Hybrid robot high-precision error compensation method based on nonlinear error model

Abstract

The invention relates to a hybrid robot high-precision error compensation method based on a non-linear error model, and the method comprises the steps: determining kinematic equations of a series mechanism and a parallel mechanism according to the configuration of a hybrid robot, embedding error parameters into the kinematic equations, building an implicit non-linear error model of the hybrid robot, and carrying out the high-precision error compensation of the hybrid robot. An error-pose mapping model of the target hybrid robot is constructed, and an identification geometric error parameter of the target hybrid robot is obtained by using the error-pose mapping model; and constructing an error-drive mapping model of the target hybrid robot by using the hybrid implicit nonlinear error model and a pre-constructed prediction-correction method, and inputting the identification geometric error parameters into the error-drive mapping model so as to perform error compensation on the action of the target hybrid robot. Therefore, the technical problems that in the prior art, an error compensation method using a linear error model is poor in precision, and an error compensation method using a nonlinear error model is high in calculation complexity and difficult to complete analytical solution are solved.

Description

Technical Field

[0001] This application relates to the field of error compensation technology, and in particular to a high-precision error compensation method for hybrid robots based on a nonlinear error model. Background Technology

[0002] In related technologies, the accuracy of error compensation methods depends on the error model that represents the mapping relationship between geometric error sources and end effector pose / driving variables. The mainstream error models are divided into two categories: linear error models and nonlinear error models.

[0003] Linear error models are derived by perturbing the nominal geometric relationships of an ideal kinematic model (e.g., using screw theory). The linear error model establishes a linear relationship between geometric errors and pose / drive errors using the Jacobian matrix and can be considered a first-order approximation of the nonlinear error model. Accordingly, linear and nonlinear error models employ different error identification strategies. The linear error model uses linear least squares to identify error parameters, which is computationally simple and easy to implement, but its compensation accuracy is insufficient when error parameters of the same dimension differ by orders of magnitude.

[0004] Nonlinear error models directly establish a nonlinear mapping between error parameters and end effector pose / driving variables (e.g., DH parameter-based methods). This method requires describing non-ideal geometric relationships, resulting in higher model complexity, but it can provide more accurate error compensation.

[0005] Nonlinear error models require nonlinear least squares methods (such as the Gauss-Newton method or the Levenberg-Marquardt algorithm) to identify error parameters, enabling them to handle cases where error parameters differ by orders of magnitude and achieving higher compensation accuracy. A nonlinear error model can be viewed as a kinematic model with embedded error parameters, allowing the identified error parameters to be directly compensated within the model. Therefore, the core characteristic of nonlinear error models lies in "error embedding," leading to the formation of non-ideal geometric features (e.g., the orthogonality between axes is no longer maintained), thus significantly increasing the complexity of the corresponding kinematic equations.

[0006] In summary, among the related technologies, the accuracy of error compensation using linear error models is poor, while the computational complexity of error compensation using nonlinear error models is high and difficult to solve analytically, thus requiring urgent improvement. Summary of the Invention

[0007] This application provides a high-precision error compensation method for hybrid robots based on a nonlinear error model, in order to solve the technical problems in related technologies, where error compensation methods using linear error models have poor accuracy, and error compensation methods using nonlinear error models have high computational complexity and are difficult to solve analytically.

[0008] The first aspect of this application provides a high-precision error compensation method for a hybrid robot based on a nonlinear error model, comprising the following steps: determining the expression form of error parameters based on at least one geometric feature in the kinematic chain of the target hybrid robot that satisfies a preset non-ideal state condition, thereby obtaining the error parameters of the target hybrid robot; combining the error parameters and the kinematic equations of the target hybrid robot to obtain a hybrid implicit nonlinear error model of the target hybrid robot; constructing an error-pose mapping model of the target hybrid robot using the hybrid implicit nonlinear error model and a pre-built prediction-correction method, and obtaining the identification geometric error parameters of the target hybrid robot using the error-pose mapping model; constructing an error-drive mapping model of the target hybrid robot using the hybrid implicit nonlinear error model and the prediction-correction method, and inputting the identification geometric error parameters into the error-drive mapping model to perform error compensation on the action of the target hybrid robot.

[0009] Optionally, in one embodiment of this application, the step of combining the error parameters and the kinematic equations of the target hybrid robot to obtain the hybrid implicit nonlinear error model of the target hybrid robot includes: constructing a first kinematic equation for the serial mechanism and a second kinematic equation for the parallel mechanism of the target hybrid robot based on the kinematic chain of the target hybrid robot; embedding the error parameters into the first kinematic equation to obtain a first implicit nonlinear error model of the serial mechanism; embedding the error parameters into the second kinematic equation to obtain a second implicit nonlinear error model of the parallel mechanism; and combining the first implicit nonlinear error model and the second implicit nonlinear error model to obtain the hybrid implicit nonlinear error model.

[0010] Optionally, in one embodiment of this application, the step of constructing the error-pose mapping model of the target hybrid robot using the hybrid implicit nonlinear error model and the pre-built prediction-correction method includes: using the preset prediction-correction method to obtain a first accurate solution that satisfies a preset accurate condition from the hybrid implicit nonlinear error model; and using the first accurate solution to construct the error-pose mapping model.

[0011] Optionally, in one embodiment of this application, obtaining an accurate solution satisfying a preset accuracy condition from the hybrid implicit nonlinear error model using a preset prediction-correction method includes: extracting a first initial equation set from the hybrid implicit nonlinear error model; performing a preset reduction process on the first initial equation set to obtain a first reduced equation set; solving the first reduced equation set to obtain a first initial solution; and substituting the initial solution into the first initial equation set to obtain the first accurate solution.

[0012] Optionally, in one embodiment of this application, obtaining the identification geometric error parameters of the target hybrid robot using the error-pose mapping model includes: selecting multiple measurement poses that meet preset working condition coverage conditions within a preset workspace, and generating a corresponding set of measurement poses using the multiple measurement poses; converting the set of measurement poses into a set of driving variables, substituting the set of driving variables into the error-drive mapping model to obtain the predicted value of the end effector pose of the target hybrid robot; constructing a residual by combining the measured value of the end effector pose and the predicted value, and generating an identification equation using the residual; and solving the identification equation to obtain the identification geometric error parameters.

[0013] Optionally, in one embodiment of this application, the step of constructing the error-drive mapping model of the target hybrid robot using the hybrid implicit nonlinear error model and the prediction-correction method includes: extracting the position vector equation and tool vector equation of the end effector of the target hybrid robot from the hybrid implicit nonlinear error model; establishing the constraint equation of the target hybrid robot based on its structure; constructing a second initial equation set by combining the position vector equation, the tool vector equation, and the constraint equation; performing a preset reduction process on the second initial equation set to obtain a second reduced equation set; solving the second reduced equation set to obtain a second initial solution; substituting the initial solution into the second initial equation set to obtain a second exact solution; and constructing the error-drive mapping model using the second exact solution.

[0014] A second aspect of this application provides a high-precision error compensation device for a hybrid robot based on a nonlinear error model, comprising: a first acquisition module, configured to determine the expression form of error parameters based on at least one geometric feature in the kinematic chain of the target hybrid robot that satisfies a preset non-ideal state condition, thereby obtaining the error parameters of the target hybrid robot; a second acquisition module, configured to combine the error parameters and the kinematic equations of the target hybrid robot to obtain a hybrid implicit nonlinear error model of the target hybrid robot; a calculation module, configured to construct an error-pose mapping model of the target hybrid robot using the hybrid implicit nonlinear error model and a pre-built prediction-correction method, and to obtain the identification geometric error parameters of the target hybrid robot using the error-pose mapping model; and a compensation module, configured to construct an error-drive mapping model of the target hybrid robot using the hybrid implicit nonlinear error model and the prediction-correction method, and to input the identification geometric error parameters into the error-drive mapping model to perform error compensation on the action of the target hybrid robot.

[0015] Optionally, in one embodiment of this application, the second acquisition module includes: a first construction unit, configured to construct a first kinematic equation for the serial mechanism and a second kinematic equation for the parallel mechanism of the target hybrid robot based on the kinematic chain of the target hybrid robot; a first acquisition unit, configured to embed the error parameters into the first kinematic equation to obtain a first implicit nonlinear error model of the serial mechanism; a second acquisition unit, configured to embed the error parameters into the second kinematic equation to obtain a second implicit nonlinear error model of the parallel mechanism; and a third acquisition unit, configured to combine the first implicit nonlinear error model and the second implicit nonlinear error model to obtain the hybrid implicit nonlinear error model.

[0016] Optionally, in one embodiment of this application, the calculation module includes: a fourth acquisition unit, used to acquire a first accurate solution that satisfies a preset accurate condition from the hybrid implicit nonlinear error model using a preset prediction-correction method; and a construction unit, used to construct the error-pose mapping model using the first accurate solution.

[0017] Optionally, in one embodiment of this application, the fourth acquisition unit includes: an extraction subunit for extracting a first initial set of equations from the hybrid implicit nonlinear error model; a processing subunit for performing a preset reduction process on the first initial set of equations to obtain a first reduced set of equations; a first calculation subunit for solving the first reduced set of equations to obtain a first initial solution; and a second calculation subunit for substituting the initial solution into the first initial set of equations to obtain the first exact solution.

[0018] Optionally, in one embodiment of this application, the calculation module includes: a first generation unit, configured to select multiple measurement poses that satisfy preset working condition coverage conditions within a preset workspace, and generate a corresponding set of measurement poses using the multiple measurement poses; a first calculation unit, configured to convert the set of measurement poses into a set of driving variables, substitute the set of driving variables into the error-driving mapping model, and obtain a predicted value of the end effector pose of the target hybrid robot; a second generation unit, configured to combine the measured value of the end effector pose and the predicted value to construct a residual, and generate an identification equation using the residual; and a second calculation unit, configured to solve the identification equation to obtain the identification geometric error parameters.

[0019] Optionally, in one embodiment of this application, the compensation module includes: an extraction unit, configured to extract the position vector equation and tool vector equation of the end effector of the target hybrid robot from the hybrid implicit nonlinear error model; a second construction unit, configured to establish the constraint equations of the target hybrid robot based on the structure of the target hybrid robot; a third construction unit, configured to combine the position vector equation, the tool vector equation, and the constraint equations to construct a second initial equation set; a processing unit, configured to perform a preset reduction process on the second initial equation set to obtain a second reduced equation set; a third calculation unit, configured to solve the second reduced equation set to obtain a second initial solution; a fourth calculation unit, configured to substitute the initial solution into the second initial equation set to obtain a second exact solution; and a fourth construction unit, configured to use the second exact solution to construct the error-drive mapping model.

[0020] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement the high-precision error compensation method for hybrid robots based on a nonlinear error model as described in the above embodiments.

[0021] A fourth aspect of this application provides a computer-readable storage medium storing computer instructions for causing the computer to execute the high-precision error compensation method for hybrid robots based on a nonlinear error model as described in the above embodiments.

[0022] A fifth aspect of this application provides a computer program product, including a computer program that, when executed, implements the above-described high-precision error compensation method for hybrid robots based on a nonlinear error model.

[0023] This application's embodiments can determine the kinematic equations of the serial and parallel mechanisms based on the hybrid robot configuration, embed error parameters into the kinematic equations, and establish an implicit nonlinear error model for the hybrid robot. Then, using the implicit nonlinear error model and a pre-built prediction-correction method, an error-pose mapping model for the target hybrid robot is constructed to obtain the identification geometric error parameters. Furthermore, using the implicit nonlinear error model and the prediction-correction method, an error-drive mapping model for the target hybrid robot is constructed. The identification geometric error parameters are input into the error-drive mapping model to compensate for the robot's motion. The prediction-correction method quickly and stably establishes the nonlinear error model of the hybrid robot, ensuring the solution efficiency and stability of the nonlinear error model and improving the global spatial pose compensation accuracy of the hybrid robot. Therefore, this solves the technical problems in related technologies where error compensation methods using linear error models have poor accuracy, and error compensation methods using nonlinear error models have high computational complexity and are difficult to solve analytically.

[0024] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0025] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart illustrating a high-precision error compensation method for a hybrid robot based on a nonlinear error model, according to an embodiment of this application. Figure 2 This is a flowchart of a high-precision error compensation method for a hybrid robot based on a nonlinear error model according to an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a five-degree-of-freedom hybrid robot according to an embodiment of this application; Figure 4 A simplified kinematic diagram of the equivalent mechanism of a five-DOF hybrid robot according to an embodiment of this application; Figure 5 Here are simplified kinematic diagrams of the parallel and serial mechanisms of a five-axis hybrid machining robot according to an embodiment of this application; Figure 6 This is a schematic diagram of a high-precision error compensation device for a hybrid robot based on a nonlinear error model, according to an embodiment of this application. Figure 7 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0026] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0027] The following describes, with reference to the accompanying drawings, a high-precision error compensation method for hybrid robots based on a nonlinear error model, according to embodiments of this application. To address the technical problems mentioned in the background section, such as the poor accuracy of error compensation methods using linear error models and the high computational complexity of error compensation methods using nonlinear error models, which are difficult to solve analytically, this application provides a high-precision error compensation method for hybrid robots based on nonlinear error models. In this method, the kinematic equations of the serial and parallel mechanisms are determined according to the hybrid robot configuration. Error parameters are embedded into the kinematic equations to establish an implicit nonlinear error model for the hybrid robot. Then, using the implicit nonlinear error model and a pre-built prediction-correction method, an error-pose mapping model for the target hybrid robot is constructed to obtain the identification geometric error parameters. Finally, using the implicit nonlinear error model and the prediction-correction method, an error-drive mapping model for the target hybrid robot is constructed. The identification geometric error parameters are input into the error-drive mapping model to compensate for the motion of the target hybrid robot. The prediction-correction method quickly and stably establishes the nonlinear error model of the hybrid robot, ensuring the solution efficiency and stability of the nonlinear error model and improving the global spatial pose compensation accuracy of the hybrid robot. This solves the technical problems in related technologies, such as the poor accuracy of error compensation methods using linear error models and the high computational complexity of error compensation methods using nonlinear error models, making it difficult to achieve analytical solutions.

[0028] Understandably, hybrid robots are increasingly being used in high-end manufacturing sectors such as aerospace, automotive manufacturing, and energy. By integrating the advantages of parallel and serial mechanisms, hybrid robots exhibit high rigidity and dynamic performance, while also possessing considerable workspace and flexibility. This balanced comprehensive performance enables hybrid robots to be applied to tasks such as machining, welding, and assembly. In precision machining applications, the performance of hybrid robots largely depends on their absolute position and attitude accuracy. However, unavoidable manufacturing and assembly errors in hybrid robots often cause pose errors in the end effector. These geometric errors are transmitted from the drive end to the actuator end through the hybrid structure, causing deviations between the commanded pose and the actual pose, resulting in the machined features exceeding the allowable tolerance range. Therefore, error compensation for hybrid robots is of great significance for improving their performance in precision machining applications.

[0029] To achieve higher pose accuracy in hybrid robots, using nonlinear error models for error compensation is an effective approach. Robot CNC systems require nonlinear error models to be solved quickly and stably; if the nonlinear error model cannot be solved within a single scan cycle, the CNC system will malfunction. However, the unique open-loop and closed-loop composite structure of hybrid robots leads to strong coupling in their kinematic equations. Furthermore, numerous active and passive joints introduce a multitude of geometric error sources, and the resulting non-ideal geometric features further exacerbate the coupling of the kinematic equations. Therefore, nonlinear error modeling for hybrid robots ultimately requires solving a large-scale, strongly coupled set of nonlinear equations. These equations typically contain complex transcendental functions, making analytical solutions nearly impossible. From a numerical perspective, such equation sets exhibit complex convergence domain shapes and distribution characteristics in high-dimensional space, making it difficult to select initial values ​​that fall within the correct convergence region. Although classical numerical methods such as homotopy methods can alleviate sensitivity to initial values, they require constructing homotopy equations and performing multiple initial value corrections, which is insufficient to meet the real-time computational requirements of CNC systems. Therefore, how to stably and quickly establish an explicit nonlinear mapping relationship between error parameters and end effector pose / driving variables remains an important challenge for current research.

[0030] Therefore, the high-precision error compensation method for hybrid robots based on nonlinear error models in this application can further improve the end-effector pose accuracy of hybrid robots on the basis of traditional kinematic calibration based on linear error models, so as to solve the technical problems of high computational complexity and difficulty in real-time compensation of error compensation methods based on nonlinear error models in related technologies.

[0031] Specifically, Figure 1 This is a flowchart illustrating a high-precision error compensation method for hybrid robots based on a nonlinear error model, provided in an embodiment of this application.

[0032] like Figure 1 As shown, the high-precision error compensation method for hybrid robots based on a nonlinear error model includes the following steps: In step S101, the expression form of the error parameter is determined based on at least one geometric feature in the kinematic chain of the target hybrid robot that satisfies the preset non-ideal state condition, so as to obtain the error parameter of the target hybrid robot.

[0033] The error parameter is the deviation between the actual and nominal geometric dimensions. In this embodiment, the expression of the error parameter can be determined based on the non-ideal geometric features that may exist in the kinematic chain of the target hybrid robot, thus obtaining the error parameter.

[0034] For example, in the embodiments of this application, desired posture data can be obtained through the control commands of the target hybrid robot, and then error parameters can be calculated based on the actual posture data and desired posture data generated by the target hybrid robot in response to the control commands.

[0035] It is understandable that the error parameters are expressed in different forms for different nonlinear error models; the above is just one such expression.

[0036] In step S102, the implicit nonlinear error model of the target hybrid robot is obtained by combining the error parameters and the kinematic equations of the target hybrid robot.

[0037] It is understood that the target hybrid robot is composed of a series mechanism and a parallel mechanism connected end to end. The series and parallel parts can be combined in any order. In the embodiments of this application, the kinematic equations of the target hybrid robot in the series and parallel parts can be constructed respectively, and error parameters can be embedded to obtain the hybrid implicit nonlinear error model.

[0038] Optionally, in one embodiment of this application, a hybrid implicit nonlinear error model of the target hybrid robot is obtained by combining error parameters and the kinematic equations of the target hybrid robot, including: constructing a first kinematic equation for the serial mechanism and a second kinematic equation for the parallel mechanism of the target hybrid robot based on the kinematic chain of the target hybrid robot; embedding error parameters into the first kinematic equation to obtain a first implicit nonlinear error model for the serial mechanism; embedding error parameters into the second kinematic equation to obtain a second implicit nonlinear error model for the parallel mechanism; and combining the first implicit nonlinear error model and the second implicit nonlinear error model to obtain a hybrid implicit nonlinear error model.

[0039] As one possible approach, embodiments of this application can determine the first kinematic equation of the serial mechanism and the second kinematic equation of the parallel mechanism based on the hybrid robot configuration, embed error parameters into the first and second kinematic equations, and establish a hybrid implicit nonlinear error model for the hybrid robot.

[0040] The hybrid implicit nonlinear error model consists of a first implicit nonlinear error model in the series part and a second implicit nonlinear error model in the parallel part. The former is described by the homogeneous transformation matrix between the coordinate systems of the series part, and the latter is described by the homogeneous transformation matrix between the coordinate systems of the parallel part. The error parameters are embedded in the kinematic equations represented by the homogeneous transformation matrix.

[0041] In step S103, the error-pose mapping model of the target hybrid robot is constructed using the hybrid implicit nonlinear error model and the pre-built prediction-correction method, and the identification geometric error parameters of the target hybrid robot are obtained using the error-pose mapping model.

[0042] In actual implementation, the embodiments of this application can use a hybrid implicit nonlinear error model and a pre-built prediction-correction method to extract the initial equation set, and then use the initial equation set to construct an error-pose mapping model. Different poses of the end effector are measured in the workspace to construct identification equations, and the actual geometric error parameters are identified iteratively using the nonlinear least squares principle, i.e., the identified geometric error parameters.

[0043] Optionally, in one embodiment of this application, an error-pose mapping model of the target hybrid robot is constructed using a hybrid implicit nonlinear error model and a pre-built prediction-correction method, including: obtaining a first accurate solution that satisfies a preset accurate condition from the hybrid implicit nonlinear error model using a preset prediction-correction method; and constructing an error-pose mapping model using the first accurate solution.

[0044] The embodiments of this application can quickly and stably establish a nonlinear error model of a hybrid robot by constructing a prediction-correction method. Based on this, the actual geometric error parameters of the hybrid robot can be effectively identified and directly compensated into the kinematic model.

[0045] The prediction-correction method can include two steps: prediction and correction. These steps correspond to solving the reduced equation system and the initial equation system, respectively, to simplify the hybrid implicit nonlinear error model and improve computational efficiency. The simplified data is then substituted back into the hybrid implicit nonlinear error model to obtain the exact solution. Finally, the exact solution is used to construct the error-pose mapping model.

[0046] Optionally, in one embodiment of this application, a preset prediction-correction method is used to obtain an accurate solution that satisfies a preset accuracy condition from a hybrid implicit nonlinear error model, including: extracting a first initial equation set from the hybrid implicit nonlinear error model; performing a preset reduction process on the first initial equation set to obtain a first reduced equation set; solving the first reduced equation set to obtain a first initial solution; and substituting the initial solution into the first initial equation set to obtain a first accurate solution.

[0047] As one possible implementation, the prediction-correction method framework constructed in this application can solve the explicit nonlinear error model of the hybrid robot, extract the initial equation set from the implicit nonlinear error model, and perform equation elimination and dimensionality reduction under ideal geometric conditions. The reduced equation set is numerically or analytically solved and the dimension of the solution is expanded to obtain a high-quality predicted solution of the initial equation set. The expanded predicted solution is used as the initial value for the numerical solution of the initial equation set, and the accurate solution is obtained after one correction.

[0048] The initial equations can be extracted from the implicit nonlinear error model. The initial equations are in the form of a nonlinear equation system composed of nonlinear scalar equations, which can be component equations of vector equations. The ideal geometric condition means that all geometric features of the hybrid robot are nominal values, that is, the error parameters are all zero.

[0049] In this process, under ideal geometric conditions, a smaller set of nonlinear equations, or reduced equations, is obtained by eliminating variables and reducing the dimension of the initial equations. The numerical solution uses the Newton-Raphson algorithm, and the initial values ​​are selected as the pose or driving variables corresponding to the initial symmetric configuration of the mechanism. In special cases, the reduced equations have analytical solutions.

[0050] Among them, the numerical or analytical solution of the reduced equation system is used as a high-quality predicted solution, and the dimensionality is increased to the initial value of the numerical solution of the initial equation system through back substitution; the larger-scale Newton-Raphson algorithm is used to obtain the exact solution of the initial equation system by correcting the predicted solution of the reduced equation system once.

[0051] Optionally, in one embodiment of this application, the identification geometric error parameters of the target hybrid robot are obtained using an error-pose mapping model, including: selecting multiple measurement poses that meet preset working condition coverage conditions within a preset workspace, and generating a corresponding set of measurement poses using the multiple measurement poses; converting the set of measurement poses into a set of driving variables, substituting the set of driving variables into the error-drive mapping model to obtain the predicted value of the end effector pose of the target hybrid robot; constructing a residual by combining the measured value and the predicted value of the end effector pose, and generating an identification equation using the residual; and solving the identification equation to obtain the identification geometric error parameters.

[0052] In actual execution, the error-pose mapping model can reflect the nonlinear function between the error parameters and the spatial pose of the end effector of the hybrid robot under the given driving variables of the target hybrid robot. It is equivalent to embedding the error parameters into the forward kinematics of the hybrid robot, which is obtained by explicitly solving the implicit nonlinear error model by the prediction-correction model.

[0053] For example, the spatial pose of the end effector of the target hybrid robot is obtained through an auxiliary tooling containing three target points and an external measuring device. The tooling coordinate system is generated using the three target points to represent the position and orientation of the end effector of the hybrid robot. Sufficient pose measurement points are selected in the workspace according to the principle of traversing the working conditions. The nonlinear identification equation of the residual is constructed by combining the pose theoretical value obtained by the error-pose mapping model. The residual components are weighted according to the standard deviation of the pose error in each direction. Nonlinear least squares methods such as the Levenberg-Marquardt algorithm are used, and appropriate regularization parameters are determined to ensure stable convergence of the algorithm. The actual geometric error parameters are iteratively identified.

[0054] In step S104, an error-drive mapping model of the target hybrid robot is constructed using a hybrid implicit nonlinear error model and a prediction-correction method. The identified geometric error parameters are input into the error-drive mapping model to compensate for the error in the action of the target hybrid robot.

[0055] The embodiments of this application can establish an error-drive mapping model of the hybrid robot based on the prediction-correction method, and directly embed the identified geometric error parameters into the model to achieve error compensation. The prediction-correction method is used to quickly and stably establish the nonlinear error model of the hybrid robot, ensuring the solution efficiency and stability of the nonlinear error model and improving the global spatial pose compensation accuracy of the hybrid robot.

[0056] Optionally, in one embodiment of this application, an error-drive mapping model of the target hybrid robot is constructed using a hybrid implicit nonlinear error model and a pre-built prediction-correction method. This includes: extracting the position vector equations and tool vector equations of the end effector of the target hybrid robot from the hybrid implicit nonlinear error model; establishing constraint equations for the target hybrid robot based on its structure; constructing a second initial equation set by combining the position vector equations, tool vector equations, and constraint equations; performing a pre-defined reduction process on the second initial equation set to obtain a second reduced equation set; solving the second reduced equation set to obtain a second initial solution; substituting the initial solution into the second initial equation set to obtain a second exact solution; and constructing the error-drive mapping model using the second exact solution.

[0057] The error-drive mapping model is a nonlinear function reflecting the relationship between error parameters and driving variables of the hybrid robot, given the spatial pose of the end effector. It is equivalent to embedding the error parameters into the inverse kinematics of the hybrid robot, which is obtained by explicitly solving the implicit nonlinear error model through the prediction-correction model. The identified error parameters are directly embedded into the error-drive mapping model to achieve direct compensation of geometric errors.

[0058] For example, embodiments of this application can establish position vector equations and tool vector equations, and combine them with constraint equations to form an initial set of equations; when the error parameter is zero, elimination is performed to obtain a reduced set of equations for the hybrid mechanism; the reduced set of equations is solved using the Newton-Raphson algorithm to obtain a solution, which is then extended to a predicted solution of the second initial set of equations, i.e., the second initial solution; the second initial set of equations is solved using the Newton-Raphson algorithm to obtain a second exact solution; the second exact solution is expressed as a homogeneous transformation matrix, and based on the driving equations and tool vector equations, the error-driving mapping models of the parallel mechanism and the series mechanism are solved respectively; the error-driving mapping models of the parallel mechanism and the series mechanism are merged into the error-driving mapping model of the hybrid robot.

[0059] Combination Figures 2 to 5 As shown, an embodiment is used to illustrate in detail the working principle of the high-precision error compensation method for hybrid robots based on a nonlinear error model in this application.

[0060] like Figure 2 As shown, embodiments of this application may include the following steps: Step S201: Determine the kinematic equations of the serial and parallel mechanisms based on the hybrid robot configuration, embed the error parameters into the kinematic equations, and establish the implicit nonlinear error model of the hybrid robot.

[0061] For example, embodiments of this application can be applied to, for example, Figure 3 The five-axis hybrid machining robot shown is composed of: a fixed platform 1, a branch chain 2, a moving platform 3, and an AC tilting head 4.

[0062] like Figure 4 As shown, the configuration of the five-axis hybrid robot is 3-SPR+RR, which consists of a 3-SPR parallel mechanism and an RR series mechanism combined sequentially. The base of the RR series mechanism is fixedly connected to the moving platform of the 3-SPR parallel mechanism. ( ) represents the three SPR branches of the parallel mechanism. ( Evenly distributed around the center of the circle. , radius is On the circle, there is the Hooke's hinge center, representing the connection between the three SPR branches and the fixed platform. ( Evenly distributed around the center of the circle. , radius is On the circle, the center of the rotating joints representing the three SPR branches connected to the moving platform is shown. Meanwhile, It is also the center of the first rotating joint of the series mechanism. This is the first link in the series mechanism. The center of the second rotating joint of the series mechanism. The first link of the series mechanism is offset. This is the second link, i.e., the end tool.

[0063] Furthermore, the kinematic diagrams of the parallel and serial mechanisms of the five-axis hybrid robot can be shown as follows: Figure 5 As shown, with A coordinate system for the fixed platform, with the origin at point A, is established and fixed to the fixed platform of the parallel mechanism. - (recorded as) ),in shaft edge direction, Axis perpendicular to - The plane in which it is located. Establish a Hookean coordinate system with point as the origin. , recorded as ,in shaft edge direction, The axis is perpendicular to the plane of the fixed platform. .by A coordinate system for the moving platform, with the origin at point A, is established and fixed to the moving platform of the parallel mechanism. (recorded as) ),in shaft edge direction, Axis perpendicular to - The plane in which it is located. Establish a coordinate system with the origin at point A. , recorded as ,in shaft edge direction, The axis is perpendicular to the plane of the moving platform. .Will Around itself Axis rotation angle This makes its own Axis and branch axis The perpendicular, rotated coordinate system is called the branched coordinate system. , recorded as . Figure 4 It shows Hooke's hinge coordinate system Rotating sub-coordinate system and branched coordinate system .

[0064] Furthermore, in establishing the implicit nonlinear error model of the hybrid robot, the embodiments of this application may include the following steps: Step S1: Write the kinematic chain of the hybrid robot in homogeneous transformation matrix form, and establish the kinematic equations of the serial and parallel mechanisms respectively.

[0065] Step S2: Embed the error parameters into the homogeneous transformation matrix and remove redundant error parameters.

[0066] Step S3: The kinematic equations of the embedded error parameters constitute an implicit nonlinear error mode.

[0067] Among them, from the fixed platform center To the dynamic platform center The kinetic chain can be represented as:

[0068]

[0069]

[0070]

[0071] in, It is expressed in the form of XYZ Euler angles. relatively The rotation matrix, Indicates the parallel mechanism motion platform center The position vector. and yes The corresponding attitude angle. and express and The actual value. The geometric meaning is of shaft and of The complementary angle between the shafts. The actual driving force of the parallel mechanism's drive pair. ( It can be expressed as the sum of the nominal driving quantity and the zero-position error parameter. .

[0072] Kinematic modeling of the RR cascade mechanism based on the DH parameter method allows the geometric error parameters of each component to be incorporated into the model. (Slave platform coordinate system) To the end tool coordinate system The homogeneous transformation matrix is ​​as follows:

[0073] in, and These represent the driving quantities of the first and second joints of the RR tandem mechanism, respectively. for Actual length value. The first offset angle (i.e.) and The actual value of the included angle. The second offset angle (i.e.) and The actual value of the included angle. for The actual value of the length, for The actual value of the length. and They are respectively Point and The offset error at the point. The actual driving amount of the RR series mechanism can be expressed as the sum of the nominal driving amount and the zero-position error parameter ( , ).when ( )hour, point The influence of the orientation error parameter on the end-effector pose can be compared with... The length error parameter is the same. When ( )hour, point The influence of orientation error parameters on end-effector pose and zero-position error parameters Same. Remove from error parameters. and The remaining error parameters are then combined into an error vector. Thus, the geometric error parameters of the hybrid mechanism are established.

[0074] The above process establishes an implicit nonlinear error model for the five-axis hybrid robot, which implicitly includes a nonlinear error mapping relationship.

[0075] Step S202: Construct a prediction-correction method framework to solve the explicit nonlinear error model of the hybrid robot, extract the initial equation set from the implicit nonlinear error model, generate the reduced equation set under ideal geometric conditions, expand the solution of the reduced equation set to the predicted solution of the initial equation set and correct it.

[0076] The prediction-correction method may include the following steps: Step S1: Extract the initial set of equations from the implicit nonlinear error model. Furthermore, under ideal geometric conditions, the system of equations is eliminated and its dimensionality is reduced.

[0077] Step S2, for reducing the system of equations Find numerical or analytical solutions High-quality predicted solutions to the initial equation system are obtained by expanding the dimension of the solution. .

[0078] Step S3: Use the extended predicted solution as the initial value for numerical solution of the initial equation system, and obtain the exact solution after one correction. .

[0079] Step S203: Establish the error-pose mapping model of the hybrid robot based on the prediction-correction method.

[0080] The process of establishing an error-pose mapping model for the hybrid robot based on the prediction-correction method may include the following steps: Step S1, establish the driving equations and constraint equations And constitute the initial set of equations for the parallel mechanism. .

[0081] Step S2, when the error parameter To perform elimination at zero, we obtain a reduced set of equations for the parallel mechanism. .

[0082] Step S3: Solve the reduced system of equations using the Newton-Raphson algorithm to obtain the solution. Extended to predictive solutions of the initial system of equations .

[0083] Step S4, with The initial values ​​are used for numerical iteration, and the exact solution is obtained by solving the initial system of equations using the Newton-Raphson algorithm. .

[0084] Step S5: Represent the exact solution as a homogeneous transformation matrix to obtain the error-pose mapping model of the parallel mechanism. .

[0085] Step S6, with the error-pose mapping model of the serial mechanism. By merging the results, the error-pose mapping model of the hybrid robot is obtained. .

[0086] Among them, the unknowns of the initial system of equations Reduce the number of unknowns in the system of equations The numerical iterative forms corresponding to the initial system of equations and the reduced system of equations are as follows:

[0087]

[0088] in, This represents the error parameters of the parallel mechanism.

[0089] Step S204: Construct identification equations based on different poses of the end effector of the strategy within the workspace, and obtain identification geometric error parameters using the nonlinear least squares method.

[0090] The process of constructing identification equations based on the different poses of the end effector within the workspace and obtaining identification geometric error parameters using a nonlinear least squares method may include the following steps: Step S1: Select a set of measurement poses within the workspace so that the poses cover more than 80% of the workspace.

[0091] Step S2: Convert the measured pose set into a set of driving variables, and substitute them into the error-driving mapping model to calculate the predicted value of the end effector pose.

[0092] Step S3: Construct residuals based on the measured and predicted pose values ​​of the end effector. This forms the identification equation.

[0093] Step S4: Solve for the residual Jacobian matrix.

[0094] Step S5: Use the Levenberg-Marquardt algorithm to iteratively identify the actual geometric error parameters.

[0095] The residual Jacobian matrix is ​​represented as follows:

[0096] in, It is the error Jacobian matrix of the parallel mechanism. It is the error Jacobian matrix of the series mechanism. It is the identity matrix after removing the third row.

[0097] Step S205: Establish the error-drive mapping model of the hybrid robot based on the prediction-correction method.

[0098] The steps involved in establishing an error-drive mapping model for a hybrid robot based on a prediction-correction method include: Step S1: Establish the position vector equation Tool vector equations .

[0099] Step S2, eliminate position vector equations using the tool vector equation. and And combine the constraint equations to form the initial system of equations. .

[0100] Step S3, when the error parameter To eliminate variables at zero, a reduced set of equations for the hybrid mechanism is obtained. .

[0101] Step S4: Solve the reduced system of equations using the Newton-Raphson algorithm to obtain the solution. And extend it to the predicted solution of the initial system of equations. .

[0102] Step S5, with The initial values ​​are used for numerical iteration, and the exact solution is obtained by solving the initial system of equations using the Newton-Raphson algorithm. .

[0103] Step S6: Express the exact solution as a homogeneous transformation matrix, and solve the error-drive mapping models for the parallel and series mechanisms based on the driving equations and tool vector equations, respectively. and .

[0104] Step S7: Merge the error-drive mapping models of the parallel and serial mechanisms into an error-drive mapping model for the hybrid robot. .

[0105] The unknowns in the position vector equation are expressed as: The unknowns of the tool vector equations and the initial equations are expressed as follows: Unknowns of the initial system of equations Reduce the number of unknowns in the system of equations The numerical iterative forms corresponding to the initial system of equations and the reduced system of equations are as follows:

[0106] .

[0107] Step S206: The identified geometric error parameters are directly embedded into the error-driven mapping model to achieve error compensation.

[0108] The high-precision error compensation method for hybrid robots based on a nonlinear error model proposed in this application determines the kinematic equations of the serial and parallel mechanisms according to the hybrid robot configuration, embeds error parameters into the kinematic equations, establishes an implicit nonlinear error model for the hybrid robot, and then constructs an error-pose mapping model for the target hybrid robot using the implicit nonlinear error model and a pre-built prediction-correction method to obtain the identification geometric error parameters of the target hybrid robot. Furthermore, it constructs an error-drive mapping model for the target hybrid robot using the implicit nonlinear error model and the prediction-correction method, inputting the identification geometric error parameters into the error-drive mapping model to compensate for the robot's motion. The prediction-correction method quickly and stably establishes the nonlinear error model of the hybrid robot, ensuring the solution efficiency and stability of the nonlinear error model and improving the global spatial pose compensation accuracy of the hybrid robot. This solves the technical problems in related technologies where error compensation methods using linear error models have poor accuracy, and error compensation methods using nonlinear error models have high computational complexity and are difficult to solve analytically.

[0109] Next, referring to the accompanying drawings, a high-precision error compensation device for hybrid robots based on a nonlinear error model, according to an embodiment of this application, is described.

[0110] Figure 6 This is a block diagram of a high-precision error compensation device for a hybrid robot based on a nonlinear error model, according to an embodiment of this application.

[0111] like Figure 6 As shown, the high-precision error compensation device 10 for hybrid robots based on a nonlinear error model includes: a first acquisition module 100, a second acquisition module 200, a calculation module 300, and a compensation module 400.

[0112] Specifically, the first acquisition module 100 is used to determine the expression form of the error parameters based on at least one geometric feature in the kinematic chain of the target hybrid robot that satisfies the preset non-ideal state conditions, so as to obtain the error parameters of the target hybrid robot.

[0113] The second acquisition module 200 is used to combine error parameters and the kinematic equations of the target hybrid robot to obtain the hybrid implicit nonlinear error model of the target hybrid robot.

[0114] The calculation module 300 is used to construct the error-pose mapping model of the target hybrid robot using the hybrid implicit nonlinear error model and the pre-built prediction-correction method, and to obtain the identification geometric error parameters of the target hybrid robot using the error-pose mapping model.

[0115] The compensation module 400 is used to construct an error-drive mapping model of the target hybrid robot using a hybrid implicit nonlinear error model and a prediction-correction method. The identified geometric error parameters are input into the error-drive mapping model to compensate for the error in the action of the target hybrid robot.

[0116] Optionally, in one embodiment of this application, the second acquisition module 200 includes: a first construction unit, a first acquisition unit, a second acquisition unit, and a third acquisition unit.

[0117] The first building unit is used to construct the first kinematic equation of the serial mechanism and the second kinematic equation of the parallel mechanism of the target hybrid robot based on the kinematic chain of the target hybrid robot.

[0118] The first acquisition unit is used to embed error parameters into the first kinematic equation to obtain the first implicit nonlinear error model of the series mechanism.

[0119] The second acquisition unit is used to embed the error parameters into the second kinematic equation to obtain the second implicit nonlinear error model of the parallel mechanism.

[0120] The third acquisition unit is used to combine the first implicit nonlinear error model and the second implicit nonlinear error model to obtain the hybrid implicit nonlinear error model.

[0121] Optionally, in one embodiment of this application, the calculation module 300 includes a fourth acquisition unit and a construction unit.

[0122] The fourth acquisition unit is used to obtain the first accurate solution that meets the preset accurate conditions from the hybrid implicit nonlinear error model using a preset prediction-correction method.

[0123] The building unit is used to construct an error-pose mapping model using the first exact solution.

[0124] Optionally, in one embodiment of this application, the fourth acquisition unit includes: an extraction subunit, a processing subunit, a first calculation subunit, and a second calculation subunit.

[0125] Among them, the extracted sub-unit is used to extract the first initial equation set from the hybrid implicit nonlinear error model.

[0126] The processing subunit is used to perform a preset reduction process on the first initial equation set to obtain the first reduced equation set.

[0127] The first computational subunit is used to solve the first reduced system of equations to obtain the first initial solution.

[0128] The second computational subunit is used to substitute the initial solution into the first initial equation system to obtain the first exact solution.

[0129] Optionally, in one embodiment of this application, the calculation module 300 includes: a first generation unit, a first calculation unit, a second generation unit, and a second calculation unit.

[0130] The first generation unit is used to select multiple measurement poses that meet the preset working condition coverage conditions within a preset workspace, and to generate a corresponding set of measurement poses using the multiple measurement poses.

[0131] The first calculation unit is used to convert the measured pose set into a set of driving variables, and substitute the set of driving variables into the error-driving mapping model to obtain the predicted value of the end effector pose of the target hybrid robot.

[0132] The second generation unit is used to combine the measured and predicted values ​​of the end effector pose to construct residuals and use the residuals to generate identification equations.

[0133] The second calculation unit is used to solve the identification equation and obtain the identification geometric error parameters.

[0134] Optionally, in one embodiment of this application, the compensation module 400 includes: an extraction unit, a second construction unit, a third construction unit, a processing unit, a third calculation unit, a fourth calculation unit, and a fourth construction unit.

[0135] The extraction unit is used to extract the position vector equation and tool vector equation of the end effector of the target hybrid robot from the hybrid implicit nonlinear error model.

[0136] The second building unit is used to establish the constraint equations of the target hybrid robot based on its structure.

[0137] The third building unit is used to combine the position vector equation, tool vector equation, and constraint equation to construct the second initial equation set.

[0138] The processing unit is used to perform a preset reduction process on the second initial equation set to obtain the second reduced equation set.

[0139] The third calculation unit is used to solve the second reduced system of equations to obtain the second initial solution.

[0140] The fourth calculation unit is used to substitute the initial solution into the second initial equation system to obtain the second exact solution.

[0141] The fourth building unit is used to construct an error-driven mapping model using the second exact solution.

[0142] It should be noted that the foregoing explanation of the embodiment of the high-precision error compensation method for hybrid robots based on nonlinear error models also applies to the high-precision error compensation device for hybrid robots based on nonlinear error models in this embodiment, and will not be repeated here.

[0143] The high-precision error compensation device for hybrid robots based on a nonlinear error model proposed in this application can determine the kinematic equations of the serial and parallel mechanisms according to the hybrid robot configuration, embed error parameters into the kinematic equations, establish an implicit nonlinear error model for the hybrid robot, and then construct an error-pose mapping model for the target hybrid robot using the implicit nonlinear error model and a pre-built prediction-correction method to obtain the identification geometric error parameters of the target hybrid robot. Furthermore, using the implicit nonlinear error model and the prediction-correction method, an error-drive mapping model for the target hybrid robot is constructed. The identification geometric error parameters are input into the error-drive mapping model to compensate for the motion of the target hybrid robot. The prediction-correction method quickly and stably establishes the nonlinear error model of the hybrid robot, ensuring the solution efficiency and stability of the nonlinear error model and improving the global spatial pose compensation accuracy of the hybrid robot. This solves the technical problems in related technologies where error compensation methods using linear error models have poor accuracy, and error compensation methods using nonlinear error models have high computational complexity and are difficult to solve analytically.

[0144] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 701, the processor 702, and the computer program stored on the memory 701 and executable on the processor 702.

[0145] When the processor 702 executes the program, it implements the high-precision error compensation method for hybrid robots based on a nonlinear error model provided in the above embodiments.

[0146] Furthermore, electronic devices also include: Communication interface 703 is used for communication between memory 701 and processor 702.

[0147] The memory 701 is used to store computer programs that can run on the processor 702.

[0148] The memory 701 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0149] If the memory 701, processor 702, and communication interface 703 are implemented independently, then the communication interface 703, memory 701, and processor 702 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 7 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0150] Optionally, in a specific implementation, if the memory 701, processor 702, and communication interface 703 are integrated on a single chip, then the memory 701, processor 702, and communication interface 703 can communicate with each other through an internal interface.

[0151] The processor 702 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0152] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described high-precision error compensation method for hybrid robots based on a nonlinear error model.

[0153] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the high-precision error compensation method for hybrid robots based on a nonlinear error model provided in this embodiment of the invention.

[0154] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0155] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0156] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0157] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0158] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0159] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0160] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0161] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A high-precision error compensation method for hybrid robots based on a nonlinear error model, characterized in that, Includes the following steps: The expression form of the error parameter is determined based on at least one geometric feature in the kinematic chain of the target hybrid robot that satisfies the preset non-ideal state condition, so as to obtain the error parameter of the target hybrid robot. By combining the error parameters and the kinematic equations of the target hybrid robot, the hybrid implicit nonlinear error model of the target hybrid robot is obtained. Using the hybrid implicit nonlinear error model and the pre-built prediction-correction method, the error-pose mapping model of the target hybrid robot is constructed, and the identification geometric error parameters of the target hybrid robot are obtained using the error-pose mapping model. Using the hybrid implicit nonlinear error model and the prediction-correction method, an error-driven mapping model of the target hybrid robot is constructed. The identified geometric error parameters are input into the error-driven mapping model to compensate for the error in the action of the target hybrid robot.

2. The method according to claim 1, characterized in that, The process of combining the error parameters and the kinematic equations of the target hybrid robot to obtain the hybrid implicit nonlinear error model of the target hybrid robot includes: Based on the kinematic chain of the target hybrid robot, the first kinematic equation of the serial mechanism and the second kinematic equation of the parallel mechanism of the target hybrid robot are constructed. The error parameters are embedded into the first kinematic equation to obtain the first implicit nonlinear error model of the series mechanism; The error parameters are embedded into the second kinematic equation to obtain the second implicit nonlinear error model of the parallel mechanism; The hybrid implicit nonlinear error model is obtained by combining the first implicit nonlinear error model and the second implicit nonlinear error model.

3. The method according to claim 1, characterized in that, The step of constructing the error-pose mapping model of the target hybrid robot using the hybrid implicit nonlinear error model and the pre-built prediction-correction method includes: Using the preset prediction-correction method, a first accurate solution satisfying the preset accuracy condition is obtained from the hybrid implicit nonlinear error model; Using the first exact solution, the error-pose mapping model is constructed.

4. The method according to claim 3, characterized in that, The step of using a preset prediction-correction method to obtain an accurate solution satisfying a preset accuracy condition from the hybrid implicit nonlinear error model includes: Extract the first set of initial equations from the hybrid implicit nonlinear error model; The first initial set of equations is subjected to a preset reduction process to obtain the first reduced set of equations; Solve the first reduced system of equations to obtain the first initial solution; Substituting the initial solution into the first set of initial equations yields the first exact solution.

5. The method according to claim 4, characterized in that, The step of using the error-pose mapping model to obtain the identification geometric error parameters of the target hybrid robot includes: Select multiple measurement poses that meet the preset working condition coverage conditions within the preset workspace, and generate a corresponding set of measurement poses using the multiple measurement poses. The measured pose set is converted into a driving variable set, and the driving variable set is substituted into the error-driving mapping model to obtain the predicted value of the end effector pose of the target hybrid robot. By combining the measured value and the predicted value of the end effector pose, a residual is constructed, and the residual is used to generate an identification equation. Solve the identification equation to obtain the identification geometric error parameters.

6. The method according to claim 1, characterized in that, The step of constructing the error-drive mapping model of the target hybrid robot using the hybrid implicit nonlinear error model and the prediction-correction method includes: From the hybrid implicit nonlinear error model, extract the position vector equation and tool vector equation of the end effector of the target hybrid robot; Based on the structure of the target hybrid robot, the constraint equations of the target hybrid robot are established; By combining the position vector equation, the tool vector equation, and the constraint equation, a second initial set of equations is constructed; The second initial set of equations is subjected to a preset reduction process to obtain the second reduced set of equations; Solve the second reduced system of equations to obtain the second initial solution; Substituting the initial solution into the second initial system of equations, we obtain the second exact solution; The error-driven mapping model is constructed using the second exact solution.

7. A high-precision error compensation device for a hybrid robot based on a nonlinear error model, characterized in that, include: The first acquisition module is used to determine the expression form of the error parameter based on at least one geometric feature in the kinematic chain of the target hybrid robot that satisfies the preset non-ideal state condition, so as to obtain the error parameter of the target hybrid robot. The second acquisition module is used to combine the error parameters and the kinematic equations of the target hybrid robot to obtain the hybrid implicit nonlinear error model of the target hybrid robot. The calculation module is used to construct the error-pose mapping model of the target hybrid robot using the hybrid implicit nonlinear error model and the pre-built prediction-correction method, and to obtain the identification geometric error parameters of the target hybrid robot using the error-pose mapping model. The compensation module is used to construct the error-drive mapping model of the target hybrid robot using the hybrid implicit nonlinear error model and the prediction-correction method, and input the identified geometric error parameters into the error-drive mapping model to compensate for the error of the target hybrid robot's movements.

8. An electronic device, characterized in that, include: The system includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to implement the high-precision error compensation method for a hybrid robot based on a nonlinear error model as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the high-precision error compensation method for hybrid robots based on a nonlinear error model as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed, it is used to implement the high-precision error compensation method for hybrid robots based on a nonlinear error model as described in any one of claims 1-6.

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