A real-time path correction method and device for an industrial robot

Abstract

The present invention relates to the technical field of industrial robot path control, and discloses a real-time path correction method for industrial robots. The method includes: collecting current position information, speed information, and acceleration information; calculating a path error value using the Euclidean distance; generating a correction matrix by combining an error feedback control method and an inverse kinematics algorithm, and adjusting the angle, angular velocity, acceleration, and angular acceleration parameters of the industrial robot joints in real time; further adjusting the motion trajectory parameters based on an improved Kalman filtering algorithm. Compared with the prior art, there are technical problems such as path correction lag and insufficient accuracy, especially in complex dynamic environments, it is difficult to achieve real-time path correction. Since this application combines the use of multi-sensor fusion, feedback control algorithms, and improved PID control algorithms to correct the path in real time and reduce error accumulation, it avoids excessive path deviation and improves the production efficiency of industrial robots in complex environments.

Description

Technical Field

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

[0002] At present, industrial robots are increasingly widely used in automated production. However, in the actual working process, the accuracy and real-time correction ability of the robot path still have deficiencies. 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 unable to fully meet the requirements of high-precision and real-time path correction. Therefore, there is an urgent need for a method that can still achieve real-time path correction in a complex working environment to improve the path accuracy and production efficiency of industrial robots and meet the increasing accuracy requirements in high-dynamic environments for automated production. Summary of the Invention

[0003] Aiming at the above-mentioned technical deficiencies, the purpose of the present invention is to propose a method for real-time path correction of industrial robots, aiming to solve the technical problems in the prior art that 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 unable to fully meet the requirements of high-precision and real-time path correction.

[0004] To solve the above technical problems, the present invention adopts the following technical solutions: The present invention provides a method for real-time path correction of industrial robots,

[0005] The method for real-time path correction of industrial robots includes:

[0006] Step S10: Based on multi-modal sensors on the industrial robot, real-time collect the current position information, speed information, and acceleration information;

[0007] Step S20: Compare the collected current position information with the target path information, and use the Euclidean distance to calculate and generate the path error value between the current position information and the target path;

[0008] Step S30: According to the path error value, use the inverse kinematics algorithm to calculate the target correction position of the end effector of the industrial robot and generate a correction matrix, and combine the improved PID control algorithm to real-time adjust the motion trajectory parameters, where the motion trajectory parameters include the angle, angular velocity, acceleration, and angular acceleration parameters of the industrial robot joints;

[0009] Step S40: Continuously collect the adjusted position information, speed information, and acceleration information in real time through the multi-modal sensor, calculate the feedback error using the generalized least squares method, and correct the feedback error based on the improved Kalman filter algorithm to further adjust the motion trajectory parameters.

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

[0011]

[0012] where, The coordinates of the current position information, are the coordinates of the target path information.

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

[0014]

[0015] where, is the updated state estimate, is the predicted state, is the dynamically adjusted Kalman gain matrix, which is adaptively adjusted according to the dynamic change of the feedback error, is the current measurement value, is the observation matrix, is the error value at the current moment, 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 the task requirements and the limitations of the robot working space.

[0017] Preferably, the sampling frequency of the multi-modal sensor is dynamically adjusted according to the change of the industrial robot motion speed.

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

[0019] Preferably, the multi-modal sensor includes a lidar, a vision sensor, and a force sensor.

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

[0021] An information acquisition module, which is used to collect the current position information, speed information, and acceleration information in real time based on the multi-modal sensor on the industrial robot;

[0022] An error calculation module, which is used to compare the collected current position information with the target path information, and calculate and generate a path error value between the current position information and the target path by using the Euclidean distance;

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

[0024] A feedback control module, which is used to continuously collect the adjusted position information, speed information, and acceleration information in real time through a multi-modal sensor, calculate the feedback error by using the 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 an industrial robot real-time path correction device, including:

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

[0027] The present invention also provides a computer program product including an industrial robot real-time path correction program, where when the industrial robot real-time path correction program is executed by a processor, the industrial robot real-time path correction method as described above is implemented.

[0028] The beneficial effect of the present invention is that: compared with the technical problems of path correction lag and insufficient accuracy in the prior art, especially in a complex dynamic environment where it is difficult to achieve real-time path correction, since the present application combines the use of multi-sensor fusion, feedback control algorithms, and improved PID control algorithms to correct the path in real time and 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 technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0030] Figure 1 It is a schematic flowchart of the first embodiment of an industrial robot real-time path correction method of the present invention.

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

[0032] Figure 3 This is a schematic diagram of the device for a real-time path correction method of an industrial robot according to the present invention. Detailed implementation manners

[0033] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0034] Embodiment 1: As Figure 1 shown, this is a flowchart of the first embodiment of the real-time path correction method of the industrial robot according to the present invention, and the first embodiment of the real-time path correction method of the industrial robot according to the present invention is proposed.

[0035] Step S10: Based on the multi-modal sensors on the industrial robot, the current position information, speed information, and acceleration information are collected in real time;

[0036] It should be noted that the multi-modal sensor refers to a sensing system that obtains the real-time state of the robot through a combination of multiple sensors. This system includes, but is not limited to, position sensors, speed sensors, and acceleration sensors, and is used to comprehensively perceive the motion state of the robot during operation. The position information refers to the current position data of the end effector or each joint of the industrial robot, usually represented in a three-dimensional coordinate system (X, Y, Z). The position information includes joint angle data and the absolute position of the robot end in the working space. The speed information refers to the current motion speed of each joint or the end effector of the industrial robot, usually represented in the form of linear speed and angular speed. The speed information includes the rotational speed of each joint or the moving speed of the end effector. The acceleration information refers to the acceleration change of each joint or the end effector of the robot during motion. The acceleration information reflects the current acceleration value of the robot and its response ability to path dynamic changes.

[0037] It can be understood that by collecting this information, the multi-modal sensor can comprehensively obtain the current motion state data of the robot, ensuring high precision and real-time response during robot path correction. The data collected by the sensor will be continuously input into the control system, serving as the basis for subsequent path correction calculations and providing accurate error information for subsequent steps.

[0038] Step S20: Compare the collected current position information with the target path information, and calculate the path error value between the current position information and the target path using the Euclidean distance;

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

[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 deviation degree of the actual motion of the robot from the preset trajectory. Calculated by the Euclidean distance formula, this error value is used to quantify the distance between the actual position and the target position of the robot in the current state. The path error value includes: the three-dimensional space distance, speed error, and acceleration error between the current position and the target position.

[0041] It should be understood that the path error value is a key indicator for judging whether the current trajectory of the robot is consistent with the target trajectory. According to the magnitude of the error value, the control system can adjust the motion parameters of the robot 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, calculate the target correction position of the end effector of the industrial robot using the inverse kinematics algorithm and generate a correction matrix, and combine the improved PID control algorithm to adjust the motion trajectory parameters in real time. 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 calculating the angles that each joint needs to reach in reverse according to the target position and posture of the end effector (the tool or the end of the arm) of the robot, so as to achieve the desired motion of the end; the target correction position of the end effector of the industrial robot refers to the specific position that the end effector should be adjusted to 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 being corrected 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 motion requirements of the end into joint angle changes; the improved PID control algorithm refers to combining an adaptive adjustment or gain optimization mechanism on the basis of the traditional PID control algorithm to perform more precise joint adjustment for real-time errors. The improved PID control can dynamically adjust the control parameters according to the actual error, making the control system response more sensitive and reducing overshoot or lag phenomena;

[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 compensates for the path error by accurately describing the correction amount of the joint. At the same time, combined with the improved PID control algorithm, the joint motion parameters are adjusted in real time to ensure smooth and accurate robot motion.

[0045] It should be understood that in the process of adopting the inverse kinematics algorithm and the improved PID control algorithm in the present invention, the requirement of real-time path correction in a complex dynamic environment is combined. Traditional inverse kinematics algorithms are usually used for path planning under ideal working conditions. However, in actual industrial applications, due to environmental disturbances, error accumulation, and the dynamic characteristics of the robot, traditional algorithms may have problems such as low calculation accuracy and slow correction. The present invention improves the inverse kinematics algorithm by adopting an adaptive optimization strategy. Based on continuously collecting error information, the joint angles are adjusted in real time to achieve faster and more accurate correction. In addition, the improved PID control algorithm dynamically adjusts according to the real-time error, improving the response speed and accuracy of the system to path deviations and avoiding the common overshoot or oscillation phenomena in traditional PID control.

[0046] Traditional PID control algorithms are difficult to handle instantaneous errors caused by environmental changes, and for different task scenarios, fixed , , values may lead to slow system response or oscillation. To solve this problem, the present invention uses an adaptive PID control algorithm to adjust the PID parameters in real time, enabling the control system to dynamically optimize according to the magnitude and change trend of the error.

[0047] The improved PID control formula is:

[0048]

[0049] Wherein, are the dynamic proportional gain, dynamic integral gain, and dynamic derivative gain respectively, which are dynamically adjusted 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 real-time monitoring the speed and amplitude of the error change, the control gain is dynamically adjusted, enabling the control system to respond more sensitively to instantaneous errors and reducing system oscillation.

[0050] For example, assume that 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 workpiece surface temperature, mechanical vibrations, etc.) cause the error between the actual path and the target path of the robot to change rapidly, resulting in a large instantaneous error. The welding robot calculates the target correction position of the end effector based on the real-time feedback data through the inverse kinematics algorithm. At this time, the actual position of the end effector is (x current , y current , z current ), while the target position is (x target , y target , z target ). After calculating the current error e(t), the inverse kinematics algorithm calculates the correction angles of the joints to make the end effector return to the target position. Based on the above error, the PID control system adjusts according to the change trend of the error . Assume that the initial error is e(t) = 3. At this time, the system will correspondingly increase the value to ensure that the joints respond quickly and shorten the path correction time. As the error gradually decreases to e(t) = 0.5, the system will reduce the value to prevent overshoot caused by overcorrection. During the welding process, the error may remain within a small range for a long time. At this time, the integral term will gradually increase to eliminate the accumulated steady-state error and ensure that the robot trajectory finally returns to the target path. When it is detected that the error changes too quickly, the system will automatically increase the to reduce instantaneous fluctuations and oscillations. Through this dynamically adjusted PID control, the welding robot can quickly adjust the path when encountering large external disturbances, return to the target trajectory, and at the same time avoid overcorrection and system oscillations, thus ensuring welding accuracy and stability.

[0051] Step S40: Continuously collect the adjusted position information, speed information, and acceleration information in real time through the multi-modal sensor, calculate the feedback error using the generalized least squares method, and correct the feedback error based on the improved Kalman filtering algorithm to further adjust the motion trajectory parameters.

[0052] It should be noted that the Generalized Least Squares (GLS) is an extended method of the least squares method for dealing with heteroscedastic and autocorrelated errors. It can effectively handle the measurement noise and errors that may exist in multi-modal sensor data and provide more accurate error estimates in the feedback loop. In the present invention, the Generalized Least Squares method fits the position information, velocity information, and acceleration information collected by the sensor to generate a more accurate feedback error value, including correcting the heteroscedastic error in the data through a weight matrix to ensure the accuracy of the error estimate; 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 measurement noise and the prediction of the system state model. Although the traditional Kalman filter performs well in noise filtering, it may be difficult to handle scenarios with drastic changes or excessive noise in complex environments. The improved Kalman filter algorithm enhances the adaptability to non-linear dynamic systems and combines an adaptive gain adjustment mechanism to dynamically adjust 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, and 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 subjected to error estimation and filtering processing during 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, assume that an industrial robot is performing a precise handling task in a factory environment with frequent vibrations. In this environment, the robot needs to move 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 ground vibrations, the robot's sensors frequently receive noisy position and velocity information.

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

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

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

[0058] Actually 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 vibrations result in lower weights. For example, for the strong vibration area, the weight is 0.5, and 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 weights 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 composed 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] Among them, is the updated state estimate, is the predicted state, is the Kalman gain matrix after dynamic adjustment, which is adaptively adjusted according to the dynamic change of the feedback error, is the current measurement value, is the observation matrix, is the error value at the current moment, is the dynamic adjustment coefficient, which is used to dynamically adjust the amplitude of state update according to the square of the error.

[0063] It can be understood that the core improvement point of this formula is to dynamically adjust the intensity of state update by introducing the parameter and adjust it in real time according to the size of the error, enhancing the response and adaptation ability to the error. This mechanism enables the system to more effectively cope with complex dynamic environments, especially under high-noise conditions, and maintain the stability and accuracy of the system.

[0064] For example, assume that an industrial robot is working in a high-noise environment, and this robot is responsible for performing precision assembly operations. Due to equipment vibrations and external force disturbances in the external environment, the position information and speed information measured by the sensors are both affected by noise interference, resulting in the actual movement trajectory of the robot deviating from the preset path. If the robot cannot correct these errors in time, it may lead to assembly failure or workpiece damage. In this case, the improved Kalman filter algorithm can more effectively cope with this high-noise environment through the introduction of an adaptive feedback mechanism. The specific implementation steps can be:

[0065] (1) Initialization phase:

[0066] When the robot starts to work, the system initializes the state vector according to the initial position and velocity information measured by the sensor , and sets the initial state covariance matrix . 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), with a certain initial error at this time.

[0067] (2) State prediction:

[0068] According to the motion model of the robot (such as uniform motion or accelerated motion), the system predicts the state of the robot at the next moment, such as position information and velocity. Assume that the robot is expected to reach the position (5.1, 3.1, 2.0) at the next time step, but due to the influence of 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), and there are also deviations in the velocity information.

[0069] (3) Feedback correction:

[0070] Through the improved Kalman filter algorithm, the system starts to correct the state. Using the measurement value = (5.25, 3.15, 2.05) and the predicted state = (5.1, 3.1, 2.0) to calculate the error:

[0071] = - = (5.25, 3.15, 2.05) - (5.1, 3.1, 2.0) = (0.15, 0.05, 0.05). At this time, the system calculates the square of the error vector , and introduces a dynamic adjustment term according to the formula to adjust the amplitude of state update. If the external noise is large, the sensor error is large, and the error vector is also large, at this time the dynamic adjustment coefficient increases, and the system will strengthen the correction of the error, making the state update at the next moment more radical, correcting the deviation in time, and avoiding system out - of - control. Since the state update formula is: , calculate the corrected state: = (5.1, 3.1, 2.0) + [(0.15, 0.05, 0.05) (1 + 0.5 0.0225)], so that the robot will quickly approach the target position through enhanced error feedback correction and reduce the error.

[0072] (4) Over time, if the environmental noise decreases and the measurement error also decreases accordingly, the system will automatically reduce the correction strength to avoid overcorrection and ensure the smooth movement of the robot. The system updates the Kalman gain matrix and error correction terms continuously to ensure high-precision path control in a noisy environment.

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

[0074] It should be noted that the adjustment range of the 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 path accuracy of the robot. In tasks such as large-scale handling or low-precision tasks, the adjustment range of the motion trajectory parameters can be appropriately relaxed. The specific adjustment range is also physically limited by the robot's working space, 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 necessary to consider avoiding collisions with other equipment or obstacles.

[0075] It can be understood that, depending on the task, the adjustment strategy of the motion trajectory parameters may vary greatly. In tasks that require high precision such as precision assembly or welding, the adjustment of the motion trajectory parameters usually needs to be maintained within a very narrow range. For handling or tasks with low 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 physical structure and working space limitations of the robot to avoid exceeding the limit positions that the robot can operate.

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

[0077] A positioning module, configured to receive Beidou satellite positioning data of a target area based on a Beidou satellite receiver, and determine the precise position of the target area by parsing the Beidou satellite positioning data;

[0078] A data acquisition and processing module, configured to obtain the current meteorological data source and historical meteorological data source according to the precise position of the target area, and obtain initial meteorological data and historical meteorological data after standardization processing;

[0079] A model construction and training module, which is used to construct an extreme weather event prediction model according to the initial meteorological data and historical meteorological data, adopt convolutional neural network and recurrent neural network algorithms for model training, and evaluate and optimize the model through cross-validation method;

[0080] A feature extraction and early warning release module, which is used to process the initial meteorological data by time series analysis method to obtain key meteorological features and input them into the trained extreme weather event prediction model, and release extreme weather event early warning information to the terminal according to the extreme weather event prediction result.

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

[0082] The present invention provides an industrial robot real-time path correction device, please refer to Figure 2, An industrial robot real-time path correction device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute an industrial robot real-time path correction method in Embodiment 1 above. An industrial robot real-time path correction device in an 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 Assistant), PADs (Portable Application Description: tablet computers), PMPs (Portable Media Player: portable multimedia players), in-vehicle terminals (such as in-vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. An industrial robot real-time path correction device is merely an example and should not impose any limitations on the functions and scope of use of embodiments of the present invention. An industrial robot real-time path correction device may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which may perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM: Random Access Memory) 1004. In the 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, the ROM 1002, and the 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 may be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, 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: Liquid Crystal Display), 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 may allow an industrial robot real-time path correction device to communicate with other devices wirelessly or wiredly to exchange data. Although an industrial robot real-time path correction device with various systems is shown in the figure, it should be understood that it is not required to implement or have all the shown systems. More or fewer systems may be alternatively implemented or had.

[0083] The present invention also provides a computer program product, including a computer program which, when executed by a processor, implements the steps of an industrial robot real-time path correction method as described above. The computer program product provided by the present invention can solve the technical problem of industrial robot real-time path correction. Compared with the prior art, the beneficial effects of the computer program product provided by the present invention are the same as those of the industrial robot real-time path correction method provided by the above embodiment, and will not be elaborated herein.

[0084] In particular, according to the embodiments disclosed by the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, an embodiment disclosed by the present invention includes a computer program product which includes a computer program carried on a computer-readable medium, and the computer program includes program codes for executing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by a processing device 1001, it executes the above functions defined in the methods of the embodiments disclosed by the present invention.

[0085] It should be understood that various parts of 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 a suitable manner in any one or more embodiments or examples.

[0086] Obviously, those skilled in the art can make various modifications and variations 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 equivalent technologies, the present invention also intends to include these modifications and variations.

Claims

1. A real-time path correction method for an industrial robot, characterized in that, The real-time path correction method for the industrial robot includes: Step S10: Based on the multi-modal sensors on the industrial robot, real-time collect the current position information, speed information, and acceleration information of the industrial robot; Step S20: Compare the collected current position information with the target path information, and use the Euclidean distance to calculate and generate a path error value between the current position information and the target path; Step S30: According to the path error value, use the inverse kinematics algorithm to calculate the target correction position of the end effector of the industrial robot and generate a correction matrix, and combine the improved PID control algorithm to adjust the motion trajectory parameters in real time. The motion trajectory parameters include the angle, angular velocity, acceleration, and angular acceleration parameters of the joints of the industrial robot. Among them, the correction matrix is a matrix used to adjust the motion parameters of each joint of the industrial robot. The correction matrix is used to describe the correction amount of each joint angle and convert the motion requirements at the end into joint angle changes. Combining the improved PID control algorithm to adjust the motion trajectory parameters in real time specifically includes: on the basis of the traditional PID control algorithm, combining an adaptive adjustment or gain optimization mechanism to adjust the motion trajectory parameters in real time. The specific formula used is: , where are the dynamic proportional gain, dynamic integral gain, and dynamic differential gain respectively, which are dynamically adjusted 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, the control gain is dynamically adjusted so that the control system can respond more sensitively to instantaneous errors and reduce system oscillation; Step S40: Continuously collect the adjusted position information, speed information, and acceleration information in real-time through the multi-modal sensors, calculate the feedback error using the generalized least squares method, and correct the feedback error based on the improved Kalman filtering algorithm to further adjust the motion trajectory parameters; wherein, the position information includes the robot joint angle data and the absolute position of the robot end in the working space; the acceleration information includes the acceleration changes of the robot joints or end effectors during movement.

2. The real-time path correction method for an industrial robot according to claim 1, wherein, The calculation method of the path error value includes the following Euclidean distance formula: Among them, is the coordinate of the current position information, is the coordinate of the target path information.

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

4. The real-time path correction method for an industrial robot according to claim 1, wherein The adjustment range of the motion trajectory parameters is dynamically adjusted according to the task requirements and the limitations of the robot working space.

5. The real-time path correction method for an industrial robot according to claim 1, wherein, The sampling frequency of the multi-modal sensors is dynamically adjusted according to the change of the industrial robot motion speed.

6. The real-time path correction method for an industrial robot according to claim 1, characterized in that, The Euclidean distance calculation is based on the Euclidean distance in the 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 multi-modal sensors include lidar, vision sensors, and force sensors.

8. A real-time path correction device for an industrial robot, which is applied to the real-time path correction method of the industrial robot according to any one of claims 1 to 7, and is characterized in that, The real-time path correction device for the industrial robot includes: An information collection module for real-time collecting the current position information, speed information, and acceleration information of the industrial robot based on the multi-modal sensors on the industrial robot; An error calculation module for comparing the collected current position information with the target path information and using the Euclidean distance to calculate and generate a path error value between the current position information and the target path; A path correction module, which is used to calculate the target correction position of the end effector of the industrial robot by using the inverse kinematics algorithm according to the path error value and generate a correction matrix, and combines an improved PID control algorithm to adjust the motion trajectory parameters in real time. The motion trajectory parameters include the angle, angular velocity, acceleration, and angular acceleration parameters of the joints of the industrial robot. Among them, the correction matrix refers to the matrix used to adjust the motion parameters of each joint of the industrial robot. The correction matrix is used to describe the correction amount of each joint angle and convert the motion requirements at the end into changes in joint angles. Combining the improved PID control algorithm to adjust the motion trajectory parameters in real time specifically includes: on the basis of the traditional PID control algorithm, combining an adaptive adjustment or gain optimization mechanism to adjust the motion trajectory parameters in real time. The specific formula used is: , where are the dynamic proportional gain, dynamic integral gain, and dynamic differential gain respectively, which are dynamically adjusted 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 more sensitively to instantaneous errors and reduce system oscillations; A feedback control module for continuously collecting the adjusted position information, speed information, and acceleration information in real-time through the multi-modal sensors, calculating the feedback error using the generalized least squares method, and correcting the feedback error based on the improved Kalman filtering algorithm to further adjust the motion trajectory parameters; wherein, the position information includes the robot joint angle data and the absolute position of the robot end in the working space; the acceleration information includes the acceleration changes of the robot joints or end effectors during movement.

9. An industrial robot real-time path correction device, applied to the industrial robot real-time path correction method according to any one of claims 1 to 7, characterized in that, The real-time path correction equipment for the industrial robot includes: A memory, a processor, and an industrial robot real-time path correction program stored on the memory and running on the processor. When the industrial robot real-time path correction program is executed by the processor, it implements the real-time path correction method for the industrial robot.

10. A computer program product, applied to the real-time path correction method of the industrial robot according to any one of claims 1 to 7, characterized in that, The computer program product includes an industrial robot real-time path correction program. When the industrial robot real-time path correction program is executed by the processor, it implements the real-time path correction method for the industrial robot.

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