A multi-axis motion mechanism error compensation method and related device

Abstract

The present application discloses an error compensation method for a multi-axis motion mechanism and a related device thereof. The method comprises: inputting the theoretical end position of the multi-axis motion mechanism into a geometric error model and a neural network respectively to obtain a geometric error value and an error fitting value; calculating a bias factor based on the geometric error value and the error fitting value; substituting the geometric error value and the bias factor into a generalized linear bias error model for calculation to obtain an actual error value; obtaining the end effector position of the multi-axis motion mechanism based on a theoretical kinematic model, superimposing the actual error value on the end effector position to obtain a new end effector position; inputting the new end effector position into a theoretical inverse kinematic model for calculation to obtain a motor motion displacement after non-geometric error compensation, and performing error compensation on the multi-axis motion mechanism based on the motor motion displacement. The present application improves the error compensation accuracy and helps to improve the end positioning accuracy of the multi-axis motion mechanism.

Description

Technical Field

[0001] The present application relates to the technical field of multi-axis motion mechanism control, and in particular to an error compensation method for a multi-axis motion mechanism and a related device thereof. Background Art

[0002] During the manufacturing and assembly process of multi-axis motion mechanisms (such as robots), errors inevitably occur, such as part machining errors and assembly errors. These errors reduce the positioning accuracy of the multi-axis motion mechanism's end points. To improve the positioning accuracy of multi-axis motion mechanisms, error compensation is necessary. Errors in multi-axis motion mechanisms are primarily categorized as geometric errors and non-geometric errors. How to compensate for these errors is a pressing technical challenge for those skilled in the art. Summary of the Invention

[0003] In order to solve the above problems, the present application provides an error compensation method for a multi-axis motion mechanism and a related device.

[0004] In view of this, a first aspect of the present application provides an error compensation method for a multi-axis motion mechanism, comprising:

[0005] Inputting the terminal theoretical pose of the multi-axis motion mechanism into the geometric error model and the neural network respectively to obtain a geometric error value and an error fitting value; calculating a bias factor based on the geometric error value and the error fitting value;

[0006] Substituting the geometric error value and the bias factor into a generalized linear bias error model for calculation to obtain an actual error value;

[0007] Acquiring an end effector position of the multi-axis motion mechanism based on a theoretical kinematic model, and superimposing the actual error value on the end effector position to obtain a new end effector position;

[0008] The new end effector position is input into a theoretical inverse kinematics model for calculation to obtain a motor motion displacement after non-geometric error compensation, and error compensation is performed on the multi-axis motion mechanism based on the motor motion displacement.

[0009] Optionally, the step of inputting the terminal theoretical pose of the multi-axis motion mechanism into a geometric error model and a neural network respectively to obtain a geometric error value and an error fitting value includes:

[0010] Inputting the theoretical position of the end of the multi-axis motion mechanism into the geometric error model to perform geometric error calculation to obtain the geometric error value of the multi-axis motion mechanism;

[0011] Inputting the terminal theoretical posture of the multi-axis motion mechanism into the first neural network to perform actual error fitting, thereby obtaining an actual error fitting value of the multi-axis motion mechanism;

[0012] Alternatively, the terminal theoretical position and posture of the multi-axis motion mechanism is input into a second neural network for performing non-geometric error fitting to obtain a first non-geometric error fitting value of the multi-axis motion mechanism;

[0013] Alternatively, the terminal theoretical position and the geometric error value of the multi-axis motion mechanism are input into a third neural network for non-geometric error fitting to obtain a second non-geometric error fitting value of the multi-axis motion mechanism.

[0014] Optionally, calculating a bias factor according to the geometric error value and the error fitting value includes:

[0015] Calculating a ratio of the actual error fitting value to the geometric error value to obtain a first bias factor;

[0016] Alternatively, a sum of the first non-geometric error fitting value and the geometric error value is calculated to obtain a first actual error calculation value, and a ratio of the first actual error calculation value to the geometric error value is calculated to obtain a second bias factor;

[0017] Alternatively, a sum of the second non-geometric error fitting value and the geometric error value is calculated to obtain a second actual error calculation value, and a ratio of the second actual error calculation value to the geometric error value is calculated to obtain a third bias factor.

[0018] Optionally, the geometric error model is:

[0019]

[0020] Where, is the geometric error parameter matrix, is the coefficient matrix, is the geometric error.

[0021] Optionally, the generalized linear bias error model is:

[0022]

[0023] Where, is the actual error, is the bias factor.

[0024] A second aspect of the present application provides an error compensation device for a multi-axis motion mechanism, comprising:

[0025] A bias factor calculation unit is used to input the theoretical position of the end of the multi-axis motion mechanism into the geometric error model and the neural network respectively to obtain a geometric error value and an error fitting value; and calculate the bias factor according to the geometric error value and the error fitting value;

[0026] an error calculation unit, configured to substitute the geometric error value and the bias factor into a generalized linear bias error model for calculation to obtain an actual error value;

[0027] a position updating unit, configured to obtain a position of an end effector of the multi-axis motion mechanism based on a theoretical kinematic model, and to superimpose the actual error value on the end effector position to obtain a new end effector position;

[0028] The error compensation unit is used to input the new end effector position into a theoretical inverse kinematics model for calculation to obtain the motor motion displacement after non-geometric error compensation, and perform error compensation on the multi-axis motion mechanism based on the motor motion displacement.

[0029] Optionally, the bias factor calculation unit includes a first calculation subunit and a second calculation subunit;

[0030] The first calculation subunit is specifically used to input the theoretical position and posture of the end of the multi-axis motion mechanism into the geometric error model to perform geometric error calculation to obtain the geometric error value of the multi-axis motion mechanism;

[0031] Inputting the terminal theoretical posture of the multi-axis motion mechanism into the first neural network to perform actual error fitting, thereby obtaining an actual error fitting value of the multi-axis motion mechanism;

[0032] Alternatively, the terminal theoretical position and posture of the multi-axis motion mechanism is input into a second neural network for performing non-geometric error fitting to obtain a first non-geometric error fitting value of the multi-axis motion mechanism;

[0033] Alternatively, the terminal theoretical position and the geometric error value of the multi-axis motion mechanism are input into a third neural network to perform non-geometric error fitting to obtain a second non-geometric error fitting value of the multi-axis motion mechanism;

[0034] The second calculation subunit is specifically configured to calculate a ratio of the actual error fitting value to the geometric error value to obtain a first bias factor;

[0035] Alternatively, a sum of the first non-geometric error fitting value and the geometric error value is calculated to obtain a first actual error calculation value, and a ratio of the first actual error calculation value to the geometric error value is calculated to obtain a second bias factor;

[0036] Alternatively, a sum of the second non-geometric error fitting value and the geometric error value is calculated to obtain a second actual error calculation value, and a ratio of the second actual error calculation value to the geometric error value is calculated to obtain a third bias factor.

[0037] Optionally, the generalized linear bias error model is:

[0038]

[0039] Where, is the actual error, is the geometric error parameter matrix, is the coefficient matrix, is the bias factor.

[0040] A third aspect of the present application provides an electronic device, the device comprising a processor and a memory;

[0041] The memory is used to store program code and transmit the program code to the processor;

[0042] The processor is used to execute the error compensation method for the multi-axis motion mechanism described in any one of the first aspects according to the instructions in the program code.

[0043] In a fourth aspect, the present application provides a computer-readable storage medium for storing program code. When the program code is executed by a processor, the error compensation method for the multi-axis motion mechanism described in any one of the first aspects is implemented.

[0044] It can be seen from the above technical solutions that this application has the following advantages:

[0045] The error compensation method for a multi-axis motion mechanism provided in this application proposes a generalized linear bias compensation method for non-geometric errors of a multi-axis motion mechanism based on the exploration of the formation mechanism of the fitting error and the analysis of its influence on the final absolute accuracy of the mechanism. A generalized linear bias error model is established on the basis of the geometric error model. Through the correlation between the numerical fitting error and the bias factor expression, a new bias factor formula is proposed to further compensate for its fitting error. The output of the geometric error model is expanded into network input data, and a new network training model is constructed to achieve optimal compensation for the non-geometric errors of the multi-axis motion mechanism, thereby improving the end positioning accuracy of the multi-axis motion mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0047] Figure 1 A schematic flow chart of an error compensation method for a multi-axis motion mechanism provided in an embodiment of the present application;

[0048] Figure 2Schematic diagram of the geometric error model and generalized linear bias error model provided in the embodiments of the present application;

[0049] Figure 3 A schematic diagram illustrating the construction principle of the first bias factor provided in an embodiment of the present application;

[0050] Figure 4 A schematic diagram illustrating the construction principle of the second bias factor provided in an embodiment of the present application;

[0051] Figure 5 A schematic diagram illustrating the construction principle of the third bias factor provided in an embodiment of the present application;

[0052] Figure 6 Another schematic flow chart of an error compensation method for a multi-axis motion mechanism provided in an embodiment of the present application;

[0053] Figure 7 A structural schematic diagram of an error compensation device for a multi-axis motion mechanism provided in an embodiment of the present application. DETAILED DESCRIPTION

[0054] In order to help those skilled in the art better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0055] For easier understanding, please refer to Figure 1 , an embodiment of the present application provides an error compensation method for a multi-axis motion mechanism, comprising:

[0056] Step 110: Input the theoretical position of the end of the multi-axis motion mechanism into the geometric error model and the neural network respectively to obtain the geometric error value and the error fitting value; and calculate the bias factor according to the geometric error value and the error fitting value.

[0057] In the process of geometric error modeling, by removing high-order terms, the nonlinear terms of the original error model can be converted into linear terms, which facilitates the solution of geometric error parameters. The geometric error model in the embodiment of the present application can be expressed as follows:

[0058]

[0059] in, is the geometric error parameter matrix, is the geometric error term, is the coefficient matrix, is the geometric error, that is, the end error that the geometric error model can compensate. (Basis function) is a pose parameter that is a known function of the independent variable, so the geometric error model can be predicted and compensated. The expression is:

[0060]

[0061] Where, is the theoretical position of the mechanism on the A, B, C, X, Y, and Z axes, are theoretical geometric parameters.

[0062] Since there are still uncompensated non-geometric errors after geometric error compensation, the actual error at the end is only approximately equal to the compensated geometric error, that is:

[0063]

[0064] in, , is the theoretical value of kinematic posture, is the actual value of the end-point pose measured before geometric error compensation. If the non-geometric error after the end-point geometric error compensation is taken into account, the following equation can be established, that is:

[0065]

[0066] This yields the following relationship:

[0067]

[0068] Where, is the actual error, is the non-geometric error, is the geometric error.

[0069] The bias factor in the embodiment of this application Defined as:

[0070]

[0071] Multiply both sides of the above equation by the bias factor , we get the generalized linear bias error model:

[0072]

[0073] Bias Factor The effect is equivalent to changing the basis function in the coefficient matrix without changing the original geometric error parameters. A certain offset is made, so the formula The equality relationship is established, that is, the bias factor The actual error is optimally compensated. Figure 2 shown.

[0074] In one embodiment, please refer to Figure 3 , this application uses the first neural network (network 1) to calculate the actual error Perform fitting directly to obtain the actual error fitting value. X ,F Y ,F Z ,F A ,F B ,F C ) is input into the first neural network for actual error fitting to obtain the actual error fitting value of the multi-axis motion mechanism . The actual error for fitting the first neural network The algebraic expression of is the fitting error that fits the actual error.

[0075] Input the theoretical end pose of the multi-axis motion mechanism into the geometric error model The geometric error calculation is performed in the multi-axis motion mechanism to obtain the geometric error value , and then calculate the actual error fitting value and geometric error value The ratio of , get the first bias factor ;

[0076]

[0077] Represents the bias factor for directly fitting the actual error (equivalent to the direct method).

[0078] In another embodiment, please refer to Figure 4 , this application uses the second neural network (network 2) to perform non-geometric error fitting to obtain the non-geometric error fitting value. That is, the terminal theoretical posture of the multi-axis motion mechanism is input into the second neural network for non-geometric error fitting, and the first non-geometric error fitting value of the multi-axis motion mechanism is obtained. ; Fitting non-geometric errors to the second neural network The algebraic expression of Non-geometric error for fitting The fitting error.

[0079] Input the theoretical end pose of the multi-axis motion mechanism into the geometric error model The geometric error calculation is performed in the multi-axis motion mechanism to obtain the geometric error value , then calculate the first non-geometric error fit value and geometric error value The sum of the first actual error calculation value and the geometric error value is calculated. The ratio of , get the second bias factor ;

[0080]

[0081] Indicates that geometric error compensation is performed first, and then the bias factor of the non-geometric error is fitted (equivalent to the indirect method).

[0082] While indirect error compensation methods offer better accuracy than direct methods, the final compensation accuracy of indirect methods still needs improvement due to fitting errors. Existing studies using numerical fitting methods for both direct and indirect methods have not considered the impact of the fitting model and its fitting errors on the final absolute accuracy of multi-axis motion mechanisms. This results in inconsistencies in the effectiveness of non-geometric error compensation for multi-axis motion mechanisms, and the compensation results remain unsatisfactory.

[0083] In yet another embodiment, the present application uses the bias factor Based on this, a new bias factor is proposed The bias factor The fitting error is converted into input data, and a new training network is constructed through dimension expansion and new weight factors to improve the non-geometric error fitting accuracy and achieve optimal compensation of non-geometric errors of multi-axis motion mechanisms. For details, please refer to Figure 5 , the terminal theoretical position and geometric error value of the multi-axis motion mechanism are input into the third neural network (network 3) for non-geometric error fitting, and the second non-geometric error fitting value of the multi-axis motion mechanism is obtained. . Algebraic expression for the non-geometric error of the third neural network fit.

[0084] Then calculate the sum of the second non-geometric error fitting value and the geometric error value to obtain the second actual error calculation value, and calculate the ratio of the second actual error calculation value to the geometric error value to obtain the third bias factor :

[0085]

[0086] The third bias factor in The biggest difference from the above method is that the output of the geometric error model is used as the input of the third neural network, which can convert the fitting error Convert it into a new network with expanded dimension to achieve the purpose of fitting error compensation.

[0087] Whether it is a direct method or an indirect method, there are numerical fitting errors, and the influence of the fitting model and its fitting error on the final absolute accuracy of the mechanism is not considered, which affects the final error compensation effect. In order to further improve the compensation effect of the positioning error of the multi-axis motion mechanism, the embodiment of the present application is to adjust the bias factor. Based on this, a new bias factor is further proposed , the fitting error is converted into input data, and a new training network is constructed through dimensional expansion (geometric error is added on the basis of the theoretical pose of the end) and new weight factors, which improves the fitting accuracy of non-geometric error and thus realizes the optimal compensation of non-geometric error of multi-axis motion mechanism.

[0088] Step 120: Substitute the geometric error value and the bias factor into the generalized linear bias error model for calculation to obtain the actual error value.

[0089] Substitute the geometric error value and bias factor into the generalized linear bias error model for calculation to obtain the actual error value :

[0090]

[0091] Where, for 、 or , To use the bias factor The actual error value calculated.

[0092] Step 130 : Acquire the end effector position of the multi-axis motion mechanism based on the theoretical kinematic model, and superimpose the actual error value on the end effector position to obtain a new end effector position.

[0093] Obtaining the end effector position of a multi-axis motion mechanism based on a theoretical kinematics model , then at the end effector position The actual error value is superimposed on the , get the new end effector position It should be noted that the theoretical kinematics model is an existing model, and the specific process of obtaining the end effector position of the multi-axis motion mechanism based on the theoretical kinematics model will not be described in detail here.

[0094] Step 140: Input the new end effector position into the theoretical inverse kinematics model for calculation to obtain the motor motion displacement after non-geometric error compensation, and perform error compensation on the multi-axis motion mechanism based on the motor motion displacement.

[0095] The new end-effector position F is input to the theoretical inverse kinematics model Finv Calculate in to obtain the motor motion displacement Q after non-geometric error compensation, , error compensation is performed on the multi-axis motion mechanism based on the motor motion displacement Q. It should be noted that the theoretical inverse kinematics model is an existing model, and the specific calculation process of solving the motor displacement through the theoretical inverse kinematics model will not be described here.

[0096] This application provides three methods for calculating the bias factor. For details, please refer to Figure 6 , through these three bias factors The error compensation is performed separately, and the motor motion displacements after non-geometric error compensation are , by obtaining the three motor motion displacements Error compensation was performed separately, and comparison showed that the error compensation effect corresponding to the third bias factor was better than that of the second bias factor and the first bias factor, and the error compensation effect corresponding to the second bias factor was better than that of the first bias factor.

[0097] Based on the algebraic equation relationship between actual error and geometric error and non-geometric error, this application establishes a generalized linear bias model for non-geometric errors of different types of mechanisms, generalizes the direct method and indirect method through the bias factor, and further proposes a bias factor expression with higher compensation accuracy, thereby improving the non-geometric error fitting accuracy and realizing the optimal compensation of non-geometric errors of multi-axis motion mechanisms.

[0098] Please refer to Figure 7 , the embodiment of the present application further provides an error compensation device for a multi-axis motion mechanism, comprising:

[0099] The bias factor calculation unit 710 is used to input the theoretical position of the end of the multi-axis motion mechanism into the geometric error model and the neural network respectively to obtain the geometric error value and the error fitting value; and calculate the bias factor based on the geometric error value and the error fitting value;

[0100] An error calculation unit 720 is used to substitute the geometric error value and the bias factor into the generalized linear bias error model for calculation to obtain an actual error value;

[0101] A position updating unit 730 is used to obtain the end effector position of the multi-axis motion mechanism based on the theoretical kinematic model, and to superimpose the actual error value on the end effector position to obtain a new end effector position;

[0102] The error compensation unit 740 is used to input the new end effector position into the theoretical inverse kinematics model for calculation, obtain the motor motion displacement after non-geometric error compensation, and perform error compensation on the multi-axis motion mechanism based on the motor motion displacement.

[0103] As a further improvement, the bias factor calculation unit 710 includes a first calculation subunit and a second calculation subunit;

[0104] The first calculation subunit is specifically used to input the theoretical position and posture of the end of the multi-axis motion mechanism into the geometric error model to perform geometric error calculation to obtain the geometric error value of the multi-axis motion mechanism;

[0105] The theoretical position of the end of the multi-axis motion mechanism is input into the first neural network to perform actual error fitting, thereby obtaining the actual error fitting value of the multi-axis motion mechanism;

[0106] Alternatively, the terminal theoretical position and posture of the multi-axis motion mechanism is input into the second neural network for non-geometric error fitting to obtain a first non-geometric error fitting value of the multi-axis motion mechanism;

[0107] Alternatively, the terminal theoretical position and geometric error value of the multi-axis motion mechanism are input into a third neural network for non-geometric error fitting to obtain a second non-geometric error fitting value of the multi-axis motion mechanism.

[0108] The second calculation subunit is specifically used to calculate the ratio of the actual error fitting value to the geometric error value to obtain a first bias factor;

[0109] Alternatively, a sum of a first non-geometric error fitting value and a geometric error value is calculated to obtain a first actual error calculation value, and a ratio of the first actual error calculation value to the geometric error value is calculated to obtain a second bias factor;

[0110] Alternatively, the sum of the second non-geometric error fitting value and the geometric error value is calculated to obtain a second actual error calculation value, and the ratio of the second actual error calculation value to the geometric error value is calculated to obtain a third bias factor.

[0111] As a further improvement, the geometric error model is:

[0112]

[0113] Where, is the geometric error parameter matrix, is the coefficient matrix, is the geometric error.

[0114] As a further improvement, the generalized linear bias error model is:

[0115]

[0116] Where, is the actual error, is the bias factor.

[0117] An embodiment of the present application further provides an electronic device, the device including a processor and a memory;

[0118] The memory is used to store program codes and transmit the program codes to the processor;

[0119] The processor is configured to execute the error compensation method for the multi-axis motion mechanism in the aforementioned method embodiment according to instructions in the program code.

[0120] An embodiment of the present application further provides a computer-readable storage medium, which is used to store program code. When the program code is executed by a processor, the error compensation method of the multi-axis motion mechanism in the aforementioned method embodiment is implemented.

[0121] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0122] In the specification of this application and the above-mentioned drawings, the terms "first," "second," "third," "fourth," etc. (if any) are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. In addition, the terms "including" and "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus that includes a series of steps or elements is not necessarily limited to those steps or elements explicitly listed, but may include other steps or elements not explicitly listed or inherent to such process, method, product, or apparatus.

[0123] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships can exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or plural.

[0124] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0125] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0126] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0127] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the method described in each embodiment of the present application through a computer device (which can be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (full name: Read-Only Memory, English abbreviation: ROM), random access memory (full name: Random Access Memory, English abbreviation: RAM), disk or optical disk, and other media that can store program code.

[0128] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for compensating an error of a multi-axis motion mechanism, characterized in that: include: The theoretical position and posture of the end of the multi-axis motion mechanism are input into the geometric error model and the neural network respectively to obtain the geometric error value and the error fitting value; Calculating a bias factor according to the geometric error value and the error fitting value; Specifically include: The theoretical end position of the multi-axis motion mechanism is input into the geometric error model to calculate the geometric error, and the geometric error value of the multi-axis motion mechanism is obtained; the geometric error model is: ； Where, is the geometric error parameter matrix, is the coefficient matrix, is the geometric error; Inputting the terminal theoretical posture of the multi-axis motion mechanism into the first neural network to perform actual error fitting, thereby obtaining an actual error fitting value of the multi-axis motion mechanism; Alternatively, the terminal theoretical position and posture of the multi-axis motion mechanism is input into a second neural network for performing non-geometric error fitting to obtain a first non-geometric error fitting value of the multi-axis motion mechanism; Alternatively, the terminal theoretical position and the geometric error value of the multi-axis motion mechanism are input into a third neural network to perform non-geometric error fitting to obtain a second non-geometric error fitting value of the multi-axis motion mechanism; Calculating a ratio of the actual error fitting value to the geometric error value to obtain a first bias factor; Alternatively, a sum of the first non-geometric error fitting value and the geometric error value is calculated to obtain a first actual error calculation value, and a ratio of the first actual error calculation value to the geometric error value is calculated to obtain a second bias factor; Alternatively, a sum of the second non-geometric error fitting value and the geometric error value is calculated to obtain a second actual error calculation value, and a ratio of the second actual error calculation value to the geometric error value is calculated to obtain a third bias factor; Substituting the geometric error value and the bias factor into a generalized linear bias error model for calculation, an actual error value is obtained; the generalized linear bias error model is: ； Where, is the actual error, is the bias factor; Acquiring an end effector position of the multi-axis motion mechanism based on a theoretical kinematic model, and superimposing the actual error value on the end effector position to obtain a new end effector position; The new end effector position is input into a theoretical inverse kinematics model for calculation to obtain a motor motion displacement after non-geometric error compensation, and error compensation is performed on the multi-axis motion mechanism based on the motor motion displacement.

2. An error compensation device for a multi-axis motion mechanism, characterized in that: include: The bias factor calculation unit is used to input the theoretical position of the end of the multi-axis motion mechanism into the geometric error model and the neural network respectively to obtain the geometric error value and the error fitting value; Calculating a bias factor according to the geometric error value and the error fitting value; The bias factor calculation unit includes a first calculation subunit and a second calculation subunit; The first calculation subunit is specifically used to input the theoretical position of the end of the multi-axis motion mechanism into the geometric error model to perform geometric error calculation to obtain the geometric error value of the multi-axis motion mechanism; the geometric error model is: ； Where, is the geometric error parameter matrix, is the coefficient matrix, is the geometric error; Inputting the terminal theoretical posture of the multi-axis motion mechanism into the first neural network to perform actual error fitting, thereby obtaining an actual error fitting value of the multi-axis motion mechanism; Alternatively, the terminal theoretical position and posture of the multi-axis motion mechanism is input into a second neural network for performing non-geometric error fitting to obtain a first non-geometric error fitting value of the multi-axis motion mechanism; Alternatively, the terminal theoretical position and the geometric error value of the multi-axis motion mechanism are input into a third neural network to perform non-geometric error fitting to obtain a second non-geometric error fitting value of the multi-axis motion mechanism; The second calculation subunit is specifically configured to calculate a ratio of the actual error fitting value to the geometric error value to obtain a first bias factor; Alternatively, a sum of the first non-geometric error fitting value and the geometric error value is calculated to obtain a first actual error calculation value, and a ratio of the first actual error calculation value to the geometric error value is calculated to obtain a second bias factor; Alternatively, a sum of the second non-geometric error fitting value and the geometric error value is calculated to obtain a second actual error calculation value, and a ratio of the second actual error calculation value to the geometric error value is calculated to obtain a third bias factor; an error calculation unit, configured to substitute the geometric error value and the bias factor into a generalized linear bias error model for calculation to obtain an actual error value; The generalized linear bias error model is: ； Where, is the actual error, is the geometric error parameter matrix, is the coefficient matrix, is the bias factor; a position updating unit, configured to obtain a position of an end effector of the multi-axis motion mechanism based on a theoretical kinematic model, and to superimpose the actual error value on the end effector position to obtain a new end effector position; The error compensation unit is used to input the new end effector position into a theoretical inverse kinematics model for calculation to obtain the motor motion displacement after non-geometric error compensation, and perform error compensation on the multi-axis motion mechanism based on the motor motion displacement.

3. An electronic device, characterized in that: The device includes a processor and a memory; The memory is used to store program code and transmit the program code to the processor; The processor is configured to execute the error compensation method for a multi-axis motion mechanism according to claim 1 according to instructions in the program code.

4. A computer-readable storage medium, characterized in that The computer-readable storage medium is used to store program codes, and when the program codes are executed by a processor, the error compensation method for a multi-axis motion mechanism according to claim 1 is implemented.

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