Mechanical arm parameter identification compensation method and system

Abstract

The application relates to a mechanical arm parameter identification compensation method, which comprises the following steps: calculating initial MDH parameters of a robot mechanical arm, constructing a robot kinematics error model, and calculating a correction amount of a deviation value of each connecting rod of the mechanical arm; setting the correction amount of the deviation value of each connecting rod of the mechanical arm as an evolutionary algorithm chromosome, calculating an adaptability function of an ideal position, and searching for a chromosome corresponding to the minimum position deviation; performing decimal coding on all the chromosomes; initializing each parameter according to the chromosome corresponding to the minimum position deviation searched, calculating a cross variation probability, and performing cross variation operation on the chromosomes to obtain kinematics parameters; adjusting the deviation value correction amount in real time according to the updated cross variation probability; and outputting optimal MDH correction values when the value of the deviation value correction amount converges. The application also relates to a mechanical arm parameter identification compensation system. The application can improve the calculation efficiency, accelerate the convergence speed of the algorithm, and make up for the shortcomings of a traditional iterative method.

Description

TECHNICAL FIELD

[0001] The application relates to a mechanical arm parameter identification compensation method and system. BACKGROUND

[0002] The absolute positioning accuracy error of a current robot is mainly caused by external environment and internal mechanism error of the robot. The external environment refers to vibration of equipment around the robot and temperature influence of the environment, and the internal environment error is caused by inaccurate geometric parameters of the robot, wherein 80% of the positioning error is caused by geometric error. In actual engineering, machining precision, assembly error and absolute positioning accuracy are generally low. In order to improve the positioning accuracy of a serial mechanical arm end execution mechanism in puncture surgery, an effective method is to accurately identify the parameters of connecting rods, that is, parameter calibration.

[0003] However, the conventional genetic algorithm in the prior art cannot utilize feedback information of a network in time, has defects such as slow convergence speed, poor local search capability and time-consuming decoding mechanism. Since the search speed of the genetic algorithm in the prior art is relatively slow, more training time is required to obtain a more accurate solution. SUMMARY

[0004] Therefore, it is necessary to provide a mechanical arm parameter identification compensation method and system.

[0005] The application provides a mechanical arm parameter identification compensation method, which comprises the following steps: a. calculating initial MDH parameters of a robot mechanical arm according to an origin analysis method, constructing a robot kinematic error model, and calculating a correction amount of a deviation value of each connecting rod of the mechanical arm; b. setting the correction amount of the deviation value of each connecting rod of the mechanical arm as an evolutionary algorithm chromosome, calculating an adaptability function of an ideal position according to a measured position of an infrared binocular device and the updated kinematic model, and searching for a chromosome corresponding to the minimum position deviation; c. performing decimal coding on all the chromosomes; d. initializing each parameter according to the chromosome corresponding to the minimum position deviation searched out, calculating a cross variation probability by using the initialized parameter, and performing cross variation operation on the chromosome, so as to calculate the kinematic parameters; e. adjusting the deviation value correction amount in real time according to the updated cross variation probability, and judging whether the value of the deviation value correction amount converges; and f. outputting the optimal MDH correction value when the value of the deviation value correction amount converges.

[0006] Preferably, the method further comprises:

[0007] In step e, if the value of the deviation value correction amount does not converge, the parameter is returned to step c for recalculation.

[0008] Preferably, the step a comprises:

[0009] The homogeneous transformation matrix between two joints in the MDH robot model is shown below:

[0010]

[0011] Where, θ i For X i－1 With X i Between them, circling Z i－1 Angle pointing positively upwards; d i For X i－1 With X i Between Z i－1 Distance on; a i For X i－1 With X i Between X i Distance on; α i For Z i－1 To Z i Between X i Angle pointing positively upwards; β i For Z i－1 To Z i Between Y i The included angle is positive; Rot is the rotation matrix, Trans is the translation matrix, and multiplying them yields the homogeneous transformation matrix.

[0012] Preferably, step a further includes:

[0013] First, the initial MDH parameters are obtained using the origin analysis method. Then, one joint of the robotic arm is rotated while the other joints are fixed. By collecting scattered points, the spatial circles of rotation of each joint are fitted. The coordinate system axes of each joint are established using the right-hand rule, and the initial parameters in the MDH model are calculated.

[0014] Preferably, step a further includes:

[0015] After obtaining the initial MDH parameters, construct the robot kinematic error model. Assuming errors exist between the links of the robot, for a six-DOF serial robot, when the coordinate system is transformed from the base coordinates to the end effector, error accumulation occurs, and the coordinate transformation relationship is as follows:

[0016]

[0017] Expand Mentioning outside:

[0018]

[0019] By matrix transformation and substituting the MDH parameter error, Δθ i , Δd i , Δa iDelta alpha i Delta beta i As a small deviation of kinematics parameters, finally simplified as AX = b, A is the Jacobian matrix calculated by matrix transformation, b is the error between the actual position and the theoretical position of the robot end execution structure, X is the MDH parameter deviation between each link of the robot arm, and finally the target is accurately estimated as a set of X values:

[0020]

[0021] Where, Delta X, Delta Y, Delta Z are the end deviation values of the robot arm, Delta theta i Delta d i Delta a i Delta alpha i Delta beta i are the MDH parameter deviation values, by calculating Delta X, Delta Y, Delta Z, the MDH parameter deviation values, that is, the correction amount of the deviation values of each link of the robot arm, can be obtained, and the initial MDH parameters are added to the MDH parameter deviation values to correct the geometric parameters of the robot.

[0022] Preferably, the step b comprises:

[0023] The fitness function is:

[0024]

[0025] Take N groups of data, the i-th group of position measurement data is represented as [x i , y i , z i ], and the corrected position data calculated according to the measured joint amount and the corrected kinematics model is represented as [x i ', y i ', z i '].

[0026] Preferably, the step b searches for the chromosome corresponding to the minimum position deviation, and specifically comprises:

[0027] The MDH parameter deviation values of the robot are represented as chromosomes, and then new chromosomes are generated by operations such as mutation and crossover on the chromosomes, and the advantages and disadvantages of each chromosome are evaluated according to the fitness function, and the excellent chromosomes are selected to be retained, and the poor chromosomes are eliminated; through continuous iteration, the optimal chromosome, that is, the best parameter combination, is finally found, so as to realize the optimal calibration of the robot.

[0028] Preferably, the decimal encoding specifically comprises:

[0029] Determining the parameter range: determining the value range of the parameters in the optimization problem;

[0030] determination precision: precision of the determined parameter;

[0031] integer conversion: scaling the parameter value according to the set precision;

[0032] integer encoding conversion back to parameter value: after optimization, the integer encoding is converted back to the actual parameter value.

[0033] Preferably, the step d specifically comprises:

[0034] initializing each parameter, and calculating the crossover and mutation probabilities with the initialized parameters, the calculation formula of the crossover probability and the mutation probability in the adaptive evolution algorithm being:

[0035]

[0036]

[0037] wherein fmax represents the maximum fitness value in the population, fvag represents the average fitness value in the population, f represents the larger fitness value of the two individuals to be crossed, f' represents the fitness value of the individual to be mutated, and Ki (i=1, 2, 3, 4) is a constant;

[0038] performing the crossover and mutation operations on the chromosome with the calculated crossover and mutation probabilities, so as to calculate the kinematic parameters.

[0039] The application provides a mechanical arm parameter identification compensation system, which comprises a deviation value correction amount calculation module, a search module, an encoding module, a kinematic parameter calculation module, an updating module, a judgment module and an output module, wherein: the deviation value correction amount calculation module is used for calculating the initial MDH parameters of a robot mechanical arm according to the origin analysis method, constructing a robot kinematic error model, and calculating the deviation value correction amounts of each link of the mechanical arm; the search module is used for setting the deviation value correction amounts of each link of the mechanical arm as the chromosomes of an evolution algorithm, calculating the fitness function of the ideal position according to the measured position of an infrared binocular device and the updated kinematic model, and searching for the chromosome corresponding to the minimum position deviation; the encoding module is used for performing decimal encoding on all the chromosomes; the kinematic parameter calculation module is used for initializing each parameter according to the chromosome corresponding to the minimum position deviation searched out, calculating the crossover and mutation probabilities with the initialized parameters, performing the crossover and mutation operations on the chromosome, and thus calculating the kinematic parameters; the updating module is used for adjusting the deviation value correction amounts in real time according to the updated crossover and mutation probabilities, and judging whether the value of the deviation value correction amount converges; the judgment module is used for adjusting the deviation value correction amounts in real time according to the updated crossover and mutation probabilities, and judging whether the value of the deviation value correction amount converges; and the output module is used for outputting the optimal MDH correction value when the value of the deviation value correction amount converges.

[0040] The adaptive genetic algorithm is adopted, the crossover probability and the mutation probability of which can be automatically changed with the use degree, so that the optimal crossover probability and the mutation probability relative to a solution are obtained, the values of the crossover and the mutation are adaptively changed according to the fitness value, the control on the whole population is adjusted in real time, the calculation efficiency is improved and the convergence speed is accelerated, the requirements are accurately met, and the shortcomings of the traditional iteration method are made up. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 A flow chart of the mechanical arm parameter identification compensation method of the present application;

[0042] Figure 2 A schematic diagram for obtaining initial MDH parameters by the origin analysis method provided by the embodiment of the present application;

[0043] Figure 3 An implementation environment schematic diagram provided by the mechanical arm parameter identification compensation system embodiment of the present application;

[0044] Figure 4 A hardware architecture diagram of the mechanical arm parameter identification compensation system of the present application. DETAILED DESCRIPTION

[0045] The present application will be further described in detail below in combination with the drawings and specific embodiments.

[0046] Referring to Figure 1 Fig. 1 is a work flow chart of the preferred embodiment of the mechanical arm parameter identification compensation method of the present application.

[0047] Step S1, the initial MDH (modified denavit-hartenberg) parameters of the robot mechanical arm are calculated according to the origin analysis method, the robot kinematics error model is constructed, and the correction amount of the deviation value of each link of the mechanical arm is calculated. The initial MDH parameters of the robot mechanical arm are rough values, which will be continuously corrected subsequently.

[0048] Specifically:

[0049] The classical DH model has singularity when the adjacent joints are approximately parallel. The MDH model solves this problem by introducing a rotation transformation around the y axis for the approximately parallel adjacent joints. The homogeneous transformation matrix between two joints based on the MDH robot model is as follows:

[0050]

[0051] Where, θ i is the angle between X i－1 and X i in the forward direction around Z i－1 ; d i is the distance between Xi－1 With X i Between Z i－1 Distance on; a i For X i－1 With X i Between X i Distance on; α i For Z i－11 To Z i Between X i Angle pointing positively upwards; β i For Z i－1 To Z i Between Y i The included angle is positive; Rot is the rotation matrix, Trans is the translation matrix, and multiplying them yields the homogeneous transformation matrix.

[0052] For the entire robotic arm, the homogeneous transformation matrix expression of the robotic arm's end effector in the robotic arm's base coordinate system is calculated as follows:

[0053]

[0054] The initial MDH parameters were calculated first using the origin analysis method (see [link to original text]). Figure 2 To obtain the initial MDH parameters, rotate one joint of the robotic arm while fixing the other joints. By collecting scattered points, fit the spatial circles of rotation for each joint. Establish the coordinate system axes of each joint using the right-hand rule, and calculate the initial parameters (i.e., the initial MDH parameters) in the MDH model.

[0055] After obtaining the initial MDH parameters, construct the robot kinematic error model. Assuming errors exist between the links of the robot, for a six-DOF serial robot, when the coordinate system is transformed from the base coordinates to the end effector, error accumulation occurs, and the coordinate transformation relationship is as follows:

[0056]

[0057] Expand Mentioning outside:

[0058]

[0059] By matrix transformation and substituting the MDH parameter error, Δθ i ,Δd i ,Δa i ,Δα i ,,Δβ iFor the small deviation of kinematic parameters, finally simplified to the form of AX = b. A is the Jacobian matrix calculated by matrix transformation, b is the error between the actual position and the theoretical position of the robot end effector, X is the MDH parameter deviation between each link of the robot arm, and finally the goal is to accurately estimate a set of X values.

[0060]

[0061] Where ΔX, ΔY, ΔZ are the end deviation values of the robot arm, Δθ i , Δd i , Δa i , Δα i , Δβ i are the MDH parameter deviation values, by calculating ΔX, ΔY, ΔZ, the MDH parameter deviation values (i.e. the correction amount of the deviation values of each link of the robot arm) can be obtained, and the initial MDH parameters plus the MDH parameter deviation values can correct the geometric parameters of the robot.

[0062] Step S2, set the correction amount of the deviation values of each link of the robot arm as the chromosome of the evolutionary algorithm, calculate the fitness function of the ideal position according to the measured position of the infrared binocular device and the updated kinematic model, and search for the chromosome corresponding to the minimum position deviation. Specifically:

[0063] Set the correction amount of the deviation values of each link of the robot arm as the chromosome of the evolutionary algorithm, take the measured position of the actual NDI infrared binocular device of the end effector and the fitness function of the ideal position calculated by the updated kinematic model, search for the chromosome corresponding to the minimum position deviation.

[0064] The fitness function is:

[0065]

[0066] Take N groups of data, the i-th group of position measurement data is represented as [x i , y i , z i ], and the corrected position data calculated according to the measured joint amount and the corrected kinematic model is represented as [x i ', y i ', z i '].

[0067] In this embodiment, the chromosome corresponding to the minimum position deviation is searched, specifically including:

[0068] The MDH parameter deviation value of the robot is expressed as a chromosome (i.e., a gene group), and then new chromosomes are generated through operations such as mutation and crossover on the chromosomes, while the advantages and disadvantages of each chromosome are evaluated according to a fitness function, and excellent chromosomes are selected to be retained and poor chromosomes are eliminated. Through continuous iteration, the optimal chromosome, i.e., the best parameter combination, is finally found, so as to realize the optimal calibration of the robot.

[0069] Step S3, all chromosomes are coded in decimal. Specifically:

[0070] All chromosomes are coded, and coding is mainly divided into binary coding and decimal coding according to different code number system types. Binary coding format is neat and easy to understand; decimal coding is easy to observe, and no number system conversion is needed, so decimal coding is adopted in this embodiment.

[0071] In this embodiment, the decimal coding specifically includes:

[0072] Determine the parameter range: determine the value range of the parameter in the optimization problem, such as the minimum value and the maximum value. Determine the accuracy: determine the accuracy of the parameter, such as the number of digits after the decimal point, or a certain fixed step. Convert the parameter to an integer: scale the parameter value according to the set accuracy, for example, scale a real number parameter x to the range of [0, 100] with an accuracy of 0.01, then multiply x by 100 and take the integer part to get an integer coding i:

[0073] i = [x x 100]

[0074] Convert the integer coding back to the parameter value: after the optimization is completed, convert the integer coding back to the actual parameter value. The following formula is used for conversion:

[0075]

[0076] For example: for an independent variable x ∈ (a, b), where a and b are given independent variable intervals, and b > a,

[0077] Divide (b-a) into ≥2 parts, and represent each number with a ≥2-digit number sequence, which is the gene coding.

[0078] Step S4, according to the chromosome corresponding to the minimum position deviation searched out in step S2, initialize each parameter, calculate the crossover mutation probability with the initialized parameters, and perform crossover mutation operation on the chromosomes, so as to calculate the kinematic parameters. Specifically:

[0079] Initialize each parameter, and calculate the crossover mutation probability with the initialized parameters. The calculation formula of the crossover probability and the mutation probability in the adaptive evolution algorithm is:

[0080]

[0081]

[0082] Wherein, fmax represents the maximum fitness value in the population, fvag represents the population average fitness value, f represents the larger fitness value of the two individuals to be crossed, f' represents the fitness value of the individual to be mutated, Ki (i = 1, 2, 3, 4) is a constant.

[0083] The chromosomes are crossed and mutated by using the calculated cross mutation probability, so that the kinematic parameters are calculated.

[0084] Step S5, according to the calculated kinematic parameters, the kinematic model is updated, the corresponding parameters are calculated, so that the cross mutation probability is updated in real time. Specifically:

[0085] According to the calculated kinematic parameters, the kinematic model is updated, the related parameters such as fmax and fvag are calculated by substituting into the fitness function, so as to update the cross mutation probability in real time.

[0086] Step S6, according to the updated cross mutation probability, the X value is adjusted in real time, and whether the X value converges is judged. Specifically:

[0087] It is judged whether the end position of formula 5 converges: if formula 5 does not converge, the above-mentioned parameters such as fmax and fvag are returned to S3 for recalculation; otherwise, if the end position deviation of formula 5 remains stable and converges, step S7 is entered.

[0088] Step S7, output the optimal MDH correction value.

[0089] Referring to Figure 3 、 4 As shown in the figure, the mechanical arm parameter identification compensation system 10 of the application comprises: a deviation value correction amount calculation module 101, a search module 102, an encoding module 103, a kinematic parameter calculation module 104, an update module 105, a judgment module 106 and an output module 107. Wherein:

[0090] The deviation value correction amount calculation module 101 is used to calculate the initial MDH (modified denavit-hartenberg) parameters of the robot arm according to the origin analysis method, to construct the robot kinematic error model, and to calculate the deviation value correction amount of each link of the mechanical arm. Wherein, the initial MDH parameters of the robot arm are rough values, which will be continuously corrected subsequently.

[0091] Specifically:

[0092] The classical DH model has singularity when the adjacent joints are approximately parallel. The MDH model solves this problem by introducing a rotation around the y-axis for the approximately parallel adjacent joints. The homogeneous transformation matrix between two joints based on the MDH robot model is as follows:

[0093]

[0094] where θ i is the angle between X i－1 and X i in the positive direction of Z i－1 ; d i is the distance between X i－1 and X i in the Z i－1 direction; a i is the distance between X i－1 and X i in the X i direction; α i is the angle between Z i－11 and Z i in the positive direction of X i ; β i is the angle between Z i－1 and Z i in the positive direction of Y i ; Rot is the rotation matrix, and Trans is the translation matrix. The homogeneous transformation matrix is obtained by multiplying the two.

[0095] For the entire robot arm, the homogeneous transformation matrix expression of the robot arm end in the robot arm base coordinate system is calculated as follows:

[0096]

[0097] The initial MDH parameters are calculated. First, the initial MDH parameters are calculated by the origin analysis method (see Figure 2 ). The robot arm is rotated, and the other joints are fixed. The space circle of each joint rotation is fitted by the collected scattered points. The coordinate system axes of each joint are established by the right-hand rule, and the initial parameters in the MDH model (i.e., the initial MDH parameters) are calculated.

[0098] After obtaining the initial MDH parameters, the robot kinematic error model is constructed Assuming that there are errors between the links of the robot, for a six-degree-of-freedom serial robot, there is error accumulation when the coordinate system is transformed from the base to the end, and the coordinate transformation relationship is as follows:

[0099]

[0100] Expand it outside: ​

[0101]

[0102] By matrix transformation, substitute MDH parameter error, Δθ i ,Δd i ,Δa i ,Δα i ,,Δβ i , for kinematics parameter small deviation, finally simplified as AX = b form. A is the Jacobian matrix calculated by matrix transformation, b is the error between the actual position of the robot end execution structure and the theoretical position, X is the MDH parameter deviation between each link of the robot arm, and finally the goal is to accurately estimate a set of X values.

[0103]

[0104] Where, ΔX, ΔY, ΔZ are the end deviation values of the robot arm, Δθ i ,Δd i ,Δa i ,Δα i ,Δβ i are the MDH parameter deviation values, by calculating ΔX, ΔY, ΔZ, the MDH parameter deviation values (that is, the correction amount of each link deviation value of the robot arm) can be obtained, and the initial MDH parameter is added to the MDH parameter deviation value to correct the robot geometric parameters.

[0105] The search module 102 is configured to set the correction amount of each link deviation value of the robot arm as an evolutionary algorithm chromosome, calculate the fitness function of the ideal position according to the measured position of the infrared binocular device and the updated kinematics model, and search for the chromosome corresponding to the minimum position deviation. Specifically:

[0106] The correction amount of each link deviation value of the robot arm is set as an evolutionary algorithm chromosome, the fitness function of the ideal position is calculated according to the measured position of the actual NDI infrared binocular device of the end effector and the updated kinematics model, and the chromosome corresponding to the minimum position deviation is searched.

[0107] The fitness function is:

[0108]

[0109] Take N groups of data, and the i-th group of position measurement data is represented as [x i , y i , z i ], and the corrected position data calculated according to the measured joint amount and the corrected kinematics model is represented as [x i ', y i ', z i '].

[0110] In this embodiment, the chromosome corresponding to the minimum position deviation is searched, and specifically comprises:

[0111] The MDH parameter deviation value of the robot is expressed as a chromosome (i.e., a gene group), and then new chromosomes are generated through operations such as mutation and crossover on the chromosomes, while the advantages and disadvantages of each chromosome are evaluated according to the fitness function, and the excellent chromosomes are selected to be retained and the poor chromosomes are eliminated. Through continuous iteration, the optimal chromosome, i.e., the best parameter combination, is finally found, so as to realize the optimal calibration of the robot.

[0112] The encoding module 103 is configured to encode all chromosomes in decimal. Specifically:

[0113] All chromosomes are encoded, and the encoding is mainly divided into binary encoding and decimal encoding according to different code number system types. The binary encoding format is neat and easy to understand; the decimal encoding is easy to observe, and does not need number system conversion, so the decimal encoding is adopted in this embodiment.

[0114] In this embodiment, the decimal encoding specifically comprises:

[0115] Determine the parameter range: determine the value range of the parameter in the optimization problem, such as the minimum value and the maximum value. Determine the precision: determine the precision of the parameter, such as the number of digits after the decimal point, or a certain fixed step. Convert the parameter to an integer: scale the parameter value according to the set precision, for example, scale a real number parameter x to the range of [0, 100] with a precision of 0.01, then multiply x by 100 and take the integer part to get an integer encoding i:

[0116] i = [x x 100]

[0117] Convert the integer encoding back to the parameter value: after the optimization is completed, convert the integer encoding back to the actual parameter value. The following formula is used for conversion:

[0118]

[0119] For example: for an independent variable x ∈ (a, b), where a and b are given independent variable intervals, and b > a,

[0120] Divide (b-a) into ≥2 parts, and represent each number with a ≥2-digit number sequence, which is the gene encoding.

[0121] The kinematic parameter calculation module 104 is configured to initialize each parameter according to the chromosome corresponding to the minimum position deviation searched, calculate the crossover and mutation probability with the initialized parameters, and perform crossover and mutation operations on the chromosomes, so as to calculate the kinematic parameters. Specifically:

[0122] Initialize each parameter, and calculate the cross mutation probability with the initialized parameter, the calculation formula of the cross probability and the mutation probability in the adaptive evolution algorithm is as follows:

[0123]

[0124]

[0125] Wherein, fmax represents the maximum fitness value in the population, fvag represents the population average fitness value, f represents the larger fitness value of the two individuals to be crossed, f' represents the fitness value of the individual to be mutated, and Ki (i=1, 2, 3, 4) is a constant.

[0126] The cross mutation probability is calculated, and the chromosome is subjected to cross mutation operation, so as to calculate the kinematics parameters.

[0127] The updating module 105 is used for updating the kinematics model according to the calculated kinematics parameters, calculating the corresponding parameters, and updating the cross mutation probability in real time. Specifically:

[0128] According to the calculated kinematics parameters, the kinematics model is updated, and the related parameters such as fmax and fvag are calculated by substituting into the fitness function, so as to update the cross mutation probability in real time.

[0129] The judging module 106 is used for adjusting the X value in real time according to the updated cross mutation probability, and judging whether the X value converges. Specifically:

[0130] It is judged whether the end position of formula 5 converges: if formula 5 does not converge, the above-mentioned parameters such as fmax and fvag are returned to the encoding module 103 for recalculation.

[0131] The output module 107 is used for outputting the optimal MDH correction value when the end position deviation of formula 5 remains stable and converges.

[0132] The robot MDH parameter model is established first, so as to construct the error model of the robot. The error is encoded to establish a population, which is brought into the evolution algorithm, and the genetic parameters are adaptively adjusted, so that the population diversity is maintained while the convergence of the algorithm is ensured. The adaptive strategy requires adaptive adjustment during the evolution process: a larger cross and mutation probability is selected in the initial stage, and such a rough search process is beneficial to maintain the population diversity, and a smaller value is adjusted in the later stage to perform detailed search, so as to prevent the optimal solution from being broken and accelerate the convergence speed.

[0133] Although the present application has been described with reference to the preferred embodiments, workers skilled in the art will recognize that the application can accommodate various modifications and improvements caused by present knowledge, and therefore, the above description should not be taken as limiting the scope of the application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the protection of the present application.

Claims

1. A method for identifying and compensating parameters of a robotic arm, characterized in that, The method includes the following steps: a. Calculate the initial MDH parameters of the robot arm using the origin analysis method, construct the robot kinematic error model, and calculate the correction amount of the deviation value of each link of the robot arm; b. Set the correction value of each link deviation of the robotic arm as the chromosome of the evolutionary algorithm, calculate the fitness function of the ideal position based on the measurement position of the infrared binocular device and the updated kinematic model, and search for the chromosome corresponding to the minimum position deviation; c. Encode all chromosomes in decimal form; d. Based on the chromosome corresponding to the smallest positional deviation found in the search, initialize each parameter, calculate the crossover mutation probability using the initialized parameters, perform crossover mutation operation on the chromosome, and thus calculate the kinematic parameters; e. Based on the updated crossover and mutation probabilities, adjust the deviation correction amount in real time, and determine whether the deviation correction amount converges; f. When the deviation correction value converges, output the optimal MDH correction value; where: The decimal encoding specifically includes: Determine the parameter range: Determine the range of values ​​for the parameters in the optimization problem; Determine the precision: Determine the precision of the parameters; Convert parameters to integers: scale parameter values ​​according to the set precision; Convert the integer encoding back to the parameter value: After optimization, convert the integer encoding back to the actual parameter value.

2. The method as described in claim 1, characterized in that, The method further includes: In step e, if the value of the deviation correction does not converge, the parameter is returned to step c for recalculation.

3. The method as described in claim 2, characterized in that, Step a includes: The homogeneous transformation matrix between two joints in the MDH robot model is shown below: Where, θ i For X i－1 With X i Between them, circling Z i－1 Angle pointing positively upwards; d i For X i－1 With X i Between Z i－1 Distance on; a i For X i－1 With X i Between X i Distance on; α i For Z i－1 To Z i Between X i Angle pointing positively upwards; β i For Z i－1 To Z i Between Y i The included angle is positive; Rot is the rotation matrix, Trans is the translation matrix, and multiplying them yields the homogeneous transformation matrix.

4. The method as described in claim 3, characterized in that, Step a further includes: First, the initial MDH parameters are obtained using the origin analysis method. Then, one joint of the robotic arm is rotated while the other joints are fixed. By collecting scattered points, the spatial circles of rotation of each joint are fitted. The coordinate system axes of each joint are established using the right-hand rule, and the initial parameters in the MDH model are calculated.

5. The method as described in claim 4, characterized in that: Step a further includes: After obtaining the initial MDH parameters, construct the robot kinematic error model. Assuming errors exist between the links of the robot, for a six-DOF serial robot, when the coordinate system is transformed from the base coordinates to the end effector, error accumulation occurs, and the coordinate transformation relationship is as follows: Expand Mentioning outside: By matrix transformation and substituting the MDH parameter error, Δθ i , Δd i , Δa i , Δα i Δβ i For small deviations in kinematic parameters, the final simplified form is AX = b, where A is the Jacobian matrix calculated through matrix transformation, b is the error between the actual and theoretical positions of the robot's end effector, and X is the MDH parameter deviation between the links of the robotic arm. The ultimate goal is to accurately estimate a set of X values: Where ΔX, ΔY, and ΔZ are the end effector deviations of the robotic arm, and Δθ i , Δd i , Δa i , Δα i Δβ i The MDH parameter deviation value can be obtained by calculating ΔX, ΔY, and ΔZ. This is also the correction amount for the deviation values ​​of each link of the robotic arm. The robot's geometric parameters can be corrected by adding the MDH parameter deviation value to the initial MDH parameters.

6. The method as described in claim 4, characterized in that, Step b includes: The fitness function is: Take N sets of data, and represent the i-th set of position measurement data as [x i ,y i ,z i The corrected position data calculated based on the measured joint values ​​and the corrected kinematic model are represented as [x]. i ',y i ',z i ').

7. The method as described in claim 5, characterized in that, Step b, which involves searching for the chromosome with the smallest positional deviation, specifically includes: The robot's MDH parameter deviation values ​​are represented as chromosomes. New chromosomes are generated by performing mutation and crossover operations on the chromosomes. At the same time, the quality of each chromosome is evaluated according to the fitness function. Excellent chromosomes are selected and retained, while poor chromosomes are eliminated. Through continuous iteration, the optimal chromosome, i.e. the best combination of parameters, is finally found, thereby achieving the optimal calibration of the robot.

8. The method as described in claim 7, characterized in that, Step d specifically includes: Initialize all parameters, and then calculate the crossover and mutation probabilities using the initialized parameters. The formulas for calculating the crossover and mutation probabilities in the adaptive evolutionary algorithm are as follows: Where fmax represents the maximum fitness value in the population, fvag represents the average fitness value of the population, f represents the larger fitness value of the two individuals to be crossed, f' represents the fitness value of the individual to be mutated, and Ki (i = 1, 2, 3, 4) is a constant; The calculated crossover mutation probability is used to perform crossover mutation on the chromosome, thereby calculating the kinematic parameters.

9. A robotic arm parameter identification and compensation system utilizing the robotic arm parameter identification and compensation method of claim 1, characterized in that, The system includes a deviation correction calculation module, a search module, an encoding module, a kinematic parameter calculation module, an update module, a judgment module, and an output module, wherein: The deviation correction calculation module is used to calculate the initial MDH parameters of the robot arm according to the origin analysis method, construct the robot kinematic error model, and calculate the deviation correction of each link of the robot arm. The search module is used to set the correction amount of the deviation value of each link of the robotic arm as the chromosome of the evolutionary algorithm, calculate the fitness function of the ideal position based on the measurement position of the infrared binocular device and the updated kinematic model, and search for the chromosome corresponding to the minimum position deviation. The encoding module is used to encode all chromosomes in decimal form; The kinematic parameter calculation module is used to initialize each parameter according to the chromosome corresponding to the smallest positional deviation found by the search, calculate the crossover mutation probability using the initialized parameters, perform crossover mutation operation on the chromosome, and thus calculate the kinematic parameters. The update module is used to adjust the deviation value correction amount in real time according to the updated crossover mutation probability, and to determine whether the value of the deviation value correction amount converges. The judgment module is used to adjust the deviation value correction amount in real time based on the updated crossover mutation probability, and to determine whether the value of the deviation value correction amount converges. The output module is used to output the optimal MDH correction value when the value of the deviation correction converges.

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