Industrial robot tolerance design method and system based on positioning accuracy reliability

Abstract

The application discloses a positioning accuracy reliability-based industrial robot tolerance design method and system, and the method comprises the following steps: performing differential transformation processing on a homogeneous transformation matrix of an industrial robot connecting rod coordinate system, and constructing a positioning accuracy limit state function; establishing an inner and outer test table for industrial robot tolerance design; calculating the failure probability of the positioning accuracy of the industrial robot according to the inner and outer test table and the positioning accuracy limit state function; and sorting the importance of the kinematic parameter tolerance according to a signal-to-noise ratio calculation formula and the positioning accuracy failure probability, and guiding the design of the kinematic parameter tolerance scheme of the industrial robot. Through the use of the application, the kinematic parameter tolerance of each component of the industrial robot can be tightened and loosened, and the positioning accuracy of the industrial robot can be effectively improved under the same production conditions. The application can be widely applied to the technical field of industrial robot tolerance design as a positioning accuracy reliability-based industrial robot tolerance design method and system.

Description

Technical Field

[0001] This invention relates to the field of industrial robot tolerance design technology, and in particular to an industrial robot tolerance design method and system based on positioning accuracy reliability. Background Technology

[0002] The development of automation technology has promoted the high efficiency, low cost, and good repeatability of industrial robots, which has further facilitated their application in various fields such as automotive manufacturing, electronics and electrical engineering, precision assembly, and aerospace. Positioning accuracy is a key performance indicator for industrial robots, affected by manufacturing and assembly errors, joint clearances, elastic deformation, and other uncertainties. Among these, the kinematic parameter errors after manufacturing and assembly have the most direct impact on positioning accuracy. Therefore, during the design phase, it is necessary to ensure the positioning accuracy of industrial robots by rationally designing kinematic parameter tolerances. In traditional industrial robot tolerance design, kinematic parameter tolerances are mainly determined by designers considering tolerance types, relevant dimensional parameters, and processing technology levels, through analogy or... Consulting mechanical design manuals can help determine this, but this strategy heavily relies on subjective experience and further complicates practical evaluation, optimization, and motion accuracy control. Existing methods also systematically change some kinematic parameters to identify error sources that significantly affect the positioning accuracy of industrial robots, thus guiding the tightening or loosening of kinematic parameter tolerances. However, most existing industrial robot tolerance test design methods are deterministic analyses, treating kinematic parameter errors as fixed values ​​and performing motion accuracy analysis on a fixed point in the workspace. In actual engineering, errors are widely uncertain, and the reliability of different positioning points varies. Therefore, current technology struggles to improve robot positioning accuracy through reasonable tolerance design. Summary of the Invention

[0003] To address the aforementioned technical problems, the present invention aims to provide an industrial robot tolerance design method and system based on positioning accuracy reliability, which can control motion parameter errors within a certain range according to the different positioning performance of the industrial robot at different working points, thereby improving the positioning accuracy of the industrial robot.

[0004] The first technical solution adopted in this invention is: an industrial robot tolerance design method based on positioning accuracy reliability, comprising the following steps:

[0005] Differential transformation is performed on the homogeneous transformation matrix of the industrial robot link coordinate system to construct the positioning accuracy limit state function;

[0006] Based on orthogonal experimental design and uniform experimental design, an internal and external test table for tolerance design of industrial robots is established.

[0007] Based on the internal and external test tables and positioning accuracy limit state function of the industrial robot tolerance design, calculate the failure probability of the industrial robot positioning accuracy.

[0008] The importance of kinematic parameter tolerances is ranked based on the signal-to-noise ratio calculation formula and the failure probability of positioning accuracy, thereby guiding the design of kinematic parameter tolerance schemes for industrial robots.

[0009] Furthermore, the step of performing differential transformation processing on the homogeneous transformation matrix of the industrial robot's link coordinate system to construct the positioning accuracy limit state function specifically includes:

[0010] Using the DH method, establish the homogeneous transformation matrix of the industrial robot's link coordinate system:

[0011]

[0012] In the above formula, i-1 T i θ represents the homogeneous transformation matrix of the i-th link coordinate system of the industrial robot relative to the i-1-th link coordinate system. i The joint angle, a, represents the kinematic parameters of link i. i The link length d represents the kinematic parameters of link i. i The link offset, α, represents the kinematic parameters of link i. i The joint twist angle represents the kinematic parameters of link i;

[0013] By performing differential transformation on the alignment transformation matrix, an error model for the industrial robot is constructed.

[0014]

[0015] In the above formula, Δ=(d x ,d y ,d z ,δ x ,δ y ,δ z ) T Let represent the error vector of the industrial robot, m represent the degrees of freedom of the industrial robot, and q represent the error vector of the industrial robot. i =(Δθ) i ,Δa i ,Δd i ,Δα i ) T H represents the kinematic parameter error, where i represents the i-th link of the industrial robot. i =J i M i Let M represent the error matrix, where M is the error matrix. i and J i They are shown below:

[0016]

[0017]

[0018] In the above formula, n i =(n ix n iy n iz ) T s i =(s ix s iy s iz ) T a i =(a ix a iy a iz ) T and p i =(p ix p iy p iz ) T These represent the normal vector, direction vector, proximity vector, and position vector of the industrial robot's end effector in the i-link coordinate system, respectively.

[0019] Based on the error model of industrial robots, a positioning accuracy limit state function is established:

[0020] G(W)=r 2 -E(W)

[0021] In the above formula, G(W) represents the positioning accuracy limit state function, r represents the allowable value of positioning error, W represents a random variable composed of kinematic parameter errors, and E(W) represents the positioning error between the actual position and the target position. The expression for E(W) is as follows:

[0022] E(W)=[d x (W)] 2 +[d y (W)] 2 +[d z (W)] 2

[0023] In the above formula, d x (W), d y (W), d z (W) represent the position error functions of the industrial robot end effector in the x, y, and z axes, respectively.

[0024] Furthermore, the step of calculating the failure probability of the industrial robot's positioning accuracy based on the internal and external test tables and positioning accuracy limit state function designed according to the industrial robot's tolerances specifically includes:

[0025] Based on the internal and external test tables for the tolerance design of industrial robots, determine the covariance matrix of the position error of the industrial robot:

[0026]

[0027] In the above formula, Cov(d) represents the variance of the positional error of the industrial robot end effector in the x, y, and z axes, respectively. x ,d y Cov(d) represents the covariance of the positional error along the x and y axes. x ,d z Cov(d) represents the covariance of the position error along the x and z axes. y ,d z () represents the covariance of the position error along the y and z axes;

[0028] Based on the covariance matrix of the position error, a cumulative generation function for the position error of the industrial robot is established:

[0029]

[0030] In the above formula, K E (s) denotes the cumulant generating function, and λ1, λ2, and λ3 denote the matrix C. d eigenvalues.

[0031] Based on the cumulative generation function of position error and the saddle point approximation theory, the failure probability of the industrial robot's positioning accuracy is calculated:

[0032]

[0033] In the above formula, p f Let Φ(·) represent the failure probability of the industrial robot's positioning accuracy, φ(·) represent the cumulative distribution function of the standard normal distribution, φ(·) represent the probability density function of the standard normal distribution, and v and w represent the saddle point approximation coefficients.

[0034] v = sgn(s t ){2[s t r 2 -K E (s t )]} 1 / 2

[0035] w = s t [K E "(s t )] 1 / 2

[0036] In the above formula, sgn(·) represents the sign function, s t Indicated by K E′ (s)=r 2 The saddle point K obtained by solving the problem E ′ (s), K E "(s0 represents K respectively) E The first and second derivatives of (s).

[0037] Furthermore, the formula for calculating the signal-to-noise ratio is as follows:

[0038]

[0039] In the above formula, S / N represents the signal-to-noise ratio, k represents the number of test tables, and p fi Let represent the failure probability of the i-th test group.

[0040] The second technical solution adopted in this invention is: an industrial robot tolerance design system based on positioning accuracy reliability, comprising:

[0041] The module is used to perform differential transformation processing on the homogeneous transformation matrix of the industrial robot link coordinate system to construct the positioning accuracy limit state function.

[0042] The design module is used to establish internal and external test tables for the tolerance design of industrial robots based on orthogonal test method and uniform test method;

[0043] The calculation module is used to calculate the failure probability of the industrial robot's positioning accuracy based on the internal and external test tables and positioning accuracy limit state function of the industrial robot tolerance design.

[0044] The optimization module is used to rank the importance of kinematic parameter tolerances based on the signal-to-noise ratio calculation formula and the failure probability of positioning accuracy, thereby guiding the design of kinematic parameter tolerance schemes for industrial robots.

[0045] The beneficial effects of the method and system of this invention are as follows: This invention transforms the linkage coordinates of an industrial robot to construct a positioning accuracy limit state function, taking into account the uncertainties existing in actual engineering. Furthermore, based on kinematic design, a kinematic parameter tolerance test table for the industrial robot is established to study the impact of kinematic parameter errors on the reliability of the industrial robot's positioning accuracy. This is then incorporated into the industrial robot's tolerance design. By calculating the failure probability of the industrial robot's positioning accuracy and analyzing the influence of the signal-to-noise ratio on the kinematic parameter tolerance, the optimal tolerance design scheme for the industrial robot is selected. This is the industrial robot tolerance design method based on positioning accuracy reliability, which can effectively identify important kinematic parameters of the industrial robot and their interactions. While evaluating the performance of the industrial robot according to the tolerance range, it guides the tightening or loosening of kinematic parameter tolerances. Attached Figure Description

[0046] Figure 1 This is a flowchart of the steps of the industrial robot tolerance design method based on positioning accuracy reliability of the present invention;

[0047] Figure 2 This is a structural block diagram of the industrial robot tolerance design system based on positioning accuracy and reliability of the present invention. Detailed Implementation

[0048] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments. The step numbers in the following embodiments are only for ease of explanation and do not limit the order of the steps. The execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art.

[0049] Reference Figure 1 This invention provides a tolerance design method for industrial robots based on positioning accuracy reliability. The method includes the following steps:

[0050] S1. Perform differential transformation on the homogeneous transformation matrix of the industrial robot link coordinate system to construct the positioning accuracy limit state function;

[0051] S11. Using the DH method, establish the homogeneous transformation matrix of the industrial robot's link coordinate system:

[0052]

[0053] In the above formula, i-1 T i θ represents the homogeneous transformation matrix of the i-th link coordinate system of the industrial robot relative to the i-1-th link coordinate system. i The joint angle, a, represents the kinematic parameters of link i. i The link length d represents the kinematic parameters of link i. i The link offset, α, represents the kinematic parameters of link i. i The joint twist angle represents the kinematic parameters of link i.

[0054] S12. Perform differential transformation on the alignment transformation matrix to construct the error model of the industrial robot;

[0055]

[0056] In the above formula, Δ=(d x ,d y ,d z ,δ x ,δ y ,δ z ) T Let represent the error vector of the industrial robot, m represent the degrees of freedom of the industrial robot, and q represent the error vector of the industrial robot. i =(Δθ)i ,Δa i ,Δd i ,Δα i ) T H represents the kinematic parameter error, where i represents the i-th link of the industrial robot. i =J i M i Let M represent the error matrix, where M is the error matrix. i and J i They are shown below:

[0057]

[0058]

[0059] In the above formula, n i =(n ix n iy n iz ) T s i =(s ix s iy s iz ) T a i =(a ix a iy a iz ) T and p i =(p ix p iy p iz ) T These represent the normal vector, direction vector, proximity vector, and position vector of the industrial robot's end effector in the i-axis coordinate system of the link, respectively.

[0060] S13. Based on the error model of the industrial robot, establish the positioning accuracy limit state function:

[0061] G(W)=r 2 -E(W)

[0062] In the above formula, G(W) represents the positioning accuracy limit state function, r represents the allowable value of positioning error, W represents a random variable composed of kinematic parameter errors, and E(W) represents the positioning error between the actual position and the target position. The expression for E(W) is as follows:

[0063] E(W)=[d x (W)] 2 +[d y (W)] 2 +[d z (W)] 2

[0064] In the above formula, d x (W), d y (W), d z (W) represent the position error functions of the industrial robot end effector in the x, y, and z axes, respectively.

[0065] In a specific embodiment of the present invention, the nominal values ​​of the kinematic parameters of the industrial robot are shown in Table 1;

[0066] Table 1 Nominal values ​​of kinematic parameters for industrial robots

[0067] link <![CDATA[θ i (°)]]> <![CDATA[a i (mm)]]> <![CDATA[d i (mm)]]> <![CDATA[α i (°)]]> 1 <![CDATA[-160≤θ1≤160]]> 200 509 90 2 <![CDATA[-70≤θ2≤170]]> 628 0 0 3 <![CDATA[-260≤θ3≤60]]> 140 0 90 4 <![CDATA[-180≤θ4≤180]]> 0 713.5 -90 5 <![CDATA[-120≤θ5≤120]]> 0 0 90 6 <![CDATA[-360≤θ6≤360]]> 0 135 0

[0068] The allowable error is set to r = 0.30 mm, and the random variables considered are W = (Δa1, Δd1, Δa2, Δa3, Δd4, Δd6). T The kinematic parameter errors for industrial robot tolerance design are a1, d1, a2, s3, d4, d6, and the corresponding tolerances are t1, t2, t3, t4, t5, t6.

[0069] S2. Based on the orthogonal test method and the uniform test method, establish the internal and external test tables for the tolerance design of industrial robots;

[0070] Specifically, in this embodiment of the invention, the kinematic parameter tolerances t1, t2, t3, t4, t5, and t6 are all divided into two levels: 0.15 mm and 0.30 mm. The internal test table for the precision design of the industrial robot established according to the orthogonal test method is shown in Table 2.

[0071] Table 2 Internal Test Table for Precision Design of Industrial Robots

[0072]

[0073]

[0074] Nine joint angles were uniformly selected within the range of motion for the joint parameters θ1, θ2, θ3, θ4, θ5, and θ6. The external test table for the precision design of the industrial robot, established based on the uniform test method, is shown in Table 3.

[0075] Table 3 External Test Table for Precision Design of Industrial Robots

[0076] experimental group <![CDATA[θ1(°)]]> <![CDATA[θ2(°)]]> <![CDATA[θ3(°)]]> <![CDATA[θ4(°)]]> <![CDATA[θ5(°)]]> <![CDATA[θ6(°)]]> 1 -160 -40 -140 0 60 270 2 -120 20 20 -160 0 180 3 -80 80 -180 40 -60 90 4 -40 140 -20 -120 -120 0 5 0 -70 -220 80 90 -90 6 40 -10 -60 -80 30 -180 7 80 50 -260 120 -30 -270 8 120 110 -100 -40 -90 -360 9 160 170 60 160 120 360

[0077] S3. Based on the internal and external test tables and positioning accuracy limit state function of the industrial robot tolerance design, calculate the failure probability of the industrial robot positioning accuracy.

[0078] S31. Based on the internal and external test tables for the industrial robot tolerance design, determine the covariance matrix of the industrial robot's position error:

[0079]

[0080] In the above formula, Cov(d) represents the variance of the positional error of the industrial robot end effector in the x, y, and z axes, respectively. x ,d y Cov(d) represents the covariance of the positional error along the x and y axes. x ,d z Cov(d) represents the covariance of the position error along the x and z axes. y ,d z ) represents the covariance of position errors in the y and z axes.

[0081] Specifically, the random variable of kinematic parameter error follows a normal distribution, i.e., W i ~N(0,t) i / 3)(t i (For the tolerance) we can obtain:

[0082]

[0083]

[0084]

[0085]

[0086]

[0087]

[0088] In the above formula, n represents the number of random variables of kinematic parameter error, and h 1i h 2i h 3i These represent the position error coefficients along the x, y, and z axes, respectively.

[0089] S32. Based on the covariance matrix of the position error, establish the cumulative generation function of the industrial robot's position error:

[0090]

[0091] In the above formula, K E (s) denotes the cumulant generating function, and λ1, λ2, and λ3 denote the matrix C. d eigenvalues.

[0092] S33. Based on the cumulative generation function of position error and the saddle point approximation theory, calculate the failure probability of the industrial robot's positioning accuracy:

[0093]

[0094] In the above formula, p f Let Φ(·) represent the failure probability of the industrial robot's positioning accuracy, φ(·) represent the cumulative distribution function of the standard normal distribution, φ(·) represent the probability density function of the standard normal distribution, and v and w represent the saddle point approximation coefficients.

[0095] v = sgn(s t ){2[s t r 2 -K E (s t )]} 1 / 2

[0096] w = s t [K″ E (s t )] 1 / 2

[0097] In the above formula, sgn(·) represents the sign function, s t Indicated by K′ E (s)=r 2 The saddle point obtained by solving, K′ E (s), K″ E (s) represent K respectively E The first and second derivatives of (s).

[0098] In a specific embodiment of the present invention, the calculation results of the positioning accuracy failure probability of each group are shown in Table 4;

[0099] Table 4 Calculation Results of Positioning Accuracy Failure Probability of Industrial Robots

[0100] experimental group 1 2 3 4 5 6 7 8 9 A 0.0041 0.0026 0.0028 0.0023 0.0013 0.0030 0.0033 0.0019 0.0035 B 0.0903 0.0889 0.0830 0.0770 0.0666 0.0846 0.0860 0.0810 0.0881 C 0.0956 0.0900 0.0949 0.0803 0.0770 0.0843 0.0967 0.0838 0.0876 D 0.1309 0.1344 0.1300 0.1274 0.1254 0.1329 0.1299 0.1286 0.1337 E 0.0903 0.0913 0.0875 0.0883 0.0838 0.1010 0.0933 0.0866 0.0994 F 0.1389 0.1311 0.1254 0.1243 0.1110 0.1273 0.1292 0.1230 0.1346 G 0.1312 0.1311 0.1262 0.1200 0.1113 0.1271 0.1284 0.1275 0.1296 H 0.0894 0.089 0.0874 0.0852 0.0815 0.0878 0.0887 0.0859 0.0883

[0101] S4. Rank the importance of kinematic parameter tolerances according to the signal-to-noise ratio calculation formula and the failure probability of positioning accuracy, so as to guide the design of kinematic parameter tolerance schemes for industrial robots.

[0102] Specifically, the signal-to-noise ratio calculation formula is as follows:

[0103]

[0104] In the above formula, S / N represents the signal-to-noise ratio, k represents the number of test tables, and p fi Let represent the failure probability of the i-th test group.

[0105] In a specific embodiment of the present invention, the signal-to-noise ratio calculation results are shown in Table 5;

[0106] Table 5. Signal-to-noise ratio calculation results

[0107] experimental group <![CDATA[t1(mm)]]> <![CDATA[t2(mm)]]> <![CDATA[t3(mm)]]> <![CDATA[t4(mm)]]> <![CDATA[t5(mm)]]> <![CDATA[t6(mm)]]> S / N A 0.15 0.15 0.15 0.15 0.15 0.15 117.07 B 0.15 0.15 0.15 0.30 0.30 0.30 49.75 C 0.15 0.30 0.30 0.15 0.15 0.30 48.60 D 0.15 0.30 0.30 0.30 0.30 0.15 40.75 E 0.30 0.15 0.30 0.15 0.30 0.15 47.84 F 0.30 0.15 0.30 0.30 0.15 0.30 41.21 G 0.30 0.30 0.15 0.15 0.30 0.30 41.43 H 0.30 0.30 0.15 0.30 0.15 0.15 48.82 <![CDATA[S1]]> 256.17 255.87 257.07 254.94 255.70 254.48 / <![CDATA[S2]]> 179.30 179.60 178.40 180.53 179.77 180.99 / <![CDATA[s1]]> 128.09 127.94 128.54 127.47 127.85 127.24 / <![CDATA[s2]]> 89.65 89.80 89.20 90.27 89.89 90.50 / R 38.44 38.14 39.34 37.20 37.96 36.74 /

[0108] Where S1 and S2 are the sums of the results of four tests under tolerance level 1 (0.15mm) and level 2 (0.30mm), respectively, and s1 and s2 are their average values; R is the range, which is equal to the difference between s1 and s2. The range values ​​in Table 5 can reflect the degree of influence of each tolerance on the reliability of positioning accuracy. Therefore, in this embodiment of the invention, the importance ranking of the kinematic parameter tolerances of the industrial robot is t3>t1>t2>t5>t4>t6. The tolerance levels can be adjusted according to the above order of importance. For example, the tolerance levels of t3, t1, and t2 can be reduced, while the tolerance levels of t5, t4, and t6 can be relaxed. That is, considering the manufacturing cost and the reliability of positioning accuracy, the scheme of test group B is better than the other test group schemes.

[0109] Reference Figure 2 An industrial robot tolerance design system based on positioning accuracy and reliability includes:

[0110] The module is used to perform homogeneous transformation and differential transformation on the links of industrial robots to construct the positioning accuracy limit state function.

[0111] The design module is used to design internal and external test tables for the kinematic parameter tolerances of industrial robots based on the kinematic parameter tolerance grades and link joint parameter levels.

[0112] The calculation module is used to calculate the failure probability of the industrial robot's positioning accuracy based on the internal and external test tables of the kinematic parameter tolerances of the industrial robot and the positioning accuracy limit state function.

[0113] The optimization module is used to rank the importance of kinematic parameter tolerances based on the signal-to-noise ratio calculation formula and the failure probability of positioning accuracy, thereby guiding the design of kinematic parameter tolerance schemes for industrial robots.

[0114] The content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0115] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. An industrial robot tolerance design method based on positioning accuracy and reliability, characterized in that, Includes the following steps: Differential transformation is performed on the homogeneous transformation matrix of the industrial robot's link coordinate system to construct the positioning accuracy limit state function, including: Using the DH method, establish the homogeneous transformation matrix of the industrial robot's link coordinate system: In the above formula, Indicates the linkage of an industrial robot Coordinate system relative to the link Homogeneous transformation matrix of the coordinate system Indicates the link The joint angles of the kinematic parameters, Indicates the link The kinematic parameters of the link length, Indicates the link Linkage bias of kinematic parameters. Indicates the link The joint twist angle of the kinematic parameters; By performing differential transformation on the alignment matrix, an error model for the industrial robot is constructed, the specific expression of which is shown below: In the above formula, This represents the error vector of an industrial robot. Indicates the degrees of freedom of an industrial robot. Indicates the error in kinematic parameters. The first industrial robot Root connecting rod, Let represent the error matrix, where and They are shown below: In the above formula, , , and These represent the end effector of the industrial robot at the link. Normal vector, direction vector, proximity vector, and position vector in a coordinate system; Based on the error model of industrial robots, a positioning accuracy limit state function is established: In the above formula, This represents the positioning accuracy limit state function. This indicates the allowable value for positioning error. This represents a random variable composed of errors in kinematic parameters. This represents the positioning error between the actual position and the target position, where The expression is as follows: In the above formula, , , These represent the end effectors of industrial robots. , , Position error function along the axis; Based on orthogonal experimental design and uniform experimental design, an internal and external test table for tolerance design of industrial robots is established. Based on the internal and external test tables and positioning accuracy limit state function of the industrial robot tolerance design, calculate the failure probability of the industrial robot positioning accuracy, including: Based on the internal and external test tables for the tolerance design of industrial robots, determine the covariance matrix of the position error of the industrial robot: In the above formula, , , These represent the end effectors of industrial robots. , , The variance of the positional error along the axial direction. express , The covariance of the position error along the axial direction. express , The covariance of the position error along the axial direction. express , Covariance of positional error along the axial direction; Based on the covariance matrix of the position error, a cumulative generation function for the position error of the industrial robot is established: In the above formula, This represents the cumulative generation function. , and Representation matrix eigenvalues; Based on the cumulative generation function of position error and the saddle point approximation theory, the failure probability of the industrial robot's positioning accuracy is calculated: In the above formula, This represents the failure probability of the positioning accuracy of an industrial robot. The cumulative distribution function represents the standard normal distribution. This represents the probability density function of the standard normal distribution. , Denotes the saddle point approximation coefficient, where: In the above formula, Represents a symbolic function. Indicates by The saddle points obtained by solving, , They represent First and second derivatives; The importance of kinematic parameter tolerances is ranked based on the signal-to-noise ratio calculation formula and the failure probability of positioning accuracy, thereby guiding the design of kinematic parameter tolerance schemes for industrial robots.

2. The industrial robot tolerance design method based on positioning accuracy reliability according to claim 1, characterized in that, The formula for calculating the signal-to-noise ratio is as follows: In the above formula, Indicates the signal-to-noise ratio. Indicates the number of test groups. Indicates the first The failure probability of the group test.

3. An industrial robot tolerance design system based on positioning accuracy and reliability, characterized in that, The method for implementing the industrial robot tolerance design method based on positioning accuracy reliability as described in claim 1 includes the following modules: The module is used to perform differential transformation processing on the homogeneous transformation matrix of the industrial robot link coordinate system to construct the positioning accuracy limit state function. The design module is used to establish internal and external test tables for the tolerance design of industrial robots based on orthogonal test method and uniform test method; The calculation module is used to calculate the failure probability of the industrial robot's positioning accuracy based on the internal and external test tables and positioning accuracy limit state function of the industrial robot tolerance design. The optimization module is used to rank the importance of kinematic parameter tolerances based on the signal-to-noise ratio calculation formula and the failure probability of positioning accuracy, thereby guiding the design of kinematic parameter tolerance schemes for industrial robots.

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