Robot polishing trajectory planning method, device and equipment for complex component and medium

Abstract

The present application relates to a kind of complex component robot grinding and polishing trajectory planning method, device, equipment and medium, its method includes: obtaining the path to be processed of complex component and corresponding robot posture data;Based on the bending tilt angle in the robot processing and the bending tilt rate of change of path, the processing stability requirement of robot is calculated, the processing efficiency requirement of robot is calculated based on the posture rigidity performance in the robot processing, and according to processing stability requirement and processing efficiency requirement, robot self-adapting weight function is established;With the processing stability and processing efficiency in self-adapting weight function as objective function, with the physical constraint of robot as constraint condition, trajectory planning model is constructed in combination with preset trajectory generation algorithm;Based on the preset intelligent optimization algorithm, the trajectory planning model is solved, and robot target grinding and polishing trajectory is generated.The present application improves the accurate control of grinding and polishing trajectory by self-adapting adjustment to the weight of grinding and polishing efficiency and processing stability objective function.

Description

Technical Field

[0001] This invention relates to the field of robot trajectory planning technology, and in particular to a method, apparatus, electronic device, and storage medium for planning the grinding and polishing trajectory of a complex component robot with adaptive weights. Background Technology

[0002] After complex components are cast, numerous burrs of varying sizes are distributed at the parting line, which generally require machining to remove. Due to the large size, complex shape, and varied machining paths of complex components, automated machining by robots is quite difficult.

[0003] Conventional trajectory planning algorithms set fixed objective function weights, which can easily lead to problems such as excessive time spent on simple paths or insufficient processing stability for complex paths. Therefore, manufacturing companies usually use manual operations for edge polishing, which has low processing efficiency and cannot meet the company's production pace. In addition, the metal dust generated during the processing can also affect the physical and mental health of workers.

[0004] Therefore, how to accurately control the robot's movement trajectory during the grinding and polishing process to ensure processing stability is a technical problem that urgently needs to be solved. Summary of the Invention

[0005] In view of this, it is necessary to provide a method, device, electronic device and storage medium for planning the grinding and polishing trajectory of a robot with adaptive weights for complex components, so as to accurately control the robot's motion trajectory during the grinding and polishing process, so as to ensure processing stability and processing efficiency.

[0006] To achieve the above objectives, in a first aspect, the present invention provides a method for planning the grinding and polishing trajectory of a robot for complex components with adaptive weights, comprising:

[0007] Obtain the processing path of complex components and the corresponding robot posture data;

[0008] The robot's processing stability requirements are calculated based on the bending and tilting angles and the bending and tilting change rate of the path during robot processing. The robot's processing efficiency requirements are calculated based on the posture stiffness performance during robot processing. An adaptive weighting function for the robot is then constructed based on the processing stability requirements and processing efficiency requirements.

[0009] Using the processing stability and processing efficiency in the adaptive weight function as the objective function, the physical constraints of the robot as the constraint condition, and a preset trajectory generation algorithm to construct a trajectory planning model;

[0010] The trajectory planning model is solved based on a preset intelligent optimization algorithm to generate the robot's target grinding and polishing trajectory.

[0011] Furthermore, acquiring the processing path of the complex component and the corresponding robot posture data includes:

[0012] Obtain the processing path of complex components generated in the simulation workstation;

[0013] Based on the processing path, adjust the robot's processing posture to be collision-free and singular-free, and obtain the robot posture data corresponding to the coordinates of the points on the processing path.

[0014] Furthermore, the calculation of the robot's machining stability requirements based on the bending tilt angle and the rate of change of the bending tilt along the path during robot machining includes:

[0015] Construct an interval mutation rate function based on the bending angle and bending change rate of the path to be processed in the horizontal plane;

[0016] Construct a line-surface gradient function based on the tilt angle and tilt change rate of the path to be processed;

[0017] The machining stability requirements of the robot are calculated based on the interval mutation rate and the line-surface gradient.

[0018] Furthermore, the calculation of the robot's processing efficiency requirements based on the posture stiffness performance in robot processing includes:

[0019] Based on the stiffness performance of the robot in different postures, construct the robot's average posture stiffness function;

[0020] The machining stability requirements of the robot are calculated based on the robot's average posture stiffness.

[0021] Furthermore, the step of constructing an adaptive weight function for the robot based on the processing stability requirements and processing efficiency requirements includes:

[0022] An adaptive weighting function for the robot is established based on the interval mutation rate, line-surface gradient, and average attitude stiffness:

[0023] Among them, w J As a weight for processing stability, w t As the processing efficiency weight, β is the interval mutation rate, Let γ be the line-surface gradient and γ be the average stiffness of the robot's posture.

[0024] Furthermore, the objective function is: minF(t) = w t f1+w J f2, where f1 is the processing efficiency objective function and f2 is the processing stability objective function;

[0025] The constraints are as follows: Where, θ iLet θ be the angle of the i-th joint angle. imin and θ imax Let ω be the minimum and maximum angles of the i-th joint angle, and ω be the minimum value. i Let ω be the angular velocity of the i-th joint angle. imax It represents the maximum angular velocity of the i-th joint angle, where i is the joint angle index.

[0026] Furthermore, the method also includes:

[0027] The robot's initial grinding and polishing trajectory is generated by calculating the path to be processed based on the preset trajectory generation algorithm.

[0028] Secondly, the present invention also provides an adaptive weighted robotic grinding and polishing trajectory planning device for complex components, comprising:

[0029] The data acquisition module is used to acquire the processing path of complex components and the corresponding robot posture data;

[0030] The function construction module is used to calculate the robot's processing stability requirements based on the bending tilt angle and the bending tilt change rate of the path in robot processing, calculate the robot's processing efficiency requirements based on the robot's posture stiffness performance in robot processing, and construct an adaptive weight function for the robot based on the processing stability requirements and processing efficiency requirements.

[0031] The model building module is used to construct a trajectory planning model by taking the processing stability and processing efficiency in the adaptive weight function as the objective function, the physical constraints of the robot as the constraint condition, and combining the preset trajectory generation algorithm.

[0032] The trajectory generation module is used to solve the trajectory planning model based on a preset intelligent optimization algorithm to generate the robot target grinding and polishing trajectory.

[0033] Thirdly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps in the above-mentioned adaptive weighted complex component robot grinding and polishing trajectory planning method.

[0034] Fourthly, the present invention also provides a computer storage medium storing a computer program, which, when executed by a processor, implements the steps in the above-described adaptive weighted complex component robot grinding and polishing trajectory planning method.

[0035] The beneficial effects of using the above embodiments are:

[0036] This invention acquires the processing path of complex components, fully considering the characteristics of complex component processing surfaces and variable paths, thus improving the adaptability of the robot's grinding and polishing trajectory. Then, it acquires the robot's posture data corresponding to the processing path, determines the robot's processing posture stiffness performance based on the posture data, and constructs an adaptive weight function. By constructing the adaptive weight function through processing path characteristics and robot posture stiffness performance, more precise control of the robot's trajectory during the grinding and polishing process is achieved. While improving the adaptability of robot grinding and polishing and ensuring processing stability, it effectively improves the processing efficiency and effect of robot grinding and polishing of complex components, demonstrating strong practical value. Attached Figure Description

[0037] Figure 1 A flowchart illustrating an embodiment of a complex component robot grinding and polishing trajectory planning method considering adaptive weights provided by the present invention;

[0038] Figure 2 This is a schematic diagram of the processing path for an automobile flywheel housing according to an embodiment of the present invention;

[0039] Figure 3 This is a schematic diagram of the interval mutation rate provided in an embodiment of the present invention;

[0040] Figure 4 This is a schematic diagram of a line-surface gradient provided in an embodiment of the present invention;

[0041] Figure 5 This is a processing experiment diagram provided in one embodiment of the present invention;

[0042] Figure 6 A schematic diagram of a structural embodiment of the adaptive weighted complex component robot grinding and polishing trajectory planning device provided by the present invention;

[0043] Figure 7 This is a schematic diagram of the structure of an electronic device provided by the present invention. Detailed Implementation

[0044] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0045] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. Furthermore, "a plurality of" means two or more, unless otherwise explicitly specified. The reference to "embodiment" herein means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0046] This invention provides a method, device, electronic device, and storage medium for planning the grinding and polishing trajectory of a robot for complex components, which considers adaptive weights. By establishing an adaptive weight function to quantitatively analyze the processing stability and efficiency requirements of the processing path, the robot can more accurately control the motion trajectory during the grinding and polishing process, thereby effectively improving processing efficiency and processing effect while ensuring processing stability.

[0047] The specific embodiments are described in detail below:

[0048] Please see Figure 1 , Figure 1 This is a flowchart illustrating an embodiment of a complex component robot grinding and polishing trajectory planning method considering adaptive weights provided by the present invention. A specific embodiment of the present invention discloses a complex component robot grinding and polishing trajectory planning method considering adaptive weights, comprising:

[0049] Step S101: Obtain the processing path of the complex component and the corresponding robot posture data;

[0050] Step S102: Calculate the robot's processing stability requirements based on the bending tilt angle and the bending tilt change rate of the path in robot processing, calculate the robot's processing efficiency requirements based on the robot's posture stiffness performance in robot processing, and construct an adaptive weight function for the robot based on the processing stability requirements and processing efficiency requirements.

[0051] Step S103: Using the processing stability and processing efficiency in the adaptive weight function as the objective function, the physical constraints of the robot as the constraint condition, and a preset trajectory generation algorithm to construct a trajectory planning model;

[0052] Step S104: Solve the trajectory planning model based on the preset intelligent optimization algorithm to generate the robot target grinding and polishing trajectory.

[0053] This invention acquires the processing path of complex components, fully considering the characteristics of complex component processing surfaces and variable paths, thus improving the adaptability of the robot's grinding and polishing trajectory. Then, it acquires the robot's posture data corresponding to the processing path, determines the robot's processing posture stiffness performance based on the posture data, and constructs an adaptive weight function. By constructing the adaptive weight function through processing path characteristics and robot posture stiffness performance, more precise control of the robot's trajectory during the grinding and polishing process is achieved. While improving the adaptability of robot grinding and polishing and ensuring processing stability, it effectively improves the processing efficiency and effect of robot grinding and polishing of complex components, demonstrating strong practical value.

[0054] In one embodiment of the present invention, obtaining the processing path of the complex component and the corresponding posture data of the robot includes:

[0055] Obtain the processing path of complex components generated in the simulation workstation;

[0056] Based on the processing path, adjust the robot's processing posture to be collision-free and singular-free, and obtain the robot posture data corresponding to the coordinates of the points on the processing path.

[0057] First, it should be noted that this invention is primarily used in simulation workstations. A simulation workstation is created using RobotStudio 3D software, and CAD models of complex components and robot end effector models are imported. Specifically, taking a car flywheel housing as an example, the CAD model of the car flywheel housing is adjusted to fit within the robot's reachable working range. Please refer to [link to relevant documentation]. Figure 2 , Figure 2 This is a schematic diagram of the processing path for an automobile flywheel housing according to an embodiment of the present invention.

[0058] Understandably, the simulation workstation autonomously generates machining paths for complex components. By adjusting the robot's machining posture to avoid collisions and singularities, the coordinates of the machining path points and the corresponding robot posture information are recorded and exported. Specifically, when generating the robot's grinding and polishing path, the normal direction of the milling cutter is aligned with the car flywheel housing. Furthermore, by adjusting the axis configuration and offsetting the radius of the end effector, the robot and end effector are made to avoid collisions with the car flywheel housing, and there are no singularities or unreachable points during the movement.

[0059] In one embodiment of the present invention, the calculation of the robot's processing stability requirements based on the bending tilt angle and the bending tilt change rate of the path during robot processing includes:

[0060] Construct an interval mutation rate function based on the bending angle and bending change rate of the path to be processed in the horizontal plane;

[0061] Construct a line-surface gradient function based on the tilt angle and tilt change rate of the path to be processed;

[0062] The machining stability requirements of the robot are calculated based on the interval mutation rate and the line-surface gradient.

[0063] It is understandable that the required stability of robot processing can be calculated based on the degree of bending and tilting in the robot processing and the degree of bending and tilting changes in the path before and after it.

[0064] Specifically, based on the curvature of the path to be processed in the horizontal plane and the degree of change before and after it, an interval abrupt change rate function is constructed. Please refer to [link / reference needed]. Figure 3 , Figure 3 This is a schematic diagram of the interval mutation rate provided in an embodiment of the present invention.

[0065] The interval mutation rate function is:

[0066] Where β is the interval mutation rate, and Cn is the curvature of the nth and (n+1)th processed line segments. k′ is the first derivative of the slope of the line segment, k″ is the second derivative of the slope of the line segment, and l is the number of intervals;

[0067] Then, based on the slope of the path and the magnitude of the change in slope before and after it, construct the line-surface gradient function. (See [link to relevant documentation]). Figure 4 , Figure 4 This is a schematic diagram of a line-surface gradient provided in an embodiment of the present invention.

[0068] The line-surface gradient function is:

[0069] in, For the line-surface gradient, α n Let be the angle between the nth machining line segment and the robot base reference plane, defined in the range of 0° to 90°.

[0070] In one embodiment of the present invention, the calculation of the robot's processing efficiency requirements based on the posture stiffness performance in robot processing includes:

[0071] Based on the stiffness performance of the robot in different postures, construct the robot's average posture stiffness function;

[0072] The machining stability requirements of the robot are calculated based on the robot's average posture stiffness.

[0073] Understandably, the required grinding and polishing efficiency of the robot is calculated based on the robot's posture stiffness performance during the processing. Specifically, firstly, an average posture stiffness function is constructed based on the stiffness performance of the robot in different postures, and then the processing stability requirements of the robot are calculated based on the average posture stiffness of the robot.

[0074] From the static stiffness model of the robot, we know that: K c =J -T (K θ -K f )J -1 K c Let K be the Cartesian stiffness matrix, J be the Jacobian matrix, and K be the Cartesian stiffness matrix. θ K is the joint angular stiffness matrix. f This is the stiffness compensation matrix.

[0075] Since the influence of the stiffness compensation matrix is ​​very small, it can be ignored. Therefore, K c =J -T K θ J -1 Based on the formula for force and stiffness, the formula Kc = J -T K θ J -1 Extending this, we get: F = J -T K θ J -1 X. Where F is the force acting on the robot's end effector, and X is the displacement produced by the force acting on the robot's end effector.

[0076] F = J -T K θ J -1 The X-transform yields:

[0077] Extraction Formula The coefficients are used to construct the compliance matrix: Where C is the compliance coefficient matrix, C tt C is a 3rd order translation compliance submatrix. tr C is a 3rd order coupling compliance submatrix. rr It is a 3rd order rotational compliance submatrix. The deformation caused by the torque is very small and can be ignored.

[0078] Therefore, the formula The simplified formula is d = C tt f.

[0079] Based on the robot's joint stiffness matrix, the robot's stiffness performance function is obtained:

[0080] From the robot stiffness performance function constructed above, the average attitude stiffness function can be obtained: γ=(E k(n) +E k(n+1) ) / 2, where γ is the average stiffness of the robot's posture, E k(n) Let represent the stiffness performance of the robot's posture at the nth processing point.

[0081] Then, an adaptive weight function for the robot is constructed based on the processing stability requirements and processing efficiency requirements.

[0082] Specifically, an adaptive weighting function for the robot is established based on the interval mutation rate, line-surface gradient, and average attitude stiffness:

[0083] Among them, w J As a weight for processing stability, w t As the processing efficiency weight, β is the interval mutation rate, Let γ be the line-surface gradient and γ be the average stiffness of the robot's posture.

[0084] In one embodiment of the present invention, the objective function is: minF(t) = w t f1+w J f2, where f1 is the processing efficiency objective function and f2 is the processing stability objective function;

[0085] The constraints are as follows: Where, θ i Let θ be the angle of the i-th joint angle. imin and θ imax Let ω be the minimum and maximum angles of the i-th joint angle, and ω be the minimum value. i Let ω be the angular velocity of the i-th joint angle. imax It represents the maximum angular velocity of the i-th joint angle, where i is the joint angle index.

[0086] Understandably, in the process of constructing the trajectory planning model, a preset cubic polynomial trajectory generation algorithm and an adaptive weight function can be used. That is, a multi-trajectory planning model can be constructed with robot grinding and polishing efficiency and processing stability as objective functions and robot physical constraints as constraint conditions.

[0087] Specifically, based on the proposed adaptive weighting function, the objective function is constructed as: min F(t) = w t f1+w J f2

[0088] Where f1 is the objective function for processing efficiency and f2 is the objective function for processing stability.

[0089] Based on the robot's physical constraints, the constraints of the trajectory planning model are obtained: Where, θ i Let θ be the angle of the i-th joint angle. imax and θ imin ω represents the maximum and minimum angles of the i-th joint angle. i Let ω be the angular velocity of the i-th joint angle. imaxThis represents the maximum permissible angular velocity of the i-th joint angle.

[0090] Please refer to Table 1, which shows the physical constraints of the IRB6700 robot.

[0091] Table 1 Physical Constraints of IRB6700 Robot

[0092]

[0093]

[0094] Based on the objective function and constraints described in the above steps, a trajectory planning model can be established: min F(t) = w t f1(t)+w J f2(t)

[0095]

[0096] Then, the established trajectory planning model based on adaptive weights is solved using a preset intelligent optimization model, such as the COVIDOA intelligent optimization algorithm, to generate the robot's optimal motion trajectory. Finally, the car flywheel housing is processed. Please refer to [link / reference]. Figure 5 , Figure 5 This is a processing experiment diagram provided in one embodiment of the present invention.

[0097] Furthermore, it should be noted that, in one embodiment of the present invention, the path to be processed can also be calculated based on the preset trajectory generation algorithm to generate the robot's initial grinding and polishing trajectory.

[0098] Understandably, for the processing path points of the generated complex components, a cubic polynomial function can be used to generate the robot's initial grinding and polishing trajectory, so as to compare it with the target grinding and polishing trajectory. In particular, this invention uses a cubic polynomial to generate the robot's grinding and polishing trajectory, making the acceleration derivative of its joint angles constant, the acceleration continuous, the motion impact-free, and the joint angle wear small.

[0099] To better implement the adaptive weighted trajectory planning method for grinding and polishing complex components in this invention, based on the adaptive weighted trajectory planning method for grinding and polishing complex components in this invention, please refer to the corresponding documentation. Figure 6 , Figure 6 This is a schematic diagram of an embodiment of the adaptive weighted complex component robot grinding and polishing trajectory planning device provided by the present invention. The embodiment of the present invention provides an adaptive weighted complex component robot grinding and polishing trajectory planning device 600, comprising:

[0100] The data acquisition module 601 is used to acquire the processing path of complex components and the corresponding robot posture data;

[0101] The function construction module 602 is used to calculate the robot's processing stability requirements based on the bending tilt angle and the bending tilt change rate of the path in robot processing, calculate the robot's processing efficiency requirements based on the robot's posture stiffness performance in robot processing, and construct an adaptive weight function for the robot based on the processing stability requirements and processing efficiency requirements.

[0102] The model building module 603 is used to build a trajectory planning model by taking the processing stability and processing efficiency in the adaptive weight function as the objective function, the physical constraints of the robot as the constraint condition, and combining the preset trajectory generation algorithm.

[0103] The trajectory generation module 604 is used to solve the trajectory planning model based on a preset intelligent optimization algorithm to generate the robot target grinding and polishing trajectory.

[0104] It should be noted that the device 600 provided in the above embodiments can implement the technical solutions described in the above method embodiments. The specific implementation principles of each module or unit can be found in the corresponding content in the above method embodiments, and will not be repeated here.

[0105] Based on the above-described adaptive weighted trajectory planning method for complex component robot grinding and polishing, this invention also provides an electronic device, including: a processor and a memory, and a computer program stored in the memory and executable on the processor; when the processor executes the computer program, it implements the steps in the adaptive weighted trajectory planning method for complex component robot grinding and polishing as described in the above embodiments.

[0106] Figure 7 The diagram shows a structural schematic of an electronic device 700 suitable for implementing embodiments of the present invention. The electronic device in the embodiments of the present invention may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 7 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0107] The electronic device includes a memory and a processor, wherein the processor may be referred to as processing device 701 below, and the memory may include at least one of read-only memory (ROM) 702, random access memory (RAM) 703 and storage device 708 below, as detailed below:

[0108] like Figure 7As shown, the electronic device 700 may include a processing unit (e.g., a central processing unit, a graphics processor, etc.) 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage device 708 into a random access memory (RAM) 703. The RAM 703 also stores various programs and data required for the operation of the electronic device 700. The processing unit 701, ROM 702, and RAM 703 are interconnected via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0109] Typically, the following devices can be connected to I / O interface 705: input devices 706 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 707 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 708 including, for example, magnetic tapes, hard disks, etc.; and communication devices 709. Communication device 709 allows electronic device 700 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 7 An electronic device 700 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0110] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 709, or installed from a storage device 708, or installed from a ROM 702. When the computer program is executed by the processing device 701, it performs the functions defined in the methods of the embodiments of the present invention.

[0111] Based on the above-described adaptive weighted trajectory planning method for complex component robot grinding and polishing, this invention also provides a computer-readable storage medium storing one or more programs that can be executed by one or more processors to implement the steps in the adaptive weighted trajectory planning method for complex component robot grinding and polishing as described in the above embodiments.

[0112] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0113] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for planning the grinding and polishing trajectory of a robot for complex components with adaptive weights, characterized in that, include: Obtain the processing path of complex components and the corresponding robot posture data; The robot's processing stability requirements are calculated based on the bending and tilting angles and the bending and tilting rate of the path during robot processing. The robot's processing efficiency requirements are calculated based on the posture stiffness performance during robot processing. An adaptive weighting function for the robot is then constructed based on the processing stability requirements and processing efficiency requirements. Using the processing stability and processing efficiency in the adaptive weight function as the objective function, the physical constraints of the robot as the constraint condition, and a preset trajectory generation algorithm to construct a trajectory planning model; The trajectory planning model is solved based on a preset intelligent optimization algorithm to generate the robot's target grinding and polishing trajectory. The calculation of the robot's machining stability requirements based on the bending tilt angle and the bending tilt change rate of the path during robot machining includes: Construct an interval mutation rate function based on the bending angle and bending change rate of the path to be processed in the horizontal plane; Construct a line-surface gradient function based on the tilt angle and tilt change rate of the path to be processed; The machining stability requirements of the robot are calculated based on the interval mutation rate and the line-surface gradient. The calculation of the robot's processing efficiency requirements based on the attitude stiffness performance in robot machining includes: Based on the stiffness performance of the robot in different postures, construct the robot's average posture stiffness function; The robot's processing efficiency requirements are calculated based on the robot's average attitude stiffness. The step of constructing an adaptive weight function for the robot based on the processing stability requirements and processing efficiency requirements includes: An adaptive weighting function for the robot is established based on the interval mutation rate, line-surface gradient, and average attitude stiffness: ,in, w J As a weight for processing stability, w t As a weight for processing efficiency, For interval mutation rate, For line-surface gradient, This represents the average attitude stiffness of the robot.

2. The adaptive weighted trajectory planning method for grinding and polishing complex components by a robot according to claim 1, characterized in that, The process of acquiring the processing path of the complex component and the corresponding robot posture data includes: Obtain the processing path of complex components generated in the simulation workstation; Based on the processing path, adjust the robot's processing posture to avoid collisions and singularities, and obtain the robot posture data corresponding to the coordinates of the points on the processing path.

3. The adaptive weighted trajectory planning method for grinding and polishing complex components by a robot according to claim 1, characterized in that, The objective function is: ,in, Let the processing efficiency be the objective function. The objective function is the processing stability function; The constraints are as follows: ,in, For the first The angle of each joint angle and For the first Minimum and maximum values ​​of the joint angles For the first angular velocity of each joint angle For the first The maximum angular velocity of each joint angle This refers to the joint angle number.

4. The adaptive weighted trajectory planning method for grinding and polishing complex components by a robot according to claim 1, characterized in that, The method further includes: The robot calculates the path to be processed based on a preset intelligent optimization algorithm to generate the initial grinding and polishing trajectory.

5. A robotic grinding and polishing trajectory planning device for complex components with adaptive weights, characterized in that, include: The data acquisition module is used to acquire the processing path of complex components and the corresponding robot posture data; The function construction module is used to calculate the robot's processing stability requirements based on the bending tilt angle and the bending tilt change rate of the path in robot processing, calculate the robot's processing efficiency requirements based on the robot's posture stiffness performance in robot processing, and construct the robot's adaptive weight function based on the processing stability requirements and processing efficiency requirements. The model building module is used to construct a trajectory planning model by taking the processing stability and processing efficiency in the adaptive weight function as the objective function, the physical constraints of the robot as the constraint condition, and combining the preset trajectory generation algorithm. The trajectory generation module is used to solve the trajectory planning model based on a preset intelligent optimization algorithm to generate the robot target grinding and polishing trajectory. The calculation of the robot's machining stability requirements based on the bending tilt angle and the bending tilt change rate of the path during robot machining includes: Construct an interval mutation rate function based on the bending angle and bending change rate of the path to be processed in the horizontal plane; Construct a line-surface gradient function based on the tilt angle and tilt change rate of the path to be processed; The machining stability requirements of the robot are calculated based on the interval mutation rate and the line-surface gradient. The calculation of the robot's processing efficiency requirements based on the attitude stiffness performance in robot machining includes: Based on the stiffness performance of the robot in different postures, construct the robot's average posture stiffness function; The robot's processing efficiency requirements are calculated based on the robot's average attitude stiffness. The step of constructing an adaptive weight function for the robot based on the processing stability requirements and processing efficiency requirements includes: An adaptive weighting function for the robot is established based on the interval mutation rate, line-surface gradient, and average attitude stiffness: ,in, w J As a weight for processing stability, w t As a weight for processing efficiency, For interval mutation rate, For line-surface gradient, This represents the average attitude stiffness of the robot.

6. An electronic device, characterized in that, The system includes a memory and a processor, wherein the memory is used to store a program; and the processor is coupled to the memory to execute the program stored in the memory to implement the steps in the adaptive weighted complex component robot grinding and polishing trajectory planning method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, Used to store computer-readable programs or instructions, which, when executed by a processor, are capable of implementing the steps in the adaptive weighted complex component robot grinding and polishing trajectory planning method described in any one of claims 1 to 4.

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