Robot milling path planning method and system considering tool nose frequency response characteristic, medium and equipment

Abstract

The invention relates to the field of machining, and discloses a robot milling path planning method and system considering the tool nose frequency response characteristic, a medium and equipment, and the method comprises the steps that a robot rigidity model is established, and the rigidity value of the tail end of a robot is calculated; constructing a main shaft system model, and solving to obtain a tool nose frequency response function; calculating the inherent frequency of the tool nose under different rigidity configurations based on a tool nose frequency response function, and calculating the rigidity weight coefficient of the tail end of the robot so as to construct a rigidity direction optimization index SDOI; and on the basis of the rigidity direction optimization index, a robot milling path planning model is constructed, and a milling path containing the optimal rigidity characteristic of the robot is obtained. According to the method, the tool nose frequency response change characteristic is taken as an entry point, the weight coefficient of the influence degree of the robot tail end rigidity on the tool nose inherent frequency is established, then the robot rigidity direction optimization index SDOI is constructed to serve as the basis for making the milling path, and the stability and the machining efficiency of the milling process are ensured.

Description

Technical Field

[0001] The present invention relates to the field of mechanical processing technology, and in particular to a robot milling processing path planning method, system, medium and equipment taking into account the frequency response characteristics of a tool tip. Background Art

[0002] Serial industrial robots have the advantages of wide working space, strong operational flexibility, easy transportation and installation, and low cost. They have broad application prospects in the fields of aviation, aerospace, rail transportation, etc., especially robot milling processing technology, which has urgent practical needs in the integrated processing and manufacturing of large and complex components. However, due to the structural characteristics of serial industrial robots, their overall stiffness is low and has posture dependence, and the stiffness characteristics under different postures are significantly different. The robot milling processing path is related to its posture, so the stiffness of the robot end under different processing paths is very different, which in turn affects the frequency response characteristics of the tool tip. The frequency response characteristics of the tool tip directly affect the stability of the robot milling process. If the milling process produces unstable chatter, it will reduce the surface quality and dimensional accuracy of the workpiece, accelerate tool wear, and even damage the robot processing equipment in severe cases. Since the frequency response characteristics of the tool tip are the key factors to ensure the dynamic stability of the industrial robot milling process, in order to accurately analyze and control the stability of the processing process, when formulating the robot milling processing path, it is necessary to comprehensively consider the stiffness characteristics of the robot end along different coordinate axes and the influence of the tool tip frequency response changes. Therefore, how to establish the mapping relationship between the robot end stiffness, tool tip frequency response change and milling path, formulate the optimal processing path, and then improve the stability of the robot milling process is a key issue that needs to be solved urgently to ensure the robot milling processing accuracy and workpiece quality. Summary of the invention

[0003] In view of the above problems, the purpose of the present invention is to provide a robot milling machining path planning method, system, medium and equipment that takes into account the frequency response characteristics of the tool tip. It takes the characteristics of the change in the frequency response of the tool tip as the starting point, establishes the weight coefficient of the influence of the robot end stiffness on the natural frequency of the tool tip, and then constructs the robot stiffness direction optimization index SDOI (StiffnessDirectionOptimization Index) as the basis for formulating the milling machining path to ensure the stability and machining efficiency of the milling process.

[0004] To achieve the above-mentioned purpose, in the first aspect, the technical solution adopted by the present invention is: a robot milling processing path planning method considering the frequency response characteristics of the tool tip, which includes: establishing a robot stiffness model and calculating the stiffness value of the robot end; constructing a spindle system model and solving the tool tip frequency response function; based on the tool tip frequency response function, calculating the natural frequency of the tool tip under different stiffness configurations, and calculating the stiffness weight coefficient of the robot end to construct a stiffness direction optimization index; based on the stiffness direction optimization index, constructing a robot milling processing path planning model to obtain a milling processing path containing the optimal stiffness characteristics of the robot.

[0005] Furthermore, a robot stiffness model is established to calculate the robot end stiffness value, including:

[0006] Define the parameters of each joint and link of the robot, establish the robot kinematic model, and construct the Jacobian matrix;

[0007] The least square method is used to calculate the joint stiffness of the robot, and the robot stiffness model is constructed by combining the Jacobian matrix and the joint stiffness matrix.

[0008] A stiffness ellipsoid model is constructed based on the robot stiffness model;

[0009] The stiffness ellipsoid model is used to obtain the stiffness values ​​of the robot end flange along the x-axis, y-axis and z-axis in the global coordinate system.

[0010] Furthermore, the spindle system model is constructed to solve the tool tip frequency response function, including:

[0011] The finite element method is used to construct the spindle system model and couple the robot with the spindle system;

[0012] Input the robot end flange stiffness constraint and solve to obtain the tool tip frequency response function; the robot end flange stiffness constraint is the stiffness value of the robot end flange along the x-axis, y-axis and z-axis, and the stiffness value is constrained in the corresponding direction of the spindle system model connection block.

[0013] Furthermore, the optimization index of stiffness direction is constructed, including:

[0014] The natural frequency of the tool tip under different stiffness configurations is calculated using the spindle system model;

[0015] Apply excitation along the y-axis of the tool tip and repeatedly calculate the natural frequency of the tool tip under different stiffness configurations. After each change in the excitation and stiffness in a certain direction, record the change in the natural frequency of the tool tip along the x-axis, y-axis, and z-axis.

[0016] By analyzing the change values ​​of the natural frequency in each direction, the comprehensive influence of the stiffness change on the robot milling system is determined, so as to calculate the proportion of the change of the natural frequency of the tool tip along the x-axis, y-axis and z-axis to the total change as the weight coefficient;

[0017] The tool tip frequency response functions under multiple groups of different excitation directions and stiffness configurations are simulated and calculated, and the natural frequency difference obtained each time in the same vibration direction is accumulated and calculated as the total change of the natural frequency in the vibration direction to obtain the final robot end stiffness weight coefficient;

[0018] The final weight coefficient is multiplied by the stiffness values ​​of the robot's end along the x-axis, y-axis, and z-axis at the specified position to obtain the robot's stiffness direction optimization index.

[0019] Furthermore, based on the optimization index of the stiffness direction, a robot milling machining path planning model is constructed to obtain a milling machining path containing the optimal stiffness characteristics of the robot, including:

[0020] Based on the optimization index of the stiffness direction, the objective function of the path planning is constructed to obtain the maximum value of the optimization index of the stiffness direction at each tool position on the processing path;

[0021] The objective function is combined with the robot kinematic constraints to build a robot milling processing posture planning model; the robot kinematic constraints include joint limit constraints, motion smoothness constraints and singularity avoidance constraints;

[0022] A plurality of tool positions are generated according to the shape of the workpiece, and the plurality of tool positions are input into the robot milling process path planning model to obtain a milling process path including the optimal stiffness characteristics of the robot.

[0023] Furthermore, the objective function of path planning is: max[SDOI(γ)], where γ is the redundant Euler angle parameter of the robot end; SDOI is the stiffness direction optimization index.

[0024] Furthermore, the robot milling machining path optimization model is:

[0025]

[0026] In the formula, θ i represents the i-th joint angle, θ min and θ max They represent the maximum and minimum limits of the corresponding joint angles respectively; δ is the maximum allowable joint angular velocity, Δt is the time interval between adjacent tool positions; κ(J AW ) is the arm-wrist Jacobian matrix condition number, and η is the dexterity threshold.

[0027] In the second aspect, the technical solution adopted by the present invention is: a robot milling processing path planning system that takes into account the frequency response characteristics of the tool tip, which includes: a stiffness acquisition module, which establishes a robot stiffness model and calculates the stiffness value of the robot end; a frequency response function module, which constructs a spindle system model and solves the tool tip frequency response function; an index construction module, which calculates the natural frequency of the tool tip under different stiffness configurations based on the tool tip frequency response function, and calculates the stiffness weight coefficient of the robot end to construct a stiffness direction optimization index; a path acquisition module, which constructs a robot milling processing path planning model based on the stiffness direction optimization index to obtain a milling processing path that includes the robot's optimal stiffness characteristics.

[0028] In a third aspect, the technical solution adopted by the present invention is: a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, and when the instructions are executed by a computing device, the computing device executes any one of the above methods.

[0029] In a fourth aspect, the technical solution adopted by the present invention is: a computing device, comprising: one or more processors, a memory and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing any of the above methods.

[0030] The present invention adopts the above technical solution, which has the following advantages:

[0031] 1. The present invention couples the robot end stiffness model with the spindle system to model the tool tip frequency response function of the robot in different positions and postures.

[0032] 2. The present invention establishes a weight coefficient that can reflect the influence of the robot end stiffness on the natural frequency of the tool tip, thereby achieving a quantitative characterization of the influence of the robot end stiffness on the dynamic characteristics of the tool tip.

[0033] 3. The present invention combines stiffness optimization with frequency response analysis, which can more comprehensively reflect the changing laws of the dynamic characteristics during the robot milling process and improve the absolute accuracy of the robot milling process path.

[0034] 4. The present invention constructs the robot stiffness direction optimization index (SDOI), which can effectively measure the anti-vibration performance of each path point and provide a theoretical basis for formulating the optimal milling path. The present invention can significantly enhance the anti-vibration performance of the robot milling process and ensure the dynamic stability of the processing process.

[0035] 5. The present invention can be widely used in posture optimization and path planning of robot milling processing of complex components, expanding the application scope of industrial robots in the field of milling processing of complex components. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 is a flow chart of a robot milling machining path planning method considering the tool tip frequency response characteristics in an embodiment of the present invention;

[0037] Figure 2 is a flowchart of calculating the stiffness of the robot end in an embodiment of the present invention;

[0038] Figure 3 is a schematic diagram of a frequency response function of a tool tip in an embodiment of the present invention;

[0039] Figure 4 It is a flow chart for constructing the optimization index of the robot end stiffness direction in an embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to obtain the optimal processing path to improve the stability of the robot milling process, the present invention provides a robot milling processing path planning method, system, medium and equipment considering the frequency response characteristics of the tool tip. The present invention establishes a weight coefficient that can reflect the influence of the robot end stiffness on the natural frequency of the tool tip, and realizes the quantitative characterization of the influence of the robot end stiffness on the dynamic characteristics of the tool tip. The present invention combines stiffness optimization with frequency response analysis, which can more comprehensively reflect the law of dynamic characteristics changes in the robot milling process, thereby improving the absolute accuracy of the robot milling processing path. The present invention constructs a robot stiffness direction optimization index (SDOI), which can effectively measure the vibration resistance of each path point, and then formulate the optimal milling processing path. Compared with traditional methods, the present invention can significantly enhance the vibration resistance of the robot milling process and ensure the dynamic stability of the processing process; it can be widely used in the posture optimization and path planning of robot milling of complex components, and expand the application scope of industrial robots in the field of complex component milling.

[0041] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0042] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0043] In one embodiment of the present invention, a robot milling machining path planning method considering the tool tip frequency response characteristics is provided. In this embodiment, Figure 1 As shown, the method comprises the following steps:

[0044] 1) Establish a robot stiffness model and calculate the stiffness value of the robot end;

[0045] In this embodiment, Figure 2 As shown, the specific steps include:

[0046] 1.1) Define the parameters of each joint and link of the robot, establish the robot kinematic model, and construct the Jacobian matrix;

[0047] Specifically, in this embodiment, the improved Denavit-Hartenberg (DH) parameter method is used to define the parameters of each joint and link of the robot, and the Jacobian matrix J is constructed, as shown in formula (1);

[0048] J=[J 1 , J 2 , J 3 , J 4 , J 5 , J 6 ] (1)

[0049] Among them, J 1 ,J 2 ,J 3 ,J 4 ,J 5 ,J 6 Represent each sub-joint matrix respectively.

[0050] 1.2) Conduct stiffness identification experiments, then use the least squares method to calculate the robot joint stiffness, combine the Jacobian matrix and joint stiffness matrix, and build the robot stiffness model;

[0051] In this embodiment, the robot arm is simplified into a rigid link, and the robot joint is equivalent to a linear torsion spring with a constant stiffness coefficient. is the joint stiffness of the i-th joint, then the robot joint space stiffness can be expressed by a 6×6 diagonal matrix K θ To describe, that is:

[0052]

[0053] Where diag represents a diagonal matrix.

[0054] Furthermore, the stiffness matrix K of the industrial robot end effector can be described as:

[0055] K=J-T K θ J -1 (3)

[0056] Where J is the Jacobian matrix.

[0057] According to the physical meaning of the robot space Cartesian stiffness matrix, K is divided into four 3×3 sub-matrices:

[0058]

[0059] In the formula, K ft , K fr , K mt and K mr They are the force-linear displacement submatrix, the force-angular displacement submatrix, the moment-linear displacement submatrix and the moment-angular displacement submatrix respectively.

[0060] 1.3) Considering the force-line displacement sub-matrix, a stiffness ellipsoid model is constructed based on the robot stiffness model:

[0061]

[0062] Among them, λ 1 , 2 With λ 3 is the square root of the eigenvalue corresponding to the eigenvector.

[0063] 1.4) Use the stiffness ellipsoid model to obtain the stiffness value K of the robot end flange along the x-axis, y-axis and z-axis in the global coordinate system x , K y , K z .

[0064] 2) Construct the spindle system model and solve the tool tip frequency response function;

[0065] In this embodiment, the following steps are specifically included:

[0066] 2.1) The spindle system model is constructed using the finite element method to couple the robot with the spindle system;

[0067] Specifically, the finite element simulation software is used to construct the spindle system model, and the stiffness constraints of the robot end along the x-axis, y-axis and z-axis directions are applied to the end of the spindle model to simulate the stiffness support of the robot end to the spindle system.

[0068] 2.2) Input the robot end flange stiffness constraint and solve to obtain the tool tip frequency response function; the robot end flange stiffness constraint is the stiffness value of the robot end flange along the x-axis, y-axis and z-axis, and the stiffness value is constrained in the corresponding direction of the spindle system model connection block.

[0069] Specifically, in this embodiment, under a given robot posture, the stiffness values ​​of the robot end flange along the x-axis, y-axis and z-axis are calculated, and the above stiffness values ​​are constrained in the corresponding directions of the spindle model connection block, and then the frequency response function of the tool tip is obtained through model simulation calculation. For example, when the angles of each joint of the robot are (-19.30°, -80.17°, 113.28°, -32.20°, -38.33°, 26.51°), the stiffness values ​​of the robot end along the x-axis, y-axis and z-axis are obtained by the stiffness model, which are 5233N / mm, 2201N / mm and 3815N / mm respectively. When it is applied to the end of the spindle system model, the tool tip frequency response function is obtained, as shown in FIG. Figure 3 shown.

[0070] 3) Based on the tool tip frequency response function, the natural frequency of the tool tip under different stiffness configurations is calculated, and the stiffness weight coefficient of the robot end is calculated to construct the stiffness direction optimization index (SDOI);

[0071] In this embodiment, Figure 4 As shown in Figure 2, constructing the stiffness direction optimization index includes the following steps:

[0072] 3.1) The natural frequency of the tool tip under different stiffness configurations is calculated using the spindle system model;

[0073] In this embodiment, finite element software is used to apply excitations of the same magnitude but in different directions to the milling cutter part of the spindle system, and the stiffness values ​​of the robot end along the x-axis, y-axis and z-axis directions are adjusted in turn, and the weight coefficient of the influence of the stiffness of the robot end along different coordinate axes on the tool tip frequency response function is calculated.

[0074] For example, applying 10N excitation along the x-axis of the tool tip will reduce the stiffness of the robot end along the x-axis from the reference value K c Increase to target value K m , while keeping the stiffness in other directions unchanged, calculate the difference in the natural frequency of the tool tip under the two stiffness values; then change the stiffness values ​​of the robot end along the y-axis and z-axis in turn, and calculate the difference in the natural frequency of the tool tip under the corresponding parameters respectively.

[0075] 3.2) Apply excitation along the y-axis of the tool tip and repeatedly calculate the natural frequency of the tool tip under different stiffness configurations. After each change in the excitation and stiffness in a certain direction, record the change in the natural frequency of the tool tip along the x-axis, y-axis and z-axis;

[0076] For example, when the stiffness of the robot end along the x-axis, y-axis and z-axis increases by ΔK x , ΔK y With ΔK z Then, the total change of the natural frequency of the tool tip along the x-axis, y-axis and z-axis directions Δf is recorded in turn. x , Δfy , Δf z .

[0077] 3.3) By analyzing the change values ​​of the natural frequency in each direction, determine the comprehensive impact of the stiffness change on the robot milling system, and calculate the proportion of the change of the natural frequency of the tool tip along the x-axis, y-axis and z-axis to the total change as the weight coefficient;

[0078] Calculate the robot end stiffness weight coefficient as shown in equations (6)-(8).

[0079]

[0080] In the formula, ω x ,ω y ,ω z The weight coefficients are the influence of the stiffness values ​​of the robot end along the x-axis, y-axis and z-axis on the dynamic characteristics of the tool tip of the milling system. The higher the value, the greater the influence. The weight coefficient obtained by the present invention can directly reflect the contribution ratio of the stiffness of the robot end along the x-axis, y-axis and z-axis to the overall dynamic characteristics of the milling cutter.

[0081] 3.4) To ensure the accuracy of the weight coefficient, the tool tip frequency response function under multiple groups of different excitation directions and stiffness configurations is simulated and calculated, and the natural frequency difference obtained in the same vibration direction each time is accumulated and calculated as the total change of the natural frequency in the vibration direction to obtain the final robot end stiffness weight coefficient;

[0082] For example, according to the above method, the robot end stiffness weight coefficients obtained in this embodiment are ω x =0.465,ω y =0.183 and ω z =0.352.

[0083] 3.5) The final weight coefficient is multiplied by the stiffness values ​​of the robot's end along the x-axis, y-axis and z-axis at the specified position to obtain the robot's stiffness direction optimization index SDOI.

[0084] The stiffness direction optimization index SDOI is:

[0085] SDOI=ω x ·K x +ω y ·K y +ω z ·K z (9)

[0086] The SDOI value can be used as a basis for formulating the robot milling processing path to ensure that the robot's posture is always in the optimal stiffness value state to improve processing stability.

[0087] 4) Based on the optimization index of stiffness direction, a robot milling process path planning model is constructed to obtain a milling process path containing the optimal stiffness characteristics of the robot;

[0088] In this embodiment, the following steps are specifically included:

[0089] 4.1) Based on the stiffness direction optimization index, the objective function of path planning is constructed to obtain the maximum value of the stiffness direction optimization index at each tool position on the machining path;

[0090] In this embodiment, a path planning method based on the tool tip frequency response characteristics is proposed to solve the stiffness optimization problem of the industrial robot during the milling process. In this embodiment, the robot end position is optimized by maximizing the stiffness direction optimization index SDOI to improve the vibration resistance of the processing path and the stability of the milling process. According to the aforementioned stiffness direction optimization index SDOI, the objective function is defined to obtain the maximum value of the stiffness direction optimization index SDOI at each tool position on the processing path to ensure that the robot has the minimum vibration and the optimal stiffness on the milling path. Therefore, the objective function of path planning is:

[0091] max[SDOI(γ)],

[0092] Among them, γ is the redundant Euler angle parameter of the robot end; SDOI is the stiffness direction optimization index.

[0093] 4.2) Combine the objective function with the robot kinematic constraints to construct a robot milling processing posture planning model;

[0094] Among them, in order to ensure the stability and feasibility of the path planning model, the robot kinematic constraints include joint limit constraints, motion smoothness constraints and singularity avoidance constraints, etc. The details are as follows:

[0095] To prevent the robot joint angle from exceeding the limit range, it is necessary to ensure that the angle of each joint θ i Within the allowed joint limits (joint limit constraints):

[0096] θ min ≤θ i ≤θ max (10)

[0097] Among them, θ i represents the i-th joint angle, θ min and θ max They represent the maximum and minimum limit values ​​of the corresponding joint angles respectively.

[0098] In order to prevent the robot from vibrating due to excessive posture changes between adjacent tool positions, the constraint shown in equation (11) is introduced to ensure the smoothness of joint motion (motion smoothness constraint):

[0099]

[0100] Among them, δ is the maximum allowable joint angular velocity, and Δt is the time interval between adjacent tool positions.

[0101] The inverse of the Jacobian matrix condition number is used as the dexterity index to ensure that the robot posture is away from singular postures, thereby improving motion stability. The constraint condition (singularity avoidance constraint) is defined as:

[0102]

[0103] Among them, κ(J AW ) is the arm-wrist Jacobian matrix condition number, and η is the dexterity threshold, which is used to ensure that the robot maintains a high dexterity on the milling path and stays away from singular postures.

[0104] Combining the above constraints and redundant characteristics analysis, the robot milling machining path optimization model is finally established as follows:

[0105]

[0106] 4.3) Generate multiple tool locations according to the shape of the workpiece, and input the multiple tool locations into the robot milling process path planning model to obtain a milling process path that includes the optimal stiffness characteristics of the robot.

[0107] Through the above path planning model, the robot end position is optimized at a given tool position, so as to maximize the stiffness direction optimization index (SDOI) in a specific processing path, and achieve stiffness enhancement and chatter suppression. The present invention can effectively improve the dynamic stability of the robot milling process, thereby ensuring the efficiency and quality of the robot milling process.

[0108] In one embodiment of the present invention, a robot milling machining path planning system considering the tool tip frequency response characteristics is provided, which comprises:

[0109] Stiffness acquisition module, which establishes the robot stiffness model and calculates the stiffness value of the robot end;

[0110] Frequency response function module, builds the spindle system model and solves the tool tip frequency response function;

[0111] The index construction module calculates the natural frequency of the tool tip under different stiffness configurations based on the tool tip frequency response function, and calculates the stiffness weight coefficient of the robot end to construct the stiffness direction optimization index;

[0112] The path acquisition module constructs a robot milling path planning model based on the stiffness direction optimization index to obtain the milling path that contains the optimal stiffness characteristics of the robot.

[0113] In the above embodiment, the robot stiffness model is established to calculate the stiffness value of the robot end joint, including:

[0114] Define the parameters of each joint and link of the robot, establish the robot kinematic model, and construct the Jacobian matrix;

[0115] The least square method is used to calculate the joint stiffness of the robot, and the robot stiffness model is constructed by combining the Jacobian matrix and the joint stiffness matrix.

[0116] The stiffness ellipsoid model is used to obtain the stiffness values ​​of the robot end flange along the x-axis, y-axis and z-axis in the global coordinate system.

[0117] In the above embodiment, a spindle system model is constructed to obtain a tool tip frequency response function by solving the following:

[0118] The finite element method is used to construct the spindle system model and couple the robot with the spindle system;

[0119] Input the robot end flange stiffness constraint and solve to obtain the tool tip frequency response function; the robot end flange stiffness constraint is the stiffness value of the robot end flange along the x-axis, y-axis and z-axis, and the stiffness value is constrained in the corresponding direction of the spindle system model connection block.

[0120] In the above embodiment, constructing the stiffness direction optimization index includes:

[0121] The natural frequency of the tool tip under different stiffness configurations is calculated using the spindle system model;

[0122] Apply excitation along the y-axis of the tool tip and repeatedly calculate the natural frequency of the tool tip under different stiffness configurations. After each change in the excitation and stiffness in a certain direction, record the change in the natural frequency of the tool tip along the x-axis, y-axis, and z-axis.

[0123] By analyzing the change values ​​of the natural frequency in each direction, the comprehensive influence of the stiffness change on the robot milling system is determined, so as to calculate the proportion of the change of the natural frequency of the tool tip along the x-axis, y-axis and z-axis to the total change as the weight coefficient;

[0124] The tool tip frequency response functions under multiple groups of different excitation directions and stiffness configurations are simulated and calculated, and the natural frequency difference obtained each time in the same vibration direction is accumulated and calculated as the total change of the natural frequency in the vibration direction to obtain the final robot end stiffness weight coefficient;

[0125] The final weight coefficient is multiplied by the stiffness values ​​of the robot's end along the x-axis, y-axis, and z-axis at the specified position to obtain the robot's stiffness direction optimization index.

[0126] In the above embodiment, based on the stiffness direction optimization index, a robot milling machining path planning model is constructed to obtain a milling machining path including the optimal stiffness characteristics of the robot, including:

[0127] Based on the optimization index of the stiffness direction, the objective function of the path planning is constructed to obtain the maximum value of the optimization index of the stiffness direction at each tool position on the processing path;

[0128] The objective function is combined with the robot kinematic constraints to build a robot milling processing posture planning model; the robot kinematic constraints include joint limit constraints, motion smoothness constraints and singularity avoidance constraints;

[0129] A plurality of tool positions are generated according to the shape of the workpiece, and the plurality of tool positions are input into the robot milling process path planning model to obtain a milling process path including the optimal stiffness characteristics of the robot.

[0130] In this embodiment, the objective function of path planning is: max[SDOI(γ)], where γ is the redundant Euler angle parameter of the robot end; SDOI is the stiffness direction optimization index.

[0131] In this embodiment, the robot milling machining path optimization model is:

[0132]

[0133] In the formula, θ i represents the i-th joint angle, θ min and θ max They represent the maximum and minimum limits of the corresponding joint angles respectively; δ is the maximum allowable joint angular velocity, Δt is the time interval between adjacent tool positions; κ(J AW ) is the arm-wrist Jacobian matrix condition number, and η is the dexterity threshold.

[0134] The system provided in this embodiment is used to execute the above-mentioned method embodiments. Please refer to the above-mentioned embodiments for specific processes and detailed contents, which will not be repeated here.

[0135] A computing device provided in one embodiment of the present invention may be a terminal, which may include: a processor, a communications interface, a memory, a display screen, and an input device. The processor, the communications interface, and the memory communicate with each other through a communication bus. The processor is used to provide computing and control capabilities. The memory includes a non-volatile storage medium and an internal memory, wherein the non-volatile storage medium stores an operating system and a computer program, and the computer program is executed by the processor to implement the methods in the above embodiments; the internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be implemented through WIFI, a management network, NFC (near field communication) or other technologies. The display screen may be a liquid crystal display screen or an electronic ink display screen, and the input device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computing device, or an external keyboard, touchpad or mouse, etc. The processor may call the logic instructions in the memory.

[0136] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0137] In one embodiment of the present invention, a computer program product is provided, wherein the computer program product includes a computer program stored on a non-transitory computer-readable storage medium, wherein the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the methods provided by the above-mentioned method embodiments.

[0138] In one embodiment of the present invention, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium stores server instructions, and the computer instructions enable a computer to execute the methods provided in the above embodiments.

[0139] The above embodiment provides a computer-readable storage medium, whose implementation principle and technical effect are similar to those of the above method embodiment, and will not be repeated here.

[0140] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0141] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0142] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0143] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A robot milling machining path planning method considering the tool tip frequency response characteristics, characterized in that: include: Establish the robot stiffness model and calculate the stiffness value of the robot end; Construct the spindle system model and solve the tool tip frequency response function; The natural frequency of the tool tip under different stiffness configurations is calculated based on the tool tip frequency response function, and the stiffness weight coefficient of the robot end is calculated to construct the stiffness direction optimization index; Based on the optimization index of stiffness direction, a robot milling machining path planning model is constructed to obtain the milling machining path containing the optimal stiffness characteristics of the robot.

2. The robot milling machining path planning method considering the tool tip frequency response characteristics as claimed in claim 1, characterized in that: Establish the robot stiffness model and calculate the robot end stiffness value, including: Define the parameters of each joint and link of the robot, establish the robot kinematic model, and construct the Jacobian matrix; The least square method is used to calculate the joint stiffness of the robot, and the robot stiffness model is constructed by combining the Jacobian matrix and the joint stiffness matrix. A stiffness ellipsoid model is constructed based on the robot stiffness model; The stiffness ellipsoid model is used to obtain the stiffness values ​​of the robot end flange along the x-axis, y-axis and z-axis in the global coordinate system.

3. The robot milling machining path planning method considering the tool tip frequency response characteristics as claimed in claim 1, characterized in that: Construct the spindle system model and solve the tool tip frequency response function, including: The finite element method is used to construct the spindle system model and couple the robot with the spindle system; Input the robot end flange stiffness constraint and solve to obtain the tool tip frequency response function; the robot end flange stiffness constraint is the stiffness value of the robot end flange along the x-axis, y-axis and z-axis, and the stiffness value is constrained in the corresponding direction of the spindle system model connection block.

4. The robot milling machining path planning method considering the tool tip frequency response characteristics as claimed in claim 1, characterized in that: Construct stiffness direction optimization indicators, including: The natural frequency of the tool tip under different stiffness configurations is calculated using the spindle system model; Apply excitation along the y-axis of the tool tip and repeatedly calculate the natural frequency of the tool tip under different stiffness configurations. After each change in the excitation and stiffness in a certain direction, record the change in the natural frequency of the tool tip along the x-axis, y-axis, and z-axis. By analyzing the change values ​​of the natural frequency in each direction, the comprehensive influence of the stiffness change on the robot milling system is determined, so as to calculate the proportion of the change of the natural frequency of the tool tip along the x-axis, y-axis and z-axis to the total change as the weight coefficient; The tool tip frequency response functions under multiple groups of different excitation directions and stiffness configurations are simulated and calculated, and the natural frequency difference obtained each time in the same vibration direction is accumulated and calculated as the total change of the natural frequency in the vibration direction to obtain the final robot end stiffness weight coefficient; The final weight coefficient is multiplied by the stiffness values ​​of the robot's end along the x-axis, y-axis, and z-axis at the specified position to obtain the robot's stiffness direction optimization index.

5. The robot milling machining path planning method considering the tool tip frequency response characteristics as claimed in claim 1, characterized in that: Based on the optimization index of stiffness direction, a robot milling machining path planning model is constructed to obtain the milling machining path containing the optimal stiffness characteristics of the robot, including: Based on the optimization index of the stiffness direction, the objective function of the path planning is constructed to obtain the maximum value of the optimization index of the stiffness direction at each tool position on the processing path; The objective function is combined with the robot kinematic constraints to build a robot milling processing posture planning model; the robot kinematic constraints include joint limit constraints, motion smoothness constraints and singularity avoidance constraints; A plurality of tool positions are generated according to the shape of the workpiece, and the plurality of tool positions are input into the robot milling process path planning model to obtain a milling process path including the optimal stiffness characteristics of the robot.

6. The robot milling machining path planning method considering the tool tip frequency response characteristics as claimed in claim 5, characterized in that: The objective function of path planning is: max[SDOI(γ)], where γ is the redundant Euler angle parameter of the robot end; SDOI is the stiffness direction optimization index.

7. The robot milling machining path planning method considering the tool tip frequency response characteristics as claimed in claim 5, characterized in that: The robot milling machining path optimization model is: In the formula, θ i represents the i-th joint angle, θ min and θ max They represent the maximum and minimum limits of the corresponding joint angles respectively; δ is the maximum allowable joint angular velocity, Δt is the time interval between adjacent tool positions; κ(J AW ) is the arm-wrist Jacobian matrix condition number, and η is the dexterity threshold.

8. A robot milling machining path planning system considering the tool tip frequency response characteristics, characterized in that: include: Stiffness acquisition module, which establishes the robot stiffness model and calculates the stiffness value of the robot end; Frequency response function module, builds the spindle system model and solves the tool tip frequency response function; The index construction module calculates the natural frequency of the tool tip under different stiffness configurations based on the tool tip frequency response function, and calculates the stiffness weight coefficient of the robot end to construct the stiffness direction optimization index; The path acquisition module constructs a robot milling path planning model based on the stiffness direction optimization index to obtain the milling path that contains the optimal stiffness characteristics of the robot.

9. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include instructions, which, when executed by a computing device, cause the computing device to perform any one of the methods of claims 1 to 7.

10. A computing device, characterized in that: include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing any one of the methods described in claims 1 to 7.

Images