Manufacturing precision control method and system for parallel robots

Abstract

The present invention relates to a method and system for controlling the manufacturing accuracy of a parallel robot. The method includes: establishing a kinematic model of a coaxial five-bar parallel mechanism based on a vector method; using a perturbation method to obtain an error model of the kinematic model regarding the mapping relationship between error sources and mechanism end-point errors; performing simulation calculations on the error model to obtain the variation characteristics of the mechanism end-point errors; performing sensitivity analysis based on statistics to obtain the sensitivity coefficients of the error sources; calculating the initial distribution of a certain value for each error source based on the required end-point errors and using the sensitivity coefficients as indicators; redistributing the initially distributed errors based on the actual end-point errors, and correcting the distribution of the certain values ​​for each error source; and performing multi-objective optimization based on a genetic algorithm to obtain optimized reference values ​​for each error source to control the manufacturing accuracy of the parallel robot. The present invention can improve the manufacturing accuracy of the parallel robot.

Description

Technical Field

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

[0002] With the development of society, more and more robots are being incorporated into industrial production processes. From a mechanics perspective, robots can be divided into two major categories: serial robots and parallel robots. Serial robots are used in industries such as packaging, medicine, and pharmaceuticals. However, serial robots can cause dynamic accumulation of joint errors and often lack sufficient stiffness, making them inadequate for high-speed applications. Parallel robots, on the other hand, offer faster speed, higher precision, greater rigidity, and greater load capacity than serial robots. Therefore, they are primarily used in high-speed, high-precision applications such as packaging, medicine, and pharmaceuticals. Currently, the most common high-precision robots on the market are delta parallel robots. Due to dimensional and assembly errors, parallel robots inevitably exhibit certain errors after production, preventing them from achieving ideal performance. Furthermore, the variation in errors is uncertain. These errors can make the robots unsuitable for practical production applications. Therefore, precision design of parallel robots is crucial during the design phase to ensure that the manufactured parallel robots meet production requirements and can be smoothly put into production.

[0003] Currently, geometric precision control primarily involves two mutually inverse problems: precision analysis and tolerance allocation. Existing precision control methods primarily establish error models, analyze the variation in robot end-point errors and the sensitivity of error sources, and optimize tolerance allocation based on control conditions such as end-point errors and manufacturing costs. However, existing technologies lack a definitive allocation formula or standardized process for robot tolerance allocation, relying instead on design experience and lacking a deterministic tolerance allocation method. Existing sensitivity analysis primarily analyzes the impact of each error source on the end-point error, identifying the most influential error source to guide 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 for 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 error sources and mechanism end errors 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, based on the required terminal error and taking the sensitivity coefficient as an indicator, calculate and obtain the initial assigned definite value of each error source; based on the actual terminal error obtained, redistribute the initially assigned error and correct the assigned definite value of each error source;

[0009] S400, performing multi-objective optimization based on a genetic algorithm to obtain reference values ​​of each optimized error source 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 through a first axis, the other end of the passive arm is rotatably connected to one end of the passive arm through a second axis, and the other ends of the two passive arms are rotatably connected through a third axis; the actuator end is arranged at the connection between the two passive arms.

[0011] Furthermore, the step S100 includes:

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

[0013] S120, let the origin of the driving arm coordinate system A be 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; [u 0,i v 0,i w 0,i ] represents the unit vector in the x, y, and z directions of the drive arm coordinate system A at the first axis of the i-th branch;

[0016] S130, let the origin of the passive arm coordinate system B be 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 posture matrix R of the passive arm coordinate system B relative to the fixed coordinate system 1,i It is expressed as follows:

[0017]

[0018] Where z 0,i represents the z-axis of the drive arm coordinate system A at the second axis of the i-th branch; 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;

[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] Where 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; i represents the length of the passive rod of the i-th 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 , 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 , we can get the closed-loop vector equations of the i-th branch of the coaxial five-bar parallel mechanism, which are expressed as follows:

[0024]

[0025] Where Δs i =(Δs x,i ,Δsy,i ), ΔS i =(ΔS x,i ,ΔS y,i ).

[0026] Furthermore, 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] Where 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] Furthermore, 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] Where, 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] Where, represents the local sensitivity index of the qth error source;

[0042] Then the global sensitivity index Ω of the qth error source is 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] Furthermore, 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 terminal error is ΔV rq The sensitivity coefficient k q and error source e q It is expressed as follows:

[0047]

[0048] Here, let the sensitivity coefficient of each error source be k, take the inverse of the sensitivity coefficient 1·k, and based on the proportion η of each error source 1 / k in the total error sources, we can get the error value E allocated to each error source, which is expressed as follows:

[0049] E=η@ΔV rq .

[0050] Furthermore, 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 e al After correction, the error value of each error source after redistribution is obtained: erd=eal(ΔVat / ΔVrq), where the distribution value of each error source: eal=E / k.

[0052] Furthermore, in step S400,

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

[0054]

[0055] Where 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 having program instructions stored thereon, 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, wherein the system includes a computer device containing the above-mentioned computer-readable storage medium.

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

[0059] The present invention addresses the problem of tolerance allocation relying on experience during the parallel robot precision design process. Through sensitivity analysis, using sensitivity coefficients as indicators, the invention achieves deterministic and standardized tolerance allocation. Cost optimization is performed on the redistributed error values ​​to determine the error values ​​of each error source that minimize cost while maintaining the required terminal error constraints. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0062] Figure 3 2 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 point 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 point error in the working space after redistribution according to the method of the present invention. DETAILED DESCRIPTION

[0066] The following will provide a clear and complete description of the concept, specific structure and technical effects of the present invention in conjunction with the embodiments and drawings to fully understand the purpose, scheme and effects 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 the other feature, or it may be indirectly fixed or connected to the other 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 commonly understood by those skilled in the art. The terms used in this specification are only for describing specific embodiments and are not intended to limit the invention. The term "and / or" used herein includes any combination of one or more related listed items.

[0068] Should be understood that, although the present disclosure may adopt the term first, second, third etc. to describe various elements, these elements should not be limited to these terms.These terms are only used to distinguish the elements of the same type 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, will not impose limitations on the scope of the present invention.

[0069] Reference Figures 1 to 6 In some embodiments, the manufacturing precision control method of the parallel robot according to the present invention includes 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 error sources and mechanism end errors 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, based on the required terminal error and taking the sensitivity coefficient as an indicator, calculate and obtain the initial assigned definite value of each error source; based on the actual terminal error obtained, redistribute the initially assigned error and correct the assigned definite value of each error source;

[0073] S400, performing multi-objective optimization based on a genetic algorithm to obtain reference values ​​of each optimized error source 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 the error model of 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 between the two passive arms. The two driving arms are rotatably arranged on the frame through two first shafts, and the two first shafts are coaxially arranged. There are two second shafts, one driving arm and one passive arm are rotatably connected through a second shaft, and the two passive arms are rotatably connected through a third shaft. Under the action of two drivers, the two driving arms can rotate around the first shaft respectively, change the angle between the two driving arms, and then drive the passive arm to rotate around the second shaft, change the angle between the driving arm and the passive arm, and drive the passive arm to rotate around the third shaft, change the angle between the two passive arms. Among them, the three axes of the first shaft, the second shaft and the third shaft 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 ​​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; [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.

[0080] Assume that 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 posture 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] Where z 0,i represents the z-axis of the drive arm coordinate system A at the second axis of the i-th branch; 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; i represents the length of the passive rod of the i-th 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 as infinitesimal rods s i and S i , 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 equations (2) can be further expressed as:

[0088]

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

[0090]

[0091] Dot product on 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] Where B i represents the error source coefficient matrix of i branches; 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 an error model, summarizes the variation characteristics of the mechanism end error, 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 to As an evaluation criterion, it reflects the degree of influence of each error source on the terminal error.

[0100] According to the uncertainty of the error, each error source of the robot end error is defined as follows: first, each error source is assumed to be independent of each other, that is, the covariance between all error sources is zero; second, each error source is assumed to obey the 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] Where, 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] Where, represents the local sensitivity index of the qth error source, then the global sensitivity index of the qth error source Ω q Obtained by integration in the workspace, i.e. Where V represents the volume of the specified working range of the parallel robot actuator end.

[0107] Here we use a specific embodiment to illustrate. Specifically, the length of the robot's driving arm is l = 100, the length of the passive arm is L = 100, and the radius of the working space 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, 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 based on the required terminal error and the sensitivity coefficient as an indicator; redistributes the initial distribution error based on 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 that the required terminal error is ΔVrq , ΔV rq The sensitivity coefficient k q and error source e q It is expressed as follows:

[0111]

[0112] Here, the sensitivity coefficient of each error source is k. The larger k, the greater its impact on the error. Under the same change, the more error it affects, so the error caused by this error source should be smaller, and the smaller the error source value. In other words, the larger k, the smaller its contribution to the error, and conversely, the smaller k, the larger its contribution to the error. Taking the inverse of the sensitivity coefficient 1 / k and calculating the contribution η of each error source to the total error sources, we can obtain the error value allocated to each error source as 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 by calculating the sensitivity coefficient. The sensitivity coefficient is obtained under the conditions of the entire workspace and is closer to the average value. Therefore, there will be some discrepancies 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 The size of the coaxial five-bar parallel mechanism is different. Based on the characteristics of the end error change 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, the terminal error ΔV obtained by only allocating the sensitivity coefficients is at The end error is ΔV rq There is a certain gap, the present invention redistributes each error source once, and 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 The corresponding coefficients. Further, the above formula (9) can be expressed as follows:

[0118]

[0119] Right now:

[0120]

[0121] The final error value e after redistribution of each error source 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 values ​​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 values ​​of each error source obtained by the above method are 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. Here, the change in cost is used as the objective function, that is:

[0124]

[0125] Where 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 By formula e rd =c k e al , we can get the error value e of each error source after redistribution rd Then e rd By bringing it 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. 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 executed 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 procedural or object-oriented programming language to communicate with the computer system. However, if desired, the program can be implemented in assembly or machine language. In any case, the language can be a compiled or interpreted language. In addition, for this purpose, the program can be run on a programmed application-specific integrated circuit.

[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 the 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 can 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 as 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 over 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 that is 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 a physical and tangible object, including a specific visual depiction of the physical and tangible object produced on the display.

[0132] The above description is merely a preferred embodiment of the present invention. The present invention is not limited to the aforementioned embodiments. As long as the technical effects of the present invention are achieved by the same means, any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention. Within the scope of protection of the present invention, various modifications and variations of the technical solutions and / or implementation methods are possible.

Claims

1. A manufacturing precision control method for a parallel robot, characterized in that: The parallel mechanism includes an end effector, a frame, two driving arms and two passive arms, one end of the driving arm is rotatably connected to the frame via a first shaft, the other end of the driving arm is rotatably connected to one end of the passive arm via a second shaft, and the other ends of the two passive arms are rotatably connected via a third shaft; The end effector is arranged at the connection between the two passive arms; 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 error sources and mechanism end errors 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, based on the required terminal error and taking the sensitivity coefficient as an indicator, calculate and obtain the initial assigned definite value of each error source; based on the actual terminal error obtained, redistribute the initially assigned error and correct the assigned definite value of each error source; S400, performing multi-objective optimization based on a genetic algorithm to obtain optimized reference values ​​of each error source to control the manufacturing accuracy of the parallel robot; Wherein, the step S100 includes: S110, establishing a fixed coordinate system on the rack , whose origin is ; S120, setting the coordinate system of the driving arm The origin is denoted as ,origin With fixed coordinate system Origin overlap, and its Axis and fixed coordinate system Axis coincidence, coordinate system Transformation matrix relative to the fixed coordinate system It is expressed as follows: Where, Represents a coordinate system fixed at the first axis of axis; Indicates the i Drive angle of the branch chain; Indicates the i Driving angle error of the branch chain; Indicates the i Coordinate system of driving arm at the first axis of branch chain of x , y , z Unit vector in direction; S130, setting the coordinate system of the passive arm The origin is denoted as , where the coordinate system of Axis and fixed coordinate system Axis-parallel, passive arm coordinate system The attitude matrix relative to the fixed coordinate system It is expressed as follows: Where, Indicates the i Passive arm coordinate system at the second axis of the branch chain of axis; Indicates the i The driven angle of the branch chain; Indicates the i Driven angle error of the branch chain; Indicates the i Passive arm coordinate system at the second axis of the branch chain of x , y , z Unit vector in direction; S130, the coordinate system of the end effector is recorded as , whose origin is ; S140, the coaxial five-bar parallel mechanism can be obtained. i The vector equation of the branched chain is expressed as follows: Where, i The values ​​1 and 2 are recorded as branch numbers; The position vector representing the end; Indicates the i The length of the driving rod of the branch chain; Indicates the i The length of the passive rod of the branch chain; Indicates along The unit vector of the axis; represents the global unit vector; S150, set the assembly errors at the passive joint and the end effector to infinitesimal rods respectively and , rod length without error and All are 0, and when there is an error, it is expressed as and , we can get the first i The closed-loop vector equations of the branched chain are expressed as follows: Where, , ; Wherein, in the step S100, The first i The closed-loop vector equations of the branched chain are perturbed to obtain: Dot product on both sides of the above equation , =0, processing can be obtained: Where, Represents the error of the end effector ; Indicates the i The length error of the driving rod of the branch chain; Indicates the i The error of the driving angle of the branch chain; Indicates the i The passive rod length error of the branch chain; Where, Indicates the i The error source coefficient matrix of the branch chain; Indicates the i Error sources of the branch chain.

2. The method according to claim 1, characterized in that In step S200: By solving the error model of the parallel mechanism end effector, we can obtain and The solution expression of , and then obtain the volume error of the parallel mechanism end effector The variance of , which is expressed as follows: Where, , express The error sources; express No. m Rank n Column elements; The Jacobian matrix representing the error solution expression; Then we can get: Where, Indicates the q The local sensitivity index of each error source; Then q Global sensitivity index of each error source It is obtained by integrating in the workspace and is expressed as follows: Where, It represents the volume of a given working range of the end effector of the parallel mechanism.

3. The method according to claim 2, characterized in that In the step S300, Set up the first q Error Sources The corresponding sensitivity coefficient is , , and the required terminal error is Using sensitivity coefficient and error sources It is expressed as follows: Among them, the sensitivity coefficient of each error source is assumed to be , take the inverse of the sensitivity coefficient , and according to the error sources The proportion of all error sources in the total , the error values ​​assigned to each error source can be obtained , which is expressed as follows: 。 4. The method according to claim 3, characterized in that In the step S300, Based on the known required end error and the actual terminal error , redistribute each error source to Correction is performed to obtain the error values ​​after redistribution of each error source. , where the distribution value of each error source is .

5. The method according to claim 4, characterized in that In the step S400, Using the change in cost as the objective function, it is expressed as follows: Where, For the i The error cost function of each error source; After optimization i The error of the error source. 6 . A computer-readable storage medium having program instructions stored thereon, wherein the program instructions are executed by a processor to implement the method according to claim 1 .

7. The manufacturing precision control system of the parallel robot is characterized by: include: A computer device comprising the computer-readable storage medium according to claim 6.

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