Compensation method and device for robot position deviation

Abstract

The invention discloses a robot position deviation compensation method and device. The method comprises the following steps: after determining that a teaching object is placed in a teaching area, acquiring a first image of the teaching object by using image acquisition equipment; detecting the first image to obtain a first teaching position of the robot; after it is detected that the total operation duration of the robot reaches an operation duration threshold value, the robot is controlled to return to the teaching area, and a second image of the teaching object is collected through image collection equipment; detecting the second image to obtain a second teaching position of the robot; determining a position deviation between the first teaching position and the second teaching position; obtaining compensation information of the position deviation according to the position deviation through a global error model; and compensating the robot according to the compensation information. The technical problems that in the related technology, a mode for compensating position precision reduction caused by mechanical abrasion, thermal deformation and other factors of a robot system is complex, and consumed time is long are solved.

Description

Technical Field

[0001] This invention relates to the field of robot control technology, and more specifically, to a method and apparatus for compensating for robot position deviation. Background Technology

[0002] In modern industrial automation, the application of robotics technology is becoming increasingly widespread, especially in manufacturing, assembly, and material handling. The positional accuracy of a robot system directly affects its operational efficiency and product quality. However, over long-term operation, the positional accuracy of a robot system may gradually decrease due to factors such as mechanical wear and thermal deformation. This necessitates regular position calibration and compensation to ensure the accuracy and reliability of the robot's operation.

[0003] Currently, contact-based detection and compensation for robot position deviations are common solutions. For example, by installing tactile sensors at the robot's end effector, the sensors detect the position of the contact point when the robot comes into contact with the workpiece, and then adjust the robot's position through control algorithms to compensate for position deviations. However, this method has some drawbacks: First, the accuracy of contact-based detection is limited by sensor performance and contact conditions, often making it difficult to achieve the same high-precision results as non-contact detection; second, recalibrating the entire system is complex, requiring the robot to stop production and consuming a significant amount of time for calibration, thus impacting production efficiency.

[0004] For non-contact position compensation, vision-based detection methods have gradually become a research hotspot due to their high accuracy and non-invasiveness. Vision-based detection methods typically involve using vision sensors fixed within the robot's workspace to determine the robot's actual position in space by identifying targets at the robot's end effector (such as a checkerboard calibration board), thereby calculating the position deviation and performing correction. This method avoids the accuracy problems of contact detection and allows for real-time detection during robot operation, improving position retrieval speed and simplifying the system calibration process.

[0005] However, existing visual inspection methods may face challenges in handling complex situations, such as significant positional deviations after long-term robot operation, and situations involving multiple links and joint axes in the robot system. These challenges include difficulties in model construction and complex parameter identification. In particular, when dealing with the case of parallel adjacent joint axes, the limitations of the traditional MDH (M Denavit and J. Hartenberg) model may lead to abrupt parameter changes in the link error model, affecting the model's continuity and accuracy.

[0006] There is currently no effective solution to the above problems. Summary of the Invention

[0007] This invention provides a method and apparatus for compensating robot position deviation, which at least solves the technical problem that the methods used in related technologies to compensate for the reduction in position accuracy of robot systems due to factors such as mechanical wear and thermal deformation are relatively complex and time-consuming.

[0008] According to one aspect of the present invention, a method for compensating for robot position deviation is provided, comprising: after determining that an image acquisition device is fixedly connected to the end effector of a robot's robotic arm, placing a teaching object in a teaching area; after determining that the teaching object is placed in the teaching area, acquiring a first image of the teaching object using the image acquisition device; detecting the first image to obtain a first teaching position of the robot; after detecting that the total running time of the robot has reached a running time threshold, controlling the robot to return to the teaching area, and acquiring a second image of the teaching object using the image acquisition device; detecting the second image to obtain a second teaching position of the robot; determining the position deviation between the first teaching position and the second teaching position; obtaining compensation information for the position deviation based on the position deviation using a global error model; and compensating the robot according to the compensation information.

[0009] Optionally, after determining that the image acquisition device is fixedly connected to the end effector of the robot's robotic arm, and before placing the teaching object in the teaching area, the compensation method further includes: placing a checkerboard calibration plate within the reachable range of the robot's robotic arm, wherein the position of the checkerboard calibration plate and the robot's base remains fixed; controlling the robot to move to multiple known positions, and recording the calibration plate images acquired by the image acquisition device and the theoretical coordinates of the robot's end effector at the multiple known positions; analyzing the calibration plate images and the theoretical coordinates to obtain the transformation matrix between the coordinate system of the image acquisition device and the end effector.

[0010] Optionally, before obtaining the compensation information for the position deviation based on the position deviation through the identification model, the compensation method further includes: establishing a kinematic model of the robot's links based on the DH parameters; generating a local error model for each link of the robot based on the DH parameters and the kinematic model; and combining the local error model with the end effector error model of the robot's end effector to obtain the robot's global error model.

[0011] Optionally, obtaining compensation information for the position deviation based on the position deviation through a global error model includes: transmitting the position deviation to the global error model to process the position deviation using the global error model, thereby obtaining the joint zero-point compensation value and the link length compensation value of the robot.

[0012] Optionally, the compensation method further includes: when the adjacent joint axes of the robot are parallel, determining a plane perpendicular to the Zi-1 axis and passing through point Oi-1; calculating the distance α between the Zi-1 axis and the Xi axis after rotation by an angle θ, as well as the rotation and translation amounts; updating the link parameter table and introducing a tool coordinate system to adapt to the error compensation requirements of special positions and attitudes; establishing a differential error model and calculating the partial differential terms of each link parameter to reflect the correlation between the end effector pose change and the link parameter error.

[0013] Optionally, compensating the robot according to the compensation information includes: updating the linkage parameters in the robot's control system using the compensation information to compensate the robot.

[0014] Optionally, the compensation method further includes: collecting actual coordinate data of the robot at different positions; comparing the actual coordinate data with theoretical pose data to evaluate the position deviation in order to verify the global error model.

[0015] According to the above-described technical solution of the present invention, a robot position deviation compensation device is provided, comprising: a processing unit, configured to place a teaching object in a teaching area after determining that an image acquisition device is fixedly connected to the end of the robot's robotic arm; a acquisition unit, configured to acquire a first image of the teaching object using the image acquisition device after determining that the teaching object is placed in the teaching area; a first detection unit, configured to detect the first image to obtain a first teaching position of the robot; a control unit, configured to control the robot to return to the teaching area and acquire a second image of the teaching object using the image acquisition device after detecting that the total running time of the robot has reached a running time threshold; a second detection unit, configured to detect the second image to obtain a second teaching position of the robot; a determination unit, configured to determine the position deviation between the first teaching position and the second teaching position; an acquisition unit, configured to obtain compensation information for the position deviation based on the position deviation using a global error model; and a compensation unit, configured to compensate the robot according to the compensation information.

[0016] Optionally, the compensation device further includes: a placement unit, configured to place a checkerboard calibration plate within the reach of the robot's robotic arm after the image acquisition device is fixedly connected to the end effector of the robot, and before placing the teaching object in the teaching area, wherein the checkerboard calibration plate remains fixed in position to the robot's base; the control unit, configured to control the robot to move to multiple known positions and record the calibration plate images acquired by the image acquisition device and the theoretical coordinates of the robot's end effector at the multiple known positions; and an analysis unit, configured to analyze the calibration plate images and the theoretical coordinates to obtain a transformation matrix between the coordinate system of the image acquisition device and the end effector.

[0017] Optionally, the compensation device further includes: a modeling unit, configured to establish a kinematic model of the robot's links based on DH parameters before obtaining compensation information for the position deviation through the identification model; a generation unit, configured to generate a local error model for each link of the robot based on the DH parameters and the kinematic model; and a combination unit, configured to combine the local error model with the end effector error model of the robot's end effector to obtain a global error model of the robot.

[0018] Optionally, the acquisition unit includes a processing module, configured to transmit the position deviation to the global error model, so as to process the position deviation using the global error model to obtain the joint zero-point compensation value of the robot and the link length compensation value of the robot's link length.

[0019] Optionally, the compensation device further includes: the determining unit, used to determine a plane perpendicular to the Zi-1 axis and passing through point Oi-1 when the adjacent joint axes of the robot are parallel; the calculation unit, used to calculate the distance α between the Zi-1 axis and the Xi axis after rotation by an angle θ, as well as the rotation and translation amount; the updating unit, used to update the link parameter table and introduce the tool coordinate system to adapt to the error compensation requirements of special positions and attitudes; the calculation unit is used to establish a differential error model and calculate the partial differential terms of each link parameter to reflect the correlation between the end effector pose change and the link parameter error.

[0020] Optionally, the compensation unit includes: an update module, used to update the link parameters in the robot's control system using the compensation information, so as to compensate the robot.

[0021] Optionally, the compensation device further includes: a data acquisition unit for acquiring actual coordinate data of the robot at different positions; and a processing unit for comparing the actual coordinate data with theoretical pose data to evaluate the position deviation and verify the global error model.

[0022] According to the technical solutions provided in the above embodiments of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein the program executes the robot position deviation compensation method described in any one of the above embodiments.

[0023] According to the above-described technical solutions provided in the embodiments of the present invention, a processor is provided, the processor being used to run a program, wherein the program executes the robot position deviation compensation method described in any one of the above-described methods during runtime.

[0024] According to the above-described technical solutions provided in the embodiments of the present invention, a computer program product is provided, including computer instructions, wherein when the computer instructions are executed by a processor, the robot position deviation compensation method described in any one of the above-described methods is executed.

[0025] In this embodiment of the invention, after confirming that the image acquisition device is fixedly connected to the end of the robot's robotic arm, the teaching object is placed in the teaching area; after confirming that the teaching object is placed in the teaching area, a first image of the teaching object is acquired using the image acquisition device; the first image is detected to obtain the robot's first teaching position; after detecting that the robot's total running time has reached a running time threshold, the robot is controlled to return to the teaching area, and a second image of the teaching object is acquired using the image acquisition device; the second image is detected to obtain the robot's second teaching position; the positional deviation between the first teaching position and the second teaching position is determined; compensation information for the positional deviation is obtained based on the positional deviation using a global error model; and the robot is compensated according to the compensation information. The technical solution provided by this invention achieves the following: using a visual sensor fixed to the end effector of a robot to detect pose deviations; applying a hand-eye calibration algorithm to establish the transformation between the camera and the robot flange coordinate system; using the MDH linear error model and the differential error model of the special link and tool coordinate system to identify errors; and using the least squares method to solve for the optimal compensation value. This simplifies the robot position compensation method and solves the technical problem that the methods used in related technologies to compensate for the reduction in position accuracy of robot systems due to mechanical wear, thermal deformation, and other factors are relatively complex and time-consuming. Attached Figure Description

[0026] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0027] Figure 1 This is a hardware structure block diagram of a mobile terminal for a robot position deviation compensation method according to an embodiment of the present invention.

[0028] Figure 2 This is a flowchart of a robot position deviation compensation method according to an embodiment of the present invention;

[0029] Figure 3 This is a flowchart of an optional robot position deviation compensation method according to an embodiment of the present invention;

[0030] Figure 4 This is a flowchart of robot error identification according to an embodiment of the present invention;

[0031] Figure 5 This is a schematic diagram of the link error when the joint axes are parallel according to an embodiment of the present invention;

[0032] Figure 6 This is a schematic diagram of a parallel rotational joint model according to an embodiment of the present invention;

[0033] Figure 7 This is a schematic diagram of a robot position deviation compensation device according to an embodiment of the present invention.

[0034] The above figures include the following reference numerals:

[0035] 102. Processor; 104. Memory; 106. Transmission device; 108. Input / output device. Detailed Implementation

[0036] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0037] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0038] As described in the background section, existing technologies for compensating for positional accuracy reduction in robot systems due to factors such as mechanical wear and thermal deformation are complex and time-consuming. This invention provides a method and apparatus for compensating for robot positional deviations, a computer-readable storage medium, a processor, and a computer program product.

[0039] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0040] The methods and embodiments provided in this invention can be executed on a mobile terminal, a computer terminal, or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for a robot position deviation compensation method according to an embodiment of the present invention. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0041] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the robot position deviation compensation method in this embodiment of the invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or send data via a network. Specific examples of the aforementioned networks may include wireless networks provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0042] Example 1

[0043] According to an embodiment of the present invention, a method embodiment for compensating robot position deviation is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0044] Figure 2 This is a flowchart of a robot position deviation compensation method according to an embodiment of the present invention, as shown below. Figure 2 As shown, the method includes the following steps:

[0045] Step S202: After confirming that the image acquisition device is fixedly connected to the end of the robot's robotic arm, place the teaching object in the teaching area.

[0046] Optionally, the image acquisition device mentioned above can be a camera; of course, it can also be other forms of information acquisition devices.

[0047] Optionally, the teaching object mentioned above is a marker object used to teach the robot. The teaching area mentioned above is the location the robot moves to.

[0048] In this embodiment, the first step is to select a suitable image acquisition device (such as an industrial camera) and mount it on the end effector of the robotic arm. The installation should ensure that the device is stable and does not experience relative displacement with the movement of the robotic arm. This typically involves using a dedicated mounting bracket or adapter and performing appropriate calibration to ensure that the relative position between the image acquisition device and the robotic arm is known.

[0049] Fixed image acquisition equipment is the foundation for non-contact position detection. The fixed connection between the equipment and the end effector of the robotic arm ensures that the image data accurately reflects the relative position of the end effector, and the analysis of the image data can be further converted into position information in the flange coordinate system of the robotic arm, which is the starting point for subsequent deviation detection and compensation.

[0050] By using a fixed connection, additional measurement errors are avoided, ensuring the reliability and consistency of position data. At the same time, this method avoids mechanical interference that may arise from contact measurements, improving the accuracy and stability of position detection.

[0051] Step S204: After determining that the teaching object is placed in the teaching area, the first image of the teaching object is acquired using an image acquisition device.

[0052] In this embodiment, the teaching object (such as a checkerboard calibration board) should be placed within the teaching area accessible to the robotic arm, keeping the relative position of the teaching object and the robot base fixed. The selection of the teaching area should take into account the range of motion of the robotic arm and the field of view of the image acquisition device, ensuring that the teaching object is not obstructed by the robotic arm or other obstacles when acquiring images.

[0053] The teachable object serves as a reference for visual inspection, and its fixed geometric features (such as the corners of a checkerboard pattern) can be captured and analyzed by image acquisition equipment. By comparing the performance of the teachable object in the images when the robot is in different positions, the positional deviation in the robotic arm's flange coordinate system can be calculated.

[0054] Using specific teaching objects and regions can simplify the deviation detection process. Fixed teaching objects provide a stable visual reference, making deviation detection more accurate and repeatable, thereby improving the efficiency and accuracy of position compensation.

[0055] Step S206: Detect the first image to obtain the robot's first teaching position.

[0056] In this embodiment, before the robot begins working, an image of the teaching object is acquired, and the robot's initial position in the flange coordinate system is determined through image processing and recognition algorithms. This typically involves steps such as image feature point extraction and pose estimation.

[0057] Image processing algorithms (such as edge detection and feature point recognition) can extract key features of the taught object from the acquired image. By comparing these features with the expected geometric features, the relative position and orientation of the robot's end effector can be calculated. This is a crucial step in establishing a reference position.

[0058] The initial teaching position obtained through image acquisition and processing provides an initial reference point for subsequent positional deviation detection, which helps to accurately identify deviations. This method not only improves the automation of detection but also reduces dependence on specific hardware (such as laser trackers), thereby lowering the cost and complexity of the system.

[0059] Step S208: After detecting that the total running time of the robot has reached the running time threshold, control the robot to return to the teaching area and acquire a second image of the teaching object through the image acquisition device.

[0060] In this embodiment, during the robot's operation, its total runtime is monitored. When a preset threshold is reached, the robot is automatically controlled to return to the teaching area and acquire images of the teaching object again. The image data also needs to undergo image processing to obtain a second teaching position.

[0061] The runtime threshold is set based on empirical data and anticipated changes in robot link parameters. When the robot operates for extended periods, link parameters may undergo slight changes due to various factors (such as wear and temperature variations), leading to positional deviations. By re-acquiring images and determining a second taught position, the resulting positional deviations can be quantified.

[0062] This method can automatically detect positional deviations periodically, ensuring that potential deviations are identified and compensated for even after long periods of operation, thus maintaining the robot's working accuracy. Furthermore, automated follow-up visits and inspections reduce the need for manual intervention, improving system efficiency and reliability.

[0063] Step S210: Detect the second image to obtain the robot's second teaching position.

[0064] In this embodiment, the teaching positions acquired in the first and second acquisitions are compared, and the difference between them is calculated. This typically involves mathematical operations, such as vector subtraction or Euclidean distance calculation, to quantify the positional deviation.

[0065] Positional deviation is determined by comparing the performance of the first and second taught positions in the flange coordinate system. Considering that the position of the robotic arm's end effector may shift due to minute changes in link parameters, the key to this step is accurately identifying these changes.

[0066] Accurately determining the positional deviation is a prerequisite for effective compensation. This step allows for the precise quantification of the deviation's magnitude and direction, providing concrete data support for subsequent correction. This helps improve the robot's positional control accuracy, especially when performing high-precision tasks.

[0067] Step S212: Determine the positional deviation between the first teaching position and the second teaching position.

[0068] In this embodiment, after determining the positional deviation, this deviation is used as input and calculated using a pre-established global error model to obtain compensation information. This may involve multiple steps, such as matrix operations and solving linear equations.

[0069] The global error model is built upon the link parameter error model, which can analyze the relationship between position deviation and link parameters. By mapping position deviation to errors in link parameters, the model can calculate the parameter values ​​that need to be adjusted, thereby generating compensation information.

[0070] The application of the global error model here makes position deviation compensation systematic and automated. The compensation information obtained through model calculation can accurately guide the parameter adjustment of the robot control system, effectively reducing or eliminating position deviation and improving the robot's working accuracy and stability.

[0071] Step S214: Obtain position deviation compensation information based on position deviation using the global error model.

[0072] Step S216: Compensate the robot according to the compensation information.

[0073] Based on the calculated compensation information, the link parameters in the robot's control system are updated. This typically requires modifying the parameters via a teach pendant or a dedicated programming interface.

[0074] The compensation information includes suggestions for adjusting the robot's link parameters. By receiving and applying this information, the control system can correct positional deviations caused by link parameter errors. This involves recalculating the robot's inverse kinematics to ensure that the end effector accurately reaches the intended position.

[0075] By implementing compensation, the long-term operational performance of robots can be significantly improved, the need for maintenance and calibration can be reduced, and production efficiency and product quality can be increased simultaneously. This method also adapts to the robot's dynamic working environment, ensuring that the robot maintains high-precision position control even in complex and constantly changing tasks.

[0076] As described above, in this embodiment of the invention, after determining that the image acquisition device is fixedly connected to the end of the robot's robotic arm, the teaching object is placed in the teaching area; after determining that the teaching object is placed in the teaching area, the first image of the teaching object is acquired using the image acquisition device; the first image is detected to obtain the robot's first teaching position; after detecting that the robot's total running time has reached the running time threshold, the robot is controlled to return to the teaching area, and the second image of the teaching object is acquired using the image acquisition device; the second image is detected to obtain the robot's second teaching position; the positional deviation between the first teaching position and the second teaching position is determined; the positional deviation compensation information is obtained based on the positional deviation using a global error model; the robot is compensated according to the compensation information, thus achieving the purpose of using a visual sensor fixed to the robot's end to detect pose deviation, applying a hand-eye calibration algorithm to establish the transformation between the camera and the robot flange coordinate system, using the MDH linear error model and the differential error model of the special link and tool coordinate system to identify errors, and using the least squares method to solve for the optimal compensation value, thereby achieving the effect of simplifying the robot position compensation method.

[0077] Therefore, the technical solutions provided by the embodiments of the present invention solve the technical problem that the methods for compensating for the reduction in position accuracy of robot systems due to factors such as mechanical wear and thermal deformation are relatively complex and time-consuming in the related technologies.

[0078] Figure 3 This is a flowchart of an optional robot position deviation compensation method according to an embodiment of the present invention, such as... Figure 3 As shown, firstly, a 3D camera is fixed to the robot's end effector to complete robot-vision calibration; then, a detection marker (i.e., the teaching object) is placed within the reach of the robotic arm, and the camera is used to detect the marker's position, recording the robot's teaching position. After the robot continues operating for a certain period, it returns to the teaching position to re-detect the marker's position; and based on the positional deviation, a kinematic error identification model is established to compensate for joint zero points and link lengths.

[0079] According to the above embodiments of the present invention, after determining that the image acquisition device is fixedly connected to the end of the robot's robotic arm, before placing the teaching object in the teaching area, the compensation method further includes: placing a checkerboard calibration plate within the reachable range of the robot's robotic arm, wherein the position of the checkerboard calibration plate and the robot's base remains fixed; controlling the robot to move to multiple known positions, and recording the calibration plate image acquired by the image acquisition device and the theoretical coordinates of the robot's end effector at the multiple known positions; analyzing the calibration plate image and the theoretical coordinates to obtain the transformation matrix between the coordinate system of the image acquisition device and the end effector.

[0080] In this embodiment, a checkerboard calibration plate with precise geometric features is placed within the robot's working range. The calibration plate is fixed to the base, ensuring that the position of the calibration plate relative to the robot remains unchanged during each measurement.

[0081] The robot is controlled to move to multiple preset positions to ensure that the checkerboard calibration board is included in the camera's field of view each time, so as to collect images and corresponding theoretical coordinate data at different angles and distances.

[0082] Using image processing and pattern recognition technologies, corner information of the chessboard is extracted from the acquired images. Combined with the theoretical coordinates of the robot at each position, the transformation matrix between the camera coordinate system and the robot end effector coordinate system is estimated through a hand-eye calibration algorithm.

[0083] The hand-eye calibration algorithm, based on geometric relationships and algebraic equations, calculates the transformation relationship between the camera coordinate system and the robot tool coordinate system using the known robot motion trajectory and captured image features. This transformation matrix is ​​crucial for subsequent visual inspection and positional deviation compensation, as it establishes a connection between visual information and the robot's actual pose.

[0084] The transformation matrix obtained by the above algorithm can transform data points in the image coordinate system to the robot coordinate system, thereby allowing for accurate identification of positional deviations, even in non-standard robot configurations or special working environments.

[0085] Through accurate hand-eye calibration, the method can more reliably identify robot positional deviations, thereby improving the accuracy of correction. The design of the calibration board and the automation of the calibration process enable the method to be flexibly applied in a variety of different robot configurations and working environments, enhancing the versatility of visual correction.

[0086] That is, first, fix the camera to the end of the robot, place the checkerboard calibration plate within the reach of the robot arm, keep the calibration plate and the robot base in a fixed position, move the robot to n positions, ensure that the calibration plate is still in the field of view of the camera at each position, use the hand-eye calibration algorithm to calibrate the position of the point in the camera coordinate system in the robot arm flange coordinate system Pt1, and obtain the transformation matrix Tcp of the two coordinate systems at the same time.

[0087] Secondly, after the robot runs for a period of time, it re-runs the previous n positions, and uses the same transformation relationship to mark the positions in the same camera coordinate system as Pt2 in the flange coordinate system; further, error parameters are identified. Figure 4 This is a flowchart of robot error identification according to an embodiment of the present invention, such as... Figure 4As shown, the process is as follows: Initialize the link parameters to nominal values, obtain the observation equations based on the nominal values ​​(J1-J6 forward solutions) and the measured values; use the least squares method to obtain the motion parameter error assessment, and update the link parameters.

[0088] According to the above embodiments of the present invention, before obtaining the position deviation compensation information based on the position deviation through the identification model, the compensation method further includes: establishing a kinematic model of the robot's links based on the DH parameters; generating a local error model of each link of the robot based on the DH parameters and the kinematic model; and combining the local error model with the end effector error model of the robot's end effector to obtain the robot's global error model.

[0089] In this embodiment, a kinematic model for each link is established based on DH parameter theory, ensuring that the model covers all possible motion scenarios of the robot. Using the link parameter model, a local differential error model for each link is generated, laying the foundation for the subsequent global error model. The local error models of all links are then connected to form a model describing the global pose deviation of the entire robot.

[0090] Denavit-Hartenberg parameters are a set of mathematical models describing the relative positions and orientations of robot links, used for solving forward and inverse kinematics problems. By performing differential analysis on the link parameters, the rate of change of positional deviation over time can be quantified, which helps to understand the root causes of the deviations and design compensation strategies accordingly.

[0091] By constructing a global error model, the method can gain a deep understanding of the contribution of each link parameter change to the pose deviation of the robot's end effector, providing a mathematical basis for accurate correction.

[0092] Based on the understanding of error sources, it is possible to design compensation strategies specifically for link parameters, which can effectively reduce robot pose deviations and improve operational accuracy.

[0093] According to the above embodiments of the present invention, obtaining position deviation compensation information based on position deviation through a global error model includes: transmitting the position deviation to the global error model, processing the position deviation using the global error model, and obtaining the joint zero-point compensation value and the link length compensation value of the robot.

[0094] In this embodiment, the positional deviation obtained from visual detection is used as input and passed to the global error model. The model calculates specific compensation values. Compensation information is generated, including correction values ​​for the robot joint zero points and adjustments to the link lengths, to compensate for the detected positional deviation.

[0095] The global error model, based on the dynamics and geometry of the links, can predict and analyze the impact of different link parameter deviations on the overall robot pose deviation. By using the error model and calculating the correction amounts for the link parameters, positional deviations caused by parameter deviations can theoretically be eliminated.

[0096] This method effectively reduces the positional deviation accumulated by the robot after long-term operation, improving the continuity and reliability of robot operations. By accurately calculating the link parameters and joint zero-point compensation information, it ensures that the robot can achieve higher operational accuracy, especially in high-precision manufacturing and assembly tasks.

[0097] According to the above embodiments of the present invention, the compensation method further includes: determining a plane perpendicular to the Zi-1 axis and passing through point Oi-1 when the adjacent joint axes of the robot are parallel; calculating the distance α between the Zi-1 axis and the Xi axis after rotation by an angle θ, as well as the rotation and translation amount; updating the link parameter table and introducing a tool coordinate system to adapt to the error compensation requirements of special positions and postures; establishing a differential error model and calculating the partial differential terms of each link parameter to reflect the correlation between the end effector pose change and the link parameter error.

[0098] It should be noted that in this embodiment of the invention, the error is defined in a local coordinate system, and the model is constructed using error propagation. First, assuming the link is rigid, the link coordinate system can be described by four scalars, and the geometric error model of the link can be represented as the differential changes of these nominal values. Therefore, the differential changes in the position and orientation of two consecutive links can be described by the differential changes in the link parameters. However, in a special case, when two consecutive joint axes are nearly parallel, continuing to use MDH features for modeling violates the above assumption (the differential changes in the position and orientation of two consecutive links can be described by the differential changes in the link parameters). This can be summarized as a sudden change in the parameters of the link error model, which needs to be addressed first.

[0099] Figure 5 This is a schematic diagram of the link error when the joint axis is parallel according to an embodiment of the present invention, as shown below. Figure 5 As shown in Figure 6, for the case where adjacent joint axes are parallel, a new link joint model different from the MDH needs to be created. Figure 6 This is a schematic diagram of parallel rotational joint modeling according to an embodiment of the present invention. Step 1: Determine the relationship with Z... i-1 The axis is perpendicular to and passes through the origin O. i-1 The plane, and the plane intersects with Z. i The intersection of the axes is O. i , connect O i-1 O i The direction of the extension line of the point is X. i Axis; Step 2: Set Z i-1Rotate the axis by an angle θ, then along the X axis. i Translate along axis 'a', around X i Axial rotation α, Y i The axial rotation β ultimately aligns adjacent coordinate systems; the formula for aligning adjacent coordinate systems obtained through the above modeling steps is expressed as: .

[0100] The updated link parameter table for the GR35 robot based on the above modeling steps is as follows:

[0101] Table 1. GR35 Robot Linkage Parameter Update Table

[0102]

[0103] Based on actual testing needs, during robot calibration, a camera or laser tracker target ball is usually fixed at the end effector for spatial position measurement (not directly measuring the TCP center pose). For sensors held at the robot end effector, a tool coordinate system TN can be introduced to represent them. At this point, the link parameters can be updated as follows:

[0104] Table 2. Linkage Parameters of the GR35 Robot with Tool Coordinate System

[0105]

[0106] The transformation matrices between each joint (non-parallel axis) are as follows: .

[0107] In the special case where joints 2 and 3 are parallel, to ensure transformation continuity, based on Hayati's method, the transformation matrix is ​​as follows: .

[0108] The end-link parameter error model can be expressed as: In the above formula , , .

[0109] For the three cases mentioned above, establish local differential error models between links and local differential error models of the end-effector system respectively. The specific calculation steps can be referred to above.

[0110] The local differential error model between non-parallel joints is as follows: , .in, , , , , .

[0111] The local differential error model for adjacent joint axes being parallel is as follows: ,in, , , ,in, , ,

[0112] , , .

[0113] The local differential error model for the end-effector is as follows: , ,in: ; ; ; , , , , .

[0114] Since the sensor is mounted on the gripper at the end of the robotic arm, it is necessary to transform the local differential error model into differential error in the tool coordinate system. The transformation method is as follows: For the calculation of differentials, higher-order infinitesimal terms are omitted by default in the above formula. After simplification, we get: Further simplification of the above formula yields: .

[0115]

[0116] Combining the above equations and rearranging, we get: Simplifying the above equation, we get: .

[0117] in, This completes the robot error propagation model, revealing the relationship between the robot's local pose error and the end-effector's pose error. Next, using known conditions, we will construct models for the end-effector's pose error and the robot's link parameter errors:

[0118] .

[0119] Simplifying the above equation, we get: .

[0120] By combining the results of the differential error and the error propagation, we can obtain the global differential error as follows:

[0121]

[0122]

[0123] Further simplification of the above equation reveals: .

[0124] The above error model construction process is quite complex, and the parameters are not uniform for different situations. Therefore, an improved kinematic model of GR35 based on MDH is proposed.

[0125] Similar to Method 1, the transformation formula for adjacent coordinate systems can be found as follows:

[0126] ,

[0127] Continuing with the same partial differential operations as in Method 1, we can obtain: Similarly, the differential bias of the current joint's orientation can be obtained as follows: .

[0128] According to the above embodiments of the present invention, compensating the robot according to the compensation information includes: updating the link parameters in the robot's control system using the compensation information to compensate the robot.

[0129] In this embodiment, compensation information obtained from the global error model (such as correction values ​​for joint zero points and link lengths) is transmitted to the robot control system to update link parameters and joint settings. After the update, the robot control system will operate with the new link parameters and can verify the correction effect through visual inspection again, making fine adjustments if necessary.

[0130] Based on the compensation information, the robot control system can adjust the linkage parameters, enabling the robot to reach the desired position more accurately in subsequent operations and reducing position deviations caused by accumulated errors.

[0131] By controlling the parameters in real time, the robot can quickly adjust its behavior, thereby correcting common positional deviations during operation without stopping the operation for manual intervention.

[0132] By periodically adjusting the linkage parameters based on visual inspection results, high-precision operation of the robot is ensured throughout its operating cycle, improving the efficiency and product quality of industrial automated production lines.

[0133] Solve the equation using the least squares method: The essence of the least squares method is to find the best function fit for the data by minimizing the sum of squared errors. This method is used to find the optimal value of the objective function, and can also be used for curve fitting or to solve regression problems. This paper addresses a multiple linear regression problem, where the output is affected by multiple factors. The goal is to find a hyperplane that uses the deviation between the actual measured pose and the theoretical pose in positive kinematics as the system input. Through multiple iterations, a set of solutions that minimizes the system deviation is obtained. The least squares method for the multiple linear regression model can be represented in matrix form. The specific solution process is omitted, and the parameter estimation model is given directly below. ,in: It is a design matrix. It is a parameter vector. It is the response variable vector.

[0134] To establish a connection between the laser tracker and the robot and to achieve data conversion between two different coordinate systems, it is necessary to determine the reference coordinate system of the entire measurement system (the system consisting of the laser tracker and the robot); since the target ball is fixed at the end of the TCP and does not coincide with the axis, it is also necessary to determine the robot tool coordinate system (the target ball is regarded as the end tool).

[0135] The first method treats the robot's base coordinate system as the system's reference coordinate system. By rotating only one and two axes and using a laser tracker to collect data, the relationship between the robot's base coordinate system and the laser tracker's coordinate system is fitted; according to the formula: ,in: Indicates the tool coordinate system. Represents the TCP coordinate system. Measurement system coordinate system.

[0136] According to the above embodiments of the present invention, the compensation method further includes: collecting actual coordinate data of the robot at different positions; comparing the actual coordinate data with the theoretical pose data, evaluating the position deviation, and verifying the global error model.

[0137] In this embodiment, actual coordinate data of the robot is collected at multiple different locations, including differences in pose and position. The collected coordinate data is compared with the coordinate data predicted based on the theoretical model to evaluate the positional deviation and verify the accuracy and effectiveness of the global error model. Based on the evaluation results of the deviation, the model can be iteratively adjusted until the model prediction matches the actual measurement with a high degree of agreement.

[0138] By ensuring that the global error model can accurately describe the robot link parameter errors, a solid theoretical foundation is provided for the formulation of subsequent compensation strategies.

[0139] For example, using the AT960 laser tracker software, the GR35A design link parameter model and the laser tracker reference position are imported to generate robot command positions (50 sets of joint angles) within the robot's Cartesian motion range and joint motion range. The command positions are imported into the robot teach pendant, and the actual robot position corresponding to each set of command positions is collected using the laser tracker. The actual and theoretical positions are used as inputs to import the calculation method used in this paper to obtain the robot's link parameter compensation values, and the compensation performance is output. The link parameter compensation values ​​are imported into the teach pendant to update the link parameters, and step two is continued to verify the deviation between the theoretical and actual positions. The calibration performance output by this calculation method and the calibration performance tested by the laser tracker are compared to confirm the above correctness.

[0140] In addition, during data preprocessing, firstly, according to the error formula: .in This is the error formula in the robot tool coordinate system. The actual measured position is in the laser tracker reference coordinate system, so a coordinate system transformation is required. Because a laser tracker is used as the data acquisition tool, the input information only includes location. Therefore, the identification matrix J only retains the first three rows, that is, it only compensates for the robot's positional accuracy.

[0141] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0142] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0143] Example 2

[0144] According to embodiments of the present invention, a robot position deviation compensation device for implementing the above-described robot position deviation compensation method is also provided. Figure 7 This is a schematic diagram of a robot position deviation compensation device according to an embodiment of the present invention, as shown below. Figure 7 As shown, the device includes: a processing unit 701, a data acquisition unit 703, a first detection unit 705, a control unit 707, a second detection unit 709, a determination unit 711, an acquisition unit 713, and a compensation unit 715. The device will be described below.

[0145] The processing unit 701 is used to place the teaching object in the teaching area after determining that the image acquisition device is fixedly connected to the end of the robot's robotic arm.

[0146] The acquisition unit 703 is used to acquire the first image of the teaching object using an image acquisition device after determining that the teaching object has been placed in the teaching area.

[0147] The first detection unit 705 is used to detect the first image to obtain the robot's first teaching position.

[0148] The control unit 707 is used to control the robot to return to the teaching area after detecting that the total running time of the robot has reached the running time threshold, and to acquire a second image of the teaching object through the image acquisition device.

[0149] The second detection unit 709 is used to detect the second image to obtain the robot's second teaching position.

[0150] The determining unit 711 is used to determine the positional deviation between the first teaching position and the second teaching position.

[0151] The acquisition unit 713 is used to obtain position deviation compensation information based on the position deviation through the global error model.

[0152] The compensation unit 715 is used to compensate the robot according to the compensation information.

[0153] It should be noted that the above-mentioned processing unit 701, acquisition unit 703, first detection unit 705, control unit 707, second detection unit 709, determination unit 711, acquisition unit 713 and compensation unit 715 correspond to steps S202 to S216 in the above embodiments. The eight units and the corresponding steps implement the same instances and application scenarios, but are not limited to the content disclosed in the above embodiments.

[0154] As can be seen from the above, in the solution described in the above embodiments of the present invention, a processing unit is used to place the teaching object in the teaching area after determining that the image acquisition device is fixedly connected to the end of the robot's robotic arm; an acquisition unit is used to acquire a first image of the teaching object using the image acquisition device after determining that the teaching object is placed in the teaching area; a first detection unit is used to detect the first image to obtain the robot's first teaching position; a control unit is used to control the robot to return to the teaching area after detecting that the robot's total running time has reached a running time threshold, and to acquire a second image of the teaching object through the image acquisition device; and a second detection unit is used to detect the second image. The system performs detection to obtain the robot's second taught position; a determination unit is used to determine the positional deviation between the first and second taught positions; an acquisition unit is used to obtain positional deviation compensation information based on the positional deviation through a global error model; and a compensation unit is used to compensate the robot according to the compensation information. This system achieves the goal of using a vision sensor fixed to the robot's end effector to detect pose deviation, applying a hand-eye calibration algorithm to establish the transformation between the camera and the robot flange coordinate system, using the MDH linear error model and a differential error model of the special link and tool coordinate system to identify errors, and using the least squares method to solve for the optimal compensation value, thus simplifying the robot's position compensation method.

[0155] Therefore, the technical solutions provided by the embodiments of the present invention solve the technical problem that the methods for compensating for the reduction in position accuracy of robot systems due to factors such as mechanical wear and thermal deformation are relatively complex and time-consuming in the related technologies.

[0156] Optionally, the compensation device further includes: a placement unit, used to place a checkerboard calibration plate within the reach of the robot's robotic arm before placing the teaching object in the teaching area after the image acquisition device is fixedly connected to the end of the robot's robotic arm, wherein the position of the checkerboard calibration plate and the robot's base remains fixed; a control unit, used to control the robot to move to multiple known positions and record the calibration plate images acquired by the image acquisition device and the theoretical coordinates of the robot's end effector at the multiple known positions; and an analysis unit, used to analyze the calibration plate images and theoretical coordinates to obtain the transformation matrix between the coordinate system of the image acquisition device and the end effector.

[0157] Optionally, the compensation device further includes: a modeling unit, used to establish a kinematic model of the robot's links based on DH parameters before obtaining compensation information for the position deviation through the identification model; a generation unit, used to generate a local error model for each link of the robot based on the DH parameters and the kinematic model; and a combination unit, used to combine the local error model with the end effector error model of the robot's end effector to obtain the robot's global error model.

[0158] Optionally, the acquisition unit includes: a processing module for transmitting the position deviation to the global error model, so as to process the position deviation using the global error model to obtain the joint zero-point compensation value and the link length compensation value of the robot.

[0159] Optionally, the compensation device further includes: a determination unit, used to determine a plane perpendicular to the Zi-1 axis and passing through point Oi-1 when the adjacent joint axes of the robot are parallel; a calculation unit, used to calculate the distance a of the translation between the Zi-1 axis and the Xi axis after rotating by an angle θ, as well as the amount of rotation and translation; an update unit, used to update the link parameter table and introduce the tool coordinate system to adapt to the error compensation requirements of special positions and attitudes; and a calculation unit, used to establish a differential error model and calculate the partial differential terms of each link parameter to reflect the correlation between the end effector pose change and the link parameter error.

[0160] Optionally, the compensation unit includes: an update module for updating the link parameters in the robot's control system using compensation information to compensate the robot.

[0161] Optionally, the compensation device further includes: a data acquisition unit for acquiring actual coordinate data of the robot at different positions; and a processing unit for comparing the actual coordinate data with the theoretical pose data to evaluate the position deviation and verify the global error model.

[0162] According to the technical solutions provided in the above embodiments of the present invention, a processor is provided, which is used to run a program, wherein the program executes any of the above-described methods for compensating robot position deviations during runtime.

[0163] According to the technical solutions provided in the above embodiments of the present invention, a computer program product is provided, including computer instructions, wherein when the computer instructions are executed by a processor, a robot position deviation compensation method is performed as described above.

[0164] According to the technical solutions provided in the above embodiments of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein the program executes the robot position deviation compensation method of any one of the above.

[0165] Optionally, in this embodiment, the computer-readable storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any communication device in a group of communication devices.

[0166] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: after determining that the image acquisition device is fixedly connected to the end of the robot's robotic arm, the teaching object is placed in the teaching area; after determining that the teaching object is placed in the teaching area, a first image of the teaching object is acquired using the image acquisition device; the first image is detected to obtain the robot's first teaching position; after detecting that the robot's total running time has reached a running time threshold, the robot is controlled to return to the teaching area, and a second image of the teaching object is acquired using the image acquisition device; the second image is detected to obtain the robot's second teaching position; the positional deviation between the first teaching position and the second teaching position is determined; compensation information for the positional deviation is obtained based on the positional deviation using a global error model; and the robot is compensated according to the compensation information.

[0167] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: after determining that the image acquisition device is fixedly connected to the end effector of the robot's robotic arm, before placing the teaching object in the teaching area, a checkerboard calibration plate is placed within the reach of the robot's robotic arm, wherein the position of the checkerboard calibration plate and the robot's base remains fixed; the robot is controlled to move to multiple known positions, and the calibration plate images acquired by the image acquisition device and the theoretical coordinates of the robot's end effector at the multiple known positions are recorded; the calibration plate images and theoretical coordinates are analyzed to obtain the transformation matrix between the coordinate system of the image acquisition device and the end effector.

[0168] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: before obtaining position deviation compensation information based on position deviation through the identification model, establishing a kinematic model of the robot's links based on DH parameters; generating a local error model for each link of the robot based on the DH parameters and the kinematic model; and combining the local error model with the end effector error model of the robot's end effector to obtain the robot's global error model.

[0169] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: passing the position deviation to the global error model to process the position deviation using the global error model to obtain the joint zero-point compensation value of the robot and the link length compensation value of the robot's link length.

[0170] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: determining a plane perpendicular to the Zi-1 axis and passing through point Oi-1 when adjacent joint axes of the robot are parallel; calculating the distance α between the Zi-1 axis and the Xi axis after rotation by an angle θ, as well as the rotation and translation amounts; updating the link parameter table and introducing a tool coordinate system to adapt to the error compensation requirements of special positions and attitudes; establishing a differential error model and calculating the partial differential terms of each link parameter to reflect the correlation between the end effector pose change and the link parameter error.

[0171] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: updating the link parameters in the robot's control system using compensation information to compensate the robot.

[0172] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: collecting actual coordinate data of the robot at different positions; comparing the actual coordinate data with the theoretical pose data, evaluating the position deviation, and verifying the global error model.

[0173] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0174] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0175] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0176] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0177] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0178] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0179] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0180] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for compensating for robot position deviation, characterized in that, include: After confirming that the image acquisition device is fixedly connected to the end of the robot's robotic arm, place the teaching object in the teaching area; After determining that the teaching object is placed in the teaching area, the first image of the teaching object is acquired using the image acquisition device; The first image is detected to obtain the robot's first teaching position; After detecting that the total runtime of the robot has reached the runtime threshold, the robot is controlled to return to the teaching area, and a second image of the teaching object is acquired through the image acquisition device. The second image is detected to obtain the robot's second teaching position; Determine the positional deviation between the first teaching position and the second teaching position; The compensation information for the position deviation is obtained based on the position deviation using a global error model; The robot is compensated according to the compensation information.

2. The robot position deviation compensation method according to claim 1, characterized in that, After confirming that the image acquisition device is fixedly connected to the end effector of the robot's robotic arm, and before placing the teaching object in the teaching area, the compensation method further includes: A checkerboard calibration plate is placed within the reach of the robot's robotic arm, wherein the checkerboard calibration plate remains fixed in position to the robot's base; The robot is controlled to move to multiple known locations, and the calibration board images acquired by the image acquisition device and the theoretical coordinates of the robot's end effector at the multiple known locations are recorded. By analyzing the calibration plate image and the theoretical coordinates, the transformation matrix between the coordinate system of the image acquisition device and the end effector is obtained.

3. The robot position deviation compensation method according to claim 1, characterized in that, Before obtaining compensation information for the position deviation based on the position deviation through the identification model, the compensation method further includes: A kinematic model of the robot's links is established based on the DH parameters; Based on the DH parameters and the kinematic model, a local error model for each link of the robot is generated; The local error model is combined with the end effector error model of the robot to obtain the global error model of the robot.

4. The robot position deviation compensation method according to claim 1, characterized in that, The compensation information for the position deviation is obtained based on the position deviation using a global error model, including: The position deviation is transmitted to the global error model, and the position deviation is processed using the global error model to obtain the joint zero-point compensation value and the link length compensation value of the robot.

5. The robot position deviation compensation method according to claim 1, characterized in that, The compensation method also includes: With the adjacent joint axes of the robot parallel, the perpendicularity to Z is determined. i-1 Axis and through O i-1 A plane of points; Calculate Z i-1 After rotating the axis by an angle θ, it intersects with X. i The distance of axis translation 'a', and the amount of rotation and translation; Update the link parameter table and introduce the tool coordinate system to accommodate error compensation requirements for special positions and attitudes; A differential error model is established to calculate the partial differential terms of each link parameter, so as to reflect the correlation between the end effector pose change and the link parameter error.

6. The robot position deviation compensation method according to claim 1, characterized in that, The robot is compensated according to the compensation information, including: The link parameters in the robot's control system are updated using the compensation information to compensate the robot.

7. The robot position deviation compensation method according to claim 3, characterized in that, The compensation method also includes: Collect the actual coordinate data of the robot at different locations; The actual coordinate data is compared with the theoretical pose data to evaluate the position deviation and verify the global error model.

8. A robot position deviation compensation device, characterized in that, include: The processing unit is used to place the teaching object in the teaching area after confirming that the image acquisition device is fixedly connected to the end of the robot's robotic arm; The acquisition unit is used to acquire a first image of the teaching object using the image acquisition device after determining that the teaching object is placed in the teaching area. The first detection unit is used to detect the first image to obtain the first teaching position of the robot; The control unit is used to control the robot to return to the teaching area after detecting that the total running time of the robot has reached the running time threshold, and to acquire a second image of the teaching object through the image acquisition device; The second detection unit is used to detect the second image to obtain the second teaching position of the robot; A determining unit is used to determine the positional deviation between the first teaching position and the second teaching position; The acquisition unit is used to obtain compensation information for the position deviation based on the position deviation through a global error model; The compensation unit is used to compensate the robot according to the compensation information.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein the program executes the robot position deviation compensation method according to any one of claims 1 to 7.

10. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by the processor, they perform the robot position deviation compensation method according to any one of claims 1 to 7.

Images