CN114043485A - Dynamic error compensation method for robot online detection system - Google Patents
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Abstract
The invention discloses a dynamic error compensation method for a robot online detection system, which comprises the following steps: constructing a robot detection system, and modeling based on the robot detection system to obtain a kinematic model and a time-varying parameter model of the robot detection system; calibrating parameters based on the kinematic model, and collecting calibration attitude information; performing iterative optimization on the time-varying parameter model according to the calibration posture information to obtain target time-varying motion parameters; and optimizing the calibration attitude information, compensating the time-varying motion parameters until the time-varying motion parameters meet a set threshold value, and evaluating and detecting. The invention further improves the precision stability and the online monitoring efficiency of the robot vision system.
Description
Technical Field
The invention belongs to the field of robot vision detection, and particularly relates to a dynamic error compensation method for a robot online detection system.
Background
Industrial robots are widely used for on-line, repetitive work tasks such as measuring, welding, assembly, etc. One of the common solutions is to install a laser scanner at the end of the robot to build the detection system. When the robot runs irregularly for a long time, internal heat generation and the ambient temperature are important factors for reducing the repeatability. Due to geometric motion calibration errors, part thermal deformation and joint lubricating viscosity change, the tail end position coordinate error of the robot is large and has time-varying property. The traditional solution is to dynamically calibrate the robot and compensate for the variability of the error based on a kinematic model. However, excessive parameters in the kinematic modeling cause model redundancy and invalid calibration poses, which can result in reduction of the dynamic accuracy and efficiency of the system.
Robot kinematics calibration and error compensation have been extensively studied. David Daney et al propose an algorithm based on a constrained optimization method for selecting the optimal pose for robot calibration. Lee s, et al, define a generalized error model that includes joint clearance errors, kinematic parameter errors, and actuator control errors. Based on a sensitivity analysis method, main error parameters influencing the positioning accuracy of the robot are analyzed, and the positioning accuracy of the robot is improved from 0.85mm to 0.26 mm. Luo X. et al propose an error model based on DH model and joint clearance, which can predict the positioning accuracy of the joint at any position. The effectiveness of the error compensation model is verified through simulation of the curved path, and the positioning accuracy of the robot can be improved by about 76.46% through the error compensation method. Zhang L. et al propose a centrosymmetric static friction model, describe the hysteresis effect of joint friction in combination with a piecewise function, and identify the kinetic parameters of 6 robot joints based on a hybrid optimization algorithm and a genetic algorithm.
Much research has focused on the effects of time-varying factors. To compensate for temperature-dependent errors caused by thermal expansion of the robot, Eastwood and Webb propose two linear models to predict length and rotation in the kinematic model. Neubauer et al developed a method to identify open-chain robot dynamic parameters and predict friction related parameters based on feed forward control to reduce the effects of temperature. Yi S. et al propose a real-time dynamic error compensation method based on a vision system, and associate kinematic parameters with temperature by a joint-by-joint method through heating and cooling robots. The Lubrano E and R.Clavel are combined with the robot motor parameters to find out the temperature dependence, and the dynamic error is compensated through the controller. R.li and y.zhao analyzed the temperature distribution of the robot based on the finite element method. A robot preheat experiment was used to analyze the significant correlation between temperature and kinematic parameters. Rafal Kluz et al used the Lillefors distribution to determine the effect of temperature on random variables and reduced the error according to the Shapiro-Wilk test. In addition, the point with the minimum error is taken as the optimal calibration posture in the assembly working space. Zhu et al propose an on-line thermal compensation method based on binocular stereo vision. After analyzing the time-varying patterns of the joint parameters, the important parameters are selected to compensate for the thermal errors of all joint parameters. By reducing the parameters corresponding to the thermal drift mode, the time consumption can be effectively reduced, and the dynamic error of the KUKA robot can be reduced to +/-0.1 mm.
According to recent research, the precision stability of the robot vision system depends on the redundancy of the model, and the efficiency of online detection depends on effective calibration pose. Therefore, the following two problems can be summarized:
(1) the redundancy of the model is reduced, and the repeatability precision of the system is improved. The research on robot deformation mainly focuses on the identification of time-varying motion parameters. However, due to the non-linearity of the robot model, the parameters may interact with each other, resulting in no time-varying correlation.
(2) The optimal position of dynamic compensation is optimized, and the dynamic compensation efficiency is improved. In order to balance the repeatability and efficiency of the system, the posture of the robot is selected to be critical. In the existing method, a Jacobian matrix is used as an optimization standard, but high-order errors are ignored, so that the calibration attitude is invalid.
Disclosure of Invention
In order to solve the above problems, the present invention provides the following solutions: a dynamic error compensation method for a robot online detection system comprises the following steps:
constructing a robot detection system, and modeling based on the robot detection system to obtain a kinematic model and a time-varying parameter model of the robot detection system;
calibrating parameters based on the kinematic model, and collecting calibration attitude information;
performing iterative optimization on the time-varying parameter model according to the calibration posture information to obtain target time-varying motion parameters;
and optimizing the calibration attitude information, compensating the time-varying motion parameters until the time-varying motion parameters meet a set threshold value, and evaluating and detecting.
Preferably, the robot detection system comprises a robot, a 3D laser scanner and a calibration ball;
performing parameter calibration based on the kinematic model, wherein the process of collecting calibration posture information comprises performing parameter calibration based on the calibration posture of the robot scanned by the calibration ball; and scanning calibration balls in different directions by changing the posture of the robot to complete long-period cyclic measurement of the calibration posture, and obtaining the calibration posture information.
Preferably, the iterative optimization of the time-varying parameter model is performed according to the calibration posture information, and the process of obtaining the target time-varying motion parameter includes identifying one parameter each time, identifying that the time-varying parameter extends to the time-varying parameter model, and identifying that the time-invariant parameter is cut from the time-varying parameter model.
Preferably, the identification process comprises, at the time of the identification,
(1) clipping time-varying parameters in an external coordinate system;
(2) identifying time-varying parameters having a sigmoidal trend;
(3) deleting redundant parameters based on error sensitivity;
(4) and (4) repeating the steps (2) and (3) to carry out iterative optimization.
Preferably, the process of identifying time-varying parameters having a sigmoidal trend comprises,
fitting an S-shaped function to obtain parameter balance time; obtaining a kinematic parameter sequence based on the calibration attitude information; normalizing the kinematic parameter sequence into data centered at zero, fitting the normalized parameter sequence by a tanh function, identifying a parameter having a maximum equilibration time as a time-varying parameter and adding to the time-varying parameter model.
Preferably, the error sensitivity-based redundancy parameter deletion is obtained by QR decomposition of a model jacobian matrix.
Preferably, the iterative optimization process further includes, when the system error fluctuation range increases, canceling the operation of expansion or clipping, and resetting to the model of the previous iteration step; otherwise, the model is updated.
Preferably, the calibration pose information is optimized by obtaining a minimized number of poses by deleting points having the same effect on the first order differential of the parameter vector.
Preferably, compensating the time-varying motion parameter to meet a set threshold comprises, before starting the detection task, scanning a designated calibration sphere to assess whether the error of the system exceeds the set threshold; if the error exceeds a threshold value, scanning an optimized calibration attitude and compensating the time-varying kinematic parameters to obtain a modified transformation relation; and if the error does not exceed the threshold value, starting a detection task of the robot vision online measurement system.
The invention discloses the following technical effects:
according to the dynamic error compensation method for the robot online detection system, provided by the invention, a kinematic model with low redundancy is rapidly obtained by combining time-varying sensitivity, error sensitivity and an iterative optimization process, and the repeatability precision of the system is greatly improved by reducing the redundancy of the model; meanwhile, the repeatability and the efficiency of the system are balanced by the attitude optimization method based on the K-means clustering algorithm, the optimal position of dynamic compensation is optimized, and the dynamic compensation efficiency is improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a flow chart of a method of an embodiment of the present invention;
FIG. 2 is a schematic diagram of a kinematic model of a robot vision system in accordance with an embodiment of the present invention;
fig. 3 is a flowchart of iterative optimization of kinematic parameters according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
As shown in FIG. 1, the invention provides a dynamic error compensation method for a robot online detection system, which comprises the steps of firstly, establishing a kinematic model of the system, and calibrating model parameters by changing the posture of the robot and scanning calibration balls from different directions. And then, completing a continuous scanning experiment of the robot from a cold state to a hot state, collecting a group of dynamic measurement data, and identifying the time-varying motion parameters of the robot by adopting an iterative optimization method. In addition, the efficiency of dynamic compensation is improved by optimizing the calibration attitude. Before starting the inspection task, the designated calibration ball is scanned to evaluate whether the error of the system exceeds a threshold value. If the error exceeds a threshold, scanning the optimized calibration attitude and compensating time-varying kinematic parameters to obtain a modified transformation relation.
The method comprises the following specific steps:
the method comprises the following steps: constructing a robot vision online detection system and acquiring long-period cycle data;
the robot vision online detection system is constructed by integrating a laser scanner into the end of a robot to construct a measurement system. The system consists of three parts: an industrial robot (FANUC LR Mate 200iD), a 3D laser scanner, and a calibration object consisting of four standard spheres. The laser scanner can directly obtain the point cloud and centroid of the measured object, and the Root Mean Square Error (RMSE) of the scanning ball can reach 0.0107 mm. The coordinates of the four spherical centers on the calibration object are measured and calibrated by a three-coordinate measuring machine, and the precision is +/-0.01 mm.
And (4) building a robot vision online detection system, and building a kinematics model and calibrating. N initial calibration postures are obtained by randomly adjusting the robot, and long-period cyclic measurement of the calibration postures is completed to obtain a group of data.
As shown in fig. 2, the calibration process for the kinematic model is as follows: the center coordinate of the sphere is defined as Pi. By moving the robot randomly, the 3D sensor scans each sphere from different poses. The center of a circle in the laser scanner Camera Coordinate System (CCS) is defined as Cij. Where j is the serial number of the scanning pose, herein equal to 200.
The conversion from the camera coordinate system to the User Coordinate System (UCS) satisfies equation (1).
Wherein,
can be divided into three parts:
in the formula (2), the reaction mixture is,
is a transformation from a reference coordinate system (BCS) to a User Coordinate System (UCS).
Is the transformation from the Tool Coordinate System (TCS) to the reference coordinate system (BCS).
Is a transformation from the Camera Coordinate System (CCS) to the Tool Coordinate System (TCS).
And
based on Euler angle modeling, an
And (3) constructing a simplified MDH model based on the robot, as shown in formulas (3) to (5).
Where Trans (x, y, z) is the translation transform, Rotx,Roty,RotzIs a rotational transformation. Joint is the number of robot joints. In order to avoid the singularity of the robot (J2 and J3 are approximately parallel), the MDH model of the robot is simplified, and six parameters are deleted, including: d2,β1,β3,β4,β5,β6。
Then, the total differential of equation (1) is calculated, and equations (2) to (5) are substituted, and the result can be further simplified to a jacobian matrix:
in the formula,
to correspond to CijThe robot joint angle vector of (a) may be obtained from a register of the robot or calculated from an inverse kinematics model of the system.
Is a vector of 30 kinematic parameters:
(x0,y0,z0,r0,w0,p0,...,θi,di,ai,αi,βi,…,xe,ye,ze,re,we,pe)T
wherein 6 parameters are limited to zero: theta1＝d1＝θ6＝d6＝a6＝α60. Based on LM (Levenberg-Marquarelt) algorithm, parameter vector can be calibrated
Due to the fact that
Since the angle error of the joint cannot be avoided because of reading from the temporary memory, the angle error can be compensated by changing the form of the jacobian matrix, as shown in equation (7). Based on LM algorithm, corrected joint angle can be obtained
Step two: identifying time-varying parameters and carrying out model iterative optimization;
one time-varying parameter is identified and extended into the model at a time, or one time-invariant parameter is identified and clipped from the model. As shown in fig. 3, the flow of time-varying parameter identification includes:
step1. cut external coordinatesTime-varying parameters in the system. Because the robot base and the detection target are arranged on the same platform, and the laser scanner and the robot are in rigid connection, the initial judgment can be performed
And
are time invariant parameters.
Identify time-varying parameters with a sigmoidal trend. Generally, during the continuous movement of the robot from start-up, the kinematic parameters are mainly dependent on the material expansion and the variation of the lube oil damping. The temperature change of each part of the robot conforms to Newton's cooling law, and when the heating power of the robot is higher than the heat exchange power with the environment, the temperature changes exponentially. When the heating power and the heat exchange power are equal, a steady state equilibrium is reached. Therefore, by fitting the sigmoid function, the equilibrium time of each parameter can be obtained quickly. From the results of the long-period cyclic measurements, a sequence of kinematic parameters can be obtained. By normalizing the parameter sequence to zero-centered data, as shown in equation (9)
Then, the normalized parameter sequence can be fitted by the tanh function and will have the maximum equilibration time (i.e., minimum a)k) Is identified as a time-varying parameter and added to the model, which parameter can be obtained by equation (10).
Where t is the sample data sequence, n is the sequence length,
is the sequence of the parameter k. c. CkIs the deviation from the steady state. a iskIs the amount of scaling of the time axis (inverse of the maximum equilibrium time). bkIs the deviation of the time axis.
Step3. redundant parameters are deleted based on error sensitivity. If the influence of a certain kinematic parameter on the disturbance of the robot tip is small, the parameter can be ignored whether or not the parameter changes with time. The most common error sensitivity analysis method is the QR decomposition of a model Jacobian matrix, as shown in equation (11). The R matrix is an upper triangular matrix. The eigenvalues of the R matrix represent the gradient of each motion parameter. The smaller the parameter characteristic value is, the smaller the influence on the tail end of the robot is, and the smaller the parameter characteristic value can be deleted from the model.
And step4, iterative optimization. In order to avoid recognition of erroneous results, steps 2 and 3 are repeated, only one time-varying parameter or time-invariant parameter is recognized each time, and the parameters are expanded/clipped one by one.
And (5) verifying the precision stability of the system according to the repeatability criterion in the formula (8). If the system error fluctuation range is increased, canceling the operation of expansion or cutting, and resetting the operation into the model of the last iteration step; otherwise, the model is updated.
Where S is the measured sample data set,
is based on a kinematic model of the parameter set M. According to the iterative method described above, when all the time invariant parameters are deleted, the final dynamic compensation model M is obtained.
Step three: optimizing the dynamic calibration posture;
as the number of calibration poses increases, the error decreases, but more time is consumed. Therefore, the efficiency of dynamic compensation is improved while the precision is ensured by optimizing the calibration posture.
The key to compensating for dynamic errors is to periodically calibrate the motion parameters. In order to avoid accuracy jumps, it is necessary to reduce the redundancy of the kinematic system. From the time dimension, ideally, time invariant parameters do not affect the dynamic error. However, since the calibration result of the nonlinear system has multiple solutions, all parameters fluctuate randomly on the time axis in practice. Furthermore, due to the redundancy of the kinematic model, the time-varying parameters will affect other parameters in their vicinity.
In order to minimize the number of poses, points having the same influence on the first order differential of the parameter vector need to be deleted. According to equation (1), for a set of postures Pi,Cij,
The corresponding residual δ can be obtained by equation (12)ij。
Wherein, Pi,Cij,
Is a constant. Substitution formula (6), first order differential of system rigid body transformation matrix
Calculated by equation (13).
Further simplified to obtain the formula (14).
Furthermore, the distance between residual errors can be calibrated according to the postures, the redundancy can be judged,i.e. the residual around it can be replaced by the residual center. Combined with K-means clustering algorithm, cluster center deltaCThe most recent data set may be replaced. Finally, deltaCIs a virtual point in the residual space, and it is difficult to find a corresponding posture in the real world, so δ is calculated from equation (15)CThe closest point.
Where KN is the clustering number of the K-means algorithm, and is also the number of the calibration poses, and the value is the carry integer of parameter number 2/3 in the model M (in this case, KN is 7).
Step four: dynamic compensation in performing detection tasks
Before starting the detection task, the designated calibration ball is scanned to evaluate whether the error of the system exceeds a set threshold. If the error exceeds a threshold, scanning the optimized calibration attitude and compensating time-varying kinematic parameters to obtain a modified transformation relation. And if the error does not exceed the threshold value, starting a detection task of the robot vision online measurement system.
The above-described embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements of the technical solutions of the present invention can be made by those skilled in the art without departing from the spirit of the present invention, and the technical solutions of the present invention are within the scope of the present invention defined by the claims.
Claims (9)
1. A dynamic error compensation method for a robot online detection system is characterized by comprising the following steps:
constructing a robot detection system, and modeling based on the robot detection system to obtain a kinematic model and a time-varying parameter model of the robot detection system;
calibrating parameters based on the kinematic model, and collecting calibration attitude information;
performing iterative optimization on the time-varying parameter model according to the calibration posture information to obtain target time-varying motion parameters;
and optimizing the calibration attitude information, compensating the time-varying motion parameters until the time-varying motion parameters meet a set threshold value, and evaluating and detecting.
2. The method for compensating dynamic error of robot online inspection system according to claim 1,
the robot detection system comprises a robot, a 3D laser scanner and a calibration ball;
performing parameter calibration based on the kinematic model, wherein the process of collecting calibration posture information comprises performing parameter calibration based on the calibration posture of the robot scanned by the calibration ball; and scanning calibration balls in different directions by changing the posture of the robot to complete long-period cyclic measurement of the calibration posture, and obtaining the calibration posture information.
3. The method for compensating dynamic error of robot online inspection system according to claim 1,
and performing iterative optimization on the time-varying parameter model according to the calibration posture information, wherein the process of obtaining the target time-varying motion parameters comprises the steps of identifying one parameter each time, extending the identified time-varying parameter to the time-varying parameter model, and cutting the identified time-invariant parameter from the time-varying parameter model.
4. The on-line robot detection system dynamic error compensation method of claim 3,
the identification process may include the steps of,
(1) clipping time-varying parameters in an external coordinate system;
(2) identifying time-varying parameters having a sigmoidal trend;
(3) deleting redundant parameters based on error sensitivity;
(4) and (4) repeating the steps (2) and (3) to carry out iterative optimization.
5. The on-line robot detection system dynamic error compensation method of claim 4,
the process of identifying time-varying parameters having a sigmoidal trend includes,
fitting an S-shaped function to obtain parameter balance time; obtaining a kinematic parameter sequence based on the calibration attitude information; normalizing the kinematic parameter sequence into data centered at zero, fitting the normalized parameter sequence by a tanh function, identifying a parameter having a maximum equilibration time as a time-varying parameter and adding to the time-varying parameter model.
6. The on-line robot detection system dynamic error compensation method of claim 4,
the error sensitivity-based redundant parameter deletion is obtained through QR decomposition of a model Jacobian matrix.
7. The on-line robot detection system dynamic error compensation method of claim 4,
the iterative optimization process also comprises the steps of canceling the operation of expansion or cutting when the fluctuation range of the system error is increased, and resetting the model to the model of the previous iteration step; otherwise, the model is updated.
8. The method for compensating dynamic error of robot online inspection system according to claim 1,
and obtaining the number of minimized postures by deleting points having the same influence on the first-order differentiation of the parameter vector, and optimizing the calibration posture information.
9. The method for compensating dynamic error of robot online inspection system according to claim 1,
compensating the time-varying motion parameter to meet a set threshold includes, prior to beginning the detection task, scanning a designated calibration sphere to assess whether the error of the system exceeds the set threshold; if the error exceeds a threshold value, scanning an optimized calibration attitude and compensating the time-varying kinematic parameters to obtain a modified transformation relation; and if the error does not exceed the threshold value, starting a detection task of the robot vision online measurement system.
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| CN115139306B (en) * | 2022-08-31 | 2022-12-27 | 中国电子产品可靠性与环境试验研究所（（工业和信息化部电子第五研究所）（中国赛宝实验室）） | Active motion precision compensation method, device, equipment and medium for industrial robot |
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