Error compensation method for compensating distance working precision of industrial robot

Abstract

The invention discloses an error compensation method for compensating the distance working precision of an industrial robot, and the method comprises the steps: obtaining an actual grid point distance error and a grid center point distance error according to a grid point nominal distance, a grid center point nominal distance, a grid point actual distance and a grid center point actual distance; by taking a grid as a unit, optimizing the first weight of the grid point and the second weight of the grid point according to an introduced distance error weight adjustment factor under the corresponding grid to obtain a comprehensive weight; obtaining a sample point distance error by using the actual grid point distance error and the comprehensive weight interpolation; decomposing the distance error of the sample points in the nominal direction to obtain the position error of the sample points in the nominal direction; and converting the position error into a joint angle error through inverse solution, and compensating the joint angle error into a demonstrator to finish compensation. According to the method, the defect that the calibration effect is limited by the opening degree of the controller is overcome, errors caused by coordinate system conversion are avoided, and the calibration range is expanded to a certain extent.

Description

Technical Field

[0001] The invention relates to an error compensation method for compensating the distance working accuracy of an industrial robot, and belongs to the field of industrial robot calibration. Background Art

[0002] The movement distance of an industrial robot is the distance that the robot moves during its operation. Its accuracy is crucial for tasks such as robot handling, assembly, cutting and milling that have certain requirements for distance accuracy. At present, the method of calibrating kinematic parameters is usually used to improve the industrial absolute position accuracy. However, for some working conditions that only require distance accuracy, improving positioning accuracy will not only make the calibration work redundant and reduce the calibration efficiency, but also the kinematic calibration only considers the error of the kinematic parameters, and the modification of the kinematic parameters is also limited by the openness of the controller.

[0003] Spatial interpolation is the most commonly used method in non-kinematic calibration. Usually, the spatial interpolation method interpolates the positioning error of the sample point through the positioning error of the grid points around the sample point. However, the process of using position information involves the conversion of the coordinate system, which can easily introduce additional errors and reduce the accuracy of the interpolation.

[0004] In view of this, the present invention is proposed. Summary of the invention

[0005] In view of the problem that industrial robots are limited by the openness of the controller in traditional kinematic calibration and the increase of error sources caused by coordinate system conversion in the process of using position information, the present invention provides an error compensation method for compensating the distance working accuracy of industrial robots, so as to solve the problem of insufficient accuracy in weight calculation in traditional spatial interpolation methods.

[0006] The technical solution of the present invention is:

[0007] According to a first aspect of the present invention, there is provided an error compensation method for compensating the distance working accuracy of an industrial robot, comprising the following steps:

[0008] Step 1, construct a kinematic model for the industrial robot and obtain the kinematic parameters of the industrial robot;

[0009] Step 2, planning grid points, grid center points, sample points and reference point P0 in the workspace of the industrial robot; obtaining grid point nominal distances and grid center point nominal distances according to the planned grid points, grid center points and reference point P0 in the workspace of the industrial robot; obtaining direction information from the reference point to the sample point as nominal direction information according to the sample point and the reference point P0;

[0010] Step 3, obtaining the actual distance from the grid point to the reference point as the actual distance of the grid point, and obtaining the actual distance from the grid center point to the reference point as the actual distance of the grid center point by measuring equipment; obtaining the actual grid point distance error and the grid center point distance error according to the grid point nominal distance, the grid center nominal distance, the grid point actual distance, and the grid center actual distance;

[0011] Step 4, taking the grid as a unit, in the corresponding grid, according to the grid point distance error and the grid center point distance error, calculate the first weight of the grid point; and calculate the second weight of the grid point using the distance from the grid point to the sample point;

[0012] Step 5, taking the grid as a unit, optimizing the first weight of the grid point and the second weight of the grid point according to the distance error weight adjustment factors μ1 and μ2 introduced in the corresponding grid to obtain a comprehensive weight;

[0013] Step 6, using the actual grid point distance error and comprehensive weight interpolation to obtain the sample point distance error;

[0014] Step 7, decomposing the distance error of the sample point in the nominal direction to obtain the position error of the sample point in the nominal direction;

[0015] Step 8: Convert the position error into joint angle error through inverse solution, and compensate the joint angle error into the teaching pendant to complete the compensation.

[0016] Furthermore, the kinematic model includes but is not limited to DH modeling and MD-H modeling.

[0017] Furthermore, the step 2 is specifically as follows: C cubic grids are divided in the workspace of the industrial robot, each cubic grid is composed of K grid points, and the kth grid point P in the cth grid ck , c=1,2,…,C,k=1,2,…,K;Get C grid center points Z in C grids c , c=1,2,…,C; randomly select a point in each of the C grids to obtain N sample points S n , n=1,2,…,N,C=N; a point in the working space different from the sample point and the grid point is taken as the reference point P0; the grid point and the grid center point are respectively subjected to Euclidean distance calculation with the coordinates of the reference point to obtain the nominal distance LP of the grid point ck , C grid center point nominal distance LZ c ; At the same time, obtain the direction information from the reference point to the sample point as nominal direction information.

[0018] Furthermore, the step 3 is specifically as follows: the actual distance from the grid point to the reference point is collected by a measuring device as the actual distance LPR of the grid point ck, collect the actual distance from the grid center point to the reference point as the actual distance of the grid center point LZR c ; The actual distance of the grid point LPR ck Nominal distance LP from grid point ck Subtract to get the grid point distance error △LPR ck ; The actual distance of the grid center point LZR c Nominal distance from grid point LZ c Subtract to get the grid center point distance error △LZR c .

[0019] Furthermore, the first weight of the grid point is obtained in step 4, specifically:

[0020] Taking the first grid as an example, the first weight of each grid point in the first grid is calculated based on the grid point distance error and the grid center point distance error. The calculation formula is:

[0021]

[0022] In the formula, w 1k , k=1,2,...K is the first weight of the kth grid point in the first grid, w 1K is the first weight of the Kth grid point in the first grid; ΔLPR 1k , k=1,2,...K is the distance error of the kth grid point in the first grid, ΔLPR 1K is the distance error of the Kth grid point in the first grid; ΔLZR1 is the distance error of the center point of the first grid; Cov() represents the covariance calculation.

[0023] Furthermore, the second weight calculation formula of the grid point is:

[0024]

[0025] In the formula, q ck represents the second weight of the kth grid point in the cth grid, d ck Represents the Euclidean distance from the kth grid point in the cth grid to the sample point in the cth grid.

[0026] Furthermore, the comprehensive weight expression is:

[0027] Q ck =μ1w ck +μ2q ck

[0028] In the formula, Q ck represents the comprehensive weight of the kth grid point in the cth grid, w ck represents the first weight of the kth grid point in the cth grid, qck Represents the second weight of the kth grid point in the cth grid.

[0029] Furthermore, the values ​​of the distance error weight adjustment factors μ1 and μ2 depend on If the grid where the cth sample point is located satisfies the inequality, then μ1 is 1 and μ2 is 0; otherwise, μ1 is 0 and μ2 is 1; where ΔLZR c The distance error of the cth grid center point where the cth sample point is located, ΔLPR ck is the distance error of the kth grid point in the cth grid where the cth sample point is located.

[0030] According to a second aspect of the present invention, there is provided an error compensation system for compensating the distance working accuracy of an industrial robot, comprising a module of any one of the methods described above.

[0031] According to a third aspect of the present invention, there is provided a terminal comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor is configured to execute the steps of any one of the above-described error compensation methods for compensating for the distance working accuracy of an industrial robot.

[0032] The beneficial effects of the present invention are:

[0033] 1) The method of the present invention does not require modification of kinematic parameters, thus overcoming the disadvantage that the calibration effect is limited by the degree of openness of the controller;

[0034] 2) The method of the present invention does not require conversion between the measurement coordinate system of the measuring device and the base coordinate system of the industrial robot, thus avoiding errors caused by coordinate system conversion;

[0035] 3) The calibration process of step 3 to step 8 of the method of the present invention is not limited by the position of the measuring device, which expands the calibration range to a certain extent.

[0036] 4) The method of the present invention proposes a method for obtaining a comprehensive weight based on the first weight, the second weight and the introduction of a distance error weight adjustment factor. The accuracy of obtaining the comprehensive weight is improved compared with the traditional weight calculation method. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is a flow chart of the method of the present invention;

[0038] Figure 2 The kinematic model using the MD-H method in the embodiment;

[0039] Figure 3 Schematic diagram of grid point distribution in the embodiment;

[0040] Figure 4 Schematic diagram of the experimental platform in the embodiment. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical scheme and advantages of the embodiments of the present invention clearer, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. It should be noted that the embodiments in this application and the features in the embodiments can be combined with each other arbitrarily without conflict.

[0042] Example 1: Figure 1-4 As shown, according to a first aspect of an embodiment of the present invention, there is provided an error compensation method for compensating for the distance working accuracy of an industrial robot, comprising the following steps: step 1, constructing a kinematic model for the industrial robot to obtain kinematic parameters of the industrial robot; step 2, planning grid points, grid center points, sample points and reference point P0 in the workspace of the industrial robot; obtaining the nominal distance of the grid points and the nominal distance of the grid center points according to the planned grid points, grid center points and reference point P0 in the workspace of the industrial robot; obtaining the direction information from the reference point to the sample point as the nominal direction information according to the sample points and the reference point P0; step 3, obtaining the actual distance from the grid point to the reference point as the actual distance of the grid point, obtaining the actual distance from the grid center point to the reference point as the actual distance of the grid center point through a measuring device; and obtaining the actual distance from the grid center point to the reference point as the actual distance of the grid center point according to the nominal distance of the grid points, the nominal distance of the grid center points and the actual distance of the grid points and the actual distance of the grid center points. The actual distance is obtained by calculating the actual grid point distance error and the grid center point distance error; step 4, taking the grid as the unit, according to the grid point distance error and the grid center point distance error under the corresponding grid, the first grid point weight is calculated; the second grid point weight is calculated by using the distance from the grid point to the sample point; step 5, taking the grid as the unit, according to the introduced distance error weight adjustment factors μ1 and μ2 under the corresponding grid, the first grid point weight and the second grid point weight are optimized to obtain the comprehensive weight; step 6, the sample point distance error is obtained by interpolation using the actual grid point distance error and the comprehensive weight; step 7, the sample point distance error is decomposed in the nominal direction to obtain the position error of the sample point in the nominal direction; step 8, according to the kinematic model of the industrial robot built in step 1, the nominal kinematic parameters of the industrial robot are used to convert the position error into the joint angle error through inverse solution, and the joint angle error is compensated to the teaching pendant to complete the compensation.

[0043] Furthermore, the kinematic model includes but is not limited to DH modeling and MD-H modeling.

[0044] Furthermore, the step 2 is specifically as follows: C cubic grids are divided in the workspace of the industrial robot, each cubic grid is composed of K grid points, and the kth grid point P in the cth grid ck , c=1,2,…,C,k=1,2,…,K;Get C grid center points Z in C grids c , c=1,2,…,C; randomly select a point in each of the C grids to obtain N sample points S n , n=1,2,…,N, C=N (in the embodiment of the present invention, a point is randomly selected in each grid as a sample point to construct N sample points); a point in the workspace different from the sample point and the grid point is used as the reference point P0; the grid point and the grid center point are respectively subjected to Euclidean distance calculation with the coordinates of the reference point to obtain the nominal distance LP of the grid point ck , C grid center point nominal distance LZ c ; At the same time, obtain the direction information from the reference point to the sample point As nominal direction information. Figure 3 Taking the workspace shown as an example, there are 64 cubic grids with a total of 125 grid points, and each cubic grid has K = 8 grid points. The grid point coordinates, grid center point coordinates, sample point coordinates, and reference point coordinates are obtained through the industrial robot teaching pendant.

[0045] Furthermore, the measuring equipment includes but is not limited to a laser tracker and a wire-drawing kinematic calibration system.

[0046] Furthermore, the actual distance from the grid point to the reference point is collected by the measuring device as the actual distance LPR of the grid point. ck , collect the actual distance from the grid center point to the reference point as the actual distance of the grid center point LZR c ; The actual distance of the grid point LPR ck Nominal distance LP from grid point ck Subtract to get the grid point distance error △LPR ck (The expression is ΔLPR ck =LPR ck -LP ck );The actual distance LZR of the grid center point c Nominal distance from grid point LZ c Subtract to get the grid center point distance error △LZR c (The expression is ΔLZR c =LZR c -LZ c ).

[0047] Further, the first grid is used for illustration. Under the first grid, the first weight of each grid point in the first grid is calculated based on the grid point distance error and the grid center point distance error. The calculation formula is:

[0048]

[0049] In the formula, w 1k , k=1,2,...K is the first weight of the kth grid point in the first grid, w 1K is the first weight of the Kth grid point in the first grid; ΔLPR 1k , k=1,2,...K is the distance error of the kth grid point in the first grid, ΔLPR 1K is the distance error of the Kth grid point in the first grid; ΔLZR1 is the distance error of the center point of the first grid; Cov() represents the covariance calculation. It should be noted that the present invention constructs a cubic grid, and each cube has 8 grid points. The first weight calculation method of each grid point in other grids is the same as above.

[0050] Furthermore, the second weight calculation formula of the grid point is:

[0051]

[0052] In the formula, q ck represents the second weight of the kth grid point in the cth grid, d ck Represents the Euclidean distance from the kth grid point in the cth grid to the sample point in the cth grid.

[0053] As an example, the Euclidean distance expression from the first grid point in the first grid to the sample point in the first grid is as follows:

[0054]

[0055] Where (x1, y1, z1) represents the coordinates of the sample point in the first grid, that is, the nominal position of the sample point, which is obtained through the industrial robot teaching pendant; (x 11 ,y 11 ,z 11 ) represents the coordinates of the first grid point in the first grid, that is, the nominal position of the grid point, which is obtained through the industrial robot teaching pendant.

[0056] Furthermore, the comprehensive weight expression is:

[0057] Q ck =μ1w ck +μ2q ck

[0058] In the formula, Q ckrepresents the comprehensive weight of the kth grid point in the cth grid, w ck represents the first weight of the kth grid point in the cth grid, q ck Represents the second weight of the kth grid point in the cth grid.

[0059] The values ​​of the distance error weight adjustment factors μ1 and μ2 depend on If the grid where the cth sample point is located satisfies the inequality, then μ1 is 1 and μ2 is 0; otherwise, μ1 is 0 and μ2 is 1.

[0060] Furthermore, the interpolation formula is: Among them, ΔLS n Indicates the distance error of the nth sample point, ΔLPR nk represents the distance error of the kth grid point in the nth grid, Q nk Represents the comprehensive weight of the kth grid point in the nth grid.

[0061] Furthermore, the step 7 is specifically as follows: for each sample point, the sample point distance error in the nominal direction Decompose it on and get the nth sample point at Position error in direction (△x n ,△y n ,△z n ).

[0062] Furthermore, the joint angle error obtained by the inverse solution is input into the teaching pendant to verify the validity of the result.

[0063] According to a second aspect of an embodiment of the present invention, there is provided an error compensation system for compensating for the distance working accuracy of an industrial robot, comprising a module of any of the above-mentioned methods, specifically comprising: a first module for executing step 1, constructing a kinematic model for the industrial robot, and obtaining kinematic parameters of the industrial robot; a second module for executing step 2, planning grid points, grid center points, sample points, and reference point P0 in the workspace of the industrial robot; obtaining nominal distances of grid points and nominal distances of grid center points based on the planned grid points, grid center points, and reference point P0 in the workspace of the industrial robot; obtaining direction information from reference points to sample points as nominal direction information based on sample points and reference point P0; a third module for executing step 3, obtaining actual distances from grid points to reference points as actual distances of grid points, and obtaining actual distances from grid center points to reference points as actual distances of grid centers through a measuring device; obtaining actual distances from grid points to reference points as actual distances of grid centers based on nominal distances of grid points, nominal distances of grid centers, and grid points; obtaining actual distances from grid points to reference points as actual distances of grid centers based on nominal distances of grid points, nominal distances of grid centers, and grid points; obtaining actual distances from grid points to reference points as actual distances of grid points, and obtaining actual distances from grid centers to reference points as actual distances of grid centers; obtaining actual distances from grid points to reference points as actual distances of grid points, and obtaining actual distances from grid centers to reference points as actual distances of grid centers; obtaining distances from grid points to reference points as actual distances of grid points, nominal distances of grid centers, and grid points; obtaining distances from grid points to reference points as actual distances of grid points, and obtaining distances from grid centers to reference points as actual distances of grid centers. The actual distance of the grid point and the actual distance of the grid center point are used to obtain the actual grid point distance error and the grid center point distance error; the fourth module is used to execute step 4, taking the grid as the unit, and calculating the first weight of the grid point according to the grid point distance error and the grid center point distance error under the corresponding grid; the second weight of the grid point is calculated using the distance from the grid point to the sample point; the fifth module is used to execute step 5, taking the grid as the unit, and optimizing the first weight of the grid point and the second weight of the grid point according to the introduced distance error weight adjustment factors μ1 and μ2 under the corresponding grid to obtain the comprehensive weight; the sixth module is used to execute step 6, and obtain the sample point distance error by interpolation using the actual grid point distance error and the comprehensive weight; the seventh module is used to execute step 7, and decompose the sample point distance error in the nominal direction to obtain the position error of the sample point in the nominal direction; the eighth module is used to execute step 8, and convert the position error into the joint angle error by inverse solution, and compensate the joint angle error to the teaching pendant to complete the compensation. The term "module" as used above can be a combination of software and / or hardware to implement a predetermined function. Although the system described in the above embodiment is preferably implemented by software, implementation by hardware, or a combination of software and hardware is also possible and conceivable. For parts that are not described in detail in each module, please refer to the relevant description of this embodiment.

[0064] According to a third aspect of an embodiment of the present invention, there is provided a terminal comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor is configured to execute the steps of any one of the above-described error compensation methods for compensating for the distance working accuracy of an industrial robot.

[0065] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0066] Embodiment 2: An optional implementation of the present invention is described below, which specifically includes:

[0067] Step 1: Establish the kinematic model of the industrial robot.

[0068] The industrial robot used in this embodiment is a serial six-degree-of-freedom industrial robot, and its kinematic model is constructed using the MD-H method, such as Figure 2 As shown, the transformation matrix of the i-th joint of the industrial robot relative to the i-1-th joint coordinate system is shown in formula (1):

[0069]

[0070] Among them, R X (α i-1 ) indicates that it is around X i-1 Axis rotation, from Z i-1 Rotate to Z i Angle; D X (a i-1 ) indicates along X i-1 Axis, from Z i-1 Move to Z i The distance R Z (θ i ) is around Z i Axis, from X i-1 Rotate to X i Angle; D Z (d i ) is along Z i Axis, from X i-1 Move to X i The nominal kinematic parameters of the industrial robot are shown in Table 1.

[0071] Table 1

[0072]

[0073] Multiply the homogeneous transformation matrices of adjacent joints of the robot to obtain the homogeneous transformation matrix of the robot's end coordinate system relative to the base coordinate system, as shown in formula (2):

[0074]

[0075] Through equations (1) and (2), the transformation matrix in the industrial robot base coordinate system can be expressed by equation (3):

[0076]

[0077] Among them, the matrix The first 3 rows and 3 columns represent the robot end posture, and the last column represents the position of the robot end tool in the robot base coordinate system.

[0078] Step 2, such as Figure 3 As shown in FIG. 1 , 64 cubic grids are divided in the workspace of the industrial robot. Each cubic grid consists of 8 grid points, for a total of 125 grid points. 64 grid center points Z are obtained in the 64 grids consisting of 125 grid points. c , (c=1,2,…,64), select 64 points as sample points S n (n=1,2,…,64), a point in the space different from the grid point, grid center point and sample point is taken as the reference point P0, so the origin (0,0,0) is selected as the reference point.

[0079] The grid point, grid center point, sample point and reference point are respectively calculated by Euclidean distance to obtain the nominal distance LP of the grid point. ck , 64 grid center points nominal distance LZ c (c=1,2,…,64) and the nominal distance LS of 64 sample points n (n=1,2,…,64); In addition, the direction from the reference point to the sample point can also be obtained:

[0080]

[0081] Step 3: The distance information between the grid points and the grid center point is collected using a laser tracker. The experimental platform diagram of the measurement equipment is as follows: Figure 4 As shown. The collected data is used as the actual distance data. The actual distance LPR of the grid point ck Nominal distance LP from grid point ck Subtract to get the grid point distance error △LPR ck , the formula is:

[0082] ΔLPR ck =LPR ck -LP ck (5)

[0083] The actual distance LZR of the grid center point c Nominal distance from grid point LZ c Subtract to get the grid center point distance error △LZR c , the formula is:

[0084] ΔLZR c =LZR c -LZ c (6)

[0085] Step 4, taking the grid as a unit, calculate the first weight of the grid point according to the grid point distance error and the grid center point distance error in the corresponding grid; calculate the second weight of the grid point using the distance from the grid point to the sample point; take the first grid as an example, calculate the first weight of each grid point in the first grid according to the grid point distance error and the grid center point distance error in the first grid, and the calculation formula is:

[0086]

[0087] In the formula, w 1k , k=1,2,...K is the first weight of the kth grid point in the first grid, w 1K is the first weight of the Kth grid point in the first grid; ΔLPR 1k , k=1,2,...K is the distance error of the kth grid point in the first grid, ΔLPR 1K is the distance error of the Kth grid point in the first grid; ΔLZR1 is the distance error of the center point of the first grid. It should be noted that the present invention constructs a cubic grid, and each cube has 8 grid points. The first weight calculation method of each grid point in other grids is the same as above.

[0088] The second weight calculation formula of the grid point is:

[0089]

[0090] In the formula, q ck represents the second weight of the kth grid point in the cth grid, d ck Represents the Euclidean distance from the kth grid point in the cth grid to the sample point in the cth grid.

[0091] As an example, the Euclidean distance expression from the first grid point in the first grid to the sample point in the first grid is as follows:

[0092]

[0093] Where (x1, y1, z1) represents the coordinates of the sample point in the first grid, that is, the nominal position of the sample point, which is obtained through the industrial robot teaching pendant; (x 11 ,y 11 ,z 11 ) represents the coordinates of the first grid point in the first grid, that is, the nominal position of the grid point, which is obtained through the industrial robot teaching pendant.

[0094] The comprehensive weight expression is:

[0095] Q ck =μ1w ck +μ2q ck (10)

[0096] In the formula, Q ck represents the comprehensive weight of the kth grid point in the cth grid, w ck represents the first weight of the kth grid point in the cth grid, q ck Represents the second weight of the kth grid point in the cth grid.

[0097] The values ​​of the distance error weight adjustment factors μ1 and μ2 depend on If the grid where the cth sample point is located satisfies the inequality, then μ1 is 1 and μ2 is 0; otherwise, μ1 is 0 and μ2 is 1.

[0098] Step 6, using the actual grid point distance error and comprehensive weight interpolation to obtain the sample point distance error; the interpolation formula is:

[0099]

[0100] Among them, ΔLS n Indicates the distance error of the nth sample point, ΔLPR nk represents the distance error of the kth grid point in the nth grid, Q nk Represents the comprehensive weight of the kth grid point in the nth grid.

[0101] The interpolation results are shown in Table 2. Compared with those before calibration, the maximum value accuracy of the method of the present invention is improved by 59.02%, the average value accuracy is improved by 67.70%, and the standard deviation accuracy is improved by 58.64%; compared with the traditional interpolation method, the maximum value accuracy is improved by 41.89%, the average value accuracy is improved by 64.67%, and the standard deviation accuracy is improved by 41.67%.

[0102] Table 2 Comparison results of distance error before and after calibration

[0103]

[0104] Step 7: For each sample point, the distance error of the sample point in the nominal direction Decompose it on and get the nth sample point at Position error in direction (△x n ,△y n ,△z n ).

[0105] Step 8: Convert the position error into joint angle error (△θ1, △θ2, …, △θ6) through inverse solution and input it into the teaching pendant to complete compensation.

[0106] By applying the above technical solution, the method of the present invention does not need to modify the kinematic parameters, overcomes the disadvantage that the calibration effect is limited by the openness of the controller, does not need to convert between the measurement coordinate system of the measuring device and the base coordinate system of the industrial robot, avoids the error caused by the coordinate system conversion, is not limited by the position of the measuring device during the calibration process, and expands the calibration range to a certain extent. A method for calculating the comprehensive weight is proposed, which has improved accuracy compared to the traditional weight calculation method.

[0107] The specific implementation modes of the present invention are described in detail above in conjunction with the accompanying drawings, but the present invention is not limited to the above implementation modes, and various changes can be made within the knowledge scope of ordinary technicians in this field without departing from the purpose of the present invention.

Claims

1. An error compensation method for compensating the distance working accuracy of an industrial robot, characterized in that: The following steps are involved: Step 1, construct a kinematic model for the industrial robot and obtain the kinematic parameters of the industrial robot; Step 2, planning grid points, grid center points, sample points and reference point P0 in the workspace of the industrial robot; obtaining grid point nominal distances and grid center point nominal distances according to the planned grid points, grid center points and reference point P0 in the workspace of the industrial robot; obtaining direction information from the reference point to the sample point as nominal direction information according to the sample point and the reference point P0; Step 3, obtaining the actual distance from the grid point to the reference point by a measuring device as the actual distance of the grid point, and obtaining the actual distance from the grid center point to the reference point as the actual distance of the grid center point; According to the nominal distance of the grid points, the nominal distance of the grid center points, the actual distance of the grid points, and the actual distance of the grid center points, the actual grid point distance error and the grid center point distance error are obtained; Step 4, taking the grid as a unit, calculating the first weight of the grid point according to the grid point distance error and the grid center point distance error under the corresponding grid; The second weight of the grid point is calculated using the distance from the grid point to the sample point; Step 5, taking the grid as a unit, optimizing the first weight of the grid point and the second weight of the grid point according to the distance error weight adjustment factors μ1 and μ2 introduced in the corresponding grid to obtain a comprehensive weight; Step 6, using the actual grid point distance error and comprehensive weight interpolation to obtain the sample point distance error; Step 7, decomposing the distance error of the sample point in the nominal direction to obtain the position error of the sample point in the nominal direction; Step 8: Convert the position error into joint angle error through inverse solution, and compensate the joint angle error into the teaching pendant to complete the compensation.

2. The error compensation method for compensating the distance working accuracy of an industrial robot according to claim 1, characterized in that: The kinematic model includes but is not limited to DH modeling and MD-H modeling.

3. The error compensation method for compensating the distance working accuracy of an industrial robot according to claim 1, characterized in that: The step 2 is specifically as follows: C cubic grids are divided in the workspace of the industrial robot, each cubic grid is composed of K grid points, and the kth grid point P in the cth grid ck , c=1,2,…,C,k=1,2,…,K;Get C grid center points Z in C grids c , c=1,2,…,C; randomly select a point in each of the C grids to obtain N sample points S n , n=1,2,…,N, C=N; A point in the working space different from the sample point and the grid point is used as the reference point P0; the grid point and the grid center point are respectively subjected to Euclidean distance calculation with the coordinates of the reference point to obtain the nominal distance LP of the grid point ck , the nominal distance LZ of C grid center points c ; At the same time, obtain the direction information from the reference point to the sample point as nominal direction information.

4. The error compensation method for compensating the distance working accuracy of an industrial robot according to claim 1, characterized in that: The step 3 is specifically: collecting the actual distance from the grid point to the reference point by measuring equipment as the actual distance LPR of the grid point ck , collect the actual distance from the grid center point to the reference point as the actual distance of the grid center point LZR c ; The actual distance of the grid point LPR ck Nominal distance LP from grid point ck Subtract to get the grid point distance error △LPR ck ; The actual distance of the grid center point LZR c Nominal distance from grid point LZ c Subtract to get the grid center point distance error △LZR c .

5. The error compensation method for compensating the distance working accuracy of an industrial robot according to claim 1, characterized in that: The first weight of the grid point is obtained in step 4, specifically: Taking the first grid as an example, the first weight of each grid point in the first grid is calculated based on the grid point distance error and the grid center point distance error. The calculation formula is: In the formula, w 1k , k=1,2,...K is the first weight of the kth grid point in the first grid, w 1K is the first weight of the Kth grid point in the first grid; ΔLPR 1k , k=1,2,...K is the distance error of the kth grid point in the first grid, ΔLPR 1K is the distance error of the Kth grid point in the first grid; ΔLZR1 is the distance error of the center point of the first grid; Cov() represents the covariance calculation.

6. The error compensation method for compensating the distance working accuracy of an industrial robot according to claim 1, characterized in that: The second weight calculation formula of the grid point is: In the formula, q ck represents the second weight of the kth grid point in the cth grid, d ck Represents the Euclidean distance from the kth grid point in the cth grid to the sample point in the cth grid.

7. The error compensation method for compensating the distance working accuracy of an industrial robot according to claim 1, characterized in that: The comprehensive weight expression is: Q ck =μ1w ck +μ2q ck In the formula, Q ck represents the comprehensive weight of the kth grid point in the cth grid, w ck represents the first weight of the kth grid point in the cth grid, q ck Represents the second weight of the kth grid point in the cth grid.

8. The error compensation method for compensating the distance working accuracy of an industrial robot according to claim 7, characterized in that: The values ​​of the distance error weight adjustment factors μ1 and μ2 depend on If the grid where the cth sample point is located satisfies the inequality, then μ1 is 1 and μ2 is 0; otherwise, μ1 is 0 and μ2 is 1; where ΔLZR c The distance error of the cth grid center point where the cth sample point is located, ΔLPR ck is the distance error of the kth grid point in the cth grid where the cth sample point is located.

9. An error compensation system for compensating the distance working accuracy of an industrial robot, characterized in that: A module comprising the method according to any one of claims 1 to 8.

10. A terminal, characterized in that: The invention comprises a processor, a memory and a computer program stored in the memory and executable on the processor, wherein the processor is configured to execute the steps of the error compensation method for compensating the distance working accuracy of an industrial robot according to any one of claims 1 to 8.

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