A dual-robot high-precision calibration and online optimization method

Abstract

The present invention belongs to the field of automatic measurement and marking of complex castings. It discloses a high-precision calibration and online optimization method for dual robots. Based on hand-eye calibration and four-point calibration, the casting-measurement-marking system components are calibrated separately when the system is deployed to obtain high-precision initial values ​​of system parameters. Calibration data is collected online during the measurement-analysis-marking process based on a visual tracking system to achieve online optimization of multi-robot system parameters. The optimized robot paths are fed back to a data control center to ensure high-precision and stable operation of the robot marking.

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 key to ensuring precise measurement-to-machining coordinate conversion and improving marking accuracy. Existing methods struggle to accurately calibrate multi-robot systems, and parameters are prone to drift during actual machining, requiring repeated offline calibration, impacting marking accuracy and stability.

[0003] Therefore, it is planned to study the calibration method of the relative posture parameters of various parts of the system, including robot-probe hand-eye calibration, robot-laser tool coordinate system, robot-robot and robot-workpiece, to construct the relative posture 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, 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-mentioned defects or improvement needs of the prior art, the present invention provides a high-precision calibration and online optimization method for dual robots in the automatic measurement and marking system of complex casting processing allowances, thereby solving the technical problems of insufficient accuracy and poor stability of the existing multi-robot system calibration method.

[0005] To achieve the above objectives, 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 a circular motion trajectory of the robot end with the laser center as the center and the focal length as the radius, and use four-point calibration to obtain the position relationship between the laser and the workpiece at any four positions on the trajectory;

[0009] S4: During the online optimization of the dual-robot system, the tracking system first tracks the array of luminous markers on the laser to obtain its pose parameter data; then, as the probe and target move, their relative pose is 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 marking.

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

[0012] Preferably, the specific method of step S2 is: controlling the movement of the fixed probe on the measuring robot, shooting the calibration plate that follows the movement of the marking robot, obtaining the matrix transformation relationship between the measuring coordinate system and the marking coordinate system, constructing the AXB=YCZ equation, introducing 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 using 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 light 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 between the center of the marking laser and the workpiece. The tracking system is used to track the array of luminous markers on the laser to obtain its posture 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 system online optimization. The specific method of 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 of the robot axis angle φ e ;use Represents the inverse kinematics function of the robot, and the axis angle can be expressed as:

[0019]

[0020] in, is the position relationship between the center of the marking laser and 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 preference, 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 solutions conceived by the present invention have 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 systems, and data feedback control of numerical control centers. It achieves accurate calibration of multi-robot systems while overcoming problems such as system parameter drift that occur during robot operation, providing a theoretical basis for ensuring high-precision and stable operation of complex casting automatic measurement and marking systems.

[0027] 2. When calibrating the relative pose of the measuring robot and the marking robot, the present invention uses the measuring robot's fixed probe to capture a calibration plate fixed to the marking robot to obtain the matrix transformation relationship between the measuring coordinate system and the marking coordinate system. The equation AXB = YCZ is constructed, and the Kronecker product is introduced to solve the equation to obtain the transformation matrix between the measuring robot's base coordinate system and the marking robot's base coordinate system. Using Gauss-Newton optimization, the relative pose calibration of the dual-robot system is achieved, reducing the calibration difficulty while effectively improving calibration accuracy.

[0028] 3. When the present invention performs online optimization on the path drift that occurs during the operation of the robot, it uses a visual tracking system to measure the actual axis angle, and feeds back the difference between the actual axis angle and the target axis angle obtained during the offline calibration process 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 Schematic diagram of the implementation system of the relative pose calibration method of the dual-robot measurement and marking system provided by the present invention;

[0031] Figure 3 Schematic diagram of the 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 objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely for the purpose of explaining 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 may be combined with each other as long as they do not conflict with each other.

[0034] See also Figures 1-4 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 a circular motion trajectory of the robot end with the laser center as the center and the focal length as the radius, and use four-point calibration to obtain the position relationship between the laser and the workpiece at any four positions on the trajectory;

[0038] S4: During the online optimization of the dual-robot system, the tracking system first tracks the array of luminous markers on the laser to obtain its pose parameter data; then, as the probe and target move, their relative pose is 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 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, if 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 homogeneous transformation matrix relationship of the following form:

[0042] AX=XB (3)

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

[0044] Currently, there are many methods for solving the classic single-robot hand-eye model in related fields. This solution chooses to modify the Rodriguez formula to solve the coordinate transformation relationship and uses Gauss-Newton optimization to further improve the accuracy, realizing single-robot hand-eye calibration and laying 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 results to realize the relative posture calibration of the dual-robot measurement and marking system.

[0046] Specifically, if Figure 2 As shown, calibration of a dual-robot system primarily involves kinematic parameter correction, flange-sensor pose calibration, dual-robot base coordinate system conversion, and flange-tool pose calibration. Most current robots have self-calibration capabilities for kinematic parameters, and the remaining calibration tasks can be converted into solving the equation AXB = YCZ. X, Y, and Z represent the unknown transformation matrices between the flange and sensor, the measurement robot base coordinate system, the marking robot base coordinate system, and the flange and tool, respectively; A, B, and C represent known matrices obtained from the robot control device and the imaging process.

[0047] like Figure 2 As shown in the figure, {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, and 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 separately 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 postures to obtain n sets of homogeneous transformation matrices A. i , B i , C i (i=1,2,…,n). Then (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 Respectively represent a set of rotation matrices and translation vectors for the i-th robot pose during the calibration process. At this point, solving 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 pose 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 posture relationship between the laser and the workpiece is obtained by four-point calibration; the posture 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 light 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 between the center of the marking laser and the workpiece. The tracking system is used to track the array of luminous markers on the laser to obtain its posture parameter data. During the movement of the probe and the target, the actual position relationship is obtained.

[0059] Specifically, if Figure 3 As shown in the figure, the axis angle position of the robot is obtained by adding an external tracking device to the system. First, the initial data of the marking robot's position parameters are obtained by shooting the array of luminous markers attached to the laser. After the high-precision calibration is completed, the dual-robot system starts measuring and 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 position 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 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 of the robot axis angle φ e ;use Represents the inverse kinematics function of the robot, and the axis angle can be expressed as:

[0063]

[0064] in, is the positional relationship from the center of the marking laser to the workpiece. 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 system is obtained by high-precision calibration process. 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 merely 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 scope of protection 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, and use four-point calibration to obtain the position relationship between the laser and the workpiece at any four positions on the trajectory; S4: During the online optimization of the dual-robot system, the tracking system first tracks the array of luminous markers on the laser to obtain its pose parameter data; then, as the probe and target move, their relative pose is 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 according to 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 according to 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 results 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 according to 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 light 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. The dual-robot high-precision calibration and online optimization method according to 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 between the center of the marking laser and the workpiece. The tracking system is used to track the array of luminous markers on the laser to obtain its posture 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 system online optimization.

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 pose error in 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, and the axis angle can be expressed as: in, is the position relationship between the center of the marking laser and 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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