Industrial robot mechanical arm control precision coordination optimization method and device

Abstract

The invention relates to the technical field of industrial robot control, and discloses an industrial robot mechanical arm control precision coordination optimization method, device and equipment and a computer program product. The method comprises the steps of obtaining an initial position and an initial angular velocity of a joint based on a D-H parameter method, and performing optimization fitting on a joint trajectory through an improved Bezier curve; dynamically calculating an optimal control path by adopting a particle swarm optimization algorithm; and dynamically adjusting the parameters of the joint controller according to the real-time optimized joint motion trail data in combination with the response time of the servo control system. Compared with the problem that high-precision track control cannot be achieved under the complex working condition in the traditional technology, the method has the advantages that through combined use of improved Bezier curve fitting and the particle swarm optimization algorithm, precise control over the track of the mechanical arm is achieved, and therefore the problem of track deviation is avoided, and the control precision of the mechanical arm is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of industrial robot control, and in particular relates to a method and a device for coordinating and optimizing the control accuracy of an industrial robot mechanical arm. Background Art

[0002] At present, with the continuous improvement of the level of industrial automation, industrial robot manipulators are increasingly widely used in scenes such as manufacturing, assembly and operation. However, the existing manipulator control technology has significant deficiencies under complex working conditions. For example, when the manipulator executes a complex motion trajectory, it is often difficult to maintain the trajectory control accuracy due to factors such as environmental noise, load changes, and mechanical wear, resulting in the actual motion trajectory of the end effector deviating from expectations, affecting production efficiency and product quality. In addition, when dealing with high-speed motion or coordinated control of multi-degree-of-freedom manipulators, the existing technology is difficult to adjust the control parameters in real time, the response speed is insufficient, and it cannot adapt to the precise control requirements in high-dynamic environments. Traditional trajectory control methods, such as those based on PID control or simple kinematic equation calculations, can perform well in static or simple tasks, but when dealing with complex nonlinear motion, dynamic loads or environmental changes, there are often problems with reduced control accuracy and stability, especially in scenarios involving multi-joint linkage or high-degree-of-freedom manipulators, which cannot meet the real-time and high-precision control requirements. Therefore, there is an urgent need for a method that can achieve high-precision trajectory control under complex working conditions, so as to improve the control accuracy and dynamic response capability of the robotic arm to meet the high requirements for intelligent manufacturing and precision operation in modern industrial automation. Summary of the invention

[0003] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to propose a method for coordinated optimization of the control accuracy of an industrial robot manipulator arm, aiming to solve the problem in the prior art that control accuracy and stability are often reduced when dealing with complex nonlinear motions, coping with dynamic loads or environmental changes, especially in scenarios involving multi-joint linkage or high-degree-of-freedom manipulator arms, and the technical problem of being unable to meet real-time, high-precision control requirements.

[0004] In order to solve the above technical problems, the present invention adopts the following technical solutions: The present invention provides a method for coordinating and optimizing the control accuracy of an industrial robot manipulator arm.

[0005] The method comprises:

[0006] Step S10: Based on the DH parameter method, the kinematic equation of the robot arm is established to obtain the initial position data and initial angular velocity data of the joint, and the joint trajectory is optimized and fitted using the improved Bezier curve to obtain preliminary optimized joint motion trajectory data;

[0007] Step S20: using the position sensor and the velocity sensor to obtain feedback data of the real-time position data and the real-time angular velocity data of the robot arm joint, and using the Kalman filter algorithm to eliminate environmental noise and sensor measurement errors to obtain the corrected position data and corrected velocity data of the robot arm joint;

[0008] Step S30: combining the corrected position data and the corrected speed data, using a particle swarm optimization algorithm to dynamically calculate an optimized control path, and adjusting the preliminary optimized joint motion trajectory data in real time according to the calculation result to obtain real-time optimized joint motion trajectory data;

[0009] Step S40: dynamically adjusting the joint controller parameters of the servo control system according to the real-time optimized joint motion trajectory data combined with the response time of the servo control system.

[0010] Preferably, the step of optimizing the joint trajectory by using the improved Bezier curve comprises:

[0011] By Bezier curve formula Fit the joint trajectory, where B i,n (t) is the Bessel basis function, P i is the control point, n is the order of the Bezier curve, and i is an index variable in the Bezier curve formula, indicating the sequence number of the Bezier basis function and control point currently being processed;

[0012] Introduce weighted optimization parameters to optimize the control points. The formula is P i ′ =P i +λ·(ω f -ω0), where λ is the optimization parameter, P i ′ is the optimized control point position, ω0 is the joint angular velocity at the initial time, ω f is the joint angular velocity at the current time.

[0013] Preferably, the process of optimizing the joint trajectory by the Bezier curve includes: taking the initial position and initial angular velocity of the joint as starting conditions to generate a smooth joint trajectory curve.

[0014] Preferably, said establishing the kinematic equations of the robot arm includes modifying said equations in combination with the constraints of the workspace to meet the requirements of real-time control.

[0015] Preferably, the frequency of obtaining feedback data is adjusted according to the response time and working conditions of the servo system.

[0016] Preferably, the position sensor and the speed sensor include an inertial measurement unit and an optical sensor.

[0017] Preferably, the servo control system is combined with a fuzzy control algorithm to handle nonlinear control problems under different loads and complex working conditions.

[0018] The present invention also provides a device, comprising:

[0019] The kinematics calculation module is used to establish the kinematics equation of the robot arm based on the DH parameter method to obtain the initial position data and initial angular velocity data of the joint, and use the Bezier curve to optimize the joint trajectory to obtain the preliminary optimized joint motion trajectory data;

[0020] The data acquisition and filtering module is used to obtain feedback data including real-time position data and real-time angular velocity data using position and velocity sensors, and obtain corrected position data and corrected velocity data of the robot arm joints after eliminating environmental noise and sensor measurement errors using the Kalman filter algorithm;

[0021] A trajectory optimization module, for combining the corrected position data and the corrected speed data, using a multi-level optimization algorithm to dynamically calculate an optimized control path, and adjusting the preliminary optimized joint motion trajectory data in real time according to the calculation result to obtain real-time optimized joint motion trajectory data;

[0022] The parameter adjustment module is used to dynamically adjust the joint controller parameters of the servo control system according to the real-time optimized joint motion trajectory data combined with the response time of the servo control system.

[0023] The present invention also provides an industrial robot mechanical arm control precision coordination optimization device, comprising:

[0024] A memory, a processor, and an industrial robot manipulator arm control accuracy coordination optimization program stored in the memory and executable on the processor, wherein the industrial robot manipulator arm control accuracy coordination optimization program implements the industrial robot manipulator arm control accuracy coordination optimization method when executed by the processor.

[0025] The present invention also provides a computer program product, including an industrial robot manipulator arm control precision coordination optimization program, and when the industrial robot manipulator arm control precision coordination optimization program is executed by a processor, the industrial robot manipulator arm control precision coordination optimization method is implemented.

[0026] The beneficial effect of the present invention is that compared with traditional technologies, when dealing with complex nonlinear motions, coping with dynamic loads or environmental changes, there are often problems of reduced control accuracy and stability, especially in scenarios involving multi-joint linkage or high-degree-of-freedom robotic arms, and the technical problem of being unable to meet real-time, high-precision control requirements, this application achieves precise control of the robotic arm trajectory by combining improved Bezier curve fitting and particle swarm optimization algorithm, thereby avoiding the problem of trajectory deviation and improving the control accuracy of the robotic arm. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0028] Figure 1 The present invention is a flowchart of a first embodiment of a method for coordinated optimization of control accuracy of an industrial robot manipulator arm.

[0029] Figure 2 The present invention is a schematic diagram of an equipment for an industrial robot manipulator arm control precision coordination optimization method.

[0030] Figure 3 The present invention is a schematic diagram of a method for coordinating and optimizing the control accuracy of an industrial robot arm. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] Embodiment 1: Figure 1 , which is a flow chart of the first embodiment of the industrial robot manipulator arm control precision coordination optimization method of the present invention, proposes the first embodiment of the industrial robot manipulator arm control precision coordination optimization method of the present invention.

[0033] In a first embodiment, the method comprises:

[0034] Step S10: Based on the DH parameter method, the kinematic equation of the robot arm is established to obtain the initial position data and initial angular velocity data of the joint, and the joint trajectory is optimized and fitted using the improved Bezier curve to obtain preliminary optimized joint motion trajectory data;

[0035] It should be noted that the DH parameter method is a modeling method commonly used to describe the kinematics of a robot arm. By setting the rotation angle, displacement, and link length of each joint of the robot arm, the joint spatial position and posture of the robot arm can be accurately described. This method can accurately obtain the initial position and angular velocity of the joint, and these data will serve as the basis for subsequent trajectory fitting and optimization.

[0036] It is understandable that the Bezier curve is a mathematical method widely used in curve fitting, and the shape of the curve is determined by a set of control points. In this embodiment, an improved Bezier curve is used for trajectory fitting, the purpose of which is to make the motion trajectory of the joint smoother and more accurate and adapt to complex motion conditions. In the curve fitting process, the initial position and angular velocity of the joint are combined, so that the speed and position of the fitting trajectory at the starting point and the end point can match the actual physical parameters.

[0037] It should be understood that by improving the fitting of the Bezier curve, the mutations and discontinuities in the joint motion trajectory can be effectively reduced, ensuring the smooth transition of the trajectory throughout the entire motion process, especially in high-dynamic tasks and complex multi-degree-of-freedom robotic arm movements. This optimization fitting method can significantly improve the accuracy and stability of trajectory control. At the same time, in the process of trajectory calculation, the position of the control point is optimized to adapt to the actual operation requirements of the robotic arm under different working conditions.

[0038] Step S20: using the position sensor and the velocity sensor to obtain feedback data including real-time position data and real-time angular velocity data, and using the Kalman filter algorithm to eliminate environmental noise and sensor measurement errors to obtain corrected position data and corrected velocity data of the robot arm joint;

[0039] It should be noted that the position sensor and velocity sensor are used to monitor the actual position and angular velocity of each joint of the robot arm in real time. In actual operation, the sensor data may be affected by environmental noise or measurement errors, which may lead to inaccurate feedback data. Therefore, it is necessary to correct the feedback data through signal processing technology to ensure the accuracy of control.

[0040] It is understandable that the Kalman filter algorithm is an effective state estimation technique, especially suitable for noise processing and measurement error correction in dynamic systems. The Kalman filter algorithm recursively estimates the actual state of the robot arm by combining the prior knowledge of the system and the feedback data of the sensor, which can eliminate or reduce the impact of noise on the data. The algorithm gradually corrects the position and velocity data through prediction and update steps, making the filtered data more accurate.

[0041] Step S30: combining the corrected position data and the corrected speed data, using a particle swarm optimization algorithm to dynamically calculate an optimized control path, and adjusting the preliminary optimized joint motion trajectory data in real time according to the calculation result to obtain real-time optimized joint motion trajectory data;

[0042] It should be noted that the particle swarm optimization algorithm is a global optimization algorithm based on swarm intelligence, which finds the optimal solution of the objective function by simulating the motion trajectory of particles in the search space. In this embodiment, the particle swarm optimization algorithm is used to dynamically calculate the optimal control path of the robot arm, so that the motion trajectory of the joint can adapt to environmental changes and adjust in real time. The dynamic characteristics of the particle swarm algorithm enable the robot arm to maintain efficient and accurate path control in complex tasks and dynamic environments.

[0043] It should be understood that the working principle of the particle swarm optimization algorithm is to gradually approach the global optimal solution of the problem through the iterative movement of multiple particles. In each iteration, the algorithm combines the corrected position and speed data of the robot arm to dynamically adjust the speed and position of each particle to optimize the motion trajectory of the robot arm. The particle swarm optimization algorithm has good global search capabilities and fast convergence speed, which is suitable for the needs of real-time path optimization. By combining the corrected data after Kalman filtering, the particle swarm optimization algorithm can take into account the current motion state of the robot arm in real-time calculations and adjust the trajectory according to the feedback data. As the particle swarm algorithm continues to iterate, the motion trajectory of the joint can be gradually optimized to ensure the best path control during the task. Especially in the complex trajectory planning of multi-degree-of-freedom robot arms, particle swarm optimization can adapt to environmental changes, reduce trajectory deviations, and improve the work efficiency and accuracy of the robot arm.

[0044] Step S40: dynamically adjusting the joint controller parameters of the servo control system according to the real-time optimized joint motion trajectory data combined with the response time of the servo control system.

[0045] It should be noted that the key task of the servo control system is to accurately control each joint of the robot arm according to the set trajectory and feedback data. The joint controller parameters in the servo system (such as gain, response speed, etc.) directly affect the movement accuracy and speed of the robot arm. Real-time optimization of joint motion trajectory data provides the servo system with precise motion targets, and the servo controller makes real-time adjustments based on these target data to ensure that the robot arm can complete operations smoothly and accurately under different tasks and working conditions.

[0046] It is understandable that the response time of the servo control system depends on the parameter settings of the joint controller, such as control gain and sampling time. Different tasks may require different response times, such as faster response at high speed and more stable control at precision operation. By combining real-time optimized joint trajectory data and dynamically adjusting the parameters of the servo controller, the control system can achieve optimal performance under different working conditions, ensuring the operating accuracy and response speed of the robot arm.

[0047] In the second example provided by the present invention, the step of optimizing the joint trajectory by using the improved Bezier curve comprises:

[0048] By Bezier curve formula Fit the joint trajectory, where B i,n (t) is the Bessel basis function, P i is the control point, n is the order of the Bezier curve, and i is an index variable in the Bezier curve formula, indicating the sequence number of the Bezier basis function and control point currently being processed;

[0049] Introduce weighted optimization parameters to optimize the control points. The formula is P i ′ =P i +λ·(ω f -ω0), where λ is the optimization parameter, P i ′ is the optimized control point position, ω0 is the joint angular velocity at the initial time, ω f is the joint angular velocity at the current time.

[0050] It should be noted that the order n of the Bezier curve is set to 5, which is the optimal choice verified by experiments. In the case of a lower order, the fitted trajectory will have a larger error, while a higher order may introduce unnecessary complexity and computational burden. Therefore, choosing a 5th-order Bezier curve can ensure accuracy while taking into account computational efficiency.

[0051] It is understandable that by introducing the weighted optimization parameter λ, the fitted Bezier curve can better adapt to the initial and terminal motion conditions of the joint, especially considering the dynamic changes of the joint, making the trajectory more in line with actual needs. This optimization method can dynamically adjust the control points according to different working conditions, thereby further improving the fitting accuracy and trajectory smoothness.

[0052] It should be understood that the improved Bezier curve can not only reduce the error in traditional trajectory fitting, but also make the trajectory fitting process more adaptable by flexibly adjusting the control points, especially when the robot arm performs complex and high-speed motion tasks, it can effectively avoid trajectory deviation and discontinuity. This allows the robot arm to maintain high control accuracy and stability under various working conditions.

[0053] For example, suppose the initial joint position of a robot arm is P0 = (1.5, 2.0, 1.2) meters, and the initial angular velocity is ω0 = 0.5 radians / second. When performing complex curve motion, after calculating the initial position and angular velocity data of the joint motion trajectory using the DH parameter method, the improved Bezier curve fitting is used to set the order of the curve to 5, and the weighted optimization parameters are introduced to adjust the control point Pi to make the fitted trajectory smoother. The control points are set to P1 = (1.7, 2.1, 1.3) and P2 = (2.0, 2.5, 1.6), and the control points are fine-tuned by the weighted optimization parameter λ = 0.2. The control point position is adjusted according to the initial and terminal angular velocities of the joint, which can reduce the final trajectory fitting error by 10%.

[0054] In addition, the present invention also provides an industrial robot mechanical arm control precision coordination optimization device, please refer to Figure 3 , the industrial robot mechanical arm control precision coordination optimization device comprises:

[0055] The kinematics calculation module is used to establish the kinematics equation of the robot arm based on the DH parameter method to obtain the initial position data and initial angular velocity data of the joint, and use the Bezier curve to optimize the joint trajectory to obtain the preliminary optimized joint motion trajectory data;

[0056] The data acquisition and filtering module is used to obtain feedback data including real-time position data and real-time angular velocity data using position and velocity sensors, and obtain corrected position data and corrected velocity data of the robot arm joints after eliminating environmental noise and sensor measurement errors using the Kalman filter algorithm;

[0057] A trajectory optimization module, for combining the corrected position data and the corrected speed data, using a multi-level optimization algorithm to dynamically calculate an optimized control path, and adjusting the preliminary optimized joint motion trajectory data in real time according to the calculation result to obtain real-time optimized joint motion trajectory data;

[0058] The parameter adjustment module is used to dynamically adjust the joint controller parameters of the servo control system according to the real-time optimized joint motion trajectory data combined with the response time of the servo control system.

[0059] The present invention provides an industrial robot mechanical arm control precision coordination optimization device, which adopts an industrial robot mechanical arm control precision coordination optimization method in the above embodiment, and can solve a technical problem of industrial robot mechanical arm control precision coordination optimization. Compared with the prior art, the beneficial effect of the industrial robot mechanical arm control precision coordination optimization device provided by the present invention is the same as the beneficial effect of the industrial robot mechanical arm control precision coordination optimization method provided by the above embodiment, and the other technical features of the industrial robot mechanical arm control precision coordination optimization device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0060] The present invention provides an industrial robot mechanical arm control precision coordination optimization device, please refer to Figure 2, an industrial robot mechanical arm control precision coordination optimization device includes: at least one processor; and, a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by at least one processor, and the instructions are executed by at least one processor, so that at least one processor can execute an industrial robot mechanical arm control precision coordination optimization method in the above-mentioned embodiment 1. An industrial robot mechanical arm control precision coordination optimization device in the embodiment of the present invention may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. An industrial robot mechanical arm control precision coordination optimization device is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention. An industrial robot arm control precision coordination optimization device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM) 1004. In RAM 1004, various programs and data required for the operation of an industrial robot arm control precision coordination optimization device are also stored. The processing device 1001, ROM 1002, and RAM 1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow an industrial robot mechanical arm control precision coordination optimization device to communicate wirelessly or wired with other devices to exchange data. Although the figure shows an industrial robot mechanical arm control precision coordination optimization device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0061] The present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for coordinating and optimizing the control accuracy of an industrial robot mechanical arm. The computer program product provided by the present invention can solve the technical problem of coordinating and optimizing the control accuracy of an industrial robot mechanical arm. Compared with the prior art, the beneficial effects of the computer program product provided by the present invention are the same as the beneficial effects of the method for coordinating and optimizing the control accuracy of an industrial robot mechanical arm provided by the above-mentioned embodiment, and will not be described in detail here.

[0062] In particular, according to the embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through a communication device, or installed from the storage device 1003, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present invention are executed.

[0063] It should be understood that the various parts disclosed in the present invention can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0064] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for coordinating and optimizing the control accuracy of an industrial robot arm, characterized in that: Methods include: Step S10: Based on the DH parameter method, the kinematic equation of the robot arm is established to obtain the initial position data and initial angular velocity data of the joint, and the joint trajectory is optimized and fitted using the improved Bezier curve to obtain preliminary optimized joint motion trajectory data; Step S20: using the position sensor and the velocity sensor to obtain feedback data of the real-time position data and the real-time angular velocity data of the robot arm joint, and using the Kalman filter algorithm to eliminate environmental noise and sensor measurement errors to obtain the corrected position data and corrected velocity data of the robot arm joint; Step S30: combining the corrected position data and the corrected speed data, using a particle swarm optimization algorithm to dynamically calculate an optimized control path, and adjusting the preliminary optimized joint motion trajectory data in real time according to the calculation result to obtain real-time optimized joint motion trajectory data; Step S40: dynamically adjusting the joint controller parameters of the servo control system according to the real-time optimized joint motion trajectory data combined with the response time of the servo control system.

2. The method for coordinating and optimizing the control accuracy of an industrial robot manipulator arm according to claim 1, characterized in that: In step S10, the step of optimizing the joint trajectory by using the improved Bezier curve includes: By Bezier curve formula Fit the joint trajectory, where B i,n (t) is the Bessel basis function, P i is the control point, n is the order of the Bezier curve, and i is an index variable in the Bezier curve formula, indicating the sequence number of the Bezier basis function and control point currently being processed; Introduce weighted optimization parameters to optimize the control points. The formula is P i ′=P i +λ·(ω f -ω0), where λ is the optimization parameter, P i ′ is the optimized control point position, ω0 is the joint angular velocity at the initial time, ω f is the joint angular velocity at the current time.

3. The method for coordinating and optimizing the control accuracy of an industrial robot manipulator arm according to claim 1, characterized in that: In step S10, the process of optimizing the joint trajectory by the Bezier curve includes: taking the initial position and initial angular velocity of the joint as starting conditions to generate a smooth joint trajectory curve.

4. The method for coordinating and optimizing the control accuracy of an industrial robot manipulator arm according to claim 1, characterized in that: In step S10, the kinematic equations of the robot arm are established, including modifying the equations in combination with the constraints of the workspace to meet the requirements of real-time control.

5. The method for coordinating and optimizing the control accuracy of an industrial robot manipulator arm according to claim 1, characterized in that: In step S20, the frequency of obtaining feedback data is adjusted according to the response time and working conditions of the servo system.

6. The method for coordinating and optimizing the control accuracy of an industrial robot manipulator arm according to claim 1, characterized in that: In step S20, the position sensor and the speed sensor include an inertial measurement unit and an optical sensor.

7. The method for coordinating and optimizing the control accuracy of an industrial robot arm according to claim 1, characterized in that: In step S40, the servo control system is combined with a fuzzy control algorithm to handle nonlinear control problems under different loads and complex working conditions.

8. An industrial robot arm control accuracy coordination optimization device, characterized in that: The device comprises: The kinematics calculation module is used to establish the kinematics equation of the robot arm based on the DH parameter method to obtain the initial position data and initial angular velocity data of the joint, and use the Bezier curve to optimize the joint trajectory to obtain the preliminary optimized joint motion trajectory data; The data acquisition and filtering module is used to obtain feedback data including real-time position data and real-time angular velocity data using position and velocity sensors, and obtain corrected position data and corrected velocity data of the robot arm joints after eliminating environmental noise and sensor measurement errors using the Kalman filter algorithm; A trajectory optimization module, for combining the corrected position data and the corrected speed data, using a multi-level optimization algorithm to dynamically calculate an optimized control path, and adjusting the preliminary optimized joint motion trajectory data in real time according to the calculation result to obtain real-time optimized joint motion trajectory data; The parameter adjustment module is used to dynamically adjust the joint controller parameters of the servo control system according to the real-time optimized joint motion trajectory data combined with the response time of the servo control system.

9. An industrial robot arm control precision coordination optimization device, characterized in that: The device comprises: A memory, a processor, and an industrial robot manipulator arm control accuracy coordination optimization program stored in the memory and executable on the processor, wherein the industrial robot manipulator arm control accuracy coordination optimization program implements the industrial robot manipulator arm control accuracy coordination optimization method when executed by the processor.

10. A computer program product, characterized in that The computer program product includes an industrial robot manipulator arm control precision coordination optimization program, and the industrial robot manipulator arm control precision coordination optimization program, when executed by a processor, implements the industrial robot manipulator arm control precision coordination optimization method.

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