Double-robot high-precision calibration and online optimization method

Abstract

The invention belongs to the field of automatic measurement and lineation of complex castings, and discloses a double-robot high-precision calibration and online optimization method, which comprises the following steps of: based on hand-eye calibration, four-point calibration and the like, respectively calibrating each part of a casting-measurement system-lineation system during system deployment to obtain high-precision system parameter initial values; on the basis of online collection of calibration data in the measurement-analysis-lineation process of a visual tracking system, online optimization of multi-robot system parameters is achieved, optimized robot paths are fed back on the basis of a data control center, and high-precision stable operation of robot lineation is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of automatic measurement and marking of complex castings, and specifically to a dual-robot high-precision calibration and online optimization method; more specifically, to a dual-robot high-precision calibration and online optimization method in an automatic measurement and marking system for complex casting machining allowances. Background Art

[0002] Accurate calibration of multi-robot system parameters is the key to ensuring accurate conversion of measurement-processing coordinates and improving marking processing accuracy. Existing methods are difficult to achieve accurate calibration of multi-robot systems, and parameters are prone to drift during actual processing, requiring repeated offline calibration, which affects marking accuracy and stability.

[0003] Therefore, it is planned to study the calibration methods of the relative pose parameters of various parts of the system including robot-probe hand-eye calibration, robot-laser tool coordinate system, robot-robot and robot-workpiece, construct the relative pose conversion relationship of the probe-laser tool coordinate system, and realize the offline precise calibration of the multi-robot system; study the online optimization method of multi-robot calibration parameters based on visual tracking to overcome the problem of system parameter drift caused by factors such as robot joint wear and production site vibration, and ensure the high-precision and stable operation of automatic marking of complex castings. Summary of the invention

[0004] In response to the above defects or improvement needs of the prior art, the present invention provides a dual-robot high-precision calibration and online optimization method in the automatic measurement and marking system of complex casting processing allowance, thereby solving the technical problems of insufficient accuracy and poor stability of the existing multi-robot system calibration method.

[0005] To achieve the above object, according to one aspect of the present invention, a dual robot high-precision calibration and online optimization method is provided, comprising the following steps:

[0006] S1: Control the robot to shoot the calibration plate at a fixed position to achieve single robot hand-eye calibration;

[0007] S2: Control the movement of the fixed probe on the measuring robot, shoot the calibration plate that follows the movement of the marking robot, and realize the relative posture calibration of the dual-robot measurement and marking system;

[0008] S3: Control the laser focus to fall on the workpiece, construct the circular motion trajectory of the robot end with the center of the laser as the center and the focal length as the radius, select four positions on the trajectory at random, and use four-point calibration to obtain the position relationship between the laser and the workpiece;

[0009] S4: During the online optimization of the dual robot system, the tracking system first tracks the array of luminous marker points on the laser to obtain its position and posture parameter data; then, during the movement of the probe and the target, their relative position and posture are obtained in real time;

[0010] S5: During the online optimization of the dual-robot system, the current feedback posture of the robot obtained in step S4 is used to construct a new equation for online optimization of the system. Finally, the robot path is adjusted using the data control center to ensure high-precision and stable operation of the robot in marking.

[0011] Preferably, the specific method of step S1 is: control the robot to shoot the calibration plate at a fixed position, convert the calibration process into the equation AX=XB, solve it by introducing the modified Rodriguez formula, and use Gauss-Newton optimization to achieve single robot hand-eye calibration.

[0012] Preferably, the specific method of step S2 is: control the movement of the fixed probe on the measuring robot, shoot the calibration plate that follows the movement of the marking robot, obtain the matrix transformation relationship between the measuring coordinate system and the marking coordinate system, construct the AXB=YCZ equation, introduce the Kronecker product to solve the equation to obtain the transformation matrix between the measuring robot base coordinate system and the marking robot base coordinate system, and use Gauss-Newton to optimize the calibration results to realize the relative posture calibration of the dual robot measurement and marking system.

[0013] As a preference, the position relationship between the marking robot laser and the workpiece in step S3 is represented by a uniform matrix

[0014] When the fixed focal length of the laser is known, the circular motion trajectory of the robot end is constructed with the spot irradiated on the workpiece as the center and the focal length as the radius; four positions are randomly selected on the trajectory to make the laser irradiate the workpiece at different angles, and the position relationship between the laser and the workpiece is obtained through the "four-point calibration" method.

[0015] Preferably, the specific method of step S4 is:

[0016] During the online optimization of the dual robot system, the uniform matrix Indicates the target position relationship from the center of the marking laser to the workpiece. The tracking system is used to track the array of luminous marker points on the laser to obtain its position parameter data. During the movement of the probe and the target, the actual position relationship is obtained.

[0017] Preferably, in step S5, the current feedback posture of the robot obtained in step S4 is used to construct a new AXB=YCZ equation for online optimization of the system. The specific method for constructing the new equation is:

[0018] Assuming the robot is six independent linear “single-input, single-output” systems, the pose error in the joint space during the marking process is quantified as the control error φ of the robot axis angle e ;use represents the inverse kinematics function of the robot, then the axis angle can be expressed as:

[0019]

[0020] in, is the position relationship from the center of the marking laser to the workpiece. The vector p contains the parameters of the kinematic robot model, the position relationship of the system, and the parameters describing the robot configuration; the target position relationship Obtained by the system's high-precision calibration process.

[0021] As a preferred method, the tracking system in step S4 is used to obtain the actual position relationship of the robot. The axis angle error of the robot during marking is expressed as:

[0022] φ e =φ t -φ a

[0023] Among them, φ e is the control error of the robot axis angle, φ t is the target axis angle, φ a is the actual shaft angle.

[0024] Preferably, the data control center is used to feed back the axis angle error to the robot end effector to adjust the marking path, and the AXB=YCZ equation reflecting the dual robot coordinate transformation relationship is re-corrected under the new path to ensure high-precision and stable operation of the robot marking.

[0025] In general, the above technical solution conceived by the present invention has the following beneficial effects compared with the prior art:

[0026] 1. Considering that it is difficult to achieve accurate calibration of multi-robot systems in existing complex casting allowance automatic measurement and marking systems, and that parameters are prone to drift during actual processing and require repeated offline calibration, affecting marking accuracy and stability, the present invention is based on methods such as robot hand-eye calibration and laser tool coordinate system calibration, online optimization of visual tracking system, and data feedback control of CNC center, which can achieve accurate calibration of multi-robot systems while overcoming problems such as system parameter drift that occur during robot operation, and provides a theoretical basis for ensuring high-precision and stable operation of complex casting automatic measurement and marking systems.

[0027] 2. When the present invention calibrates the relative posture of the measuring robot and the marking robot, the fixed probe of the measuring robot is used to shoot the calibration plate fixed on the marking robot to obtain the matrix conversion relationship between the measuring coordinate system and the marking coordinate system. The AXB=YCZ equation is constructed, and the Kronecker product is introduced to solve the equation to obtain the conversion matrix between the base coordinate system of the measuring robot and the base coordinate system of the marking robot, and Gauss-Newton optimization is used to realize the relative posture calibration of the dual robot system, reduce the calibration difficulty and effectively improve the calibration accuracy.

[0028] 3. When the present invention performs online optimization on the path drift occurring during the operation of the robot, the visual tracking system is used to measure the actual axis angle, and the difference between the actual axis angle and the target axis angle obtained during the offline calibration process is fed back to the end of the robot through the numerical control system to achieve path correction and ensure high-precision operation of the complex casting measurement and marking system. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 A schematic diagram of the implementation process of a dual-robot high-precision calibration and online optimization method in an automatic measurement and marking system for complex casting machining allowances provided by the present invention;

[0030] Figure 2 A schematic diagram of a system for implementing a relative posture calibration method for a dual-robot measurement and marking system provided by the present invention;

[0031] Figure 3 A schematic diagram of a dual-robot path tracking and online optimization system provided by the present invention;

[0032] Figure 4 This is a flow chart of the online optimization and path correction feedback solution for the dual robot system provided by the present invention. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0034] See also Figure 1-Figure 4 The present invention provides a dual robot high-precision calibration and online optimization method, comprising the following steps:

[0035] S1: Control the robot to shoot the calibration plate at a fixed position to achieve single robot hand-eye calibration;

[0036] S2: Control the movement of the fixed probe on the measuring robot, shoot the calibration plate that follows the movement of the marking robot, and realize the relative posture calibration of the dual-robot measurement and marking system;

[0037] S3: Control the laser focus to fall on the workpiece, construct the circular motion trajectory of the robot end with the center of the laser as the center and the focal length as the radius, select four positions on the trajectory at random, and use four-point calibration to obtain the position relationship between the laser and the workpiece;

[0038] S4: During the online optimization of the dual robot system, the tracking system first tracks the array of luminous marker points on the laser to obtain its position and posture parameter data; then, during the movement of the probe and the target, their relative position and posture are obtained in real time;

[0039] S5: During the online optimization of the dual-robot system, the current feedback posture of the robot obtained in step S4 is used to construct a new equation for online optimization of the system. Finally, the robot path is adjusted using the data control center to ensure high-precision and stable operation of the robot in marking.

[0040] The specific method of step S1 is: control the robot to shoot the calibration plate at a fixed position, convert the calibration process into the equation AX=XB, solve it by introducing the modified Rodriguez formula, and use Gauss-Newton optimization to achieve single robot hand-eye calibration.

[0041] Specifically, Figure 2 As shown in the figure, when the camera is fixed at the end of the robot arm, the hand-eye calibration problem is transformed into finding the transformation matrix from the robot end flange coordinate system to the camera coordinate system. This problem can be classified as solving the following form of homogeneous transformation matrix relationship:

[0042] AX=XB (3)

[0043] Among them, X is the hand-eye conversion matrix to be determined, A is the conversion matrix between the robot end coordinate system and the base coordinate system obtained by the robot forward kinematics, and B is the conversion matrix between the camera coordinate system and the calibration coordinate system, which is obtained by shooting the fixed position calibration plate with the camera.

[0044] There are many methods in the related field for solving the classic single robot hand-eye model. This scheme chooses to modify the Rodriguez formula to solve the coordinate transformation relationship, and uses Gauss-Newton optimization to further improve the accuracy, realize single robot hand-eye calibration, and lay the foundation for subsequent dual robot collaborative calibration.

[0045] The specific method of step S2 is: control the movement of the fixed probe on the measuring robot, shoot the calibration plate that follows the movement of the marking robot, obtain the matrix transformation relationship between the measuring coordinate system and the marking coordinate system, construct the AXB=YCZ equation, introduce the Kronecker product to solve the equation to obtain the transformation matrix between the base coordinate system of the measuring robot and the base coordinate system of the marking robot, and use Gauss-Newton to optimize the calibration result to realize the relative posture calibration of the dual robot measurement and marking system.

[0046] Specifically, Figure 2 As shown in the figure, the calibration work of the dual robot system mainly includes the correction of kinematic parameters, flange-sensor pose calibration, dual robot base coordinate system coordinate conversion, flange-tool pose calibration, etc. Among them, the current robots basically have the self-calibration function of kinematic parameters, and other calibration work can be converted into the solution problem of the AXB=YCZ equation. Among them, X, Y, Z represent the unknown flange-sensor, measurement robot base coordinate system-marking robot base coordinate system and flange-tool conversion matrix respectively; A, B, C represent the known matrices obtained from the robot control device and the shooting process.

[0047] like Figure 2 As shown, {O1}, {E1}, {S}, {O2}, {E2} and {T} represent the base coordinate system, end flange coordinate system, camera coordinate system of robot 1, base coordinate system, end flange coordinate system and target coordinate system of robot 2, respectively. At this time, X, Y, Z in the equation AXB=YCZ represent the homogeneous transformation matrices from {E1} to {S}, {O1} to {O2} and {E2} to {T}, respectively, which can be expressed as:

[0048]

[0049] Where R T Represents a 3×3 rotation matrix, t T represents a 3×1 translation vector. At this time, solving the equation AXB=YCZ can be converted into solving the rotation part and translation part as shown below:

[0050] R A R X R B =R Y R C R Z (5)

[0051] R A R X t B +R A t X +t A =R Y RC t Z +R Y t C +t Y (6)

[0052] During the calibration process, the two robots need to continuously adjust their positions to obtain n sets of homogeneous transformation matrices A. i , B i , C i (i=1,2,…,n). In this case, (3) and (4) are extended to:

[0053] R Ai R X R Bi =R Y R Ci R Z (7)

[0054] R Ai R X t Bi +R Ai t X +t Ai =R Y R Ci t Z +R Y t Ci +t Y (8)

[0055] where i = 1, 2, ..., n and R Ai , R Bi , R Ci , t Ai , t Bi , t ci They represent a set of rotation matrices and translation vectors under the i-th robot posture during the calibration process. At this time, the solution of the equation AXB = YCZ is transformed into solving n sets of equations (7) and (8). By introducing the Kronecker product to solve the unknown rotation matrix and translation vector in the equation, and then optimizing the result through Gauss-Newton, the relative posture calibration of the dual robot measurement-marking system is completed.

[0056] In step S3, the laser focus is controlled to fall on the workpiece, and the circular motion trajectory of the robot end is constructed with the center of the laser as the center and the focal length as the radius. Four positions are randomly selected on the trajectory, and the position relationship between the laser and the workpiece is obtained by four-point calibration; the position relationship between the marking robot laser and the workpiece is expressed as a uniform matrix

[0057] When the fixed focal length of the laser is known, the circular motion trajectory of the robot end is constructed with the spot irradiated on the workpiece as the center and the focal length as the radius; four positions are randomly selected on the trajectory to make the laser irradiate the workpiece at different angles, and the position relationship between the laser and the workpiece is obtained through the "four-point calibration" method.

[0058] The specific method of step S4 is: during the online optimization process of the dual robot system, the uniform matrix Indicates the target position relationship from the center of the marking laser to the workpiece. The tracking system is used to track the array of luminous marker points on the laser to obtain its position parameter data. During the movement of the probe and the target, the actual position relationship is obtained.

[0059] Specifically, Figure 3 As shown in the figure, the robot's axis angle posture is obtained by adding an external tracking device to the system. First, the initial data of the marking robot's posture parameters are obtained by shooting the array of luminous marker points attached to the laser. After the high-precision calibration of the dual robot system starts to measure the marking with the preset path, due to a series of influencing factors such as workpiece rigidity and on-site vibration during the processing, the robot's target axis angle posture obtained in the calibration Drift will occur. The actual position at this moment is obtained through the external tracking system Provide data support for online path adjustment.

[0060] The specific method of step S4 is: during the online optimization process of the dual robot system, the new AXB=YCZ equation is constructed using the current feedback posture of the robot to perform online optimization of the system, and finally the robot path is adjusted using the data control center to ensure high-precision and stable operation of the robot in marking.

[0061] The specific method of constructing the new equation is:

[0062] The robot is assumed to be six independent linear "single-input, single-output (SISO)" systems, and the pose error in the joint space during the marking process is quantified as the control error φ of the robot axis angle e ;use represents the inverse kinematics function of the robot, then the axis angle can be expressed as:

[0063]

[0064] in, is the positional relationship from the center of the scribing laser to the workpiece, and the vector p contains the parameters of the kinematic robot model, the positional relationship of the system (robot to workpiece, laser to robot flange) and the parameters describing the robot configuration;

[0065] Target position relationship The actual position of the robot is obtained by the high-precision calibration process of the system. In the actual processing process, the tracking system in step S4 is used to obtain the actual position relationship of the robot. The axis angle error of the robot during marking is expressed as:

[0066] φ e =φ t -φ a (2)

[0067] Among them, φ e is the control error of the robot axis angle, φ t is the target axis angle, φ a is the actual shaft angle.

[0068] like Figure 4 As shown in the process, the data control center is used to feed back the axis angle error to the robot end effector to adjust the marking path, and the AXB=YCZ equation reflecting the dual robot coordinate transformation relationship is re-corrected under the new path to ensure high-precision and stable operation of the robot marking.

[0069] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A dual robot high-precision calibration and online optimization method, characterized in that: The following steps are involved: S1: Control the robot to shoot the calibration plate at a fixed position to achieve single robot hand-eye calibration; S2: Control the movement of the fixed probe on the measuring robot, shoot the calibration plate that follows the movement of the marking robot, and realize the relative posture calibration of the dual-robot measurement and marking system; S3: Control the laser focus to fall on the workpiece, construct a circular motion trajectory of the robot end with the laser center as the center and the focal length as the radius, select four positions on the trajectory at random, and use four-point calibration to obtain the position relationship between the laser and the workpiece; S4: During the online optimization of the dual robot system, the tracking system first tracks the array of luminous marker points on the laser to obtain its position and posture parameter data; then, during the movement of the probe and the target, their relative position and posture are obtained in real time; S5: During the online optimization of the dual robot system, the current feedback posture of the robot obtained in step S4 is used to construct a new equation for online optimization of the system. Finally, the robot path is adjusted using the data control center to ensure high-precision and stable operation of the robot in marking.

2. A dual robot high-precision calibration and online optimization method as claimed in claim 1, characterized in that: The specific method of step S1 is: control the robot to shoot the calibration plate at a fixed position, convert the calibration process into the equation AX=XB, solve it by introducing the modified Rodriguez formula, and use Gauss-Newton optimization to achieve single robot hand-eye calibration.

3. A dual robot high-precision calibration and online optimization method as claimed in claim 1, characterized in that: The specific method of step S2 is: control the movement of the fixed probe on the measuring robot, shoot the calibration plate that follows the movement of the marking robot, obtain the matrix transformation relationship between the measuring coordinate system and the marking coordinate system, construct the AXB=YCZ equation, introduce the Kronecker product to solve the equation to obtain the transformation matrix between the base coordinate system of the measuring robot and the base coordinate system of the marking robot, and use Gauss-Newton to optimize the calibration result to realize the relative posture calibration of the dual robot measurement and marking system.

4. A dual robot high-precision calibration and online optimization method as claimed in claim 1, characterized in that: In step S3, the position relationship between the marking robot laser and the workpiece is expressed as a uniform matrix When the fixed focal length of the laser is known, the circular motion trajectory of the robot end is constructed with the spot irradiated on the workpiece as the center and the focal length as the radius; four positions are randomly selected on the trajectory to make the laser irradiate the workpiece at different angles, and the position relationship between the laser and the workpiece is obtained through the "four-point calibration" method.

5. A dual robot high-precision calibration and online optimization method as claimed in claim 1, characterized in that: The specific method of step S4 is: During the online optimization of the dual robot system, the uniform matrix Indicates the target position relationship from the center of the marking laser to the workpiece. The tracking system is used to track the array of luminous marker points on the laser to obtain its position parameter data. During the movement of the probe and the target, the actual position relationship is obtained.

6. A dual robot high-precision calibration and online optimization method as claimed in claim 4, characterized in that: In step S5, the current feedback posture of the robot obtained in step S4 is used to construct a new AXB=YCZ equation for online optimization of the system.

7. A dual robot high-precision calibration and online optimization method as claimed in claim 6, characterized in that: The specific method of constructing the new equation in step S5 is: Assuming the robot is six independent linear "single-input, single-output" systems, the position error of the joint space during the marking process is quantified as the control error of the robot axis angle φ e ;use represents the inverse kinematics function of the robot, then the axis angle can be expressed as: in, is the position relationship from the center of the marking laser to the workpiece. The vector p contains the parameters of the kinematic robot model, the position relationship of the system, and the parameters describing the robot configuration; the target position relationship Obtained by the system's high-precision calibration process.

8. A dual robot high-precision calibration and online optimization method as claimed in claim 7, characterized in that: Use the tracking system in step S4 to obtain the actual position relationship of the robot The axis angle error of the robot during marking is expressed as: f e =φ t -f a Among them, φ e is the control error of the robot axis angle, φ t is the target axis angle, φ a is the actual shaft angle.

9. A dual robot high-precision calibration and online optimization method as claimed in claim 8, characterized in that: The data control center is used to feed back the axis angle error to the robot end effector to adjust the marking path, and the AXB=YCZ equation reflecting the dual robot coordinate transformation relationship is re-corrected under the new path to ensure high-precision and stable operation of the robot marking.

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