Partition error compensation method and device for parallel robot

Abstract

The invention relates to the technical field of manufacturing equipment error compensation, in particular to a partition error compensation method and device for a parallel robot, and the method comprises the steps: respectively building a kinematics error model and a gravity deformation prediction model of the parallel robot; obtaining a mapping relation between the tail end pose error and the structural error parameter and a gravity deformation predicted value, and further solving the structural error; the working area of the parallel robot is divided into a plurality of sub-areas, and transition areas are added among the sub-areas; constructing an identification model according to the transition region and the tail end pose error, and performing parameter identification on the interior of each sub-region by using the model to obtain a transition function; and performing error compensation on the tail end of the parallel robot through the transition function and the gravity deformation prediction model. Therefore, the problems of inaccurate input data in the identification process, limited error compensation effect and the like caused by neglect of pose errors caused by gravity deformation of components in an existing method for improving the precision of the parallel processing equipment are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of manufacturing equipment error compensation, and in particular to a partition error compensation method and device for a parallel robot. Background Art

[0002] Five-axis machining equipment plays an important role in the machining of complex components such as turbine blades, engine casings, and fuselage frames. Compared with traditional machine tools and serial machining equipment, parallel machining equipment has the advantages of high dynamic characteristics, high flexibility, and a high effective load-to-mass ratio, and has the potential to perform five-axis machining with high efficiency and high precision. However, the characteristics of multiple closed-loop branches and multiple passive joints lead to a complex coupling mechanism of the various geometric error sources on the end-point posture error. In addition, the relatively low stiffness of parallel machining equipment leads to non-negligible end-point posture errors caused by the gravity deformation of the component. These two factors make it difficult to maintain the end-point accuracy of parallel machining equipment, which is an important reason restricting its widespread application in five-axis machining.

[0003] The current method for improving the accuracy of parallel machining equipment primarily relies on kinematic calibration. First, the relationship between pose error and structural error is determined through error modeling. The measured pose error is then fed into an identification model, and finally, the identified structural error is compensated for inverse kinematic control. However, as previously mentioned, the measured pose error also includes pose error caused by component gravity deformation, which is typically ignored in kinematic calibration. This leads to inaccurate input data for the identification process and limited error compensation effectiveness. Some researchers have attempted to ensure the pose accuracy of parallel machining equipment by predicting gravity deformation and combining it with control command compensation. However, when predicting gravity deformation, they employ a force system simplification method based on theoretical mechanics, which fails to consider the influence of kinematic pairs and component elastic deformation. This causes component deformation energy to change during the force system simplification process, thereby affecting the accuracy of gravity deformation prediction and the effectiveness of compensation.

[0004] Therefore, how to more accurately predict gravity deformation and obtain more accurate structural error parameters to further improve the posture accuracy of the parallel robot still needs further research. Summary of the Invention

[0005] The present invention provides a partition error compensation method and device for a parallel robot to solve the problem that the existing method for improving the accuracy of parallel processing equipment ignores the posture error caused by the gravity deformation of the component, which leads to inaccurate input data in the identification process and limited error compensation effect.

[0006] A first embodiment of the present invention provides a partition error compensation method for a parallel robot, comprising the following steps:

[0007] A kinematic error model of the target parallel robot is established to obtain a mapping relationship between a first terminal posture error and a structural error parameter; a gravity deformation prediction model of the target parallel robot is established to predict a gravity deformation prediction value as a second terminal posture error; the structural error of the target parallel robot is solved according to the mapping relationship between the first terminal posture error and the structural error parameter and the second terminal posture error; the working area of ​​the target parallel robot is divided into a plurality of sub-areas, and a transition area is added between each sub-area; an identification model of each sub-area is constructed according to the transition area and the structural error, and the identification model of each sub-area is used to perform parameter identification inside each sub-area to obtain a transition function within the transition area; the kinematic error model is compensated using the structural error and the transition function, and the gravity deformation prediction model is used to perform terminal control instruction compensation on the target parallel robot.

[0008] Optionally, establishing a kinematic error model of the target parallel robot to obtain a mapping relationship between a first end position error and a structural error parameter includes:

[0009] Establish a kinematic model of the target parallel robot, wherein the kinematic model includes closed-loop equations and constraint equations of the parallel robot; based on the perturbation principle, construct a kinematic error model of the target parallel robot according to the closed-loop equations and the constraint equations; and use the kinematic error model to obtain a mapping relationship between the first end posture error and the structural error parameter.

[0010] Optionally, the step of establishing a gravity deformation prediction model of the target parallel robot and using the predicted gravity deformation value as the second terminal pose error includes:

[0011] A parallel robot stiffness model of the target parallel robot is established to obtain multiple driving stiffness values ​​and one constraint stiffness value of the target parallel robot; each component of the target parallel robot is equivalent to a multi-node unit; based on the deformation energy equality criterion, the terminal gravity equivalent external load is calculated according to the multiple node units; the gravity deformation prediction model is constructed according to the multiple driving stiffness values, the constraint stiffness values ​​and the terminal gravity equivalent external load, so as to use the gravity deformation prediction model to predict the gravity deformation prediction value, and the gravity deformation prediction value is used as the second terminal posture error.

[0012] Optionally, the calculating the terminal gravity equivalent external load according to the plurality of node elements based on the deformation energy equality criterion includes:

[0013] Based on the deformation energy equality criterion, the node equivalent loads of the multiple node units are calculated; and the node equivalent loads are translated according to the kinematic pair form to obtain the terminal gravity equivalent external load.

[0014] Optionally, solving the first end pose error of the target parallel robot according to the mapping relationship between the first end pose error and the structural error parameter and the second end pose error includes:

[0015] Obtain an experimental end-point pose error of the parallel robot; remove the second end-point pose error from the experimental end-point pose error to obtain a first end-point pose error caused only by the structural error; and solve the structural error of the target parallel robot based on the first end-point pose error caused only by the structural error and a mapping relationship between the end-point pose error and a structural error parameter.

[0016] Optionally, constructing an identification model for each sub-region based on the transition region and the structural error, and performing parameter identification on the interior of each sub-region using the identification model of each sub-region to obtain a transition function within the transition region includes:

[0017] The transition region and the structural error are stacked to construct an identification model of each sub-region; the identification model of each sub-region is iteratively identified using a weighted regularized least squares method and a parallel robot kinematics forward solution to obtain structural error parameters in each sub-region; and a transition function in the transition region is constructed according to the structural error parameters in each sub-region.

[0018] A second embodiment of the present invention provides a partition error compensation device for a parallel robot, comprising:

[0019] An acquisition module is used to establish a kinematic error model of the target parallel robot to obtain a mapping relationship between a first terminal posture error and a structural error parameter; a prediction module is used to establish a gravity deformation prediction model of the target parallel robot to predict a gravity deformation prediction value as a second terminal posture error; a solution module is used to solve the structural error of the target parallel robot based on the mapping relationship between the first terminal posture error and the structural error parameter and the second terminal posture error; a division module is used to divide the working area of ​​the target parallel robot into several sub-areas and add a transition area between each sub-area; a parameter identification module is used to construct an identification model of each sub-area based on the several sub-areas, the transition area and the structural error, so as to use the identification model of each sub-area to perform parameter identification on the inside of each sub-area to obtain a transition function within the transition area; an error compensation module is used to compensate the kinematic error model using the structural error and the transition function, and to use the gravity deformation prediction model to perform terminal control instruction compensation on the target parallel robot.

[0020] An embodiment of the third aspect of the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the partition error compensation method of the parallel robot as described in the above embodiment.

[0021] A fourth aspect of the present invention provides a computer program product, which implements the above-mentioned partition error compensation method for the parallel robot when the computer program / instructions are executed by a processor.

[0022] A fifth aspect of the present invention provides a computer-readable storage medium, which stores a computer program. When the program is executed by a processor, it implements the partition error compensation method of the parallel robot as described above.

[0023] The partition error compensation method and device for the parallel robot proposed in the embodiment of the present invention ensure the posture accuracy of the parallel processing equipment by predicting gravity deformation and combining it with control instruction compensation, and take into account the influence of the elastic deformation of the kinematic pairs and components, so that the deformation of the components does not change during the simplification of the force system, thereby improving the gravity deformation prediction accuracy and compensation effect, and thus further improving the posture accuracy of the terminal of the parallel robot on the basis of kinematic calibration.

[0024] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0026] Figure 1 A flowchart of a partition error compensation method for a parallel robot provided by an embodiment of the present invention;

[0027] Figure 2 A parallel processing device and a top view thereof applied to a parallel robot partition error compensation method provided by an embodiment of the present invention;

[0028] Figure 3 A schematic diagram of a process for establishing a parallel robot driving stiffness model provided by an embodiment of the present invention;

[0029] Figure 4 A schematic diagram of a process for establishing a constraint stiffness model of a parallel robot provided by an embodiment of the present invention;

[0030] Figure 5 A schematic flow chart of a gravity force system equivalent simplification method provided by an embodiment of the present invention;

[0031] Figure 6 A schematic diagram of a process for obtaining unit equivalent nodal loads provided by an embodiment of the present invention;

[0032] Figure 7 A schematic diagram of a motor, a lead screw, and a spindle provided by an embodiment of the present invention being equivalent to a multi-node unit;

[0033] Figure 8 Schematic diagrams of a two-node unit subjected to an axial load before and after simplification of the force system provided by an embodiment of the present invention, wherein (a) shows the force of the two-node unit subjected to the axial load before the force system simplification, and (b) shows the force of the two-node unit subjected to the axial load after the force system simplification;

[0034] Figure 9 Schematic diagrams of a force system of a two-node unit subjected to a load perpendicular to the axial direction before and after simplification, provided by an embodiment of the present invention. (a) shows the force of the two-node unit subjected to a load perpendicular to the axial direction before simplification, and (b) shows the force of the two-node unit subjected to a load perpendicular to the axial direction after simplification.

[0035] Figure 10 A schematic diagram of a process for dividing a working area into several sub-areas provided by an embodiment of the present invention;

[0036] Figure 11 A schematic diagram of eight sub-areas and a transition area provided by an embodiment of the present invention;

[0037] Figure 12A schematic diagram of a process for establishing identification models for each sub-region provided by an embodiment of the present invention;

[0038] Figure 13 A schematic diagram of a kinematic model compensation process provided by an embodiment of the present invention;

[0039] Figure 14 A schematic diagram of a kinematics forward solution process provided by an embodiment of the present invention;

[0040] Figure 15 A schematic diagram of a process for iteratively identifying structural error parameters in each sub-region provided by an embodiment of the present invention;

[0041] Figure 16 A schematic diagram of four types of working areas provided by an embodiment of the present invention;

[0042] Figure 17 A schematic diagram of a flow chart of a transition function construction provided by an embodiment of the present invention;

[0043] Figure 18 A schematic diagram of a mirror compensation process provided by an embodiment of the present invention, wherein (a) is the target pose, (b) is the actual pose predicted by gravity deformation, and (c) is the control command pose after mirror compensation;

[0044] Figure 19 A block diagram of a partition error compensation device for a parallel robot provided by an embodiment of the present invention;

[0045] Figure 20 The present invention provides a schematic structural diagram of an electronic device.

[0046] Explanation of the accompanying drawings: 1-frame, 2-spindle, 11-first fixed component, 12-first force-bearing component, 13-first variable-length component, 21-second fixed component, 22-second force-bearing component, 23-second variable-length component, 31-motor, 32-screw, 33-spindle, 41-large sub-area, 42-transition area, 51-large sub-area, 52-large surface transition area, 53-long strip transition area, 54-rectangular transition area, 190-partition error compensation device of parallel robot, 1901-acquisition module, 1902-prediction module, 1903-solution module, 1904-division module, 1905-parameter identification module and 1906-error compensation module. DETAILED DESCRIPTION

[0047] The following describes embodiments of the present invention in detail, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and are not to be construed as limiting the present invention.

[0048] The following describes a partition error compensation method and device for a parallel robot according to an embodiment of the present invention with reference to the accompanying drawings.

[0049] Figure 1 A schematic flow chart of a partition error compensation method for a parallel robot provided in an embodiment of the present invention.

[0050] like Figure 1 As shown, the partition error compensation method of the parallel robot includes the following steps:

[0051] In step S101, a kinematic error model of the target parallel robot is established to obtain a mapping relationship between the first end position error and the structural error parameter.

[0052] In some embodiments, establishing a kinematic error model of the target parallel robot to obtain a mapping relationship between the first end position error and the structural error parameter includes:

[0053] Establishing a kinematic model of the target parallel robot, wherein the kinematic model includes closed-loop equations and constraint equations of the parallel robot;

[0054] Based on the perturbation principle, the kinematic error model of the target parallel robot is constructed according to the closed-loop equations and constraint equations.

[0055] The kinematic error model is used to obtain the mapping relationship between the first terminal pose error and the structural error parameters.

[0056] In the actual implementation process, Figure 2 As shown, B i (i=1~5) is the center of the Hooke's joint connecting the branch chain i and the frame 01, P1 is the rotation sub-center connecting the branch chain 1 and the main shaft 02, P i(i=2~5) is the center of the Hooke's hinge connecting branches 2~5 to the main shaft. B1~B5 are located on the circumference of the same circle, and P1~P5 are divided into two layers, among which P1~P3 are located on the lower circle with point p' as the center, and P4 and P5 are located on the upper circle with point s' as the center. The frame coordinate system {O1:O1-x1y1z1} is fixed to the frame 01, the coordinate origin O1 is the center of the circle shared by B1~B5, the y1 axis is along the O1B1 direction, the z1 axis is perpendicular to the plane where B1~B5 are located, and the x1 axis is determined by the right-hand rule. The tool coordinate system {O2:O2-x2y2z2} is fixed to the main shaft 02, the coordinate origin O2 is located at the tool tip point, the y2 axis is along the p'P1 direction, the z2 axis is along the p's' direction, and the x2 axis is determined by the right-hand rule.

[0057] The closed-loop equation of the target parallel robot is given by O1-B i -P i -O2-O1 (i=1~5) is a vector equation, and its specific expression can be:

[0058] Rp i +O2=b i +L i l i

[0059] Among them, vector O1B i The expression in the coordinate system {O1} is denoted as b i , Vector O2P i The expression in the coordinate system {O2} is denoted as p i , the expression of tool tip point O2 in coordinate system {O1} is recorded as O2, the rotation matrix of coordinate system {O2} relative to coordinate system {O1} is recorded as R, and the length and unit vector of each branch are recorded as L i and l i .

[0060] The constraint equation of the target parallel robot is that the rotation axis at P1 is always perpendicular to the plane P1O1O2. The specific expression can be:

[0061]

[0062] in, is the unit direction vector of the R secondary axis at P1, θ r is the angle between the R secondary axis and the z2 axis, It is the angle between the projection of the R minor axis on the x2O2y2 plane and the x2 axis.

[0063] The mapping relationship between the first terminal posture error and the structural error parameters is obtained by using the perturbation principle, that is, the terminal posture and the structural parameters with possible errors in the closed-loop equation and constraint equation are fully differentiated, and both ends of the closed-loop equation are multiplied by l on the left.i T , sorted into matrix form, we get:

[0064]

[0065] in, is the attitude error of the secondary axis of branch chain 1.

[0066] The specific expression of the kinematic error model of the five-axis parallel machining robot can be:

[0067] δp k =J ε ε

[0068] Among them, δp k =(δO2 T δR T ) T is the 6-dimensional end-end pose error of the five-axis parallel machining robot, is the structural error parameter, a total of 37 error parameters, ε p is the position error of the center point of the Hooke's hinge of branch 1 to branch 5 at the main axis, a total of 15 error parameters, ε b is the position error of the center point of the Hooke's hinge of branch chain 1 to branch chain 5 located at the frame, a total of 15 error parameters, ε L is the motor zero position error of branch 1 to branch 5, a total of 5 error parameters, δθ r and is the posture error of the R secondary axis of the first branch chain.

[0069] In step S102, a gravity deformation prediction model of the target parallel robot is established to predict the gravity deformation prediction value as the second terminal pose error.

[0070] In some embodiments, establishing a gravity deformation prediction model of the target parallel robot to predict the gravity deformation prediction value as the second terminal pose error includes:

[0071] Establishing a parallel robot stiffness model of the target parallel robot to obtain multiple driving stiffness values ​​and a constraint stiffness value of the target parallel robot;

[0072] Each component of the target parallel robot is equivalent to a multi-node unit;

[0073] Based on the criterion of equal deformation energy, the terminal gravity equivalent external load is calculated according to multiple node elements;

[0074] A gravity deformation prediction model is constructed according to multiple driving stiffness values, constraint stiffness values ​​and terminal gravity equivalent external load, so as to use the gravity deformation prediction model to predict the gravity deformation prediction value, and the gravity deformation prediction value is used as the second terminal posture error.

[0075] In the actual implementation process, Figure 3 As shown, in the simulation software, 11 is set as a fixed component, the length of 13 is set to l, a force of F in the direction of the arrow is applied at 12, and the deformation at 12 is δl, so that the driving flexibility of the branch chain at this time is C = δl / F. The pressure application process is iteratively performed to obtain at least two sets of corresponding relationships between δl and F (the more groups, the better the fitting effect); according to the driving flexibility value C = δl / F and the corresponding relationships between at least two sets of δl and F, according to C a =C a1 l+C a2 The driving stiffness of branches 1 to 5 is fitted, where C a is the driving flexibility of the branch chain from 1 to 5, and l is the length of 13.

[0076] like Figure 4 As shown, in the simulation software, set 22 as a fixed component, set the length of 23 to l, apply a force of F in the direction of the arrow at 21, and obtain the deformation of 21 as δl, so that the constraint flexibility of the branch chain at this time is C = δl / F, repeat the pressure process to obtain at least 4 sets of corresponding relationships between δl and F (the more groups, the better the fitting effect); according to the constraint flexibility C = δl / F, at least 4 sets of corresponding relationships between δl and F, according to C c =C c1 l 3 +C c2 l 2 +C c3 l+C c4 Fit the constraint stiffness of branch 1, where C c is the constraint flexibility of branch 1, and l is the length of 23.

[0077] Furthermore, if Figure 5 and 6 As shown, each component of the five-axis parallel robot is equivalent to a multi-node unit. Specifically, the motor 31 in the five-axis parallel machining robot is equivalent to a two-node unit, the lead screw 32 is equivalent to a two-node unit, and the main shaft 33 is equivalent to a six-node unit.

[0078] Furthermore, the node equivalent loads of multiple node units are calculated according to the deformation energy equality criterion, and then the node equivalent loads are translated according to the form of the kinematic pair, and finally the gravity equivalent concentrated load acting on the end is obtained.

[0079] Among them, Figure 7-9 As shown in Figure 2, the calculation of the nodal equivalent loads of multiple nodal elements based on the deformation energy equality criterion specifically includes:

[0080] The load on the unit is divided into the load along the unit axis and the load perpendicular to the unit axis to determine the linear density of the two loads. The linear density of the load is denoted as p(x) and the unit length is L.

[0081] Write the node displacement boundary conditions according to the unit degree of freedom. Taking the two-degree-of-freedom unit as an example, the displacement at node 1 is v1, and the displacement at node 2 is v2. Then its displacement boundary conditions are:

[0082] v(x=0)=v1;v(x=L)=v2

[0083] Write the displacement field function, and the specific expression of the normalized displacement field function can be written as:

[0084] v(x)=N(x)q e

[0085] q e =[v1v2] T

[0086] According to the displacement field function and load distribution, the expression of the internal deformation energy of the unit can be written as:

[0087] W=∫p(x)v(x)dx

[0088] Substituting the displacement field function, the deformation energy is written as "node load × node displacement" to obtain the equivalent node load, that is:

[0089] ∫p(x)v(x)dx=[∫p(x)N(x)dx]q e =[F1 F2]q e

[0090] Then the equivalent forces of the gravity of motor 41 at node 1 and node 2 are obtained as F M1i and F M2i , the equivalent moments at node 1 and node 2 are M M1i and M M2i The equivalent forces of the screw 42 at nodes 1 and 2 are F s1i and F s2i , the equivalent moments at node 1 and node 2 are M s1i and M s2i .

[0091] The spindle 43 is a multi-node unit. According to the theoretical mechanics force system equivalent method, the gravity distribution force of the spindle is directly equivalent to the external load at the tool tip, which is written as:

[0092] F sp =m sp ge g

[0093] M sp =m sp g((l sp z2)×e g )

[0094] Among them, l sp The distance from the spindle center of mass to the tool tip.

[0095] In the stiffness model of the embodiment of the present invention, the frame is assumed to be a rigid body, and the 30 nodal forces and nodal moments located at the frame can be ignored. Since the revolute pair does not transmit moments along the corresponding axis of rotation, and the linear forces are translated to the tool tip according to the force translation theorem, the equivalent external load at the end can be obtained as:

[0096]

[0097] The specific expression of the gravity deformation prediction model is written as:

[0098]

[0099] In step S103, the structural error of the target parallel robot is solved according to the mapping relationship between the first terminal pose error and the structural error parameter and the second terminal pose error.

[0100] In the actual implementation process, Figure 10 As shown, the experimental end-point pose error of the parallel robot is obtained; the second end-point pose error is removed from the experimental end-point pose error to obtain the first end-point pose error caused only by the structural error; and the structural error of the target parallel robot is solved according to the first end-point pose error caused only by the structural error and the mapping relationship between the end-point pose error and the structural error parameter.

[0101] In step S104, the working area of ​​the target parallel robot is divided into several sub-areas, and a transition area is added between each sub-area.

[0102] In step S105 , an identification model of each sub-region is constructed according to the transition region and the structural error, and the identification model of each sub-region is used to perform parameter identification inside each sub-region to obtain a transition function within the transition region.

[0103] In step S106, the kinematic error model is compensated using the structural error and the transition function, and the end control command of the target parallel robot is compensated using the gravity deformation prediction model.

[0104] In some embodiments, an identification model for each sub-region is constructed based on the transition region and the structural error, and the identification model of each sub-region is used to perform parameter identification inside each sub-region to obtain a transition function within the transition region, including:

[0105] The transition region and the structural error are stacked to construct the identification model of each sub-region;

[0106] The identification model of each sub-region is iteratively identified using the weighted regularized least squares method and the forward kinematic solution of the parallel robot to obtain the structural error parameters in each sub-region.

[0107] A transition function in the transition region is constructed based on the structural error parameters in each sub-region.

[0108] In the actual implementation process, Figure 11 As shown in , the working area is divided into sub-areas that match the structural characteristics of the parallel robot, and transition areas of appropriate width are added at the junction of the sub-areas. Specifically, Figure 12 As shown, due to the structural symmetry of the five-axis parallel machining module, the parallel robot is divided into eight large sub-areas 41 and a transition area 42.

[0109] Furthermore, according to the measurement information of the parallel robot in different postures in each sub-area, the identification model of each sub-area is stacked and recorded at the i-th measurement posture. The kinematic error model is δp ki =J εi ε, the experimental end pose error value is δp i , the predicted value of gravity deformation is C i τ gi , then the first terminal pose error caused only by the structural error is (δp i -C i τ gi ), if there are n measured poses, the identification model can be written as:

[0110] δp + =J ε + ε

[0111]

[0112] Specifically, if Figure 13 As shown, based on the kinematic error model, a set of actual motor drive quantities and a set of initial terminal postures are given; the motor drive quantities corresponding to the initial terminal postures are obtained by the inverse kinematic solution, which are recorded as theoretical motor drive quantities; the difference between the actual motor drive quantities and the theoretical motor drive quantities is obtained; the difference is applied to the kinematic error model to obtain the difference between the theoretical terminal posture and the actual terminal posture caused by the motor drive quantity difference; the difference is added to the theoretical terminal posture and the theoretical terminal posture is updated at the same time; the above kinematic inverse solution process is repeated until the difference between the theoretical motor drive quantity and the actual motor drive quantity is small enough, so that the actual terminal posture under the actual motor drive quantity can be obtained, and then the identification model can be constructed.

[0113] Furthermore, if Figure 14-15 As shown in the figure, the identification model of each sub-region is used to identify the structural error parameters in each sub-region to obtain the structural error. The specific execution process is as follows:

[0114] An identification model is established based on the terminal posture measurement value, the terminal posture nominal value and the gravity deformation prediction value; a set of structural error parameters are obtained by using the weighted regularized least squares method; these are accumulated to the nominal error parameters; a new set of terminal posture nominal values ​​is obtained based on the kinematic positive solution; it is determined whether the structural error obtained by the solution is small enough, or whether the difference between the terminal posture nominal value and the terminal posture measurement value is small enough; if so, the structural error parameters are accumulated to obtain the structural error; if not, the above steps are repeated until the judgment is yes.

[0115] Furthermore, if Figure 14 As shown in Figure 1, a transition function is constructed in the transition region according to the structural error, and then the inverse kinematics control model parameters are modified according to the transition function to complete the kinematic model compensation. The specific execution process is as follows:

[0116] like Figure 16 As shown, the four types of working areas of the five-axis parallel machining module involved in the embodiment of the present invention include a large sub-area 51, a large surface transition area 52, a long strip transition area 53 and a rectangular transition area 54.

[0117] like Figure 17 As shown, a set of linearly independent basis functions are selected to construct the transition function in the rectangular transition region, where the basis function can be a polynomial function, an exponential function, a logarithmic function, a trigonometric function, etc.; the values ​​of the structural error parameters in the large sub-region are used as boundary conditions to determine the coefficients of the basis functions of the transition function in the rectangular transition region, that is, the values ​​of the eight vertices of the rectangular transition region are required to be the same as the values ​​of the eight large sub-regions; according to the boundary conditions satisfied by the rectangular transition region, the large-surface transition region and the long-strip transition region, the transition functions in the large-surface transition region and the long-strip transition region are determined, that is, the structural error is required to be continuous at the junction of the transition region; and it is verified whether the transition function satisfies continuity, symmetry and polelessness in the transition region.

[0118] It should be noted that the embodiments of the present invention further stipulate the rationality of the properties: continuity, that is, in the transition area, the structural error is a function of the end position of the parallel robot and there is no discontinuity; symmetry, that is, the form of the transition function is symmetrical, so that the various components of the end position of the parallel robot have the same effect on it; and polelessness, that is, avoiding the situation where the structural error parameters satisfy continuity but change dramatically.

[0119] like Figure 18As shown in the figure, the target pose is known; the actual pose caused by gravity deformation is predicted; and the control command pose is corrected according to the target pose and the actual pose.

[0120] In summary, the partition error compensation method of the parallel robot proposed in the embodiment of the present invention ensures the posture accuracy of the parallel processing equipment by predicting the gravity deformation and combining it with control instruction compensation, and takes into account the influence of the elastic deformation of the kinematic pair and the component, so that the component deformation energy does not change during the simplification of the force system, thereby improving the gravity deformation prediction accuracy and compensation effect, and thus the terminal posture accuracy of the parallel robot can be further improved on the basis of kinematic calibration.

[0121] Next, the partition error compensation device of the parallel robot proposed in accordance with the embodiment of the present invention will be described with reference to the accompanying drawings.

[0122] Figure 19 A block diagram of a partition error compensation device for a parallel robot provided by an embodiment of the present invention.

[0123] like Figure 19 As shown, the partition error compensation device 190 of the parallel robot includes: an acquisition module 1901, a prediction module 1902, a solution module 1903, a partition module 1904, a parameter identification module 1905 and an error compensation module 1906.

[0124] The acquisition module 1901 is used to establish a kinematic error model for the target parallel robot to obtain a mapping relationship between the first terminal pose error and the structural error parameter. The prediction module 1902 is used to establish a gravity deformation prediction model for the target parallel robot to predict the gravity deformation value as the second terminal pose error. The solution module 1903 is used to solve the structural error of the target parallel robot based on the mapping relationship between the first terminal pose error and the structural error parameter and the second terminal pose error. The division module 1904 is used to divide the working area of ​​the target parallel robot into several subareas and add transition regions between each subarea. The parameter identification module 1905 is used to construct an identification model for each subarea based on the several subareas, the transition regions, and the structural error, and use the identification model of each subarea to perform parameter identification within each subarea to obtain a transition function within the transition region. The error compensation module 1906 is used to compensate the kinematic error model using the structural error and the transition function, and to compensate the terminal control command of the target parallel robot using the gravity deformation prediction model.

[0125] In some embodiments, the acquisition module 1901 includes:

[0126] A first construction unit is configured to establish a kinematic model of a target parallel robot, wherein the kinematic model includes closed-loop equations and constraint equations of the parallel robot;

[0127] The second construction unit is used to construct a kinematic error model of the target parallel robot based on the perturbation principle, the closed-loop equation and the constraint equation;

[0128] The acquisition unit is used to obtain the mapping relationship between the first end position error and the structural error parameter by using the kinematic error model.

[0129] In some embodiments, the prediction module 1902 includes:

[0130] a stiffness acquisition unit, used for establishing a parallel robot stiffness model of a target parallel robot to obtain a plurality of driving stiffness values ​​and a constraint stiffness value of the target parallel robot;

[0131] Equivalent unit, used to convert each component of the target parallel robot into a multi-node unit;

[0132] A calculation unit is used to calculate the equivalent external load of terminal gravity based on multiple node elements based on the criterion of equal deformation energy;

[0133] The prediction unit is used to construct a gravity deformation prediction model based on multiple driving stiffness values, constraint stiffness values ​​and terminal gravity equivalent external load, so as to use the gravity deformation prediction model to predict the gravity deformation prediction value, and use the gravity deformation prediction value as the second terminal posture error.

[0134] In some embodiments, the equivalent unit includes:

[0135] The calculation sub-unit is used to calculate the nodal equivalent loads of multiple nodal elements based on the deformation energy equality criterion;

[0136] The translation subunit is used to translate the node equivalent load according to the form of the kinematic pair to obtain the end gravity equivalent external load.

[0137] In some embodiments, the solution module 1903 includes:

[0138] An error acquisition unit is used to obtain the experimental end-point posture error of the parallel robot;

[0139] a removal unit, configured to remove the second terminal pose error from the experimental terminal pose error to obtain the first terminal pose error caused only by the structural error;

[0140] The solving unit is used to solve the structural error of the target parallel robot according to the terminal posture error caused only by the structural error and the mapping relationship between the first terminal posture error and the structural error parameter.

[0141] In some embodiments, the parameter identification module 1905 includes:

[0142] A stacking unit, used to stack the transition region and the structural error to construct an identification model for each sub-region;

[0143] An iterative identification unit is used to iteratively identify the identification model of each sub-region using a weighted regularized least squares method and a forward solution of the parallel robot kinematics to obtain a structural error parameter in each sub-region;

[0144] The third construction unit is configured to construct a transition function in the transition region according to the structural error parameter in each sub-region.

[0145] It should be noted that the above explanation of the embodiment of the partition error compensation method of the parallel robot is also applicable to the partition error compensation device of the parallel robot of this embodiment, and will not be repeated here.

[0146] The partition error compensation device of the parallel robot proposed in an embodiment of the present invention ensures the posture accuracy of the parallel processing equipment by predicting gravity deformation and combining it with control instruction compensation, and takes into account the influence of the elastic deformation of the kinematic pairs and components, so that the deformation of the components does not change during the simplification of the force system, thereby improving the gravity deformation prediction accuracy and compensation effect, and thus further improving the posture accuracy of the terminal of the parallel robot on the basis of kinematic calibration.

[0147] Figure 20 The present invention provides a schematic structural diagram of an electronic device.

[0148] The electronic device may include:

[0149] Memory 2001 , processor 2002 , and computer programs stored in the memory 2001 and executable on the processor 2002 .

[0150] When the processor 2002 executes the program, the partition error compensation method of the parallel robot provided in the above embodiment is implemented.

[0151] Furthermore, the electronic device further includes:

[0152] The communication interface 2003 is used for communication between the memory 2001 and the processor 2002 .

[0153] The memory 2001 is used to store computer programs that can be run on the processor 2002.

[0154] The memory 2001 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0155] If the memory 2001, processor 2002, and communication interface 2003 are implemented independently, the communication interface 2003, memory 2001, and processor 2002 can be interconnected via a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be divided into address buses, data buses, control buses, etc. For ease of representation, Figure 20 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0156] Optionally, in a specific implementation, if the memory 2001, the processor 2002 and the communication interface 2003 are integrated on a chip, the memory 2001, the processor 2002 and the communication interface 2003 can communicate with each other through an internal interface.

[0157] The processor 2002 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 the present invention.

[0158] An embodiment of the present invention further provides a computer program product, which implements the above partition error compensation method for the parallel robot when the computer program / instructions are executed by a processor.

[0159] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned partition error compensation method for the parallel robot.

[0160] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples without contradiction.

[0161] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "N" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0162] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or N executable instructions for implementing a custom logical function or step of a process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0163] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the 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 (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program 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 the following: an electrical connection with one or N wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program can be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing it in other suitable ways as necessary, and then storing it in a computer memory.

[0164] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiment, the N steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0165] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0166] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing module, or each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or in the form of software functional modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium.

[0167] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and are not to be construed as limiting the present invention. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A partition error compensation method for a parallel robot, characterized in that: The following steps are involved: Establish a kinematic error model of the target parallel robot to obtain the mapping relationship between the first end position error and the structural error parameters; Establishing a gravity deformation prediction model for the target parallel robot, and using the predicted gravity deformation value as the second terminal pose error; Solving the structural error of the target parallel robot according to the mapping relationship between the first end position error and the structural error parameter and the second end position error; Dividing the working area of ​​the target parallel robot into several sub-areas and adding transition areas between each sub-area; constructing an identification model for each sub-region according to the transition region and the structural error, and performing parameter identification on the interior of each sub-region using the identification model of each sub-region to obtain a transition function within the transition region; The kinematic error model is compensated using the structural error and the transition function, and the gravity deformation prediction model is used to compensate the terminal control instruction of the target parallel robot.

2. The partition error compensation method of the parallel robot according to claim 1, characterized in that: The kinematic error model of the target parallel robot is established to obtain a mapping relationship between the first end position error and the structural error parameter, including: Establishing a kinematic model of the target parallel robot, wherein the kinematic model includes closed-loop equations and constraint equations of the parallel robot; Based on the perturbation principle, a kinematic error model of the target parallel robot is constructed according to the closed-loop equation and the constraint equation; The kinematic error model is used to obtain a mapping relationship between the first terminal posture error and the structural error parameter.

3. The partition error compensation method of the parallel robot according to claim 1, characterized in that: The step of establishing a gravity deformation prediction model of the target parallel robot and using the predicted gravity deformation value as the second terminal position error includes: establishing a parallel robot stiffness model of the target parallel robot to obtain a plurality of driving stiffness values ​​and a constraint stiffness value of the target parallel robot; Equivalently converting each component of the target parallel robot into a multi-node unit; Based on the deformation energy equality criterion, calculating the terminal gravity equivalent external load according to the plurality of node elements; The gravity deformation prediction model is constructed according to the multiple driving stiffness values, the constraint stiffness values ​​and the terminal gravity equivalent external load, so as to use the gravity deformation prediction model to predict the gravity deformation prediction value, and the gravity deformation prediction value is used as the second terminal posture error.

4. The partition error compensation method of the parallel robot according to claim 3, characterized in that: The calculating the terminal gravity equivalent external load according to the plurality of node elements based on the deformation energy equality criterion includes: Calculating nodal equivalent loads of the plurality of nodal elements based on a deformation energy equality criterion; The node equivalent load is translated according to the form of the kinematic pair to obtain the terminal gravity equivalent external load.

5. The partition error compensation method of the parallel robot according to claim 1, characterized in that: Solving the structural error of the target parallel robot according to the mapping relationship between the first end position error and the structural error parameter and the second end position error includes: Obtaining an experimental end-point pose error of the parallel robot; Subtracting the second terminal pose error from the experimental terminal pose error to obtain a first terminal pose error caused only by the structural error; The structural error of the target parallel robot is solved according to the first terminal posture error caused only by the structural error and the mapping relationship between the first terminal posture error and the structural error parameter.

6. The partition error compensation method of the parallel robot according to claim 1, characterized in that: The step of constructing an identification model for each sub-region based on the transition region and the structural error, and performing parameter identification on the interior of each sub-region using the identification model of each sub-region to obtain a transition function within the transition region, includes: stacking the transition region and the structural error to construct an identification model for each sub-region; Iteratively identifying the identification model of each sub-region by using a weighted regularized least squares method and a parallel robot kinematics forward solution to obtain a structural error parameter in each sub-region; A transition function in the transition region is constructed according to the structural error parameters in each sub-region.

7. A partition error compensation device for a parallel robot, characterized in that: include: An acquisition module is used to establish a kinematic error model of the target parallel robot to obtain a mapping relationship between the first end position error and the structural error parameter; A prediction module is used to establish a gravity deformation prediction model of the target parallel robot to predict the gravity deformation prediction value as the second terminal posture error; a solving module, configured to solve the structural error of the target parallel robot according to a mapping relationship between the first end position error and the structural error parameter and the second end position error; A division module, configured to divide the working area of ​​the target parallel robot into a plurality of sub-areas and add a transition area between each sub-area; a parameter identification module, configured to construct an identification model for each sub-region based on the plurality of sub-regions, the transition region, and the structural error, and perform parameter identification within each sub-region using the identification model of each sub-region to obtain a transition function within the transition region; An error compensation module is used to compensate the kinematic error model using the structural error and the transition function, and to compensate the terminal control instruction of the target parallel robot using the gravity deformation prediction model.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the partition error compensation method for the parallel robot according to any one of claims 1 to 6.

9. A computer program product, characterized in that When the computer program / instruction is executed by a processor, the partition error compensation method of the parallel robot according to any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the partition error compensation method of the parallel robot according to any one of claims 1 to 6.

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