Robot scene simulation method applied to digital robot industry chain

Abstract

The invention discloses a robot scene simulation method applied to a digital robot industry chain, and relates to the technical field of scene simulation. Three-dimensional shape data of a clamp in an actual operation environment is obtained, data fusion correction is carried out in combination with geometric structure information, and a simulation result is obtained; the form deviation caused by manufacturing tolerance, abrasion and mechanical errors is effectively compensated; in the robot grabbing modeling process, part posture changes and grabbing errors in the clamping process are calculated through simulation analysis, the deviation condition of parts in the clamping process is analyzed through a rotation transformation matrix and a rigidity matrix, grabbing points are dynamically adjusted, and assembly deviation caused by the clamping errors is effectively reduced; in the assembly path optimization stage, the path of the robot executing the assembly task is adjusted through the correction parameters in the clamp database, the insertion force and the assembly track of the robot are optimized by integrating the grabbing error and the clamp form deviation, the robot can adjust the assembly strategy in a self-adaptive mode, and the assembly precision is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of scenario simulation, and particularly to a robot scenario simulation method applied to the digital robot industrial chain. Background Art

[0002] In the existing simulation methods of the digital robot industrial chain, the robot assembly task relies on the CAD model for path planning, and combines methods such as point cloud data and finite state machine control logic to model the working state of the fixture.

[0003] When the robot actually performs grasping, clamping, and assembly operations, due to the mechanical errors of the fixture itself, the instability of the clamping force, and the slight deformation of the parts, it is difficult to accurately simulate these physical factors, resulting in slight deviations between the posture of the parts grasped by the robot in actual operation and the simulation results.

[0004] In view of the above problems, some traditional simulation schemes preset fixed clamping forces and standard clamping positions during the fixture simulation process to approximately simulate the fixture forces in the real environment; in addition, some schemes combine a vision detection system to perform secondary position correction after the robot grasps the part; however, the rigid modeling cannot dynamically reflect the subtle changes of the fixture under different working conditions, and the vision-based error compensation can often only be adjusted after grasping, and cannot optimize the robot's motion path in advance, affecting the overall assembly efficiency. Therefore, there is an urgent need for a robot scenario simulation scheme to solve such problems. Summary of the Invention

[0005] In view of the existing problems described above, the present invention is proposed.

[0006] The present invention provides a robot scenario simulation method applied to the digital robot industrial chain to solve the problems that the existing robot simulation methods are difficult to accurately simulate the clamping force of the fixture, mechanical errors, and part deformation, resulting in deviation of the robot's grasping posture and decline in assembly accuracy.

[0007] To solve the above technical problems, the present invention provides the following technical solutions:

[0008] An embodiment of the present invention provides a robot scenario simulation method applied to the digital robot industrial chain, which includes:

[0009] Step S1, based on the geometric structure information of the fixture, combined with the morphological deviation of the fixture in the actual operation environment, construct a digital twin model of the robot and the tooling fixture;

[0010] Step S2: Based on the digital twin model of the fixture in Step S1, model the robot grasping process, simulate the interaction between the robot's actuator and the fixture in the simulation environment; calculate the part attitude change and grasping error during the clamping process, and adjust the grasping points of the robot actuator based on the simulation analysis.

[0011] Step S3: According to the grasping error calculated in Step S2, obtain the correction parameters from the fixture database and adjust the path for the robot to perform the assembly task.

[0012] Step S4: Perform the assembly simulation according to the optimized path in Step S3, and collect the part position deviation in the actual assembly process through the vision sensor, and compare it with the simulation data.

[0013] As a preferred solution of the robot scenario simulation method applied to the digital robot industry chain of the present invention, wherein: in Step S1, obtain the point cloud data or visual measurement data of the fixture in the actual operating environment, correct the geometric structure information, and store the corrected fixture geometric model, form deviation parameters and clamping force compensation parameters in the fixture database.

[0014] As a preferred solution of the robot scenario simulation method applied to the digital robot industry chain of the present invention, wherein: the step of constructing the digital twin model of the robot and the tooling fixture based on the geometric structure information of the fixture and combining the form deviation of the fixture in the actual operating environment is as follows.

[0015] Obtain the three-dimensional point cloud data of the fixture in the actual operating environment through a laser scanner or a vision measurement device, and define the point cloud set as P:

[0016] P = {p i |p i = (x i , y i , z i ), i = 1, 2, …, N,

[0017] wherein, P represents the point cloud data set of the fixture, p i represents the i-th sampling point, x i , y i , z i respectively represent the coordinate values of the point p i in the three-dimensional coordinate system, and N represents the total number of sampling points.

[0018] Based on the point cloud data P, use the least squares method or the surface fitting method to reconstruct the geometric surface of the fixture, and the reconstruction process is expressed as:

[0019]

[0020] Among them, S(x, y) represents the equation of the fixture surface after fitting, M represents the highest order of fitting, a mn is the fitting coefficient to be solved, m and n respectively represent the power exponents of the fitting equation, controlling the order of the surface shape, x m , y n respectively represent the calculated values of coordinates x and y under different powers,

[0021] By comparing the point cloud data P with the ideal fixture geometric model S0(x, y), the shape deviation is calculated. The calculation formula is:

[0022]

[0023] Among them, Δd i represents the shape deviation value of the i-th point, x i,0 , y i,0 , z i,0 are the coordinate values of the corresponding points of the ideal geometric model of the fixture,

[0024] A shape deviation compensation model is constructed to correct the geometric model of the fixture. The model formula is:

[0025]

[0026] Among them, represents the geometric model of the fixture after compensation, S(x, y) is the uncorrected fitting surface equation, w i is the weight coefficient, and Δd i represents the shape deviation correction value.

[0027] As a preferred solution of the robot scene simulation method applied to the digital robot industry chain described in the present invention, wherein: the step of correcting the geometric structure information and storing the corrected fixture geometric model, shape deviation parameters and clamping force compensation parameters in the fixture database is,

[0028] Based on the shape deviation compensation model, update the geometric structure information of the fixture and introduce an error distribution function. The function formula is:

[0029]

[0030] Among them, E(x, y) represents the mean square deviation of the shape error, is the average shape deviation of all sampling points,

[0031] By shape error correction, calculate the clamping force compensation parameter. The calculation formula is:

[0032]

[0033] Among them, F cDenote the corrected clamping force, \(F_0\) is the clamping force in the ideal state, and \(k\) is the form deviation influence factor.

[0034] The corrected fixture geometric model Form deviation parameter \(\Delta d\) i and the clamping force compensation parameter \(F\) c are stored in the fixture database DB:

[0035]

[0036] wherein, DB represents the fixture database, stores the corrected geometric model data, \(\Delta d\) stores the form deviation data, and \(F\) c stores the clamping force compensation parameter.

[0037] As a preferred solution of the robot scene simulation method applied to the digital robot industry chain described in the present invention, wherein: in step S2, the corrected grasping point data, the clamping force compensation parameter, and the simulation correction data are stored in the fixture database.

[0038] As a preferred solution of the robot scene simulation method applied to the digital robot industry chain described in the present invention, wherein: the step of modeling the robot grasping process, simulating the interaction between the robot actuator and the fixture in the simulation environment; calculating the part attitude change and the grasping error during the clamping process, and adjusting the grasping point of the robot actuator based on the simulation analysis is,

[0039] Based on the digital twin model of the fixture, define the grasping equation of the robot actuator:

[0040]

[0041] wherein, \(G(x,y,z)\) represents the grasping position of the robot actuator, \(R(\theta)\) is the rotation transformation matrix, and \(\theta\) is the rotation angle. is the compensated fixture geometric model, \(T\) is the translation vector, representing the displacement of the robot actuator relative to the fixture.

[0042] Calculate the part attitude change during the clamping process, and the calculation formula is:

[0043] \(\varPhi = J\) -1 \(F\) c ,

[0044] wherein, \(\varPhi\) is the angle offset vector of the part, \(J\) is the clamping stiffness matrix, representing the elastic deformation relationship between the fixture and the part, -1 represents the inverse operation of the matrix, and \(F\) c is the corrected clamping force.

[0045] Calculate the grasping error \(e\) based on the simulation analysis and adjust the grasping point:

[0046] e = ||G - G0||,

[0047] G' = G - αe,

[0048] Wherein, e is the grasping error, representing the Euclidean distance between the actual grasping point G and the target grasping point G0, G0 is the target grasping point, representing the grasping position of the robot in the ideal state, G' is the corrected grasping point, and α is the adjustment coefficient.

[0049] As a preferred solution of the robot scenario simulation method applied to the digital robot industry chain of the present invention, wherein: during the adjustment process of the path, based on the corrected grasping point data, clamping force compensation parameters, and fixture form deviation, the insertion force and assembly trajectory of the robot are optimized.

[0050] As a preferred solution of the robot scenario simulation method applied to the digital robot industry chain of the present invention, wherein: the step of obtaining the correction parameters from the fixture database and adjusting the path of the robot to perform the assembly task is,

[0051] Based on the grasping error e, correct the assembly path, and the correction formula is:

[0052] P' = P - βe,

[0053] Wherein, P is the original assembly path, representing the initial trajectory of the robot to perform the assembly task, P' is the corrected assembly path, and β is the path correction coefficient.

[0054] Combined with the fixture form deviation, optimize the insertion force F a and the optimization formula is:

[0055]

[0056] Wherein, F' a is the optimized insertion force, F a is the initial insertion force, representing the initial force value when the robot performs the assembly task, γ is the form deviation influence factor, w i is the weight coefficient, representing the influence degree of different form deviations on the insertion force, Δd i is the form deviation correction value.

[0057] As a preferred solution of the robot scenario simulation method applied to the digital robot industry chain of the present invention, wherein: in step S4, based on the comparison result, dynamically correct the form deviation parameters and clamping force compensation parameters in the fixture database, and update the fixture digital twin model.

[0058] As a preferred solution of the robot scenario simulation method applied to the digital robot industry chain described in the present invention, wherein: the step of dynamically correcting the form deviation parameters and the clamping force compensation parameters in the fixture database based on the comparison result and updating the fixture digital twin model is as follows:

[0059] Compare the visual detection with the error, and measure the part position deviation d based on the visual sensor v And calculate the correction value:

[0060] Δd v = d v - d0,

[0061] wherein, d v is the part position deviation measured visually, d0 is the simulation reference value, that is, the expected part position in the simulation calculation, and Δd v is the error correction value, indicating the difference between the actual deviation and the simulation reference value.

[0062] Update the form deviation parameter Δd and the clamping force compensation parameter F c , and the update formula is:

[0063] Δd' = Δd + λΔd v ,

[0064] F' c = F c + μΔd v ,

[0065] wherein, Δd' is the updated form deviation parameter, Δd is the original form deviation parameter, λ is the update weight coefficient, F' c is the updated clamping force compensation parameter, F c is the original clamping force compensation parameter, and μ is the clamping force compensation adjustment coefficient.

[0066] The beneficial effects of the present invention are as follows: In the present invention, the three-dimensional form data of the fixture in the actual operation environment is obtained, and data fusion and correction are carried out in combination with the geometric structure information, effectively compensating for the form deviation caused by manufacturing tolerances, wear and mechanical errors; in the process of robot grasping modeling, the part attitude change and grasping error during the clamping process are calculated through simulation analysis, and the offset of the part during the clamping process is analyzed by using the rotation transformation matrix and the stiffness matrix, and the grasping point is dynamically adjusted, effectively reducing the assembly deviation caused by the clamping error.

[0067] In the present invention, in the stage of optimizing the assembly path, the path of the robot performing the assembly task is adjusted through the correction parameters in the fixture database, and the robot insertion force and the assembly trajectory are optimized by integrating the grasping error and the fixture form deviation, so that the robot can adaptively adjust the assembly strategy and improve the assembly accuracy.

[0068] After the assembly simulation is executed, the present invention uses a vision sensor to collect the part position deviation in the actual assembly process, compares it with the simulation data, calculates the error correction value, and dynamically updates the form deviation parameters and clamping force compensation parameters in the fixture database.

[0069] In summary, compared with the traditional simulation method that only relies on the CAD model or fixed clamping force for approximate simulation, it can more effectively compensate for the virtual-real synchronization error, improve the adaptability of the simulation system to the mechanical error of the fixture, the morphological deformation of the part, and the clamping force fluctuation, enable the robot to have higher precision and stability in complex assembly tasks, and significantly reduce the assembly failure rate caused by the accumulation of clamping errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0071] Figure 1 It is a schematic flow chart of a robot scene simulation method applied to the digital robot industry chain of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0072] In order to make the above-mentioned objects, features, and advantages of the present invention more obvious and understandable, the following will make a detailed description of the specific embodiments of the present invention with reference to the drawings in the specification.

[0073] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar promotions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0074] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The "in one embodiment" that appears in different places in this specification does not all refer to the same embodiment, nor is it a separate or alternative embodiment that mutually excludes other embodiments.

[0075] Embodiment 1, referring to Figure 1 This embodiment provides a robot scene simulation method applied to the digital robot industry chain, including the following steps:

[0076] Step S1: Based on the geometric structure information of the fixture and combining the form deviation of the fixture in the actual operation environment, construct a digital twin model of the robot and the tooling fixture;

[0077] In step S1, obtain the point cloud data or visual measurement data of the fixture in the actual operation environment, correct the geometric structure information, and store the corrected fixture geometric model, form deviation parameters, and clamping force compensation parameters in the fixture database;

[0078] The steps of constructing a digital twin model of the robot and the tooling fixture based on the geometric structure information of the fixture and combining the form deviation of the fixture in the actual operation environment are as follows:

[0079] Through a laser scanner or a visual measurement device, obtain the three-dimensional point cloud data of the fixture in the actual operation environment, and define the point cloud set as P:

[0080] P = {p i |p i = (x i , y i , z i ), i = 1, 2,..., N,

[0081] where P represents the point cloud data set of the fixture, p i represents the i-th sampling point, x i , y i , z i respectively represent the coordinate values of the point p i in the three-dimensional coordinate system, and N represents the total number of sampling points.

[0082] Based on the point cloud data P, use the least squares method or the surface fitting method to reconstruct the geometric surface of the fixture. The reconstruction process is expressed as:

[0083]

[0084] where S(x, y) represents the surface equation of the fixture after fitting, M represents the highest order of fitting, a mn is the fitting coefficient to be obtained, m and n respectively represent the power exponents of the fitting equation, control the order of the surface form, and x m , y n respectively represent the calculated values of the coordinates x and y under different powers.

[0085] By comparing the point cloud data P with the ideal fixture geometric model S0(x, y), calculate the form deviation. The calculation formula is:

[0086]

[0087] where Δd i represents the form deviation value of the i-th point, xi,0 , y i,0 , z i,0 are the coordinate values of the corresponding points of the ideal geometric model of the fixture.

[0088] Construct a shape deviation compensation model to correct the geometric model of the fixture. The model formula is:

[0089]

[0090] Among them, represents the geometric model of the fixture after compensation, S(x, y) is the fitting surface equation before correction, w i is the weight coefficient, and Δd i represents the shape deviation correction value;

[0091] Specifically, obtain the point cloud data of the fixture and perform fitting to construct a geometric model of the fixture based on real measurement. By calculating the shape deviation, correct the geometric structure of the fixture; introduce a shape deviation compensation model to enable the digital twin model of the fixture to be dynamically adjusted and improve the modeling accuracy;

[0092] The steps of correcting the geometric structure information and storing the corrected geometric model of the fixture, shape deviation parameters, and clamping force compensation parameters in the fixture database are as follows:

[0093] Based on the shape deviation compensation model, update the geometric structure information of the fixture and introduce an error distribution function. The function formula is:

[0094]

[0095] Among them, E(x, y) represents the mean square deviation of the shape error, is the average shape deviation of all sampling points,

[0096] By shape error correction, calculate the clamping force compensation parameter. The calculation formula is:

[0097]

[0098] Among them, F c represents the corrected clamping force, F0 is the clamping force in the ideal state, and k is the shape deviation influence factor.

[0099] Store the corrected geometric model of the fixture shape deviation parameter Δd i and clamping force compensation parameter F c in the fixture database DB:

[0100]

[0101] Among them, DB represents the fixture database. Δd stores the modified geometric model data, and Δd stores the form deviation data, F c Store the clamping force compensation parameters;

[0102] Specifically, based on the calculation results of the form deviation, the geometric model of the fixture is corrected, and the error distribution is further calculated to optimize the clamping force compensation parameters; the corrected data is stored in the database, which can truly reflect the actual working conditions in the digital twin environment;

[0103] Step S2: Based on the digital twin model of the fixture in Step S1, model the robot grasping process, and simulate the interaction between the robot actuator and the fixture in the simulation environment; calculate the part attitude change and grasping error during the clamping process, and adjust the grasping point of the robot actuator based on the simulation analysis;

[0104] In Step S2, store the corrected grasping point data, clamping force compensation parameters, and simulation correction data in the fixture database;

[0105] The steps of modeling the robot grasping process, simulating the interaction between the robot actuator and the fixture in the simulation environment, calculating the part attitude change and grasping error during the clamping process, and adjusting the grasping point of the robot actuator based on the simulation analysis are as follows:

[0106] Based on the digital twin model of the fixture, define the grasping equation of the robot actuator:

[0107]

[0108] where G(x, y, z) represents the grasping position of the robot actuator, R(θ) is the rotation transformation matrix, and θ is the rotation angle, is the compensated geometric model of the fixture, T is the translation vector, representing the displacement of the robot actuator relative to the fixture,

[0109] Calculate the part attitude change during the clamping process. The calculation formula is:

[0110] Φ = J -1 F c ,

[0111] where Φ is the angular offset vector of the part, J is the clamping stiffness matrix, representing the elastic deformation relationship between the fixture and the part, -1 represents the inverse operation of the matrix, F c is the corrected clamping force,

[0112] Calculate the grasping error e based on the simulation analysis and adjust the grasping point:

[0113] e = ||G - G0||,

[0114] G' = G - αe,

[0115] Among them, e is the grasping error, which represents the Euclidean distance between the actual grasping point G and the target grasping point G0. G0 is the target grasping point, representing the grasping position of the robot in the ideal state. G' is the corrected grasping point, and α is the adjustment coefficient;

[0116] Specifically, based on the digital twin model, the interaction between the robot actuator and the fixture is modeled, and the part pose change and grasping error are calculated. The offset of the part during the robot clamping process is analyzed through the rotation transformation matrix and the stiffness matrix, and the grasping point is adjusted based on the error analysis. The corrected grasping point ensures that the robot can accurately grasp the part and reduce the assembly deviation caused by the clamping error;

[0117] Step S3: According to the grasping error calculated in step S2, obtain the correction parameters from the fixture database and adjust the path of the robot to perform the assembly task;

[0118] During the adjustment of the path, based on the corrected grasping point data, the clamping force compensation parameter, and the fixture form deviation, optimize the insertion force and the assembly trajectory of the robot;

[0119] The steps of obtaining the correction parameters from the fixture database and adjusting the path of the robot to perform the assembly task are as follows:

[0120] Based on the grasping error e, correct the assembly path. The correction formula is:

[0121] P' = P - βe,

[0122] Among them, P is the original assembly path, representing the initial trajectory of the robot to perform the assembly task. P' is the corrected assembly path, and β is the path correction coefficient.

[0123] Combined with the fixture form deviation, optimize the insertion force F a The optimization formula is:

[0124]

[0125] Among them, F' a is the optimized insertion force, F a is the initial insertion force, representing the initial force value when the robot performs the assembly task. γ is the form deviation influence factor, w i is the weight coefficient, representing the influence degree of different form deviations on the insertion force. Δd i is the form deviation correction value;

[0126] Specifically, the robot assembly path is corrected according to the grasping error here to reduce the cumulative deviation during the assembly process. By calculating the influence of the error on the path and adjusting the insertion force, the assembly task is made more precise, enabling the robot to adapt to the fixture form deviation and grasping error during the execution of the assembly task and improving the assembly accuracy;

[0127] Step S4: Execute the assembly simulation according to the path optimized in step S3, and collect the part position deviation during the actual assembly process through the vision sensor for comparison with the simulation data;

[0128] In step S4, based on the comparison result, dynamically correct the form deviation parameter and the clamping force compensation parameter in the fixture database, and update the fixture digital twin model;

[0129] The steps of dynamically correcting the form deviation parameter and the clamping force compensation parameter in the fixture database based on the comparison result and updating the fixture digital twin model are as follows:

[0130] Compare the visual detection with the error, and measure the part position deviation d based on the vision sensor v And calculate the correction value:

[0131] Δd v = d v - d0,

[0132] where d v is the part position deviation measured visually, d0 is the simulation reference value, that is, the expected part position in the simulation calculation, and Δd v is the error correction value, representing the difference between the actual deviation and the simulation reference value,

[0133] Update the form deviation parameter Δd and the clamping force compensation parameter F c , and the update formula is:

[0134] Δd' = Δd + λΔd v ,

[0135] F' c = F c + μΔd v ,

[0136] where Δd' is the updated form deviation parameter, Δd is the original form deviation parameter, λ is the update weight coefficient, F' c is the updated clamping force compensation parameter, F c is the original clamping force compensation parameter, and μ is the clamping force compensation adjustment coefficient;

[0137] Specifically, by comparing the actual part positions through assembly simulation and vision sensors, the form deviation parameters and clamping force compensation parameters in the fixture database are dynamically adjusted to achieve continuous optimization of the digital twin model;

[0138] By updating the fixture database in real time, the robot can adaptively optimize the grasping and assembly processes in a changing environment, improving the accuracy and stability of long-term operation.

[0139] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A robot scene simulation method applied to a digital robot industry chain, characterized in that: include, Step S1, based on the geometric structure information of the fixture and combined with the morphological deviation of the fixture in the actual operating environment, a digital twin model of the robot and the fixture is constructed; Step S2, based on the digital twin model of the fixture in step S1, modeling the robot grasping process, simulating the interaction between the robot actuator and the fixture in a simulation environment; calculating the part posture change and grasping error during the clamping process, and adjusting the grasping point of the robot actuator based on the simulation analysis; Step S3, obtaining correction parameters from the fixture database according to the grasping error calculated in step S2, and adjusting the path of the robot to perform the assembly task; Step S4, performing assembly simulation according to the path optimized in step S3, and collecting part position deviations in the actual assembly process through visual sensors, and comparing them with simulation data.

2. A robot scene simulation method applied to a digital robot industry chain as claimed in claim 1, characterized in that: In step S1, the point cloud data or visual measurement data of the fixture in the actual operating environment is obtained, the geometric structure information is corrected, and the corrected fixture geometric model, morphological deviation parameters and clamping force compensation parameters are stored in the fixture database.

3. A robot scene simulation method applied to a digital robot industry chain as claimed in claim 2, characterized in that: The steps of constructing the digital twin model of the robot and the fixture based on the geometric structure information of the fixture and the morphological deviation of the fixture in the actual operating environment are as follows: Through laser scanning or visual measurement equipment, obtain the three-dimensional point cloud data of the fixture in the actual operating environment, and define the point cloud set as P: P={p i |p i =(x i ,y i ,z i )},i=1,2,…,N, Among them, P represents the point cloud data set of the fixture, p i represents the i-th sampling point, x i ,y i ,z i Respectively represent point p i Coordinate value in the three-dimensional coordinate system, N represents the total number of sampling points, Based on the point cloud data P, the least squares method or surface fitting method is used to reconstruct the geometric surface of the fixture. The reconstruction process is expressed as: Among them, S(x,y) represents the fixture surface equation after fitting, M represents the highest order of fitting, and a mn is the fitting coefficient to be determined, m and n represent the power exponent of the fitting equation, respectively, and control the order of the surface morphology, x m ,y n Respectively represent the calculated values ​​of coordinates x and y at different powers, By comparing the point cloud data P with the ideal fixture geometry model S0 (x, y), the morphological deviation is calculated using the following formula: Where, Δd i Indicates the shape deviation value of the i-th point, x i,0 ,y i,0 ,z i,0 is the coordinate value of the corresponding point of the ideal geometric model of the fixture, Construct a morphological deviation compensation model to correct the geometric model of the fixture. The model formula is: in, represents the geometry model of the fixture after compensation, S(x,y) is the uncorrected fitting surface equation, and w i is the weight coefficient, Δd i Indicates the morphological deviation correction value.

4. A robot scene simulation method applied to a digital robot industry chain as claimed in claim 3, characterized in that: The step of correcting the geometric structure information and storing the corrected fixture geometric model, morphological deviation parameters and clamping force compensation parameters in the fixture database is: Based on the morphological deviation compensation model, the geometric structure information of the fixture is updated, and the error distribution function is introduced. The function formula is: Among them, E(x,y) represents the mean square deviation of the morphological error, is the average morphological deviation of all sampling points, Through the correction of morphological error, the clamping force compensation parameters are calculated, and the calculation formula is: Among them, F c represents the corrected clamping force, F0 is the clamping force under ideal conditions, k is the influencing factor of the morphological deviation, The corrected fixture geometry model Morphological deviation parameter Δd i And clamping force compensation parameter F c Stored in fixture database DB: Among them, DB represents the fixture database, Store the corrected geometric model data, Δd stores the morphological deviation data, F c Stores the gripping force compensation parameters.

5. A robot scene simulation method applied to a digital robot industry chain as claimed in claim 4, characterized in that: In step S2, the corrected grasping point data, clamping force compensation parameters and simulation correction data are stored in the fixture database.

6. A robot scene simulation method applied to a digital robot industry chain as claimed in claim 5, characterized in that: The steps of modeling the robot grasping process, simulating the interaction between the robot actuator and the fixture in a simulation environment, calculating the part posture change and grasping error during the clamping process, and adjusting the grasping point of the robot actuator based on the simulation analysis are as follows: Based on the digital twin model of the fixture, define the grasping equation of the robot actuator: Among them, G(x,y,z) represents the grasping position of the robot actuator, R(θ) is the rotation transformation matrix, θ is the rotation angle, is the geometric model of the compensated fixture, T is the translation vector, which represents the displacement of the robot actuator relative to the fixture, Calculate the change in the part posture during the clamping process. The calculation formula is: Φ=J -1 F c , Among them, Φ is the angular offset vector of the part, J is the clamping stiffness matrix, which represents the elastic deformation relationship between the fixture and the part, -1 represents the inverse operation of the matrix, and F c is the corrected clamping force, Calculate the grasping error e based on simulation analysis and adjust the grasping point: e=||G-G0||, G′=G-αe, Among them, e is the grasping error, which represents the Euclidean distance between the actual grasping point G and the target grasping point G0, G0 is the target grasping point, which represents the grasping position of the robot under ideal conditions, G' is the corrected grasping point, and α is the adjustment coefficient.

7. A robot scene simulation method applied to a digital robot industry chain as claimed in claim 6, characterized in that: During the adjustment of the path, the robot insertion force and assembly trajectory are optimized based on the corrected grasping point data, clamping force compensation parameters and fixture shape deviation.

8. A robot scene simulation method applied to a digital robot industry chain as claimed in claim 7, characterized in that: The step of obtaining correction parameters from the fixture database and adjusting the path of the robot to perform the assembly task is: Based on the grasping error e, the assembly path is corrected, and the correction formula is: P′=P-βe, Among them, P is the original assembly path, which represents the initial trajectory of the robot to perform the assembly task, P' is the corrected assembly path, β is the path correction coefficient, Combined with the fixture shape deviation, the insertion force F a Optimize, the optimization formula is: Among them, F' a is the optimized insertion force, F a is the initial insertion force, which indicates the initial force value when the robot performs the assembly task, γ is the influence factor of the morphological deviation, and w i is the weight coefficient, which indicates the influence of different morphological deviations on the insertion force, Δd i is the morphological deviation correction value.

9. A robot scene simulation method applied to a digital robot industry chain as claimed in claim 8, characterized in that: In step S4, based on the comparison results, the morphological deviation parameters and clamping force compensation parameters in the fixture database are dynamically corrected, and the fixture digital twin model is updated.

10. A robot scene simulation method applied to a digital robot industry chain as claimed in claim 9, characterized in that: The steps of dynamically correcting the morphological deviation parameters and clamping force compensation parameters in the fixture database based on the comparison results and updating the fixture digital twin model are as follows: Compare visual inspection and error, measure part position deviation based on visual sensor v And calculate the correction value: Δd v =d v -d0, Among them, d v is the part position deviation measured by vision, d0 is the simulation reference value, i.e. the expected part position in the simulation calculation, Δd v is the error correction value, which represents the difference between the actual deviation and the simulated reference value. Update the shape deviation parameter Δd and the clamping force compensation parameter F c , the update formula is: Δd′=Δd+λΔd v , F′ c =F c +μΔd v , Among them, Δd' is the updated morphological deviation parameter, Δd is the original morphological deviation parameter, λ is the updated weight coefficient, and F' c is the updated clamping force compensation parameter, F c is the original clamping force compensation parameter, and μ is the clamping force compensation adjustment coefficient.

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