Method and device for correcting real-time path of industrial robot

Abstract

The invention relates to the technical field of industrial robot path control, and discloses an industrial robot real-time path correction method, which comprises the following steps: acquiring current position information, speed information and acceleration information; calculating and generating a path error value by adopting an Euclidean distance; an error feedback control method and an inverse kinematics algorithm are combined to generate a correction matrix, and the angle, angular velocity, acceleration and angular acceleration parameters of the industrial robot joint are adjusted in real time; and further adjusting the motion trail parameters based on an improved Kalman filtering algorithm. Compared with the technical problems that in the prior art, path correction lags behind, precision is insufficient, and real-time path correction is difficult to achieve especially in a complex dynamic environment, due to the fact that multi-sensor fusion, a feedback control algorithm and an improved PID control algorithm are combined for use, the path is corrected in real time, error accumulation is reduced, and therefore the situation that path deviation is too large is avoided; and the production efficiency of the industrial robot in a complex environment is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of industrial automation control, and in particular relates to a real-time path correction method and device for an industrial robot. Background Art

[0002] At present, industrial robots are increasingly used in automated production. However, in actual work, the accuracy and real-time correction capabilities of robot paths are still insufficient. Existing path control systems usually rely on pre-set trajectories and cannot be effectively adjusted according to changes in the real-time environment, resulting in insufficient path accuracy in complex dynamic environments. Especially when dealing with variable tasks, path errors cannot be corrected in a timely manner, affecting production efficiency and product quality, and failing to fully meet the needs of high-precision, real-time path correction. Therefore, there is an urgent need for a method that can still achieve real-time path correction in complex working environments to improve the path accuracy and production efficiency of industrial robots and meet the increased accuracy requirements of automated production in high dynamic environments. Summary of the invention

[0003] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to propose a real-time path correction method for an industrial robot, aiming to solve the technical problem that the path control system in the prior art usually relies on a pre-set trajectory and cannot be effectively adjusted according to changes in the real-time environment, resulting in insufficient path accuracy in complex dynamic environments, especially when processing variable tasks. The path error cannot be corrected in time, affecting production efficiency and product quality, and cannot fully meet the needs of high-precision, real-time path correction.

[0004] In order to solve the above technical problems, the present invention adopts the following technical solutions: The present invention provides a real-time path correction method for an industrial robot.

[0005] The industrial robot real-time path correction method comprises:

[0006] Step S10: collecting current position information, speed information and acceleration information in real time based on the multimodal sensor on the industrial robot;

[0007] Step S20: comparing the collected current position information with the target path information, and using Euclidean distance calculation to generate a path error value between the current position information and the target path;

[0008] Step S30: according to the path error value, an inverse kinematics algorithm is used to calculate the target correction position of the end effector of the industrial robot and generate a correction matrix, and the motion trajectory parameters are adjusted in real time in combination with an improved PID control algorithm, wherein the motion trajectory parameters include the angle, angular velocity, acceleration and angular acceleration parameters of the joints of the industrial robot;

[0009] Step S40: continuously collecting the adjusted position information, velocity information and acceleration information in real time through the multimodal sensor, calculating the feedback error using the generalized least squares method, and correcting the feedback error based on the improved Kalman filter algorithm to further adjust the motion trajectory parameters.

[0010] Preferably, the method for calculating the path error value includes the following Euclidean distance formula:

[0011]

[0012] Among them, x c ,y c , z c is the current location information, x t ,y t , z t The coordinates of the target path information.

[0013] Preferably, the process of correcting the feedback error based on the improved Kalman filter algorithm adopts the following state update formula:

[0014]

[0015] in, is the updated state estimate, is the predicted state, K k is the dynamically adjusted Kalman gain matrix, which is adaptively adjusted according to the dynamic changes of the feedback error. k is the current measurement value, H is the observation matrix, E k is the error value at the current moment, and α is the dynamic adjustment coefficient, which is used to dynamically adjust the amplitude of the state update according to the square of the error.

[0016] Preferably, the adjustment range of the motion trajectory parameters is dynamically adjusted according to task requirements and limitations of the robot's workspace.

[0017] Preferably, the sampling frequency of the multimodal sensor is dynamically adjusted according to changes in the movement speed of the industrial robot.

[0018] Preferably, the Euclidean distance calculation method is based on the Euclidean distance in three-dimensional space, and is used to calculate the error between the current position information and the target path.

[0019] Preferably, the multimodal sensor includes a laser radar, a visual sensor and a force sensor.

[0020] Preferably, the industrial robot real-time path correction device comprises:

[0021] An information acquisition module is used to acquire the current position information, speed information and acceleration information in real time based on the multimodal sensors on the industrial robot;

[0022] The error calculation module is used to compare the collected current position information with the target path information, and use Euclidean distance calculation to generate a path error value between the current position information and the target path;

[0023] A path correction module is used to calculate the target correction position of the end effector of the industrial robot according to the path error value by using an inverse kinematics algorithm and generate a correction matrix, and to adjust the motion trajectory parameters in real time in combination with an improved PID control algorithm, wherein the motion trajectory parameters include the angle, angular velocity, acceleration and angular acceleration parameters of the joints of the industrial robot;

[0024] The feedback control module is used to continuously collect the adjusted position information, velocity information and acceleration information in real time through a multimodal sensor, calculate the feedback error using a generalized least squares method, and correct the feedback error based on an improved Kalman filter algorithm to further adjust the motion trajectory parameters.

[0025] The present invention also provides a real-time path correction device for an industrial robot, comprising:

[0026] A memory, a processor, and an industrial robot real-time path correction program stored in the memory and executable on the processor. When the industrial robot real-time path correction program is executed by the processor, the industrial robot real-time path correction method is implemented.

[0027] The present invention also provides a computer program product including an industrial robot real-time path correction program, and the industrial robot real-time path correction program implements the industrial robot real-time path correction method when executed by a processor.

[0028] The beneficial effects of the present invention are as follows: compared with the technical problems of path correction lag and insufficient precision in the prior art, especially in complex dynamic environments, which make it difficult to achieve real-time path correction, the present application uses a combination of multi-sensor fusion, feedback control algorithm and improved PID control algorithm to correct the path in real time to reduce error accumulation, thereby avoiding excessive path deviation and improving the production efficiency of industrial robots in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] 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.

[0030] Figure 1 The figure is a flow chart of a first embodiment of a method for real-time path correction of an industrial robot according to the present invention.

[0031] Figure 2 The present invention is a schematic diagram of a device for a real-time path correction method for an industrial robot.

[0032] Figure 3 The present invention is a schematic diagram of a device for a real-time path correction method for an industrial robot. DETAILED DESCRIPTION

[0033] 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.

[0034] Embodiment 1: Figure 1 , which is a flow chart of the first embodiment of the real-time path correction method for an industrial robot according to the present invention, provides the first embodiment of the real-time path correction method for an industrial robot according to the present invention.

[0035] Step S10: collecting current position information, speed information and acceleration information in real time based on the multimodal sensor on the industrial robot;

[0036] It should be noted that a multimodal sensor refers to a sensing system that obtains the real-time status of a robot through a combination of multiple sensors. The system includes but is not limited to position sensors, velocity sensors, and acceleration sensors, which are used to fully perceive the motion state of the robot during work. Position information refers to the current position data of the end effector or each joint of an industrial robot, usually expressed in a three-dimensional coordinate system (X, Y, Z). Position information includes joint angle data and the absolute position of the end of the robot in the workspace. Velocity information refers to the current movement speed of each joint or end effector of an industrial robot, usually expressed in the form of linear velocity and angular velocity. Velocity information includes the rotation speed of each joint or the movement speed of the end effector. Acceleration information refers to the acceleration change of each joint or end effector of the robot during movement. Acceleration information reflects the current acceleration value of the robot and its ability to respond to dynamic changes in the path.

[0037] It is understandable that by collecting this information, the multimodal sensor can fully obtain the current motion state data of the robot, ensuring high accuracy and real-time response when the robot corrects its path. The data collected by the sensor will be continuously input into the control system as the basis for subsequent path correction calculations and provide accurate error information for subsequent steps.

[0038] Step S20: comparing the collected current position information with the target path information, and using Euclidean distance calculation to generate a path error value between the current position information and the target path;

[0039] It should be noted that the target path information refers to the preset ideal motion trajectory or path that the robot should follow, including the three-dimensional spatial coordinates (X, Y, Z) that the robot should reach at each time or position and the corresponding time information.

[0040] It can be understood that the path error value refers to the difference between the current position information and the target path information, reflecting the degree of deviation between the actual movement of the robot and the preset trajectory. The error value is used to quantify the distance between the actual position of the robot and the target position in the current state through the Euclidean distance formula. The path error value includes: the three-dimensional space distance between the current position and the target position, the speed error, and the acceleration error.

[0041] It should be understood that the path error value is a key indicator for determining whether the robot's current trajectory is consistent with the target trajectory. According to the size of the error value, the control system can adjust the robot's motion parameters in real time to ensure that the robot always moves along the target path and achieve precise control through error feedback.

[0042] Step S30: according to the path error value, an inverse kinematics algorithm is used to calculate the target correction position of the end effector of the industrial robot and generate a correction matrix, and the motion trajectory parameters are adjusted in real time in combination with an improved PID control algorithm, wherein the motion trajectory parameters include the angle, angular velocity, acceleration and angular acceleration parameters of the joints of the industrial robot;

[0043] It should be noted that the inverse kinematics algorithm refers to the reverse calculation of the angles that each joint needs to reach based on the target position and posture of the robot's end effector (tool or end of the arm) to achieve the desired movement of the end; the target correction position of the industrial robot's end effector refers to the specific position to which the end effector should be adjusted, calculated through the inverse kinematics algorithm, to compensate for the current path error. This position is the three-dimensional space point or posture that the robot end should reach after correction based on the real-time path error; the correction matrix refers to the matrix used to adjust the motion parameters of each joint of the industrial robot, which is generated by the inverse kinematics algorithm. This matrix is ​​used to describe the correction amount of each joint angle and convert the end motion requirements into joint angle changes; the improved PID control algorithm refers to the combination of adaptive adjustment or gain optimization mechanism on the basis of the traditional PID (proportional-integral-differential) control algorithm to make more accurate joint adjustments for real-time errors. The improved PID control can dynamically adjust the control parameters according to the actual error, making the control system more responsive and reducing overshoot or lag;

[0044] It can be understood that the inverse kinematics algorithm is used to convert the end correction target into the adjustment requirements of each joint, and the correction matrix achieves the compensation of the path error by accurately describing the correction amount of the joint. At the same time, the improved PID control algorithm is combined to adjust the joint motion parameters in real time to ensure smooth and accurate robot motion.

[0045] It should be understood that the present invention combines the needs of real-time path correction in complex dynamic environments in the process of adopting the inverse kinematics algorithm and the improved PID control algorithm. Traditional inverse kinematics algorithms are usually used for path planning under ideal working conditions, but in actual industrial applications, due to the influence of environmental disturbances, error accumulation and robot dynamic characteristics, traditional algorithms may have problems such as low calculation accuracy and slow correction. The present invention improves the inverse kinematics algorithm and adopts an adaptive optimization strategy. On the basis of continuously collecting error information, it adjusts the joint angle in real time to achieve faster and more accurate correction. In addition, the improved PID control algorithm dynamically adjusts the real-time error, improves the system's response speed and accuracy to path deviations, and avoids overshoot or oscillation phenomena common in traditional PID control.

[0046] Traditional PID control algorithms are difficult to handle instantaneous errors caused by environmental changes, and for different mission scenarios, a fixed K p , K i , K d The value may cause the system to respond too slowly or oscillate. In order to solve this problem, the present invention uses an adaptive PID control algorithm to adjust the PID parameters in real time, so that the control system can be dynamically optimized according to the size and change trend of the error.

[0047] The improved PID control formula is:

[0048]

[0049] Among them, K p (t), K i (t), K d (t) are dynamic proportional gain, dynamic integral gain and dynamic differential gain, which are adjusted dynamically with time t to ensure that the system can respond quickly and stably under different error amplitudes and change rates. e(t) is the current error. By monitoring the speed and amplitude of the error change in real time and dynamically adjusting the control gain, the control system can respond to instantaneous errors more sensitively and reduce system oscillations.

[0050] For example, suppose a welding robot is performing high-precision welding work, and its end effector must move along a preset curved trajectory to ensure welding accuracy. During the process, changes in the external environment (such as fluctuations in the surface temperature of the workpiece, mechanical vibration, etc.) cause the error between the actual path of the robot and the target path to change rapidly, resulting in a large instantaneous error. The welding robot uses an inverse kinematics algorithm to calculate the target correction position of the end effector based on real-time feedback data. At this time, the actual position of the end effector is (x current ,y current , z current ), and the target position is (x target ,y target , z target ),. After calculating the current error e(t), the inverse kinematics algorithm calculates the correction angle of the joint to make the end effector return to the target position. Based on the above error, the PID control system adjusts K according to the change trend of the error. p (t), K i (t), K d (t). Assuming the initial error is e(t) = 3, the system will increase K accordingly. p The value of K ensures that the joint responds quickly and shortens the path correction time. As the error gradually decreases to e(t) = 0.5, the system will reduce K p The value of K can prevent overshoot caused by excessive correction. During the welding process, the error may remain in a small range for a long time. i It will gradually increase to eliminate the accumulated steady-state error and ensure that the robot trajectory eventually returns to the target path. When the error is detected to change too quickly, the system will automatically increase K d Reduce instantaneous fluctuations and oscillations. Through this dynamically adjusted PID control, the welding robot can quickly adjust the path when encountering large external disturbances and restore to the target trajectory, while avoiding overcorrection and system oscillation, thereby ensuring welding accuracy and stability.

[0051] Step S40: continuously collecting the adjusted position information, velocity information and acceleration information in real time through the multimodal sensor, calculating the feedback error using the generalized least squares method, and correcting the feedback error based on the improved Kalman filter algorithm to further adjust the motion trajectory parameters.

[0052] It should be noted that the generalized least squares method (Generalized Least Squares, GLS) is a least squares extension method for processing heteroscedasticity and autocorrelation errors. It can effectively process the measurement noise and error that may exist in multimodal sensor data, and provide more accurate error estimation in the feedback link. In the present invention, the generalized least squares method generates a more accurate feedback error value by fitting the position information, velocity information and acceleration information collected by the sensor, including correcting the heteroscedasticity error in the data through the weight matrix to ensure the accuracy of the error estimation; the Kalman filter algorithm is a recursive algorithm for estimating the state of a dynamic system, which can dynamically adjust the error according to the prediction of the measurement noise and the system state model. Although the traditional Kalman filter performs well in noise filtering, it may be difficult to cope with scenes with drastic changes or excessive noise in complex environments. The improved Kalman filter algorithm enhances the adaptability to nonlinear dynamic systems, combines the adaptive gain adjustment mechanism, and dynamically adjusts the gain of the filter according to the error change, ensuring that unnecessary interference signals can still be accurately filtered out in the case of large noise. The improved Kalman filter can more effectively correct the feedback error and keep the system state stable.

[0053] It should be understood that the generalized least squares method is used to calculate the data error collected by the sensor, while the Kalman filter algorithm further optimizes the motion trajectory parameters by correcting the feedback error. The position information, velocity information and acceleration information collected by the sensor need to be error estimated and filtered in the feedback control process to eliminate noise and interference and ensure the accuracy and stability of the robot joint movement. Through the combination of these algorithms, the present invention can maintain high-precision path control in a complex dynamic environment.

[0054] For example, suppose an industrial robot is performing a precision handling task in a factory environment with frequent vibrations. In this environment, the robot needs to carry a sensitive component from position A (2.00, 3.00, 1.00) to position B (5.00, 8.00, 1.00). During the handling process, due to the vibration of the ground, the robot's sensor frequently receives noisy position and velocity information.

[0055] Ideal position sequence: A(2.00,3.00,1.00),B(5.00,8.00,1.00)

[0056] The actual collected position information: Position 1: (1.98, 3.02, 1.00), Position 2: (5.02, 7.95, 1.00)

[0057] Ideal speed: (1.0, 1.67, 0.0)

[0058] The actual collected speed information: Speed ​​1: (1.02, 1.70, 0.02), Speed ​​2: (0.98, 1.65, -0.02)

[0059] Among them, the application steps of the generalized least squares method (GLS) can be: according to the variance characteristics of the sensor noise, set the weight matrix W, higher vibration leads to low weight, for example, for the strong vibration area, the weight is 0.5, for the low vibration area, the weight is 1.0. W = diag (0.5, 1.0, 0.5, 1.0, 0.5, 1.0) (corresponding to the weight of each measurement), and then use the generalized least squares formula to calculate the error correction value and update the position information: Where X is the design matrix, which consists of position data; y is the target position vector.

[0060] In the second embodiment, the process of correcting the feedback error based on the improved Kalman filter algorithm adopts the following state update formula:

[0061]

[0062] in, is the updated state estimate, is the predicted state, K k is the dynamically adjusted Kalman gain matrix, which is adaptively adjusted according to the dynamic changes of the feedback error. k is the current measurement value, H is the observation matrix, E k is the error value at the current moment, and α is the dynamic adjustment coefficient, which is used to dynamically adjust the amplitude of the state update according to the square of the error.

[0063] It is understandable that the core improvement of this formula is to introduce the parameter α·‖E k ‖ 2 The system dynamically adjusts the state update intensity and makes real-time adjustments according to the size of the error, which enhances the response and adaptability to the error. This mechanism enables the system to more effectively cope with complex dynamic environments, especially in high-noise conditions, and maintain the stability and accuracy of the system.

[0064] For example, suppose an industrial robot is working in a high-noise environment and is responsible for precision assembly operations. Due to equipment vibration and external force interference in the external environment, the position information and speed information measured by the sensor are interfered by noise, causing the actual motion trajectory of the robot to deviate from the preset path. If the robot cannot correct these errors in time, it may cause assembly failure or damage to the workpiece. In this case, the improved Kalman filter algorithm can more effectively cope with this high-noise environment by introducing an adaptive feedback mechanism. The specific implementation steps can be:

[0065] (1) Initialization phase:

[0066] When the robot starts working, the system initializes the state vector according to the initial position and speed information measured by the sensor And set the initial state covariance matrix P0. Assume that the initial ideal target position is (x, y, z) = (5.0, 3.0, 2.0), and the initial position measurement collected by the sensor is (4.95, 3.02, 2.03), and there is a certain initial error.

[0067] (2) Status prediction:

[0068] According to the robot's motion model (such as uniform motion or accelerated motion), the system predicts the robot's state at the next moment, such as position information and speed. Assume that the robot is expected to reach the position (5.1, 3.1, 2.0) in the next time step, but due to vibration and external interference, the position information measured by the sensor has a large deviation, and the actual measurement value is (5.25, 3.15, 2.05). At the same time, the speed information also has deviations.

[0069] (3) Feedback correction:

[0070] Through the improved Kalman filter algorithm, the system begins to correct the state. Using the measured value z k =(5.25,3.15,2.05) and predicted state The error between:

[0071] At this point, the system calculates the square of the error vector ‖E k ‖ 2 , and introduce the dynamic adjustment term α·‖E according to the formula k ‖ 2 To adjust the amplitude of the state update. If the external noise is large, the sensor error is large, and the error vector is also large, the dynamic adjustment coefficient α increases, and the system will strengthen the correction of the error, making the state update at the next moment more aggressive, correcting the deviation in time, and avoiding system out of control. Since the state update formula is: The corrected state is calculated: In this way, the robot will quickly move toward the target position and reduce the error by strengthening the error feedback correction.

[0072] (4) As time goes by, if the environmental noise decreases, the measurement error will also decrease accordingly, and the system will automatically reduce the correction force to avoid over-correction and ensure the smoothness of the robot's movement. The system continuously updates the Kalman gain matrix and error correction terms to ensure high-precision path control in a noisy environment.

[0073] In a third embodiment, the adjustment range of the motion trajectory parameters is dynamically adjusted according to task requirements and limitations of the robot's workspace.

[0074] It should be noted that the adjustment range of motion trajectory parameters includes control parameters such as the angle, angular velocity, acceleration and angular acceleration of the industrial robot joints. The adjustment range depends on the specific task requirements. For example, in high-precision tasks, the allowable adjustment range is smaller to ensure the robot path accuracy. In large-scale handling or lower-precision tasks, the adjustment range of motion trajectory parameters can be appropriately relaxed. The specific adjustment range is also subject to the physical limitations of the robot's workspace, such as the extension limit of the robot arm and the rotation angle range of the joints. In addition, when adjusting the trajectory, it is also necessary to consider avoiding collisions with other equipment or obstacles.

[0075] It is understandable that the adjustment strategies for motion trajectory parameters may vary greatly depending on the task. In tasks that require high precision, such as precision assembly or welding, the adjustment of motion trajectory parameters usually needs to be kept within a very narrow range. For tasks with low handling or repeatability, the adjustment range of the trajectory can be appropriately expanded to ensure that the robot has higher speed and flexibility. At the same time, the adjustment range must be combined with the limitations of the robot's physical structure and workspace to avoid exceeding the robot's operable limit position.

[0076] In addition, the present invention also provides a real-time path correction device for an industrial robot, please refer to Figure 3 , the industrial robot real-time path correction device comprises:

[0077] A positioning module, used for receiving Beidou satellite positioning data of a target area based on a Beidou satellite receiver, and determining a precise position of the target area by analyzing the Beidou satellite positioning data;

[0078] A data acquisition and processing module is used to acquire the current meteorological data source and the historical meteorological data source according to the precise location of the target area, and obtain the initial meteorological data and the historical meteorological data through standardized processing;

[0079] A model building and training module is used to build an extreme weather event prediction model based on the initial meteorological data and historical meteorological data, use convolutional neural network and recurrent neural network algorithms to train the model, and evaluate and optimize the model through a cross-validation method;

[0080] The feature extraction and warning issuance module is used to process the initial meteorological data through a time series analysis method to obtain key meteorological features and input them into the trained extreme weather event prediction model, and issue extreme weather event warning information to the terminal according to the extreme weather event prediction results.

[0081] The real-time path correction device for an industrial robot provided by the present invention adopts the real-time path correction method for an industrial robot in the above embodiment, and can solve the technical problem of real-time path correction for an industrial robot. Compared with the prior art, the beneficial effects of the real-time path correction device for an industrial robot provided by the present invention are the same as the beneficial effects of the real-time path correction method for an industrial robot provided by the above embodiment, and the other technical features of the real-time path correction device for an industrial robot are the same as the features disclosed in the method of the above embodiment, which will not be described in detail here.

[0082] The present invention provides a real-time path correction device for industrial robots. Figure 2, an industrial robot real-time path correction device comprises: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the industrial robot real-time path correction method in the above-mentioned embodiment 1. An industrial robot real-time path correction 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 real-time path correction 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 real-time path correction device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, 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 real-time path correction device are also stored. The processing device 1001, ROM 1002, and RAM 1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow an industrial robot real-time path correction device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows an industrial robot real-time path correction 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 can be implemented or have alternatively.

[0083] The present invention also provides a computer program product, including a computer program, wherein when the computer program is executed by a processor, the steps of the above-mentioned method for correcting a real-time path of an industrial robot are implemented. The computer program product provided by the present invention can solve the technical problem of correcting a real-time path of an industrial robot. 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 correcting a real-time path of an industrial robot provided by the above-mentioned embodiment, and will not be described in detail here.

[0084] 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.

[0085] 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.

[0086] 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 real-time path correction method for an industrial robot, characterized in that: The industrial robot real-time path correction method comprises: Step S10: collecting the current position information, speed information and acceleration information of the industrial robot in real time based on the multimodal sensor on the industrial robot; Step S20: comparing the collected current position information with the target path information, and using Euclidean distance calculation to generate a path error value between the current position information and the target path; Step S30: according to the path error value, an inverse kinematics algorithm is used to calculate the target correction position of the end effector of the industrial robot and generate a correction matrix, and the motion trajectory parameters are adjusted in real time in combination with an improved PID control algorithm, wherein the motion trajectory parameters include the angle, angular velocity, acceleration and angular acceleration parameters of the joints of the industrial robot; Step S40: continuously collecting the adjusted position information, velocity information and acceleration information in real time through the multimodal sensor, calculating the feedback error using the generalized least squares method, and correcting the feedback error based on the improved Kalman filter algorithm to further adjust the motion trajectory parameters.

2. The real-time path correction method for an industrial robot according to claim 1, characterized in that: The calculation method of the path error value includes the following Euclidean distance formula: Among them, x c ,y c , z c is the coordinate of the target path information, x t ,y t , z t The coordinates of the target path information.

3. The real-time path correction method for an industrial robot according to claim 1, characterized in that: The process of correcting the feedback error based on the improved Kalman filter algorithm adopts the following state update formula: in, is the updated state estimate, is the predicted state, K k is the dynamically adjusted Kalman gain matrix, which is adaptively adjusted according to the dynamic changes of the feedback error. k is the current measurement value, H is the observation matrix, E k is the error value at the current moment, and α is the dynamic adjustment coefficient, which is used to dynamically adjust the amplitude of the state update according to the square of the error.

4. The real-time path correction method for an industrial robot according to claim 1, characterized in that: The adjustment range of the motion trajectory parameters is dynamically adjusted according to the task requirements and the limitations of the robot's workspace.

5. The real-time path correction method for an industrial robot according to claim 1, characterized in that: The sampling frequency of the multimodal sensor is dynamically adjusted according to the change of the movement speed of the industrial robot.

6. The real-time path correction method for an industrial robot according to claim 1, characterized in that: The Euclidean distance calculation method is based on the Euclidean distance in three-dimensional space and is used to calculate the error between the current position information and the target path.

7. The real-time path correction method for an industrial robot according to claim 1, characterized in that: The multimodal sensor includes a laser radar, a visual sensor and a force sensor.

8. A real-time path correction device for an industrial robot, characterized in that: The industrial robot real-time path correction device comprises: An information acquisition module is used to acquire the current position information, speed information and acceleration information in real time based on the multimodal sensors on the industrial robot; The error calculation module is used to compare the collected current position information with the target path information, and use Euclidean distance calculation to generate a path error value between the current position information and the target path; A path correction module is used to calculate the target correction position of the end effector of the industrial robot according to the path error value by using an inverse kinematics algorithm and generate a correction matrix, and to adjust the motion trajectory parameters in real time in combination with an improved PID control algorithm, wherein the motion trajectory parameters include the angle, angular velocity, acceleration and angular acceleration parameters of the joints of the industrial robot; The feedback control module is used to continuously collect the adjusted position information, velocity information and acceleration information in real time through a multimodal sensor, calculate the feedback error using a generalized least squares method, and correct the feedback error based on an improved Kalman filter algorithm to further adjust the motion trajectory parameters.

9. A real-time path correction device for an industrial robot, characterized in that: The industrial robot real-time path correction device comprises: A memory, a processor, and an industrial robot real-time path correction program stored in the memory and executable on the processor. When the industrial robot real-time path correction program is executed by the processor, the industrial robot real-time path correction method is implemented.

10. A computer program product, characterized in that The computer program product includes an industrial robot real-time path correction program, and the industrial robot real-time path correction program implements the industrial robot real-time path correction method when executed by a processor.

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