A dual-arm robot arm posture calibration method and system

Abstract

The present invention provides a dual-arm robot arm posture calibration method and system. The method includes: collecting parameters during the coordinated motion of the manipulators and the nominal posture of a calibration plate, constructing differential constraint equations for the calibration points of the two manipulators, combining the differential constraint equations to establish multi-point posture constraint equations for the dual-arm robot manipulators; iteratively solving the multi-point posture constraint equations using a weighted least squares method, and calibrating the kinematic parameters of the two manipulators using the kinematic parameter deviations obtained from the iterative solution; establishing a relative posture conversion matrix for the manipulators based on the posture constraint relationship between a camera and the bases of the two manipulators, and iteratively solving the relative posture relationship between the two manipulator base coordinate systems using an alternating minimization method based on the distance deviations of the calibration points on the calibration plates of the two manipulators, and calibrating the manipulator arm postures. This solution can reduce the cost of manipulator arm posture calibration, simplify the calibration process, and effectively improve calibration efficiency.

Description

Technical Field

[0001] The present invention belongs to the technical field of robots, and in particular relates to a method and system for calibrating the posture of a dual-arm robot manipulator. Background Art

[0002] Compared with the traditional single-arm working mode, dual-arm collaboration can expand the workspace, increase load capacity, and improve flexibility. The high positioning accuracy of the dual-arm robot is the core foundation for completing high-quality collaborative tasks. The positioning error of the robot mainly comes from the manufacturing and assembly of the robot. Therefore, it is of great significance to develop a high-precision, high-efficiency, and low-cost calibration algorithm to improve the positioning accuracy of the robot.

[0003] At present, the commonly used robot calibration methods include: calibration method using laser tracker, which installs a target ball at the end of the robot's manipulator arm, uses laser to obtain the end point data of the robot in different postures to establish constraints, and uses quadratic sequence programming method to solve the problem to calibrate the kinematic geometric parameters; dual-arm robot self-calibration method using ballbar, the ballbar system consists of three parts: linear sensor and dynamic and static magnetic bases. This method uses the ballbar system to measure the posture of the two manipulator arms, and realizes robot self-calibration by identifying the mapping relationship between the ballbar measurement value and the error source.

[0004] However, calibration using specialized measuring instruments, such as laser trackers and ballbars, is costly, bulky, and difficult to carry, hindering widespread industrial adoption. Furthermore, deploying numerous calibration devices at the end of the robotic arm complicates the operation and requires specialized knowledge. Furthermore, existing common dual-arm calibration algorithms typically employ centralized identification during the calibration process, simultaneously calculating the internal parameters and relative pose of both robotic arms. This leads to coupling between the two arm parameters, resulting in slow calculations and lengthy calibration times, making them difficult to adapt to the demands of high industrial production efficiency. Summary of the Invention

[0005] In view of this, an embodiment of the present invention provides a dual-arm robot manipulator arm posture calibration method and system for solving the current problems of high cost, complex operation and long calibration time of manipulator arm posture calibration.

[0006] In a first aspect of an embodiment of the present invention, a method for calibrating a dual-arm robot arm posture is provided, comprising:

[0007] Collect the robot's arm parameters, joint angles, and nominal position of the calibration plate during the coordinated motion of the robot's two arms;

[0008] Among them, two calibration plates are respectively installed at the ends of the two robotic arms of the robot, and the camera is installed on the head of the dual-arm robot, and the two robotic arms are kept in the field of view of the camera to move in coordination. Multiple calibration points are set on the calibration plates;

[0009] According to the robot arm parameters, joint angles and the nominal pose of the calibration plate, the differential constraint equations of the calibration points of the two robot arms are constructed. The differential constraint equations are combined to establish the multi-point pose constraint equations of the dual-arm robot arm.

[0010] Iteratively solving the multi-point pose constraint equations by weighted least squares method, and calibrating the kinematic parameters of the two manipulators using the kinematic parameter deviations obtained by the iterative solution;

[0011] After calibrating the kinematic parameters of the manipulator, the relative pose transformation matrix of the manipulator is established based on the pose constraint relationship between the camera and the bases of the two manipulators. Combined with the distance deviation of the calibration points on the calibration plates of the two manipulators, the relative pose relationship between the two manipulator base coordinate systems is iteratively solved using the alternating minimization method.

[0012] The robot arm posture is calibrated according to the relative posture relationship between the two robot arm base coordinate systems.

[0013] In a second aspect of an embodiment of the present invention, a dual-arm robot arm posture calibration system is provided, comprising:

[0014] The data acquisition module is used to collect the robot's arm parameters, joint angles, and nominal position of the calibration plate during the coordinated motion of the robot's two arms;

[0015] Among them, two calibration plates are respectively installed at the ends of the two robotic arms of the robot, and the camera is installed on the head of the dual-arm robot, and the two robotic arms are kept in the field of view of the camera to move in coordination. Multiple calibration points are set on the calibration plates;

[0016] A first calculation module is used to construct differential constraint equations for the calibration points of the two manipulators based on the manipulator parameters, joint angles, and the nominal pose of the calibration plate, combine the differential constraint equations to establish multi-point pose constraint equations for the dual-arm robot manipulator, and iteratively solve the multi-point pose constraint equations using a weighted least squares method;

[0017] A first calibration module is used to calibrate the kinematic parameters of the two manipulators using the kinematic parameter deviations obtained by iterative solution;

[0018] The second calculation module is used to calibrate the kinematic parameters of the manipulator and then establish the relative pose transformation matrix of the manipulator based on the pose constraint relationship between the camera and the two manipulator bases. Combined with the distance deviation of the calibration points on the calibration plates of the two manipulators, the relative pose relationship between the two manipulator base coordinate systems is iteratively solved through the alternating minimization method;

[0019] The second calibration module is used to calibrate the posture of the robotic arm according to the relative posture relationship between the two robotic arm base coordinate systems.

[0020] In a third aspect of an embodiment of the present invention, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein the processor implements the steps of the method described in the first aspect of the embodiment of the present invention when executing the computer program.

[0021] In a fourth aspect of an embodiment of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method provided in the first aspect of the embodiment of the present invention are implemented.

[0022] In an embodiment of the present invention, a calibration plate is installed at the end of a robotic arm and a camera is mounted on the robot head. The kinematic parameters and relative pose relationships of the robotic arms are calculated and calibrated by collecting pose data of calibration points during the multi-pose coordinated motion of the two robotic arms. This device is not only compact, low-cost, and portable, meeting the needs of industrial applications, but also easy to deploy, simple to operate, and highly efficient in calibration. Furthermore, the parameter decoupling and relative pose calculations are fast, and the calibration accuracy is high, meeting the high-precision and high-efficiency requirements of industrial production. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0024] Figure 1 A schematic flow chart of a method for calibrating the position and posture of a dual-arm robot manipulator provided in one embodiment of the present invention;

[0025] Figure 2 A schematic diagram of the calibration plate and camera installation provided in one embodiment of the present invention;

[0026] Figure 3 A schematic diagram of a calibration plate provided in accordance with an embodiment of the present invention;

[0027] Figure 4 A schematic structural diagram of a dual-arm robot arm posture calibration system provided by one embodiment of the present invention;

[0028] Figure 5 The present invention provides a schematic structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to make the purpose, features, and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0030] It should be understood that the terms "including" and similar expressions in the specification, claims, and drawings of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, or apparatus comprising a series of steps or units is not limited to the listed steps or units. Furthermore, the terms "first" and "second" are used to distinguish between different objects and are not intended to describe a specific order.

[0031] See also Figure 1 , a schematic flow chart of a dual-arm robot manipulator posture calibration method provided by an embodiment of the present invention includes:

[0032] S101, collecting robot arm parameters, joint angles, and nominal position and posture of the calibration plate during the coordinated motion of the two robot arms;

[0033] Among them, two calibration plates are respectively installed at the ends of the two robotic arms of the robot, and the camera is installed on the head of the dual-arm robot, and the two robotic arms are kept moving collaboratively within the field of view of the camera. Multiple calibration points are set on the calibration plate.

[0034] like Figure 2 As shown in the figure, two calibration plates are installed at the ends of the two arms of the dual-arm robot respectively, and the camera is installed on the head of the dual-arm robot, and the calibration plates are kept within the field of view of the camera.

[0035] For example, an asymmetric dot calibration plate is used as a measurement target. Figure 3As shown, the spacing between calibration points in the same group on the calibration plate is 10 mm, and the spacing between different groups of calibration points at the same location is 84.8528 mm. For example, calibration points 0 and 1 are in the same group, while points 2 and 3 and 4 and 5 are in different groups. The camera model used is MV-CA050-20UC, with a fixed-focus lens model MVL-KF1628M-12MP, a resolution of 2592 × 2048, and a field of view of 20 × 20 cm. The camera's intrinsic parameters are calibrated using Zhang's calibration method, and the calibration results show a measurement accuracy of approximately 0.015 mm within a 20 cm field of view.

[0036] The robotic arm parameters may include the robotic arm link length, link torsion angle and link offset, etc. The joint angle refers to the relative angle between the joints of the robotic arm, and the nominal posture refers to the posture of the reference plate in an ideal state, that is, the target posture.

[0037] S102, constructing differential constraint equations for the calibration points of the two manipulators based on the manipulator parameters, joint angles, and the nominal pose of the calibration plate, and combining the differential constraint equations to establish multi-point pose constraint equations for the manipulators of the dual-arm robot;

[0038] Among them, the robot parameters, joint angles and visual measurement results are obtained as input, and the initial hand-eye conversion matrix of the two robotic arms is constructed through the hand-eye calibration algorithm, and the initial hand-eye conversion matrix is ​​used as the initial value of the conversion matrix from the camera to the robotic arm.

[0039] Optionally, according to the nominal pose and actual pose of the calibration point in the calibration plate under different collaborative poses of the manipulator, after eliminating the actual pose by the difference method, a differential constraint equation is established, and the differential constraint equation is expressed as:

[0040]

[0041] Where, Indicates the The identifiable parameter variation matrix of the robot arm, Indicates the The change in the identifiable parameters of the robot arm, Indicates the Calibration plate for the robot arm The Jacobian matrix of the point pose change, For the Calibration plate for the robot arm Point The pose Jacobian matrix of the collaborative pose, Indicates the Calibration plate for the robot arm The nominal pose change of the point, For the Calibration plate for the robot arm Point The nominal pose of the collaborative pose.

[0042] Calculate the i The deviation between the nominal pose and actual pose of a single calibration point on the calibration plate of a manipulator is expressed as:

[0043] ;

[0044] Where, Indicates the position deviation of the calibration point of the manipulator, Indicates the i The actual pose of the robot arm calibration point, Indicates the i The nominal pose of the robot arm calibration points, express Collaborative movement postures, and Respectively represent i The identifiable parameters of each robotic arm and their changes.

[0045] No. i The nominal pose of the calibration points on the calibration plate of the manipulator is obtained by forward kinematics calculation:

[0046] ;

[0047] ;

[0048] Where, For the i The end coordinate system of the robot arm to the i The transformation matrix of the manipulator base coordinate system, For the i The calibration plate coordinate system of the first robot arm is i The transformation matrix of the robot end coordinate system; From the camera coordinate system to the i The transformation matrix of the calibration plate coordinate system of the manipulator, k is the number of robot arm axes, base=0,end=k .

[0049] No. i The transformation matrix of adjacent links of a robotic arm is expressed as ,and , j is less than or equal to k A positive integer, c represents the cosine function, s represents the sine function, a is the connecting rod length, α is the connecting rod torsion angle,d is the offset, θ is the joint angle.

[0050] Since the calibration plate is installed at the end of the robotic arm, is an unknown constant. To ensure the stability of parameter identification, quaternion is used and translation vectors Indicates that, and 、 The robot arm model adopts the DH parameter model, then i The identifiable parameters of the robot arm are: ,in, For the i Each robot arm can correct the joint zero position parameters, 、 For the i The robot arm can modify the link parameters.

[0051] The first-order Taylor expansion is used to expand the calibration point posture deviation:

[0052] ;

[0053] The above formula can be simplified as: , For the i The Jacobian matrix of the robot arm pose.

[0054] According to the above formula, we can get:

[0055] ;

[0056] Based on the collected data and i The robot arm calibrates the plate in different positions l The nominal pose data of the calibration points are used to establish the constraint equation:

[0057]

[0058] Where, For the i Calibration plate for the robot arm l Point n The pose Jacobian matrix of the collaborative pose, No. i Calibration plate for the robot arm l The actual pose of the point, For the i Calibration plate for the robot arm l Point n The nominal pose of the collaborative pose.

[0059] Using the difference method, eliminating the actual pose in the constraint equation can obtain the constrained difference equation:

[0060]

[0061] The first i The corresponding robotic arms f The constraint equations of the calibration points are combined to construct a multi-point pose constraint equation group:

[0062] .

[0063] S103, iteratively solving the multi-point posture constraint equation by weighted least squares method, and calibrating the kinematic parameters of the two manipulators using the kinematic parameter deviations obtained by the iterative solution;

[0064] The kinematic parameters mainly include DH parameters, which generally refer to parameters used to describe the relative position and orientation between each joint and link of the robot arm, including link length, link offset, and joint angle. The currently collected kinematic parameters are corrected based on the iteratively solved kinematic parameter deviation.

[0065] The iterative stopping condition is set to the maximum number of iterations or the terminal winding point error. The terminal winding point error is the average value of the average position error of the posture obtained by each measurement relative to the calibration point after multiple posture measurements of each calibration point.

[0066] The multi-point pose constraint equations are:

[0067]

[0068] The multi-point pose constraint equations are expressed as:

[0069] ;

[0070] In the formula, I is the unit matrix, λ is the weight, and The variable step size optimization is performed based on the changing trend of

[0071] The weighted least squares method is used to solve the multi-point pose constraint equations of the manipulators. The iteration stopping condition is set to the maximum number of iterations or the end winding error. The kinematic parameter deviations obtained from the iterative solution are used to calibrate the kinematic parameters of the two manipulators.

[0072] S104. After calibrating the kinematic parameters of the manipulators, establish a relative pose transformation matrix for the manipulators based on the pose constraint relationship between the camera and the bases of the two manipulators. Combined with the distance deviation of the calibration points on the calibration plates of the two manipulators, the relative pose relationship between the base coordinate systems of the two manipulators is iteratively solved using an alternating minimization method.

[0073] The pose constraints between the camera and the robot base are determined through eye-to-hand calibration. This involves mounting the camera on the robot head and calibrating the transformation matrix between the camera and the robot base coordinate systems. The relative pose transformation matrix of the robot arm refers to the pose transformation between the two robot base coordinate systems.

[0074] The distance deviation of a calibration point refers to the difference between the nominal distance and the actual distance between calibration points at the same location on two calibration plates. The alternating minimization method is an iterative process that fixes some variables and optimizes others, alternating until convergence.

[0075] Specifically, based on the pose constraint relationship between the camera and the bases of the two robotic arms, the relative pose transformation matrix between the two robotic arms is constructed;

[0076] The collaborative positioning distance error of the two manipulators is set as the distance deviation between the measured distance and the nominal distance between the corresponding calibration points on the calibration plate at the end of the manipulator arm;

[0077] According to the collaborative positioning distance error, an index function is constructed and it is iteratively solved through alternating minimization.

[0078] The indicator function is:

[0079] ;

[0080] Where, Indicates the indicator value, represents the collaborative pose, n represents the number of collaborative poses, represents the calibration point count variable, q Indicates the number of calibration point groups, Indicates the Collaborative postures Collaborative positioning distance error of group calibration points, Indicates the second robot arm calibration board The measured position of the calibration points in the camera coordinate system, Indicates the first robot calibration board The measured position of the calibration points in the camera coordinate system, Represents the transformation matrix from the first robotic arm base coordinate system to the camera, Represents the transpose of the transformation matrix from the second manipulator base coordinate system to the camera, Indicates the second robot arm calibration board The position of the calibration point in the second robot arm base coordinate system, Indicates the first robot calibration board The position of the calibration point in the first robot arm base coordinate system.

[0081] According to the constraint relationship between the camera and the robotic arm, the relative pose transformation matrix between the two robotic arms can be obtained as follows:

[0082]

[0083] Where, Represents the transformation relationship between the two robot base coordinate systems, since , the above formula is simplified to:

[0084]

[0085] Transform the above formula into:

[0086]

[0087] The collaborative positioning error is defined as the distance deviation between the measured distance and the nominal distance between the corresponding calibration points on the calibration plate at the end of the two manipulator arms of the dual-arm robot:

[0088]

[0089] Where, For two calibration plates The measured distance between the group calibration points, For two calibration plates Nominal distance between group calibration points:

[0090]

[0091]

[0092] Where, For the second robot arm calibration board The measured position of the calibration points in the camera coordinate system, For the first robot arm calibration board The measured position of the calibration points in the camera coordinate system, For the second robot arm calibration board The position of the calibration point in the second robot arm base coordinate system, For the first robot arm calibration board The position of the calibration point in the first robot arm base coordinate system.

[0093] According to the distance deviation, the indicator function is defined as:

[0094] ;

[0095] Where, Indicates the kind of posture, Indicates the Use step 3 to identify multiple groups of 、 The value of is solved by alternating minimization method. The value of the smallest group 、 , and then obtain .

[0096] S105: Calibrate the robot arm posture according to the relative posture relationship between the two robot arm base coordinate systems.

[0097] Based on the relative posture relationship between the two robot arm base coordinate systems, the postures of the two robot arms are adjusted. The posture of one robot arm can be adjusted separately or the postures of both robot arms can be adjusted.

[0098] In this embodiment, there is no need to use expensive calibration equipment. The equipment used only involves calibration plates and common industrial cameras. It is small in size, low in cost, easy to carry, and can meet the needs of large-scale industrial use. The calibration equipment is simple to deploy. It only needs to install calibration plates on the ends of the two robotic arms and industrial cameras on the robot head respectively. There is no need to measure the actual position of the calibration plates during the calibration process. The operation is simple and the calibration efficiency is high. By first performing kinematic calibration on each robotic arm of the dual-arm robot and completing the kinematic calibration of the two robotic arms, a posture constraint conversion matrix is ​​established through the posture constraint relationship between the camera and the ends of the two robotic arms. Combining the posture conversion constraint matrix with the single-arm identification result, the relative posture relationship of the base coordinate system between the left arm and the right arm is identified. The whole process has fast calibration speed and high accuracy, and the parameter decoupling and relative posture calculation speed are fast, which can meet the needs of high precision and high efficiency in industrial production.

[0099] It should be understood that the sequence numbers of the steps in the above embodiments do not imply a specific order of execution; the order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0100] Figure 4 A schematic structural diagram of a dual-arm robot arm posture calibration system provided in an embodiment of the present invention, the system comprising:

[0101] The data acquisition module 410 is used to collect the robot arm parameters, joint angles and nominal position of the calibration plate during the coordinated motion of the two robot arms;

[0102] Among them, two calibration plates are respectively installed at the ends of the two robotic arms of the robot, and the camera is installed on the head of the dual-arm robot, and the two robotic arms are kept in the field of view of the camera to move in coordination. Multiple calibration points are set on the calibration plates;

[0103] A first calculation module 420 is configured to construct differential constraint equations for the calibration points of the two manipulators based on the manipulator parameters, joint angles, and the nominal pose of the calibration plate, combine the differential constraint equations to establish multi-point pose constraint equations for the dual-arm manipulator, and iteratively solve the multi-point pose constraint equations using a weighted least squares method;

[0104] Among them, the robot parameters, joint angles and visual measurement results are obtained as input, and the initial hand-eye conversion matrix of the two robotic arms is constructed through the hand-eye calibration algorithm, and the initial hand-eye conversion matrix is ​​used as the initial value of the conversion matrix from the camera to the robotic arm.

[0105] Specifically, the differential constraint equations for the calibration points of the two manipulators are constructed based on the manipulator parameters, joint angles, and the nominal pose of the calibration plate, including:

[0106] According to the nominal pose and actual pose of the calibration point in the calibration plate under different collaborative poses of the manipulator, the differential constraint equation is established after eliminating the actual pose by the differential method. The differential constraint equation is expressed as:

[0107]

[0108] Where, Indicates the The identifiable parameter variation matrix of the robot arm, Indicates the The change in the identifiable parameters of the robot arm, Indicates the Calibration plate for the robot arm The Jacobian matrix of the point pose change, For the Calibration plate for the robot arm Point The pose Jacobian matrix of the collaborative pose, Indicates the Calibration plate for the robot arm The nominal pose change of the point, For the Calibration plate for the robot arm Point The nominal pose of the collaborative pose.

[0109] The iterative stopping condition is set to the maximum number of iterations or the terminal winding point error. The terminal winding point error is the average value of the average position error of the posture obtained by each measurement relative to the calibration point when multiple posture measurements are performed on multiple calibration points.

[0110] A first calibration module 430 is configured to calibrate the kinematic parameters of the two manipulators using the kinematic parameter deviations obtained through iterative solution;

[0111] The second calculation module 440 is used to establish the relative pose transformation matrix of the manipulators after calibrating the kinematic parameters of the manipulators, based on the pose constraint relationship between the camera and the bases of the two manipulators. The relative pose relationship between the two manipulator base coordinate systems is iteratively solved by an alternating minimization method based on the distance deviation of the calibration points on the calibration plates of the two manipulators.

[0112] Specifically, based on the pose constraint relationship between the camera and the bases of the two robotic arms, the relative pose transformation matrix between the two robotic arms is constructed;

[0113] The collaborative positioning distance error of the two manipulators is set as the distance deviation between the measured distance and the nominal distance between the corresponding calibration points on the calibration plate at the end of the manipulator arm;

[0114] According to the collaborative positioning error, an index function is constructed and it is iteratively solved through alternating minimization.

[0115] The indicator function is:

[0116] ;

[0117] Where, Indicates the indicator value, Indicates the Collaborative postures, n represents the number of collaborative poses, Indicates the group calibration point, q Indicates the number of calibration point groups, Indicates the Collaborative postures Collaborative positioning distance error of group calibration points, Indicates the second robot arm calibration board The measured position of the calibration points in the camera coordinate system, Indicates the first robot calibration board The measured position of the calibration points in the camera coordinate system, Represents the transformation matrix from the first robotic arm base coordinate system to the camera, Represents the transpose of the transformation matrix from the second manipulator base coordinate system to the camera, Indicates the second robot arm calibration board The position of the calibration point in the second robot arm base coordinate system, Indicates the first robot calibration board The second calibration module 450 is used to calibrate the posture of the robot arm according to the relative posture relationship between the two robot arm base coordinate systems.

[0118] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems and modules can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0119] Figure 5 1 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device is used for calibrating the posture of a dual-arm robot arm. Figure 5 As shown, the electronic device 5 of this embodiment includes: a memory 510, a processor 520 and a system bus 530, wherein the memory 510 includes an executable program 5101 stored thereon. It can be understood by those skilled in the art that Figure 5 The electronic device structure shown in the figure does not constitute a limitation to the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0120] The following combination Figure 5 A detailed introduction to the various components of electronic equipment:

[0121] Memory 510 can be used to store software programs and modules. Processor 320 executes the various functional applications and data processing of the electronic device by running the software programs and modules stored in memory 510. Memory 510 may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as sound playback or image playback). The data storage area may store data generated based on the use of the electronic device (such as cached data). Memory 510 may also include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state memory device.

[0122] The memory 510 includes an executable program 5101 for the network request method. The executable program 5101 can be divided into one or more modules / units. The one or more modules / units are stored in the memory 510 and executed by the processor 520 to implement robotic arm posture calibration, etc. The one or more modules / units can be a series of computer program instruction segments that can perform specific functions. The instruction segments are used to describe the execution process of the computer program 5101 in the electronic device 5. For example, the computer program 5101 can be divided into functional modules such as a data acquisition module, a first calculation module, a first calibration module, a second calculation module, and a second calibration module.

[0123] The processor 520 is the control center of the electronic device. It connects the various components of the entire electronic device using various interfaces and lines. By running or executing software programs and / or modules stored in the memory 510 and accessing data stored in the memory 510, it performs various functions of the electronic device and processes data, thereby monitoring the overall status of the electronic device. Optionally, the processor 520 may include one or more processing units; preferably, the processor 520 may integrate an application processor and a modem processor, wherein the application processor primarily processes the operating system, application programs, etc., and the modem processor primarily handles wireless communications. It is understood that the modem processor described above may not be integrated into the processor 520.

[0124] The system bus 530 connects the various functional components within the computer and can transmit data, address information, and control information. It can be a PCI bus, an ISA bus, a CAN bus, or other bus types. Instructions from the processor 520 are transmitted to the memory 510 via the bus, and the memory 510 feeds data back to the processor 520. The system bus 530 is responsible for the exchange of data and instructions between the processor 520 and the memory 510. Of course, the system bus 530 can also connect to other devices, such as network interfaces and display devices.

[0125] In an embodiment of the present invention, the executable program executed by the processing 520 included in the electronic device includes:

[0126] Collect the robot's arm parameters, joint angles, and nominal position of the calibration plate during the coordinated motion of the robot's two arms;

[0127] Among them, two calibration plates are respectively installed at the ends of the two robotic arms of the robot, and the camera is installed on the head of the dual-arm robot, and the two robotic arms are kept in the field of view of the camera to move in coordination. Multiple calibration points are set on the calibration plates;

[0128] According to the robot arm parameters, joint angles and the nominal pose of the calibration plate, the differential constraint equations of the calibration points of the two robot arms are constructed. The differential constraint equations are combined to establish the multi-point pose constraint equations of the dual-arm robot arm.

[0129] Iteratively solving the multi-point pose constraint equations by weighted least squares method, and calibrating the kinematic parameters of the two manipulators using the kinematic parameter deviations obtained by the iterative solution;

[0130] After calibrating the kinematic parameters of the manipulator, the relative pose transformation matrix of the manipulator is established based on the pose constraint relationship between the camera and the bases of the two manipulators. Combined with the distance deviation of the calibration points on the calibration plates of the two manipulators, the relative pose relationship between the two manipulator base coordinate systems is iteratively solved using the alternating minimization method.

[0131] The robot arm posture is calibrated according to the relative posture relationship between the two robot arm base coordinate systems.

[0132] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and modules can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0133] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0134] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for calibrating the position and posture of a dual-arm robot arm, characterized in that: include: Collect the robot's arm parameters, joint angles, and nominal position of the calibration plate during the coordinated motion of the robot's two arms; Among them, two calibration plates are respectively installed at the ends of the two robotic arms of the robot, and the camera is installed on the head of the dual-arm robot, and the two robotic arms are kept in the field of view of the camera to move in coordination. Multiple calibration points are set on the calibration plates; According to the robot arm parameters, joint angles and the nominal pose of the calibration plate, the differential constraint equations of the calibration points of the two robot arms are constructed. The differential constraint equations are combined to establish the multi-point pose constraint equations of the dual-arm robot arm. The differential constraint equations for constructing the calibration points of the two manipulators based on the manipulator parameters, joint angles, and the nominal pose of the calibration plate include: Taking the robot arm parameters, joint angles, and camera vision measurement results as input, the initial hand-eye conversion matrix of the two robot arms is constructed through the hand-eye calibration algorithm, and the initial hand-eye conversion matrix is ​​used as the initial value of the camera-to-robot arm conversion matrix; Among them, according to the nominal pose and actual pose of the calibration point in the calibration plate of the manipulator in different collaborative poses, the differential constraint equation is established after eliminating the actual pose by the differential method. The differential constraint equation is expressed as: Where, Indicates the The identifiable parameter variation matrix of the robot arm, Indicates the The change in the identifiable parameters of the robot arm, Indicates the Calibration plate for the robot arm The Jacobian matrix of the point pose change, For the Calibration plate for the robot arm Point The pose Jacobian matrix of the collaborative pose, Indicates the Calibration plate for the robot arm The nominal pose change of the point, For the Calibration plate for the robot arm Point The nominal pose of the collaborative pose; Iteratively solving the multi-point pose constraint equations by weighted least squares method, and calibrating the kinematic parameters of the two manipulators using the kinematic parameter deviations obtained by the iterative solution; After calibrating the kinematic parameters of the manipulator, the relative pose transformation matrix of the manipulator is established based on the pose constraint relationship between the camera and the bases of the two manipulators. Combined with the distance deviation of the calibration points on the calibration plates of the two manipulators, the relative pose relationship between the two manipulator base coordinate systems is iteratively solved using the alternating minimization method. The robot arm posture is calibrated according to the relative posture relationship between the two robot arm base coordinate systems.

2. The method according to claim 1, characterized in that The iteratively solving the multi-point pose constraint equation by weighted least squares method includes: The iteration stopping condition is set to the maximum number of iterations or the terminal winding point error. The terminal winding point error is the average value of the position error of each calibration point relative to the average position error of the calibration point after multiple posture measurements.

3. The method according to claim 1, characterized in that The method includes establishing a relative pose transformation matrix of the robotic arms based on the pose constraint relationship between the camera and the two robotic arm bases, and iteratively solving the relative pose relationship between the two robotic arm base coordinate systems by an alternating minimization method in combination with the distance deviation between the two robotic arms. Based on the pose constraint relationship between the camera and the bases of the two robotic arms, the relative pose transformation matrix between the two robotic arms is constructed; The collaborative positioning distance error of the two manipulators is set as the distance deviation between the measured distance and the nominal distance between the corresponding calibration points on the calibration plate at the end of the manipulator arm; According to the collaborative positioning distance error, an index function is constructed and it is iteratively solved through alternating minimization. The indicator function is: ； Where, Indicates the indicator value, represents the collaborative pose, n represents the number of collaborative poses, represents the calibration point count variable, q Indicates the number of calibration point groups, Indicates the Collaborative postures Collaborative positioning distance error of group calibration points, Indicates the second robot arm calibration board The measured position of the calibration points in the camera coordinate system, Indicates the first robot calibration board The measured position of the calibration points in the camera coordinate system, Represents the transformation matrix from the first robotic arm base coordinate system to the camera, Represents the transpose of the transformation matrix from the second manipulator base coordinate system to the camera, Indicates the second robot arm calibration board The position of the calibration point in the second robot arm base coordinate system, Indicates the first robot calibration board The position of the calibration point in the first robot arm base coordinate system.

4. A dual-arm robot arm posture calibration system, characterized in that: include: The data acquisition module is used to collect the robot's arm parameters, joint angles, and nominal position of the calibration plate during the coordinated motion of the robot's two arms; Among them, two calibration plates are respectively installed at the ends of the two robotic arms of the robot, and the camera is installed on the head of the dual-arm robot, and the two robotic arms are kept in the field of view of the camera to move in coordination. Multiple calibration points are set on the calibration plates; A first calculation module is used to construct differential constraint equations for the calibration points of the two manipulators based on the manipulator parameters, joint angles, and the nominal pose of the calibration plate, combine the differential constraint equations to establish multi-point pose constraint equations for the dual-arm robot manipulator, and iteratively solve the multi-point pose constraint equations using a weighted least squares method; The differential constraint equations for constructing the calibration points of the two manipulators based on the manipulator parameters, joint angles, and the nominal pose of the calibration plate include: Taking the robot arm parameters, joint angles, and camera vision measurement results as input, the initial hand-eye conversion matrix of the two robot arms is constructed through the hand-eye calibration algorithm, and the initial hand-eye conversion matrix is ​​used as the initial value of the camera-to-robot arm conversion matrix; Among them, according to the nominal pose and actual pose of the calibration point in the calibration plate of the manipulator in different collaborative poses, the differential constraint equation is established after eliminating the actual pose by the differential method. The differential constraint equation is expressed as: Where, Indicates the The identifiable parameter variation matrix of the robot arm, Indicates the The change in the identifiable parameters of the robot arm, Indicates the Calibration plate for the robot arm The Jacobian matrix of the point pose change, For the Calibration plate for the robot arm Point The pose Jacobian matrix of the collaborative pose, Indicates the Calibration plate for the robot arm The nominal pose change of the point, For the Calibration plate for the robot arm Point The nominal pose of the collaborative pose; A first calibration module is used to calibrate the kinematic parameters of the two manipulators using the kinematic parameter deviations obtained by iterative solution; The second calculation module is used to calibrate the kinematic parameters of the manipulator and then establish the relative pose transformation matrix of the manipulator based on the pose constraint relationship between the camera and the two manipulator bases. Combined with the distance deviation of the calibration points on the calibration plates of the two manipulators, the relative pose relationship between the two manipulator base coordinate systems is iteratively solved through the alternating minimization method; The second calibration module is used to calibrate the posture of the robotic arm according to the relative posture relationship between the two robotic arm base coordinate systems.

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method for calibrating the posture of a dual-arm robot arm are implemented as described in any one of claims 1 to 3.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed, the steps of a dual-arm robot manipulator arm posture calibration method according to any one of claims 1 to 3 are implemented.

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