Manufacturing precision control method and system of parallel robot

Abstract

The invention relates to a manufacturing precision control method and system of a parallel robot. The method comprises the steps of establishing a kinematic model of the coaxial five-rod parallel mechanism based on a vector method, obtaining an error model of the kinematic model about a mapping relation between an error source and a mechanism tail end error by adopting a perturbation method, and performing simulation calculation on the error model to obtain change characteristics of the mechanism tail end error, the method comprises the steps of performing sensitivity analysis based on statistics to obtain a sensitivity coefficient of an error source, performing calculation according to a required tail end error by taking the sensitivity coefficient as an index to obtain a determined value distributed for the first time of each error source, performing redistribution on the error distributed for the first time according to an actually obtained tail end error, and correcting the determined value distributed for each error source. And performing multi-objective optimization based on a genetic algorithm, and solving to obtain the reference value of each optimized error source so as to control the manufacturing precision of the parallel robot. The manufacturing precision of the parallel robot can be improved.

Description

Technical Field

[0001] The invention relates to a manufacturing precision control method and system of a parallel robot, belonging to the technical field of robots. Background Art

[0002] With the development of society, more and more robots are being put into industrial production processes. From the perspective of mechanism, robots can be divided into two categories: serial robots and parallel robots. Serial robots are used in packaging, medicine, and pharmaceutical industries. However, serial robots will cause dynamic accumulation of joint errors, and in most cases, the stiffness is insufficient and cannot meet high-speed application conditions. Compared with serial robots, parallel robots have the characteristics of fast speed, high precision, high rigidity, and strong load capacity, so most of them are used in high-speed and high-precision occasions such as packaging, medicine, and pharmaceutical industries. At present, the common high-precision robots on the market are mainly Delta parallel robots. Due to the existence of errors such as dimensional errors and assembly errors, parallel robots will inevitably have certain errors after production, and cannot completely reach the ideal state, and there is uncertainty in the change of errors. The existence of errors will make the robot unable to be used in actual production. Therefore, it is also necessary to carry out precision design of parallel robots in the design stage to ensure the precision of the manufactured parallel robots, so that they can meet the production requirements and be smoothly put into production.

[0003] At present, geometric precision control mainly includes two types of inverse problems: precision analysis and tolerance allocation. The existing precision control method mainly establishes an error model, analyzes the error change of the robot end and the sensitivity of the error source, and finally optimizes the tolerance allocation according to the control conditions such as the end error and manufacturing cost. However, the existing technology does not have a definite allocation formula and standardized process when allocating tolerances for robots. It relies more on the experience in the design process and lacks a deterministic tolerance allocation method. The existing use of sensitivity analysis is mainly to analyze the influence of each error source on the end error, find the error source with greater influence, and guide the error compensation. Summary of the invention

[0004] The present invention provides a manufacturing precision control method and system for a parallel robot, aiming to solve at least one of the technical problems existing in the prior art.

[0005] The technical solution of the present invention relates to a manufacturing precision control method of a parallel robot, and the method according to the present invention comprises the following steps:

[0006] S100, establishing a kinematic model of a coaxial five-bar parallel mechanism based on a vector method; and obtaining an error model of the kinematic model regarding a mapping relationship between an error source and an end error of the mechanism by using a perturbation method;

[0007] S200, performing simulation calculation on the error model to obtain the variation characteristics of the end error of the mechanism; performing sensitivity analysis based on statistics to obtain the sensitivity coefficient of the error source;

[0008] S300, according to the required terminal error, using the sensitivity coefficient as an indicator, calculating and obtaining the initial distribution of the determined value of each error source; according to the actual terminal error obtained, redistributing the initially distributed error, and correcting the determined value distributed to each error source;

[0009] S400, performing multi-objective optimization based on a genetic algorithm to obtain reference values ​​of each error source after optimization to control the manufacturing accuracy of the parallel robot.

[0010] Furthermore, the parallel mechanism includes an actuator end, a frame, two driving arms and two passive arms, one end of the passive arm is rotatably connected to the frame via a first axis, the other end of the passive arm is rotatably connected to one end of the passive arm via a second axis, and the other ends of the two passive arms are rotatably connected via a third axis; the actuator end is arranged at the connection between the two passive arms.

[0011] Further, the step S100 includes:

[0012] S110, establishing a fixed coordinate system OXY on the frame, with its origin denoted as O;

[0013] S120, assuming that the origin of the drive arm coordinate system A is denoted as A i , origin A i coincides with the origin O of the fixed coordinate system OXY, and its z 0,i The axis coincides with the z-axis of the fixed coordinate system, and the transformation matrix R of the coordinate system A relative to the fixed coordinate system 0,i It is expressed as follows:

[0014] R 0,i =Rot(z,θ i +Δθ i )=[u 0,i v 0,i w 0,i ]

[0015] Where z represents the z-axis of the coordinate system OXY fixed at the first axis; θ i represents the driving angle of the i-th branch; Δθ i represents the driving angle error of the i-th branch chain; [u 0,i v 0,i w 0,i ] represents the unit vector in the x, y, z directions of the drive arm coordinate system A at the first axis of the i-th branch;

[0016] S130, assuming that the origin of the passive arm coordinate system B is B i , where z of coordinate system B is 1,i The axis is parallel to the z-axis of the fixed coordinate system, and the attitude matrix R of the passive arm coordinate system B relative to the fixed coordinate system 1,i It is expressed as follows:

[0017]

[0018] In the formula, z 0,i represents the z-axis of the drive arm coordinate system A at the second axis of the i-th branch chain; represents the driven angle of the i-th branch; represents the passive angle error of the i-th branch; [u 1,i v 1,i w 1,i ] represents the unit vector in the x, y, z directions of the passive arm coordinate system B at the second axis of the i-th branch;

[0019] S130, the coordinate system of the end effector is recorded as O′XY, and its origin is O′;

[0020] S140, the vector equation of the i-th branch of the coaxial five-bar parallel mechanism can be obtained, which is expressed as follows:

[0021]

[0022] In the formula, i is 1 or 2, which are the branch numbers; r represents the position vector of the end; l i L represents the length of the driving rod of the i-th branch chain; i represents the length of the passive rod of the ith branch; represents the unit vector along the x-axis; represents the global unit vector;

[0023] S150, set the assembly errors at the passive joint and the end effector to infinitesimal rods s i and S i , the rod length s without error i and S i All are 0, and when there is an error, it is expressed as s i +Δs i and S i +ΔS i , the closed-loop vector equation group of the i-th branch of the coaxial five-bar parallel mechanism can be obtained, which is expressed as follows:

[0024]

[0025] In the formula, Δs i =(Δs x,i ,Δsy,i ), ΔS i =(ΔS x,i ,ΔS y,i ).

[0026] Further, in step S100,

[0027] By perturbing the closed-loop vector equations of the i-th branch of the coaxial five-bar parallel mechanism, we can obtain:

[0028]

[0029] Dot product on both sides of the above equation Processing can be obtained:

[0030]

[0031] Where Δr represents the error of the end effector [ΔxΔy] T ; Δl i represents the length error of the driving rod of the i-th branch chain; Δθ i represents the error of the driving angle of the i-th branch; ΔL i represents the passive rod length error of the i-th branch chain; where,

[0032]

[0033] e i =[Δl i ΔL i Δθ i Δs x,i Δs y,i ΔS x,i ΔS y,i ] T

[0034] In the formula, B i represents the error source coefficient matrix of the i-th branch; e i represents the error source of the i-th branch.

[0035] Further, in step S200:

[0036] By solving the error model of the parallel robot actuator end, the solution expressions of Δx and Δy are obtained [ΔxΔy] T =Je, and then the variance of the volume error ΔV at the end of the parallel robot actuator is obtained, which is expressed as follows:

[0037]

[0038] In the formula, e qrepresents the qth error source in e; J mn represents the m-th row and n-th column element of J, where J represents the Jacobian matrix of the error solution expression;

[0039] Then we can get:

[0040]

[0041] In the formula, represents the local sensitivity index of the qth error source;

[0042] Then the global sensitivity index of the qth error source is Ω q It is obtained by integrating in the workspace and is expressed as follows:

[0043]

[0044] Where V represents the volume of the given working range at the end of the parallel robot actuator.

[0045] Further, in step S300,

[0046] Assume that the qth error source e q The corresponding sensitivity coefficient is k q (q=1,2,3…14), k q =Ω q , and the required end error is ΔV rq The sensitivity coefficient k q With the error source e q It is expressed as follows:

[0047]

[0048] In which, assuming that the sensitivity coefficient of each error source is k, taking the inverse of the sensitivity coefficient 1·k, and according to the proportion η of 1 / k of each error source in the total error sources, the error value E allocated to each error source can be obtained, which is expressed as follows:

[0049] E=η@ΔV rq .

[0050] Further, in step S300,

[0051] According to the known required end error ΔV rq and the actual terminal error ΔV at , redistribute each error source to al After correction, the error value of each error source after redistribution is obtained as erd=eal(ΔVat / ΔVrq), wherein the distribution value of each error source is eal=E / k.

[0052] Further, in step S400,

[0053] Using the change in cost as the objective function, it is expressed as follows:

[0054]

[0055] In the formula, g i (*) is the error cost function of the i-th error source; ei,o is the error of the i-th error source after optimization.

[0056] The technical solution of the present invention also relates to a computer-readable storage medium on which program instructions are stored, and the above-mentioned method is implemented when the program instructions are executed by a processor.

[0057] The technical solution of the present invention also relates to a manufacturing precision control system for a parallel robot, the system comprising a computer device, which contains the above-mentioned computer-readable storage medium.

[0058] The beneficial effects of the present invention are as follows:

[0059] The manufacturing precision control method and system of the parallel robot of the present invention can improve the manufacturing precision of the parallel robot. The present invention solves the problem that the tolerance allocation depends on experience in the precision design process of the parallel robot. Through sensitivity analysis, the sensitivity coefficient is used as an indicator to achieve the deterministic and standardized allocation of tolerance. The redistributed error value is cost-optimized to obtain the error value of each error source when the cost is minimized while maintaining the terminal error constraint required by the design. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 is a basic flow chart of the method according to the present invention.

[0061] Figure 2 is a schematic structural diagram of a parallel robot according to an embodiment of the present invention.

[0062] Figure 3 is a schematic diagram of a coaxial five-bar parallel mechanism according to an embodiment of the present invention.

[0063] Figure 4 is a schematic diagram of the sensitivity coefficients of the method according to the present invention.

[0064] Figure 5 Schematic diagram of the end error in the working space after the initial distribution according to the method of the present invention.

[0065] Figure 6 Schematic diagram of the end error in the working space after redistribution according to the method of the present invention. DETAILED DESCRIPTION

[0066] The concept, specific structure and technical effects of the present invention will be clearly and completely described below in combination with the embodiments and drawings to fully understand the purpose, scheme and effect of the present invention.

[0067] It should be noted that, unless otherwise specified, when a feature is referred to as being "fixed" or "connected" to another feature, it may be directly fixed or connected to another feature, or it may be indirectly fixed or connected to another feature. The singular forms "a", "said" and "the" used herein are also intended to include the plural forms, unless the context clearly indicates otherwise. In addition, unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art. The terms used in this specification are intended only to describe specific embodiments and are not intended to limit the invention. The term "and / or" used herein includes any combination of one or more of the related listed items.

[0068] It should be understood that, although the term first, second, third etc. may be adopted to describe various elements in the present disclosure, these elements should not be limited to these terms. These terms are only used to distinguish the same type of elements from each other. For example, without departing from the scope of the present disclosure, the first element may also be referred to as the second element, and similarly, the second element may also be referred to as the first element. The use of any and all examples or exemplary language ("for example", "such as" etc.) provided herein is only intended to better illustrate embodiments of the present invention, and unless otherwise required, the scope of the present invention will not be limited.

[0069] Reference Figures 1 to 6 In some embodiments, the manufacturing precision control method of the parallel robot according to the present invention comprises at least the following steps:

[0070] S100, establishing a kinematic model of a coaxial five-bar parallel mechanism based on a vector method; and obtaining an error model of the kinematic model regarding a mapping relationship between an error source and an end error of the mechanism by using a perturbation method;

[0071] S200, performing simulation calculation on the error model to obtain the variation characteristics of the end error of the mechanism; performing sensitivity analysis based on statistics to obtain the sensitivity coefficient of the error source;

[0072] S300, according to the required terminal error, using the sensitivity coefficient as an indicator, calculating and obtaining the initial distribution of the determined value of each error source; according to the actual terminal error obtained, redistributing the initially distributed error, and correcting the determined value distributed to each error source;

[0073] S400, performing multi-objective optimization based on a genetic algorithm to obtain reference values ​​of each error source after optimization to control the manufacturing accuracy of the parallel robot.

[0074] Specific implementation of step S100

[0075] The present invention first establishes a kinematic model of the coaxial five-bar parallel mechanism based on the vector method, and then uses the perturbation method to obtain an error model for the kinematic model. The error model is the mapping relationship between the error source and the end error of the mechanism. Figure 2 The parallel robot of the present invention includes a coaxial five-bar parallel mechanism, which includes an actuator end, a frame, two driving arms and two passive arms. The two ends of the driving arm are respectively connected to the frame and one end of the passive arm, and the other ends of the two passive arms are connected. The actuator end is arranged at the connection of the two passive arms. The two driving arms are rotatably arranged on the frame through two first axes, and the two first axes are coaxially arranged. There are two second axes, a driving arm and a passive arm are rotatably connected through a second axis, and the two passive arms are rotatably connected through a third axis. Under the action of two drivers, the two driving arms can rotate around the first axis respectively to change the angle between the two driving arms, thereby driving the passive arm to rotate around the second axis to change the angle between the driving arm and the passive arm, and driving the passive arm to rotate around the third axis to change the angle between the two passive arms. Among them, the three axes of the first axis, the second axis and the third axis are parallel.

[0076] Specifically, see Figure 2 and Figure 3 , establish a fixed coordinate system OXY on the frame, with its origin at O ​​(at the first axis), i takes the values ​​of 1 and 2 as the branch chain numbers, corresponding to two driving arms and two passive arms respectively. A branch chain includes a connected driving arm and a passive arm.

[0077] Assume the origin A of the drive arm coordinate system A i coincides with the origin O of the fixed coordinate system OXY, and its z 0,i The axis coincides with the z-axis (i.e., the first axis) of the fixed coordinate system, then the transformation matrix R of the coordinate system A relative to the fixed coordinate system is 0,i It can be expressed as:

[0078] R 0,i =Rot(z,θ i +Δθ i )=[u 0,i v 0,i w 0,i ]

[0079] Where z represents the z-axis of the coordinate system OXY fixed at the first axis; θ i represents the driving angle of the i-th branch; Δθ i represents the driving angle error of the i-th branch chain; [u 0,i v 0,i w0,i ] represents the unit vector in the x, y, and z directions of the driving arm coordinate system A at the first axis of the i-th branch chain.

[0080] Assume the origin of the passive arm coordinate system B is B i (at the second axis), where z of coordinate system B is 1,i The axis (i.e., the second axis) is parallel to the z-axis of the fixed coordinate system, then the attitude matrix R of the passive arm coordinate system B relative to the fixed coordinate system is 1,i It can be expressed as:

[0081]

[0082] In the formula, z 0,i represents the z-axis of the drive arm coordinate system A at the second axis of the i-th branch chain; represents the driven angle of the i-th branch; represents the passive angle error of the i-th branch; [u 1,i v 1,i w 1,i ] represents the unit vector in the x, y, and z directions of the passive arm coordinate system B at the second axis of the i-th branch.

[0083] The coordinate system of the end effector is denoted as O′XY, and its origin is O′ (at the third axis).

[0084] Therefore, the vector equation of the i-th branch of the coaxial five-bar parallel mechanism is:

[0085]

[0086] Where r represents the position vector of the end; l i L represents the length of the driving rod of the i-th branch chain; i represents the length of the passive rod of the ith branch; represents the unit vector along the x-axis; represents the global unit vector;

[0087] The assembly errors at the passive joint and the end effector are set to infinitesimal rods s i and S i , the rod length s without error i and S i All are 0, and when there is an error, it is expressed as s i +Δs i and S i +ΔS i , where Δs i =(Δs x,i ,Δs y,i ), ΔS i =(ΔS x,i ,ΔSy,i ). Then the closed-loop vector equation group (2) can be further expressed as:

[0088]

[0089] By perturbing equation (3), we can obtain:

[0090]

[0091] Take the dot product of both sides of equation (4) Right now Processing can be obtained:

[0092]

[0093] Where Δr represents the error of the end effector [ΔxΔy] T ; Δl i represents the length error of the driving rod of the i-th branch chain; Δθ i represents the error of the driving angle of the i-th branch; ΔL i represents the passive rod length error of the i-th branch chain; where,

[0094]

[0095] e i =[Δl i ΔL i Δθ i Δs x,i Δs y,i ΔS x,i ΔS y,i ] T

[0096] In the formula, B i represents the error source coefficient matrix of the i-th branch chain; e i represents the error source of the i-th branch.

[0097] Specific implementation of step S200

[0098] The present invention performs simulation calculations based on the error model, summarizes the variation characteristics of the end error of the mechanism, and performs sensitivity analysis in a statistical sense to obtain the sensitivity coefficient of the error source.

[0099] Specifically, the present invention uses the volume error of the end of the parallel robot actuator As an evaluation criterion, it reflects the influence of each error source on the terminal error.

[0100] According to the uncertainty of the error, each error source of the robot terminal error is defined as follows: first, assume that each error source is independent of each other, that is, the covariance between all error sources is zero; second, assume that each error source obeys a standard normal distribution with a mean of zero, and the interval between the error sources is within six standard deviations.

[0101] By solving the error model of the coaxial five-bar parallel mechanism actuator end, the solution expressions of Δx and Δy can be obtained [ΔxΔy] T =Je, and then the variance of the volume error ΔV at the end of the robot is expressed as follows:

[0102]

[0103] In the formula, e q represents the qth error source in e; J mn Represents the element in the mth row and nth column of J.

[0104] Then we can get:

[0105]

[0106] In the formula, represents the local sensitivity index of the qth error source, then the global sensitivity index of the qth error source is Ω q Obtained by integration in the workspace, namely Where V represents the volume of the specified working range of the end of the parallel robot actuator.

[0107] Here is a specific example to illustrate. Specifically, the driving arm length of the robot is l = 100, the passive arm length is L = 100, and the working space radius is (30, 190). The parameter unit is mm, then the sensitivity coefficient is as follows: Figure 4 It should be noted that, because the sensitivity coefficient of the driving angle is related to the rod length of the driving rod, its sensitivity coefficient is very different from other error sources, and its sensitivity coefficient increases with the increase of the rod length.

[0108] Specific implementation of step S300

[0109] The present invention calculates the initial distribution determination value of each error source according to the required terminal error and takes the sensitivity coefficient as an index; redistributes the initial distribution error according to the actual terminal error, and corrects the distribution determination value of each error source.

[0110] Specifically, let the sensitivity coefficient corresponding to the qth error source eq be k q (q=1,2,3…14), that is, k q =Ω q . Assume the required end error is ΔVrq , ΔV rq The sensitivity coefficient k q With the error source e q It is expressed as follows:

[0111]

[0112] Among them, the sensitivity coefficient of each error source is k. The larger k is, the greater its influence on the error. Under the same change, the more errors it affects, so the error caused by the error source should be smaller, and the value of the error source is smaller. That is, the larger k is, the smaller its proportion in the error, and vice versa, the smaller k is, the greater its proportion in the error. Take the reciprocal of the sensitivity coefficient 1 / k, and calculate the proportion η of 1 / k of each error source in the total error sources, so that the error value assigned to each error source is E=η·ΔV rq .

[0113] It should be noted that the distribution value e of each error source can be obtained through formula (9): al =E / k. The distribution value of the error source calculated here is obtained through the sensitivity coefficient, which is obtained under the conditions of the entire working space. It is closer to the average value, so there will be a certain difference in the actual terminal error of a certain point. Based on the above situation, it is also necessary to substitute the calculated distribution value into the error model to calculate the actual terminal error ΔV of each error source under this distribution value. at .

[0114] It is understandable that the error of the end point will change with the change of posture. at Based on the characteristics of the end error change of the coaxial five-bar parallel mechanism in the workspace, the coaxial five-bar parallel mechanism is calculated when the end error is the largest, and the structural parameters in this case are obtained. The actual end error ΔV is calculated by formula (9): at .

[0115] Furthermore, due to the terminal error ΔV obtained only by the sensitivity coefficient distribution at The end error is ΔV rq There is a certain gap. The present invention redistributes each error source once. al Make corrections. According to the known required end error ΔV rq and the actual terminal error ΔV at , multiply both sides of equation (9) by ΔV at / ΔV rq , we can get:

[0116]

[0117] in: α i , β i represents e in Δx and Δy respectively i,al Corresponding coefficients. Further, the above formula (9) can be expressed as follows:

[0118]

[0119] Right now:

[0120]

[0121] The error value e after redistribution of each error source is finally obtained rd =e al (ΔV at / ΔV rq ). It should be noted that through e rd The calculated terminal error can meet the terminal error required by the present invention.

[0122] Specific implementation of step S400

[0123] The present invention is based on a multi-objective optimization performed by a genetic algorithm to solve the reference value of each error source after optimization to control the manufacturing accuracy of the parallel robot. Specifically, since there are many solutions to the end error that meet the design requirements, the error value of each error source obtained by the above method is only one of the solutions under the end error constraint that meets the design requirements. In the actual production process, it is not enough to get only one set of solutions, and other constraints, such as cost, need to be considered. The error values ​​of each error source obtained by simply allocating and redistributing the sensitivity coefficients may have the problem of excessive cost. After all, the cost corresponding to the error size of different parts is also different, and according to the characteristics of the error cost curve, the smaller the error, the greater the cost of reducing the same error. Therefore, the present invention finally performs cost optimization on the redistributed error value, that is, the error value of each error source when the cost is minimized while maintaining the end error constraint of the design requirements. The change in cost is used as the objective function here, that is:

[0124]

[0125] In the formula, g i (*) is the error cost function of the i-th error source; ei,o is the error of the i-th error source after optimization.

[0126] The manufacturing precision control method of the parallel robot of the present invention is actually tested. Specifically, the end error required by the design is ΔV rq =0.2mm, the sensitivity coefficient of the error source is k, then the proportion of each error source η can be obtained, and then the error value allocated to the error source E = η·ΔV rq Then through the formula eal =E / k, the initial distribution value of each error source can be obtained. Substituting it into the error model, the actual error value ΔV after the initial distribution can be obtained at The end error in the working space is as follows: Figure 5 shown.

[0127] Furthermore, the maximum value of the actual end error ΔV in the working space can be obtained by calculation: max , then the correction coefficient c of the error distribution k =ΔV rq / ΔV max . Through the formula e rd =c k e al , we can get the error value e of each error source after redistribution rd . Then e rd Substituted into the error model, we can get the terminal error in the redistributed workspace. Figure 6 It can be seen that the maximum value of the end error in the workspace is 0.2mm, and the final error distribution value can be obtained by optimizing the multi-objective optimization model.

[0128] It should be appreciated that the method steps in the embodiments of the present invention can be implemented or implemented by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer readable memory. The method can use standard programming techniques. Each program can be implemented in a high-level process or object-oriented programming language to communicate with a computer system. However, if necessary, the program can be implemented in an assembly or machine language. In any case, the language can be a compiled or interpreted language. In addition, the program can be run on a programmed ASIC for this purpose.

[0129] Furthermore, the operations of the processes described herein may be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes described herein (or variations and / or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) that is executed collectively on one or more processors, by hardware, or a combination thereof. The computer program includes a plurality of instructions that may be executed by one or more processors.

[0130] Further, the method can be implemented in any type of computing platform that is operably connected to a suitable computer, including but not limited to a personal computer, a minicomputer, a mainframe, a workstation, a network or distributed computing environment, a separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, etc. Various aspects of the present invention can be implemented in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, an optical read and / or write storage medium, an RSM, a ROM, etc., so that it can be read by a programmable computer, and when the storage medium or device is read by the computer, it can be used to configure and operate the computer to perform the process described herein. In addition, the machine-readable code, or portions thereof, can be transmitted via a wired or wireless network. When such media includes instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor, the invention described herein includes these and other different types of non-transitory computer-readable storage media. When programmed according to the methods and techniques of the present invention, the present invention can also include the computer itself.

[0131] The computer program can be applied to input data to perform the functions described herein, thereby converting the input data to generate output data stored in a non-volatile memory. The output information can also be applied to one or more output devices such as a display. In a preferred embodiment of the present invention, the converted data represents physical and tangible objects, including specific visual depictions of physical and tangible objects produced on the display.

[0132] The above is only a preferred embodiment of the present invention. The present invention is not limited to the above implementation. As long as the technical effect of the present invention is achieved by the same means, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the scope of protection of the present invention. Within the scope of protection of the present invention, its technical scheme and / or implementation method may have various modifications and changes.

Claims

1. A manufacturing precision control method for a parallel robot, characterized in that: The method comprises the following steps: S100, establishing a kinematic model of a coaxial five-bar parallel mechanism based on a vector method; and obtaining an error model of the kinematic model regarding a mapping relationship between an error source and an end error of the mechanism by using a perturbation method; S200, performing simulation calculation on the error model to obtain the variation characteristics of the end error of the mechanism; performing sensitivity analysis based on statistics to obtain the sensitivity coefficient of the error source; S300, according to the required terminal error, using the sensitivity coefficient as an indicator, calculating and obtaining the initial distribution of the determined value of each error source; according to the actual terminal error obtained, redistributing the initially distributed error, and correcting the determined value distributed to each error source; S400, performing multi-objective optimization based on a genetic algorithm to obtain reference values ​​of each error source after optimization to control the manufacturing accuracy of the parallel robot.

2. The method according to claim 1, characterized in that The parallel mechanism includes an actuator end, a frame, two driving arms and two passive arms, one end of the passive arm is rotatably connected to the frame via a first axis, the other end of the passive arm is rotatably connected to one end of the passive arm via a second axis, and the other ends of the two passive arms are rotatably connected via a third axis; the actuator end is arranged at the connection between the two passive arms.

3. The method according to claim 2, characterized in that The step S100 includes: S110, establishing a fixed coordinate system OXY on the frame, with its origin denoted as O; S120, assuming that the origin of the drive arm coordinate system A is denoted as A i , origin A i coincides with the origin O of the fixed coordinate system OXY, and its z 0,i The axis coincides with the z-axis of the fixed coordinate system, and the transformation matrix R of the coordinate system A relative to the fixed coordinate system 0,i It is expressed as follows: R 0,i =Rot(z,θ i +Δθ i )=[u 0,i in 0,i In 0,i ] Where z represents the z-axis of the coordinate system OXY fixed at the first axis; θ i represents the driving angle of the i-th branch; Δθ i represents the driving angle error of the i-th branch chain; [u 0,i v 0,i w 0,i ] represents the unit vector in the x, y, z directions of the drive arm coordinate system A at the first axis of the i-th branch; S130, assuming that the origin of the passive arm coordinate system B is B i , where z of coordinate system B is 1,i The axis is parallel to the z-axis of the fixed coordinate system, and the attitude matrix R of the passive arm coordinate system B relative to the fixed coordinate system 1,i It is expressed as follows: In the formula, z 0,i represents the z-axis of the drive arm coordinate system A at the second axis of the i-th branch chain; represents the driven angle of the i-th branch; represents the passive angle error of the i-th branch; [u 1,i v 1,i w 1,i ] represents the unit vector in the x, y, z directions of the passive arm coordinate system B at the second axis of the i-th branch; S130, the coordinate system of the end effector is recorded as O′XY, and its origin is O′; S140, the vector equation of the i-th branch of the coaxial five-bar parallel mechanism can be obtained, which is expressed as follows: In the formula, i is 1 or 2, which are the branch numbers; r represents the position vector of the end; l i L represents the length of the driving rod of the i-th branch chain; i represents the length of the passive rod of the ith branch; represents the unit vector along the x-axis; represents the global unit vector; S150, set the assembly errors at the passive joint and the end effector to infinitesimal rods s i and S i , the rod length s without error i and S i All are 0, and when there is an error, it is expressed as s i +Δs i and S i +ΔS i , the closed-loop vector equation group of the i-th branch of the coaxial five-bar parallel mechanism can be obtained, which is expressed as follows: where Δs i =(Δs x,i , Δs y,i ), ΔS i =(ΔS x,i , ΔS y,i ).

4. The method according to claim 3, characterized in that In the step S100, By perturbing the closed-loop vector equations of the i-th branch of the coaxial five-bar parallel mechanism, we can obtain: Dot product on both sides of the above equation Processing can be obtained: Where Δr represents the error of the end effector [ΔxΔy] T ; Δl i represents the length error of the driving rod of the i-th branch chain; Δθ i represents the error of the driving angle of the i-th branch; ΔL i represents the passive rod length error of the i-th branch chain; where, e i =[Δl i ΔL i Δθ i Δs x,i Δs y,i ΔS x,i ΔS y,i ] T In the formula, B i represents the error source coefficient matrix of the i-th branch; e i represents the error source of the i-th branch.

5. The method according to claim 4, characterized in that In step S200: By solving the error model of the parallel robot actuator end, the solution expressions of Δx and Δy are obtained [ΔxΔy] T =Je, and then the variance of the volume error ΔV at the end of the parallel robot actuator is obtained, which is expressed as follows: In the formula, e q represents the qth error source in e; J mn represents the m-th row and n-th column element of J; J represents the Jacobian matrix of the error solution expression; Then we can get: In the formula, represents the local sensitivity index of the qth error source; Then the global sensitivity index of the qth error source is Ω q It is obtained by integrating in the workspace and is expressed as follows: Where V represents the volume of the given working range at the end of the parallel robot actuator.

6. The method according to claim 5, characterized in that In the step S300, Assume that the qth error source e q The corresponding sensitivity coefficient is k q (q=1,2,3…14), k q =Ω q , and the required end error is ΔV rq The sensitivity coefficient k q With the error source e q It is expressed as follows: Among them, assuming that the sensitivity coefficient of each error source is k, taking the inverse of the sensitivity coefficient 1 / k, and according to the proportion η of 1 / k of each error source in the total error sources, the error value E allocated to each error source can be obtained, which is expressed as follows:. E=η·ΔV rq 7. The method according to claim 6, characterized in that In the step S300, According to the known required end error ΔV rq and the actual terminal error ΔV at , redistribute each error source to al After correction, the error value of each error source after redistribution is obtained as erd=eal(ΔVat / ΔVrq), wherein the distribution value of each error source is eal=E / k.

8. The method according to claim 7, characterized in that In the step S400, Using the change in cost as the objective function, it is expressed as follows: In the formula, g i (*) is the error cost function of the i-th error source; ei,o is the error of the i-th error source after optimization. 9 . A computer-readable storage medium having program instructions stored thereon, wherein the program instructions, when executed by a processor, implement the method according to any one of claims 1 to 8.

10. A manufacturing precision control system for a parallel robot, characterized in that: include: A computer device comprising a computer readable storage medium according to claim 9.

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