A Method for Identifying Kinematic Parameters of a Joint Robot Based on Laser Sensors

Abstract

In order to solve the problems existing in the existing kinematic parameter identification methods for articulated robots, where the differential motion of a target ball installed at the end of the robot is measured using a laser tracker to identify the kinematic parameters of the robot; or a special calibration block is used for kinematic parameter identification, which has the problems of cumbersome calibration steps and expensive calibration equipment, a kinematic parameter identification method for articulated robots based on a laser sensor is provided. In the present invention, a laser sensor is installed at the end of the articulated robot, a heavy object is suspended in the working space of the robot, and then a parameter identification method using a genetic algorithm based on the laser sensor is used to realize the identification of the kinematic parameters of the articulated robot. The calibration process is relatively simple, the absolute positioning accuracy of the robot is improved, the calibration time is shortened, and the calibration efficiency is enhanced.

Description

Technical Field

[0001] The present invention belongs to the technical field of industrial robots, and particularly relates to a method for identifying kinematic parameters of a joint robot based on a laser sensor. Background Art

[0002] With the continuous expansion of the application scope and task complexity of industrial robots in industrial production, the requirements for the position and attitude accuracy of industrial robots are also getting higher and higher. Currently, industrial robots have a very high repeat positioning accuracy, reaching the order of 0.1 mm. However, the absolute positioning accuracy is very low, only at the order of 1 cm, which severely limits the application scope of industrial robots. There are many reasons for the low positioning accuracy of industrial robots, and the most important one is the parameter deviation of the geometric structure in the kinematic model. Calibration technology is an effective method to compensate for these parameter deviations, so it has become a research hotspot. Calibration is to identify the accurate parameters of the robot model by applying advanced measurement means and model-based parameter identification methods, thereby improving the robot's positioning accuracy.

[0003] For joint robots, errors during the manufacturing and installation process and long-term wear will cause the actual kinematic parameters of the joint robot to deviate from the theoretical values, resulting in a deterioration of the robot's absolute positioning accuracy. Currently, for the method of identifying the kinematic parameters of joint robots, most of them identify the kinematic parameters of the robot by measuring the differential motion of the target ball installed at the end of the robot using a laser tracker; or use a special calibration block to identify the kinematic parameters. The calibration steps are cumbersome, and the calibration equipment is expensive.

[0004] For example, Chinese Patent CN114800526A discloses an industrial robot calibration method based on a laser tracker to establish a coordinate system through points, lines, and planes. It establishes a robot error model, derives an error transfer formula using mathematical theory, gives the preparatory work before data acquisition, the method for establishing the point-line-plane coordinate system based on the laser tracker and related precautions. During the parameter identification process, it gives the derivation process of the singular value decomposition least squares method and the execution flow of the program algorithm. At the same time, it obtains the DH compensation parameters through the forward test set and verifies the effectiveness of the DH compensation parameters through the reverse verification set to achieve the improvement of the absolute positioning accuracy of industrial robots. However, the laser tracker is expensive, the installation steps are cumbersome, and the calibration accuracy is affected by the reference coordinate system. Once the reference coordinate system is established incorrectly, it will seriously affect the calibration accuracy.

[0005] For example, Chinese Patent CN105066808A discloses a simple calibration device for kinematic parameters of an industrial robot and its calibration method. The calibration device includes a calibration block and a calibration rod. The calibration block has two calibration planes perpendicular to each other. The calibration rod is fixedly and offset-mounted at the end of the robot body, and a dial indicator is installed along the axis at the end of the calibration rod. The calibration method is that the small ball of the dial indicator probe contacts the calibration plane at more than three different positions, and the normal direction of the calibration plane is calculated. After obtaining the normal directions of the two calibration planes, according to the perpendicular constraint of these two normal directions, a constraint equation containing calibration parameters can be listed. By changing the position of the calibration block and selecting different contact points, a series of constraint equations can be obtained, and the calibration results of the kinematic parameters of the industrial robot are obtained by the least squares method. However, this method seriously depends on the premise that the two calibration surfaces on the calibration block are flat and strictly perpendicular. When this condition cannot be met, it is difficult to improve the calibration effect.

[0006] For example, Chinese Patent CN109304730A discloses a method for calibrating kinematic parameters of a robot based on a laser rangefinder. The calibration device includes a laser rangefinder and a calibration board. The surface of the calibration board is a plane, the calibration board is fixed in the working space of the robot, and the laser rangefinder is installed at the end of the robot. The calibration method is to control the movement of the robot so that the calibration board is within the range of the laser rangefinder. Every time the end is at a different position, the joint angle values of each joint and the reading value of the laser rangefinder are collected once. After obtaining multiple sets of measurement data, based on the obtained mapping relationship and multiple sets of measurement data, multiple points are obtained, and the kinematic parameter error of the robot is determined based on the obtained multiple points and the coplanarity condition. However, this method has high requirements for the flatness of the surface of the calibration board, which is often difficult to meet in practice. At the same time, this method has relatively strict requirements for the accuracy of the transformation matrix from the end joint coordinate system to the laser rangefinder coordinate system. In engineering practice, when using the multi-point method to calibrate the tool coordinate system, the accuracy often cannot meet the requirements. Summary of the Invention

[0007] The object of the present invention is to solve the problems existing in the existing methods for identifying kinematic parameters of joint robots, that is, using a laser tracker to measure the differential motion of a target ball installed at the end of the robot to identify the kinematic parameters of the robot; or using a special calibration block to identify kinematic parameters, which have the problems of cumbersome calibration steps and expensive calibration equipment, and to provide a method for identifying kinematic parameters of joint robots based on a laser sensor.

[0008] To achieve the above object, the technical solution adopted by the present invention is:

[0009] A method for identifying kinematic parameters of a joint robot based on a laser sensor, characterized in that it includes the following steps:

[0010] Step 1, build an auxiliary device

[0011] The auxiliary device includes a laser sensor, a bracket, and a heavy object suspended on the bracket by a rope; the laser sensor is fixedly installed on the end flange of the articulated robot, and the heavy object is located within the working space of the articulated robot;

[0012] Step 2: Obtain the coordinate information of the sampling points

[0013] 2.1. Move the end of the articulated robot through the teach pendant to make the end of the articulated robot approach the rope and ensure that it is within the scanning range of the laser sensor;

[0014] 2.2. Perform N scans and samplings at different positions of the rope, read and record the coordinate values p of the sampling points scanned by the laser sensor on the rope in the laser sensor coordinate system, Si as well as the joint angle values of each joint of the robot at this position, i = 1,..., N;

[0015] Step 3: Establish the kinematic model of the articulated robot;

[0016] Step 4: Calibrate the laser sensor using the multi-point method to obtain the transformation matrix from the end joint coordinate system to the laser sensor coordinate system m T S , where m is the number of joints of the articulated robot;

[0017] Step 5: Using the joint angle values obtained in Step 2, as well as the nominal kinematic parameters of each joint and the kinematic error parameters of each joint, convert the coordinate values p of the N sampling points in the laser sensor coordinate system Si into the coordinate values p in the base coordinate system Bi ;

[0018] Step 6: Sort the N sampling points in ascending order according to the X-axis coordinate values in the base coordinate system to obtain the point set in the base coordinate system;

[0019] Step 7: According to the point set in the base coordinate system obtained in Step 6, set the initial values of the kinematic parameter errors Δα, Δd, and Δa of each joint, and calculate the total error within the range of kinematic parameter errors;

[0020] Step 8: Take the minimization of the total error E as the optimization objective function of the optimization algorithm, perform the optimization algorithm with the set number of calculations, and use the vector group corresponding to the minimum value in the calculated objective function values, or the vector group corresponding to when the objective function value is less than the set threshold Q as the final kinematic parameter error values of each joint.

[0021] Further, in Step 5, the coordinate values p of the sampling points in the laser sensor coordinate system Si are converted into the coordinate values p in the base coordinate system Bi , specifically:

[0022] p Bi =( 0 A1 + d 0 A1)·( 1 A2 + d 1 A2)·...·( j A j+1 + d j A j+1 )·...( m-1 A m + d m-1 A m )· m T S ·p Si

[0023] where p Bi represents the coordinate value of the i-th sampling point in the base coordinate system; p Si represents the coordinate value of the i-th sampling point in the laser sensor coordinate system; 1 ≤ i ≤ N;

[0024] 0 A1 represents the transformation matrix from the base coordinate system to the first coordinate system;

[0025] j A j+1 represents the transformation matrix from the j-th coordinate system to the (j + 1)-th coordinate system, and each element in the matrix is a function of the kinematic parameters link twist α, link offset d, link length a, and joint angle θ; its specific form is as follows:

[0026]

[0027] where cθ j+1 represents cosθ j+1 , sθ j+1 represents sinθ j+1 ;

[0028] d 0 A1 represents the differential motion from the base coordinate system to the first coordinate system caused by kinematic parameter errors;

[0029] d j A j+1 represents the differential motion from the j-th coordinate system to the (j + 1)-th coordinate system caused by kinematic parameter errors, and each element in the matrix is a function of the kinematic parameter errors link twist error Δα, link offset error Δd, link length error Δa, and the kinematic parameters link twist α, link offset d, link length a, and joint angle θ; its specific form is as follows:

[0030]

[0031] m T S represents the transformation matrix from the end-effector joint coordinate system to the laser sensor coordinate system.

[0032] Furthermore, in step 7, the total error E within the range of kinematic parameter errors is as follows:

[0033]

[0034] In the formula, x i and y i respectively represent the X-axis coordinate value and Y-axis coordinate value of the i-th sampling point in the base coordinate system after sorting; x N-i+1 and y N-i+1 represent the X-axis coordinate value and Y-axis coordinate value of the (N - i + 1)-th sampling point in the base coordinate system.

[0035] Furthermore, in step 7, the range of kinematic parameter errors is: -0.5 ≤ Δα ≤ 0.5; -0.5 ≤ Δd ≤ 0.5; -0.5 ≤ Δa ≤ 0.5.

[0036] Furthermore, in step 7, the initial values of the kinematic parameter errors of each joint, namely the link twist error Δα, the link offset error Δd, and the link length error Δa, are all set to 0.

[0037] Furthermore, in step 2, the number N of sampling points is an even number, which is convenient for grouped calculation when calculating the total error E within the range of kinematic parameter errors.

[0038] Furthermore, in step 8, the threshold Q = E0 / 200;

[0039] where E0 is the total error within the range of kinematic parameter errors calculated when the kinematic parameter errors of each joint, namely the link twist error Δα, the link offset error Δd, and the link length error Δa, are all 0.

[0040] Furthermore, in step 3, the modeling method of the kinematic model is the improved DH method, the 5-parameter MDH method, the CPC model, the S model, or the POE model.

[0041] Furthermore, in step 8, the optimization algorithm is the genetic algorithm.

[0042] Furthermore, in step 1, the greater the mass of the heavy object, the more accurate it is. The material of the rope for hanging the heavy object is a uniform material, and the greater the stiffness, the better.

[0043] Compared with the prior art, the beneficial technical effects of the present invention are as follows:

[0044] The kinematic parameter identification method of the articulated robot based on the laser sensor proposed by the present invention does not require the introduction of additional measurement devices or special calibration blocks. A laser sensor is installed at the end of the articulated robot, a heavy object is suspended in the working space of the robot, and then a parameter identification method using the genetic algorithm based on the laser sensor is used to realize the identification of the kinematic parameters of the articulated robot. The calibration process is relatively simple, which improves the absolute positioning accuracy of the robot, shortens the calibration time, and improves the calibration efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 FIG. is a flowchart of an embodiment of the kinematic parameter identification method of the articulated robot based on the laser sensor of the present invention;

[0046] Figure 2 FIG. is a schematic structural diagram of the articulated robot in the implementation of the present invention;

[0047] Figure 3 FIG. is a schematic diagram of the motion coordinate system of the articulated robot using the improved D-H method for kinematic modeling in the implementation of the present invention;

[0048] Reference numerals:

[0049] 1 - Laser sensor, 2 - Bracket, 3 - Rope, 4 - Heavy object. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0050] In order to make the objectives, advantages and features of the present invention clearer, the following further describes in detail a kinematic parameter identification method of an articulated robot based on a laser sensor proposed by the present invention with reference to the accompanying drawings and specific embodiments. Those skilled in the art should understand that these embodiments are only used to explain the technical principle of the present invention, and the purpose is not to limit the protection scope of the present invention.

[0051] A kinematic parameter identification method of an articulated robot based on a laser sensor includes the following steps:

[0052] Step 1, Build an auxiliary device

[0053] As Figure 1 shown, the auxiliary device includes a laser sensor 1, a bracket 2, and a heavy object 4 suspended on the bracket through a rope 3. The laser sensor is fixedly installed on the end flange of the articulated robot by using an adapter, and the heavy object is located in the working space of the articulated robot. The larger the mass of the heavy object, the more accurate it is. The material of the rope for suspending the heavy object should be as uniform as possible, and the greater the stiffness, the better.

[0054] Step 2, Scan and sample to obtain the coordinate information of the sampling points

[0055] 2.1, Move the end of the articulated robot through the teach pendant so that the end of the articulated robot approaches the rope and ensure that it is within the scanning range of the laser sensor.

[0056] 2.2. Repeat the scanning and sampling N times at different positions of the rope, read and record the coordinate values p of the sampling points scanned by the laser sensor on the rope in the coordinate system of the laser sensor Si , and the angle values of each joint when the robot is at this position; the number N of sampling points is an even number, i = 1,..., N.

[0057] Step 3. Establish the kinematic model of the articulated robot

[0058] Common modeling methods for robot kinematic models include the improved DH method, the 5-parameter MDH method, the CPC model, the S model, and the POE model, etc. The classical DH method is widely used in the field of industrial robots compared with other modeling methods because of its simple principle and easy understanding. In this embodiment, the improved DH method is used to establish the improved DH model of the articulated robot.

[0059] The improved DH model establishes a joint coordinate system on each link joint of the industrial robot and uses four parameters to characterize each link, thereby establishing the space coordinate system of each link of the robot. For Figure 1 the 6-joint robot shown, the improved D-H method is used for kinematic modeling, and its motion coordinate system is as Figure 2 shown.

[0060] Define the 6 joints connected in sequence from bottom to top as the first joint, the second joint,..., the sixth joint. With the joint centroid as the coordinate origin, establish the first coordinate system, the second coordinate system,..., and the end joint coordinate system respectively.

[0061] For each joint, there are 4 kinematic parameters, namely link twist α, link offset d, link length a, and joint angle θ. Assuming that except for the joint angle θ, other kinematic parameters have errors, and the errors can be expressed as Δα, Δd, and Δa respectively. These three variables are called kinematic parameter errors. Assume that the positioning error at the end is caused by the kinematic parameter errors of each joint.

[0062] Step 4. Calibrate the laser sensor using the multi-point method to obtain the transformation matrix from the end joint coordinate system to the laser sensor coordinate system 6 T S .

[0063] Regardless of how the laser sensor is installed at the end of the robot, the rope is parallel to the Z axis in the base coordinate system. Therefore, theoretically, regardless of the specific value of the transformation matrix 6 T S from the end joint coordinate system to the laser sensor coordinate system, the X values and Y values in the coordinate values of each sampling point on the rope in the base coordinate system are equal. Therefore, when using the method in this patent, there is no need to overly pursue the transformation matrix6 T S with high precision.

[0064] Step 5. Using the joint angle values, the nominal kinematic parameters of each joint, and the kinematic error parameters of each joint obtained in Step 1, convert the coordinate values p of the N sampling points in the laser sensor coordinate system Si to the coordinate values p in the base coordinate system Bi ;

[0065] The nominal kinematic parameters of each connecting axis can be obtained from the arm length parameters and the coordinate system transformation relationship in the modeling process. The nominal arm length parameters of the robot can be obtained from the manufacturer, and the nominal link twist and nominal link offset are obtained during the kinematic modeling of the robot. The kinematic parameter errors are very small compared with the nominal kinematic parameters. The actual transformation matrix can be regarded as the nominal transformation matrix plus the differential transformation caused by the kinematic parameter errors. Then, the conversion of a point in the laser sensor coordinate system to the base coordinate system can be expressed as:

[0066] p Bi = ([[]] 0 A1 + d 0 A1)·([[]] 1 A2 + d 1 A2)·...·([[]] j A j+1 + d j A j+1 )·...([[]] 5 A6 + d 5 A6)· 6 T S ·p Si

[0067] where p Bi represents the coordinate value of the i-th sampling point in the base coordinate system; p Si represents the coordinate value of the i-th point in the laser sensor coordinate system; 1 ≤ i ≤ N;

[0068] 0 A1 represents the transformation matrix from the base coordinate system to the first coordinate system;

[0069] j A j+1 represents the transformation matrix from the j-th coordinate system to the j + 1-th coordinate system, 0 ≤ j ≤ 5, and each element in the matrix is a function of the kinematic parameters α, d, a, θ; its specific form is as follows:

[0070]

[0071] where cθ j+1 represents cosθ j+1 , sθ j+1 represents sinθj+1 ;

[0072] d 0 A1 represents the differential motion from the base coordinate system to the first coordinate system caused by kinematic parameter errors;

[0073] d j A j+1 represents the differential motion from the j-th coordinate system to the (j + 1)-th coordinate system caused by kinematic parameter errors, and each element in the matrix is a function of the kinematic parameter errors Δα, Δd, Δa;

[0074] Its specific form is as follows:

[0075]

[0076]

[0077] 6 T S represents the transformation matrix from the end-effector joint coordinate system to the laser sensor coordinate system.

[0078] Step 6: Sort the N sampling points in ascending order according to the X-axis coordinate values in the base coordinate system to obtain the point set in the base coordinate system;

[0079] Actually, due to the errors between the nominal kinematic parameters and the actual kinematic parameters, the X coordinate values of these n points are not equal, and the Y coordinate values are not equal either.

[0080] Step 7: According to the point set in the base coordinate system obtained in Step 6, set the initial values of the kinematic parameter errors Δα, Δd, Δa of each joint, and calculate the total error E within the range of kinematic parameter errors. Denote the total error when the kinematic error parameters of each joint are all 0 as E0;

[0081] Gives the method of converting a point in the laser sensor coordinate system to the base coordinate system when a set of kinematic parameter errors is given. According to the point set in the base coordinate system, the total error E can be obtained:

[0082]

[0083] In the formula, x i and y i respectively represent the X-axis coordinate value and the Y-axis coordinate value of the i-th sampling point in the base coordinate system after sorting; x N-i+1 and y N-i+1 represent the X-axis coordinate value and the Y-axis coordinate value of the (N - i + 1)-th sampling point in the base coordinate system after sorting. Selecting the data of two relatively far sampling points to calculate the error, the results obtained by multiple optimization algorithms are more accurate.

[0084] Step 8: Minimize the total error E as the optimization objective of the optimization algorithm, perform the optimization algorithm with a set number of calculations, and take the vector group corresponding to the minimum value in the calculated objective function values, or the vector group corresponding when the objective function value is less than the set threshold Q as the final kinematic parameter error values of each joint.

[0085] In this embodiment, the genetic algorithm is selected, and specifically, the genetic algorithm toolbox in the MATLAB optimization toolbox is used to solve the kinematic parameter errors of each joint. When using the genetic algorithm to solve the kinematic parameter errors of each joint, there are no non - linear constraints, linear equality constraints, or linear inequality constraints.

[0086] Take the total error E as the objective function of the genetic algorithm, that is, the fitness function in the MATLAB genetic algorithm toolbox. The kinematic parameter errors of each joint Δα, Δd, Δa are variables to be solved, and their initial values are all 0; since the kinematic parameter errors are relatively small, generally within 0.5, the upper limits of the kinematic parameter errors are all set to 0.5, and the lower limits are all set to - 0.5.

[0087] Take error minimization as the optimization objective of the optimization algorithm, and use the multiple genetic optimization algorithms to calculate the minimum value of the total error E within the range of kinematic parameter errors, or when the value of the fitness function E is less than the set threshold Q = E0 / 200 after the calculation, the kinematic parameter errors of each joint Δα, Δd, Δa are used as the final kinematic parameter error values of each joint.

[0088] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the present invention.

Claims

1. A method for identifying kinematic parameters of a joint robot based on a laser sensor, characterized in that, Including the following steps: Step 1: Set up the auxiliary device The auxiliary device includes a laser sensor, a bracket, and a heavy object suspended on the bracket by a rope; the laser sensor is fixedly installed on the end flange of the articulated robot, and the heavy object is located within the working space of the articulated robot; Step 2: Obtain the coordinate information of the sampling points 2.1 Move the end of the articulated robot through the teach pendant to make the end of the articulated robot approach the rope and ensure it is within the scanning range of the laser sensor; 2.

2. Conduct N scanning samplings at different positions of the rope, read and record the coordinate values p of the sampling points scanned by the laser sensor on the rope in the coordinate system of the laser sensor Si , and the joint angle values of the robot at this position, where i = 1, …, N; Step 3: Establish the kinematic model of the articulated robot; Step 4: Calibrate the laser sensor using the multi-point method to obtain the transformation matrix from the end-effector joint coordinate system to the laser sensor coordinate system m T S , where m is the number of joints of the articulated robot; Step 5: Using the joint angle values obtained in Step 2, as well as the nominal kinematic parameters and kinematic error parameters of each joint, convert the coordinate values p of the N sampling points in the laser sensor coordinate system Si into the coordinate values p in the base coordinate system Bi ; Step 6: Sort the N sampling points in ascending order according to the X-axis coordinate values in the base coordinate system to obtain the point set in the base coordinate system; Step 7: According to the point set in the base coordinate system obtained in Step 6, set the initial values of the kinematic parameter errors Δα, Δd, and Δa of each joint, and calculate the total error within the kinematic parameter error range; Step 8: Take the minimization of the total error E as the optimization objective function of the optimization algorithm, perform the optimization algorithm with the set number of calculations, and take the vector group corresponding to the minimum value among the calculated objective function values, or the vector group corresponding when the objective function value is less than the set threshold Q as the final kinematic parameter error values of each joint.

2. The method for identifying the kinematic parameters of an articulated robot based on a laser sensor according to claim 1, wherein: In step 5, the coordinate value p of the sampling point in the coordinate system of the laser sensor Si is converted into the coordinate value p in the base coordinate system Bi , specifically: p Bi =( 0 A1 + d 0 A1)·( 1 A2 + d 1 A2)·...·( j A j+1 + d j A j+1 )·...( m-1 A m + d m-1 A m )· m T S ·p Si where p Bi represents the coordinate value of the i-th sampling point in the base coordinate system; p Si represents the coordinate value of the i-th sampling point in the laser sensor coordinate system; 1 ≤ i ≤ N; 0 A1 represents the transformation matrix from the base coordinate system to the first coordinate system; j A j+1 represents the transformation matrix from the j-th coordinate system to the (j + 1)-th coordinate system, where 0 ≤ j ≤ m - 1. Each element in the matrix is a function of the kinematic parameters: link twist α, link offset d, link length a, and joint angle θ. Its specific form is as follows: where, cθ j+1 represents cosθ j+1 , sθ j+1 represents sinθ j+1 ; d 0 A1 represents the differential motion from the base coordinate system to the first coordinate system caused by kinematic parameter errors; d j A j+1 Denotes the differential motion from the j-th coordinate system to the (j + 1)-th coordinate system caused by kinematic parameter errors. Each element in the matrix is a function of the kinematic parameter errors, link twist error Δα, link offset error Δd, link length error Δa, and the kinematic parameters link twist α, link offset d, link length a, and joint angle θ. Its specific form is as follows: m T S represents the transformation matrix from the end-effector joint coordinate system to the laser sensor coordinate system.

3. The method for identifying the kinematic parameters of an articulated robot based on a laser sensor according to claim 2, wherein: In Step 7, the total error E within the kinematic parameter error range is: where x i and y i respectively represent the X-axis coordinate value and the Y-axis coordinate value of the i-th sampling point in the base coordinate system after sorting; x N-i+1 and y N-i+1 represent the X-axis coordinate value and the Y-axis coordinate value of the (N - i + 1)-th sampling point in the base coordinate system.

4. The method for identifying the kinematic parameters of an articulated robot based on a laser sensor according to claim 3, wherein: In Step 7, the kinematic parameter error range is: -0.5 ≤ Δα ≤ 0.5; -0.5 ≤ Δd ≤ 0.5; -0.5 ≤ Δa ≤ 0.

5.

5. The method for identifying the kinematic parameters of an articulated robot based on a laser sensor according to claim 4, wherein: In Step 7, the initial values of the kinematic parameter errors of each joint, namely the link twist error Δα, the link offset error Δd, and the link length error Δa, are all set to 0.

6. The method for identifying the kinematic parameters of an articulated robot based on a laser sensor according to any one of claims 1-5, wherein: In Step 8, the threshold Q = E0 / 200; wherein, E0 is the total error within the kinematic parameter error range calculated when the kinematic parameter errors of each joint, namely the link twist error Δα, the link offset error Δd, and the link length error Δa, are all 0.

7. The method for identifying the kinematic parameters of an articulated robot based on a laser sensor according to claim 6, wherein: In Step 3, the modeling method of the kinematic model is the improved DH method, the 5-parameter MDH method, the CPC model, the S model, or the POE model.

8. The method for identifying the kinematic parameters of an articulated robot based on a laser sensor according to claim 7, wherein: In Step 8, the optimization algorithm is a genetic algorithm.

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