechanism according to claim 1, wherein the step of obtaining the task feature parameters of the target simulation scene, constructing an adaptive weighted fusion model according to the task feature parameters, and performing parameter fusion on the joint static stiffness coefficient and the joint dynamic stiffness characteristic by using the adaptive weighted fusion model comprises the following steps: Analyzing a robot motion track file in the target simulation scene, and extracting a speed vector set and an acceleration vector set of track points; Calculating a root mean square speed value of the speed vector set, performing spectrum analysis on the speed vector set to identify a dominant frequency component, and taking the root mean square speed value and the dominant frequency component together as the task characteristic parameters; constructing a self-adaptive weight distribution strategy based on a Sigmoid function, and calculating a dynamic weight coefficient according to the root mean square speed value in the task characteristic parameters; performing linear interpolation on the joint static stiffness coefficient and the joint dynamic stiffness characteristic by utilizing the dynamic weight coefficient to generate the joint comprehensive stiffness parameter; the self-adaptive weighted fusion model specifically comprises the following steps: , Wherein, the Representing the overall stiffness parameter of the joint, Representing the dynamic weight coefficient of the model, Representing the dominant frequency component; the dynamic weight coefficient The calculation formula of (2) is as follows: , Wherein, the Representing the root mean square velocity value in question, Representing a critical speed threshold that distinguishes static from dynamic scenes, The sensitivity coefficient indicating weight switching.
  8. 8. The method for solving the dynamic simulation damping parameters based on the gravity compensation mechanism according to claim 1, further comprising the steps of sensor calibration and calibration before acquiring the joint basic parameter set of the industrial robot to be tested: Connecting an external torque sensor and an external displacement sensor for data acquisition in series to standard metering equipment to obtain a standard reading sequence; Calculating a linear deviation ratio between the measured value of the external torque sensor and the standard reading sequence; If the linear deviation rate exceeds a preset precision tolerance range, generating a calibration compensation matrix, and updating the internal parameters of the external torque sensor by using the calibration compensation matrix; And checking a timestamp synchronization protocol of the external torque sensor and the external displacement sensor, and ensuring that a clock synchronization error when the static response data sequence and the dynamic time domain response signal are acquired is smaller than a preset microsecond threshold value.
  9. 9. The method for solving the dynamic simulation damping parameters based on the gravity compensation mechanism according to claim 1, further comprising the steps of parameter verification and optimization after obtaining the joint comprehensive stiffness parameters for physical simulation: importing the joint comprehensive rigidity parameters into a physical simulation engine to construct a virtual robot model; reproducing loading conditions corresponding to the static response data sequence in the physical simulation engine, and outputting simulation deformation; Calculating a relative error rate between the simulated deformation and the actually measured deformation; And if the relative error rate is greater than a preset simulation precision standard, correcting the model parameters in the self-adaptive weighted fusion model, and re-executing the parameter fusion step until the relative error rate meets the preset simulation precision standard.
  10. 10. The gravity compensation mechanism-based dynamic simulation damping parameter solving method according to claim 1, wherein the joint basic parameter set comprises a joint type identifier, a rated torque parameter, a reduction ratio parameter and a motor inertia parameter, and the joint type identifier is used for distinguishing a rotary joint from a linear joint; When the joint type identifier indicates a rotary joint, the unit of the joint static stiffness coefficient is configured to be newton meters per degree; when the joint type identifier indicates a linear joint, the units of the joint static stiffness coefficient are configured to be newtons per millimeter; The step of constructing the test task sequence further comprises the step of automatically matching the corresponding load loading direction and displacement measurement dimension according to the joint type identifier, so as to ensure that the test coordinate system is consistent with the joint motion freedom degree direction.

Description

Dynamic simulation damping parameter solving method based on gravity compensation mechanism Technical Field The invention relates to the technical field of industrial robots, in particular to a dynamic simulation damping parameter solving method based on a gravity compensation mechanism. Background Along with the development of industrial robots to 'fine and force control' (such as the requirement of +/-0.1 mm positioning precision of semiconductor packaging), high-precision physical simulation becomes a core support, and the actual debugging cost and risk can be reduced by simulating and verifying track precision and force control stability, and joint rigidity is a key parameter for determining simulation precision. In the prior art, when the stiffness parameters of the robot joint are obtained, experimental values are generally obtained by depending on theoretical design parameters provided by manufacturers or through a general static test platform. The theoretical value of a manufacturer is usually set based on the average design standard of batch products, and individual characteristics of the single robot in terms of manufacturing tolerance, assembly clearance difference, abrasion aging caused by long-term operation and the like are ignored, so that inherent deviation exists between physical properties of a simulation model and an actual physical object. Meanwhile, the traditional testing means is excessively focused on static loading testing under a steady state condition, so that the linear deformation resistance of the joint in a static or extremely low-speed state can be only represented, and nonlinear dynamic characteristics and frequency domain response differences of the robot joint in high-speed dynamic operation or high-frequency force control operation cannot be captured. Therefore, improvements are needed. Disclosure of Invention The invention aims to solve the defects in the prior art, and provides a dynamic simulation damping parameter solving method based on a gravity compensation mechanism. In order to achieve the above purpose, the invention adopts the following technical scheme that the method for solving the dynamic simulation damping parameters based on the gravity compensation mechanism comprises the following steps: Acquiring a joint basic parameter set of an industrial robot to be tested, determining a test boundary condition according to the joint basic parameter set, and constructing a test task sequence based on the test boundary condition; performing zero load attitude calibration on the industrial robot to be detected, monitoring tail end attitude change data, and determining a zero load deformation reference position according to the tail end attitude change data; Loading a hierarchical static load to the industrial robot to be tested based on the zero load deformation reference position, collecting a static response data sequence under the hierarchical static load, and processing the static response data sequence by using a weighted least square method to obtain a joint static stiffness coefficient; applying dynamic frequency domain excitation to the industrial robot to be detected, collecting dynamic time domain response signals of the industrial robot to be detected under the dynamic frequency domain excitation, and extracting joint dynamic stiffness characteristics corresponding to the dynamic time domain response signals through cross power spectral density analysis; acquiring task characteristic parameters of a target simulation scene, constructing a self-adaptive weighted fusion model according to the task characteristic parameters, and carrying out parameter fusion on the joint static stiffness coefficient and the joint dynamic stiffness characteristic by utilizing the self-adaptive weighted fusion model to obtain the joint comprehensive stiffness parameter for physical simulation. Preferably, the step of performing zero load gesture calibration on the industrial robot to be tested, monitoring terminal pose change data, and determining a zero load deformation reference position according to the terminal pose change data includes: Controlling the industrial robot to be tested to move to a preset initial test posture, and locking a joint brake of a non-test shaft; Applying a preset standard trace test load to the tail end of the industrial robot to be tested under the initial test gesture, and acquiring the gesture change data of the tail end in real time by using a displacement sensor; calculating displacement deviation in the terminal pose change data, and comparing the displacement deviation with a preset rigid body deformation threshold; If the displacement deviation is larger than the preset rigid body deformation threshold, adjusting the joint angle of the industrial robot to be tested, and repeating the steps of applying the standard trace test load and collecting the terminal pose change data until the displacement deviation is smaller than or equal to the pres