Robot zero point calibration method, device, computer storage medium
By using 2D vision technology and nonlinear optimization models, the problems of high cost and low accuracy in robot zero-point calibration have been solved, achieving low-cost and high-precision automatic zero-point calibration.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- ADTECH SHENZHEN TECH
- Filing Date
- 2023-04-23
- Publication Date
- 2026-05-12
AI Technical Summary
Existing robot zero-point calibration methods involve expensive equipment, complex calibration processes, and manual operation, which can easily introduce human error and lead to a decrease in the robot's absolute positioning accuracy.
Using 2D vision technology, the robot moves to several teaching points and records the end-effector coordinates and joint positions to build a nonlinear optimization model. The zero-point parameters, including the zero-point offset value and the tool offset value, are solved using the offset estimate, thus achieving automatic calibration.
It reduced calibration costs, improved calibration accuracy, simplified the operation process, reduced human error, and achieved high-precision zero-point calibration.
Smart Images
Figure CN116394254B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of robot kinematic parameter calibration technology, and in particular to a zero-point calibration device and method for a robot, as well as a computer storage medium. Background Technology
[0002] Absolute positioning accuracy is a crucial technical indicator for evaluating robot performance. Industrial processes (such as component insertion, soldering, and screw driving) place increasingly higher demands on robot absolute positioning accuracy. Robot absolute positioning accuracy is affected by numerous factors, including part machining precision, assembly errors, joint friction and wear, and zero-point offset or loss. These factors cause deviations between the robot's actual kinematic parameters and the controller's theoretical values, necessitating calibration of the robot's kinematic parameters to improve absolute positioning accuracy. Among these, zero-point offset or loss is the most significant factor affecting robot absolute positioning accuracy.
[0003] Existing robot zero-point calibration methods have the following problems: the calibration equipment is expensive, the calibration process is complex, and the calibration cost is high; they require manual operation and visual observation, which introduces human error. Summary of the Invention
[0004] This application provides a zero-point calibration method, a zero-point calibration device, and a computer storage medium for a robot.
[0005] One technical solution adopted in this application is to provide a zero-point calibration method for a robot, the zero-point calibration method comprising:
[0006] The robot is moved to several first teaching points, and from each first teaching point it is moved to a standard point. The robot end coordinates corresponding to the movement from different first teaching points to the standard point are recorded. The standard point is the position of the robot when the center of the marker point is aligned with the main point of the camera.
[0007] Using the robot end-effector coordinates, the offset estimate of the tool coordinate system is calculated;
[0008] The robot is moved to several second teaching points, and from each second teaching point it is moved to the standard point, and the robot joint positions corresponding to the movement from different second teaching points to the standard point are recorded.
[0009] Based on the invariance of the coordinates of the center of the marker point in the robot's base coordinate system, a nonlinear optimization model is constructed using the robot's joint positions.
[0010] Using the offset estimate as an initial value, the nonlinear optimization model is solved to obtain the zero-point parameters, wherein the zero-point parameters include the robot's zero-point offset value and the tool offset value.
[0011] The zero-point offset value of the robot includes the zero-point offset values between each joint of the robot.
[0012] The zero-point calibration method further includes, after obtaining the zero-point parameters:
[0013] Obtain the first homogeneous transformation matrix from the tool coordinate system to the robot end effector coordinate system, and the second homogeneous transformation matrix between the joints of the robot;
[0014] Based on the first homogeneous transformation matrix and the second homogeneous transformation matrix, a third homogeneous transformation matrix is constructed for the center of the marker point in the robot base coordinate system;
[0015] The zero-point parameters are used to solve the third homogeneous transformation matrix to obtain the zero-point coordinates of the center of the marker point in the robot's base coordinate system.
[0016] The error of the zero-point parameter is calculated based on the zero-point coordinates.
[0017] The step of constructing a nonlinear optimization model based on the invariant coordinates of the marker center in the robot's base coordinate system and utilizing the robot joint positions includes:
[0018] Obtain the original posture of each teaching point;
[0019] The attitude angle between each pair of teaching points is obtained based on the original attitude of each teaching point;
[0020] The attitude angle is used to perform attitude interpolation on the pairwise teaching points to obtain the interpolated attitude between the pairwise teaching points.
[0021] Based on the invariance of the coordinates of the center of the marker point in the robot's base coordinate system, the nonlinear optimization model is constructed using the robot joint positions, the original pose, and the interpolated pose.
[0022] The step of obtaining the attitude angle between any two teaching points based on the original attitude of each teaching point includes:
[0023] Convert the attitude angle of the original attitude of each teaching point into a unit quaternion;
[0024] Based on the unit quaternions of the pairwise teaching points, the attitude angle of the short attitude path is obtained.
[0025] The step of calculating the offset estimate of the tool coordinate system based on the robot end-effector coordinates includes:
[0026] Based on the robot end-effector coordinates, construct a homogeneous transformation matrix from the robot end-effector coordinate system to the robot base coordinate system;
[0027] Construct the homogeneous transformation matrix from the tool coordinate system to the robot end effector coordinate system;
[0028] The offset estimate of the tool coordinate system is calculated using the homogeneous transformation matrix from the robot end-effector coordinate system to the robot base coordinate system and the homogeneous transformation matrix from the tool coordinate system to the robot end-effector coordinate system.
[0029] The recording of the robot end-effector coordinates corresponding to the movement from different first teaching points to the standard point includes:
[0030] Obtain the coordinates of the camera principal point and the pixel coordinates of the marker point corresponding to each first teaching point;
[0031] Based on the camera principal point coordinates and the marker point pixel coordinates, the required offset of the robot end effector is obtained;
[0032] Based on the required offset and the original robot end coordinates at the first teaching point, the robot end coordinates at the standard point are obtained.
[0033] The step of obtaining the required offset of the robot's end effector based on the camera principal point coordinates and the marker point pixel coordinates includes:
[0034] Obtain the deviation between the coordinates of the camera principal point and the pixel coordinates of the marker point;
[0035] Based on the camera's pixel equivalent and the deviation value, the required offset of the robot's end effector is obtained;
[0036] Before obtaining the required offset of the robot end effector based on the camera's pixel equivalent and the deviation value, the zero-point calibration method further includes:
[0037] Take a picture at any teaching point to obtain the coordinates of the first teaching pixel;
[0038] The robot end effector moves according to a preset step size and preset direction, and takes a picture to obtain the coordinates of the second teaching pixel;
[0039] Based on the first taught pixel coordinates and the second taught pixel coordinates, the rotation angle between the robot base coordinate system and the camera coordinate system is obtained;
[0040] A free vector transformation equation is constructed using the first taught pixel coordinates, the second taught pixel coordinates, and the rotation angle.
[0041] The pixel equivalent of the camera is obtained by jointly calculating using multiple sets of free vector transformation equations.
[0042] Another technical solution adopted in this application is to provide a zero-point calibration device, which includes a memory and a processor coupled to the memory;
[0043] The memory is used to store program data, and the processor is used to execute the program data to implement the zero-point calibration method as described above.
[0044] Another technical solution adopted in this application is to provide a computer storage medium for storing program data, which, when executed by a computer, is used to implement the zero-point calibration method described above.
[0045] The beneficial effects of this application are as follows: The zero-point calibration device moves the robot to several first teaching points, moves it from each first teaching point to a standard point, and records the robot end-effector coordinates corresponding to the movement from different first teaching points to the standard point, wherein the standard point is the position of the robot when the center of the marker point is aligned with the main point of the camera; using the robot end-effector coordinates, the offset estimate of the tool coordinate system is solved; the robot is moved to several second teaching points, moves it from each second teaching point to the standard point, and records the robot joint positions corresponding to the movement from different second teaching points to the standard point; based on the invariant coordinates of the center of the marker point in the robot's base coordinate system, a nonlinear optimization model is constructed using the robot joint positions; using the offset estimate as the initial value, the nonlinear optimization model is solved to obtain zero-point parameters, wherein the zero-point parameters include the robot's zero-point offset value and the tool offset value. The zero-point calibration device of this application adopts 2D vision and uses the method of aligning the camera principal point with the center of the marker point to perform zero-point calibration of the robot. It does not require a high-precision calibration board and can reduce calibration costs while ensuring high-precision calibration results. Attached Figure Description
[0046] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0047] Figure 1 This is a flowchart illustrating an embodiment of the zero-point calibration method provided in this application;
[0048] Figure 2This is a schematic diagram of the operation flow of the robot zero-point calibration method based on 2D vision provided in this application;
[0049] Figure 3 This is a physical image of the robot zero-point calibration method based on 2D vision provided in this application;
[0050] Figure 4 yes Figure 1 The diagram shows the detailed process flow of step S12 in the zero-point calibration method.
[0051] Figure 5 yes Figure 1 The diagram shows the detailed process of zero-point calibration method S14.
[0052] Figure 6 This is a schematic diagram of an embodiment of the zero-point calibration device provided in this application;
[0053] Figure 7 This is a schematic diagram of another embodiment of the zero-point calibration device provided in this application;
[0054] Figure 8 This is a schematic diagram of the structure of an embodiment of the computer storage medium provided in this application. Detailed Implementation
[0055] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.
[0056] Please see details. Figure 1 and Figure 2 , Figure 1 This is a flowchart illustrating an embodiment of the zero-point calibration method provided in this application. Figure 2 This is a schematic diagram illustrating the operation flow of the 2D vision-based robot zero-point calibration method provided in this application. The zero-point calibration method of this application can be applied to a zero-point calibration device, such as a collaborative robot or a handling robot, or other types of zero-point calibration devices. It can also be applied to the processing system mounted in the zero-point calibration device, or to a control system other than the zero-point calibration device.
[0057] Before introducing the operational procedures of the zero-point calibration method, this application first introduces the relevant hardware department; please refer to [link / reference needed]. Figure 3 , Figure 3 This is a physical image of the robot zero-point calibration method based on 2D vision provided in this application.
[0058] by Figure 3Taking a six-axis robot as an example, the hardware components include, but are not limited to: a fixture with circular mark points, a 2D camera, a vision tooling, and a QC620 drive and control integrated machine.
[0059] The zero point is the reference point for the robot's joint position; without a zero point, the robot cannot determine its own position. Typically, the robot's mechanical parameters are calibrated before leaving the factory, specifying the parameters and zero point positions of each link. However, in special circumstances, such as battery replacement, exceeding mechanical limits, collisions with the environment, or manual movement of robot joints, the zero point can be lost. In such situations, easily finding the robot's current zero point position is crucial for ensuring precise motion control.
[0060] like Figure 1 As shown, the zero-point calibration method of this application embodiment may specifically include the following steps:
[0061] Step S11: Move the robot to several first teaching points, move it from each first teaching point to a standard point, and record the robot end coordinates corresponding to the movement from different first teaching points to the standard point. The standard point is the position of the robot when the center of the marker point is aligned with the main point of the camera.
[0062] In this embodiment, the zero-point calibration device presets some calibration parameters, including but not limited to: camera configuration parameters, step size s, camera pixel value, and tool coordinate system number. A fixture is fixedly installed at the end effector of the robot, and a solid circular mark is affixed to the fixture.
[0063] In this embodiment, the user manually adjusts the robot's pose, moving the solid circle near the center of the camera's field of view to establish a visual template of the solid circle mark point. This robot position is used as teaching point 1. Then, the user manually controls the robot to rotate by a certain angle according to attitude angles A, B, and C, and manually controls the robot to adjust a certain displacement according to positions X and Y, moving the solid circle mark point near the center of the camera's field of view. This robot position is used as teaching point 2. This operation is repeated once to obtain the robot position as teaching point 3, and the vision system is then run.
[0064] The zero-point calibration device triggers an image at teaching point 1 or another teaching point to obtain pixel coordinates, i.e., the first teaching pixel coordinates (u1, v1). Then, the robot end effector moves a distance s along the x-direction of the robot base coordinate system, triggering an image to obtain pixel coordinates, i.e., the second teaching pixel coordinates (u2, v2); the robot end effector moves a distance s along the y-direction of the robot base coordinate system, triggering an image to obtain pixel coordinates (u3, v3).
[0065] The zero-point calibration device calculates the rotation angle between the robot's base coordinate system and the camera coordinate system based on the above-taught pixel coordinates:
[0066] α=atan2(v2-v1,u2-u1) (1)
[0067] Based on the above data, the transformation from the free vector (incremental coordinates) in the camera coordinate system to the free vector (incremental coordinates) in the robot base coordinate system can be obtained as follows:
[0068]
[0069] Similarly, we can conclude that:
[0070]
[0071] Combining equations (2) and (3) above, we can calculate the pixel equivalent, that is, the actual physical size represented by one pixel in the image:
[0072]
[0073] Furthermore, let the camera principal point coordinates be (u0, v0). The robot moves to teaching point 1, triggering the image capture mark, and obtains the pixel coordinates (u, v). At this point, the robot's end effector coordinates, i.e., the original robot end effector coordinates, are (x, y, z, a, b, c). Then, the deviation between the pixel coordinates (u, v) and the camera principal point coordinates (u0, v0) is calculated:
[0074]
[0075] The offset required for the robot's end effector to move when the pixel coordinates (u,v) coincide with the camera principal point coordinates (u0,v0) is calculated as follows:
[0076]
[0077] Calculate the next position the robot's end effector should move to:
[0078]
[0079] Multiple iterations of motion ensure that the camera's principal point is precisely aligned with the center of the mark point, thus obtaining the robot's end-effector coordinates (x1, y1, z1, a1, b1, c1).
[0080] Similarly, the robot moves to teaching point 2, and through multiple iterations, the camera's principal point is precisely aligned with the center of the mark point, obtaining the robot's end effector coordinates (x2, y2, z2, a2, b2, c2). The robot moves to teaching point 3, and through multiple iterations, the camera's principal point is precisely aligned with the center of the mark point, obtaining the robot's end effector coordinates (x3, y3, z3, a3, b3, c3).
[0081] Step S12: Using the robot end-effector coordinates, calculate the offset estimate of the tool coordinate system.
[0082] In this embodiment of the application, the zero-point calibration device calculates the offset estimate of the tool coordinate system based on the multiple robot end coordinates determined in step S11. The offset estimate is used as the initial value for subsequent calculation of calibration parameters.
[0083] Please continue reading for details. Figure 4 , Figure 4 yes Figure 1 The diagram shows the detailed process flow of step S12 in the zero-point calibration method.
[0084] like Figure 4 As shown, the zero-point calibration method of this application embodiment may specifically include the following steps:
[0085] Step S121: Based on the robot end-effector coordinates, construct the homogeneous transformation matrix from the robot end-effector coordinate system to the robot base coordinate system.
[0086] In this embodiment, the zero-point calibration device constructs a homogeneous transformation matrix from the robot's end-effector coordinate system to the robot's base coordinate system:
[0087]
[0088] In this embodiment of the application, the zero-point calibration device maps the (x1, y1, z1) coordinates of the robot's end effector to the transformed coordinates (x1, y1, z1) in the homogeneous transformation matrix. e y e , z e This transforms the coordinates (a1, b1, c1) in the robot's end effector into a rotation matrix that is a homogeneous transformation matrix.
[0089] Step S122: Construct the homogeneous transformation matrix from the tool coordinate system to the robot end effector coordinate system.
[0090] In this embodiment, the zero-point calibration device constructs a homogeneous transformation matrix from the tool coordinate system to the robot end-effector coordinate system:
[0091]
[0092] Step S123: Calculate the offset estimate of the tool coordinate system using the homogeneous transformation matrix from the robot end-effector coordinate system to the robot base coordinate system and the homogeneous transformation matrix from the tool coordinate system to the robot end-effector coordinate system.
[0093] In this embodiment, the homogeneous transformation matrix from the tool coordinate system to the robot base coordinate system is:
[0094]
[0095] Since the mark point center (TCP, Tool CenterPoint) is precisely aligned with the camera's principal point, and the camera remains stationary, then... For data from multiple teaching points, the following relationship exists:
[0096]
[0097] According to the above formula (9), the zero-point calibration device can obtain the estimated values t of the tool's x, y, and z direction offsets using the least squares method. x t y t z .
[0098] Step S13: Move the robot to several second teaching points, move it from each second teaching point to the standard point, and record the robot joint positions corresponding to the movement from different second teaching points to the standard point.
[0099] After the zero-point calibration device estimates the TCP, switch to the tool coordinate system and manually set the attitude angles A, B, and C to a certain angle. Teach point 4. Repeat this operation twice to teach points 5 and 6. This ensures that the TCP height remains unchanged to avoid affecting visual template recognition, and also allows for simple and quick point teaching even when the robot is moving at all joints.
[0100] Step S14: Based on the relationship that the coordinates of the center of the marker point remain unchanged in the robot's base coordinate system, a nonlinear optimization model is constructed using the robot's joint positions.
[0101] In this embodiment, the zero-point calibration device constructs a nonlinear optimization model based on the invariant coordinates of the marker center in the robot's base coordinate system. Prior to this, the zero-point calibration device also needs to perform attitude interpolation and automatic visual alignment.
[0102] Please refer to the details. Figure 5 , Figure 5 yes Figure 1 The diagram shows the specific process flow of zero-point calibration method S14.
[0103] like Figure 5 As shown, the zero-point calibration method of this application embodiment may specifically include the following steps:
[0104] Step S141: Obtain the original pose of each teaching point.
[0105] In this embodiment of the application, taking the attitude between teaching point 1 and teaching point 2 as an example, attitude interpolation is used to generate the attitude, and the specific implementation method is as follows:
[0106] The zero-point calibration device acquires the attitude angles (a1, b1, c1) of teaching point 1 and the attitude angles (a2, b2, c2) of teaching point 2.
[0107] Step S142: Obtain the attitude angle between any two teaching points based on the original attitude of each teaching point.
[0108] In this embodiment, the zero-point calibration device converts the attitude angle of teaching point 1 into a unit quaternion q1, and the attitude angle of teaching point 2 into a unit quaternion q2. If q1·q2 < 0, the shortest attitude path is taken, then q2 = -q2. The angle between the two unit quaternions is calculated:
[0109] θ=acos(q1·q2) (10)
[0110] Step S143: Use the attitude angle to perform attitude interpolation on the pairwise teaching points to obtain the interpolated attitude between the pairwise teaching points.
[0111] In this embodiment, the zero-point calibration device uses unit quaternion spherical linear interpolation (SLERP) for attitude interpolation:
[0112]
[0113] Where t∈[0,1].
[0114] After the zero-point calibration device interpolates to obtain a unit quaternion, it converts the unit quaternion into a rotation matrix, and then the rotation matrix is converted into attitude angles (a, b, c). Assuming that the number of attitude interpolations between two attitudes is N, then six attitudes can generate 5N attitudes.
[0115] Step S144: Based on the relationship that the coordinates of the center of the marker point remain unchanged in the robot's base coordinate system, a nonlinear optimization model is constructed using the robot's joint positions, original pose, and interpolated pose.
[0116] In the embodiments of this application, the robot moves to the i-th (i = 1, 2, ..., 5N) pose and performs a visual automatic alignment function to ensure that the principal point of the camera is accurately aligned with the center of the mark point, thereby obtaining the position of the robot joint.
[0117] Step S15: Using the offset estimate as the initial value, solve the nonlinear optimization model to obtain the zero-point parameters, where the zero-point parameters include the robot's zero-point offset value and the tool offset value.
[0118] In this embodiment, since the robot TCP is precisely aligned with the camera principal point and the camera remains stationary, the x and y coordinates of the TCP in the robot's base coordinate system remain unchanged. Let p = [Δθ2, Δθ3, Δθ4, Δθ5, t x ,ty ,t z ] T Solve the system of equations:
[0119]
[0120] Where Δθ2, Δθ3, Δθ4, and Δθ5 are the zero-point offset values of each joint of the robot, and t x ,t y ,t z This is the tool offset value.
[0121] The nonlinear equations of the above formula (12) are transformed into a nonlinear optimization model:
[0122]
[0123] For the nonlinear optimization model of equation (13), the zero-point calibration device can be solved using the quasi-Newton method. The initial value of the zero-point offset of each joint is 0, and the initial value of TCP is the estimated value determined in step S13.
[0124] Furthermore, since the zero point of joint 1 only affects the establishment of the robot's base coordinate system orientation and does not affect the robot's absolute positioning accuracy, the zero point of joint 6 only affects the TCP calibration result and does not affect the robot's absolute positioning accuracy. For a six-axis robot, the parameters to be calibrated are the zero point offsets Δθ2, Δθ3, Δθ4, and Δθ5 from joint 2 to joint 5 and the xyz offset t of the tool coordinate system. x ,t y ,t z There are a total of 7 parameters. The homogeneous transformation matrix of TCP in the robot's base coordinate system is:
[0125]
[0126] Among them, T i (i = 1, 2, ..., 6) is the homogeneous transformation matrix from joint i to joint i-1.
[0127] After the zero-point calibration device iteratively calculates the zero-point offset value and TCP, it uses the position of each joint of the robot to calculate the x and y coordinates of TCP in the robot base coordinate system according to Equation (14), and uses the minimum covering circle algorithm to calculate its radius to measure the calibration error.
[0128] In this embodiment, the zero-point calibration device moves the robot to several first teaching points, moves it from each first teaching point to a standard point, and records the robot end-effector coordinates corresponding to the movement from different first teaching points to the standard point. The standard point is the robot's position when the center of the marker point is aligned with the camera's principal point. Based on the robot end-effector coordinates, the offset estimate of the tool coordinate system is calculated. The robot is then moved to several second teaching points, moves it from each second teaching point to the standard point, and records the robot joint positions corresponding to the movement from different second teaching points to the standard point. Based on the invariant coordinates of the marker point center in the robot's base coordinate system, a nonlinear optimization model is constructed using the robot joint positions. Using the offset estimate as initial values, the nonlinear optimization model is solved to obtain zero-point parameters, which include the robot's zero-point offset value and the tool offset value. The zero-point calibration device of this application adopts 2D vision and uses the method of aligning the camera principal point with the center of the marker point to perform zero-point calibration of the robot. It does not require a high-precision calibration board and can reduce calibration costs while ensuring high-precision calibration results.
[0129] It should be noted that this application is also applicable to robots with 4 or more axes, and the shape of the mark point does not have to be a solid circle; it can also be other shapes.
[0130] The zero-point calibration method of this application can achieve the following advantages:
[0131] (1) Lower calibration cost: Only 2D vision is required, without expensive equipment such as 3D vision or laser trackers. Only a clear and distinguishable mark point printed by a regular printer is needed, without the need for a high-precision calibration board. Compared with laser levels, high-precision probes and standard balls, 3D vision, and laser trackers, the calibration cost is greatly reduced.
[0132] (2) Easy to use: Only one visual template needs to be built and six points need to be taught to complete the calibration.
[0133] (3) Automatic: One-click automatic calibration.
[0134] (4) High precision: The independently developed vision-based automatic alignment algorithm can enable the robot TCP to automatically and accurately align with the main point of the camera, greatly improving the calibration accuracy.
[0135] The above embodiments are merely one common example of this application and do not constitute any limitation on the technical scope of this application. Therefore, any minor modifications, equivalent changes, or alterations made to the above content based on the substance of the solution of this application shall still fall within the scope of the technical solution of this application.
[0136] Please continue reading Figure 6, Figure 6 This is a schematic diagram of an embodiment of the zero-point calibration device provided in this application. The zero-point calibration device 30 includes a teaching module 31, an estimation module 32, a construction module 33, and a calibration module 34.
[0137] The teaching module 31 is used to move the robot to several first teaching points, move it from each first teaching point to a standard point, and record the robot end coordinates corresponding to the movement from different first teaching points to the standard point. The standard point is the position of the robot when the center of the marker point is aligned with the main point of the camera.
[0138] The estimation module 32 is used to calculate the offset estimate of the tool coordinate system using the coordinates of the robot end effector.
[0139] The teaching module 31 is used to move the robot to several second teaching points, move it from each second teaching point to the standard point, and record the robot joint positions corresponding to the movement from different second teaching points to the standard point.
[0140] The construction module 33 is used to construct a nonlinear optimization model based on the invariant coordinates of the center of the marker point in the robot's base coordinate system and the position of the robot joint.
[0141] The calibration module 34 is used to solve the nonlinear optimization model using the offset estimate as the initial value to obtain the zero-point parameters, wherein the zero-point parameters include the robot's zero-point offset value and the tool offset value.
[0142] Please continue reading Figure 7 , Figure 7 This is a schematic diagram of another embodiment of the zero-point calibration device provided in this application. The zero-point calibration device 500 of this application includes a processor 51, a memory 52, an input / output device 53, and a bus 54.
[0143] The processor 51, memory 52, and input / output device 53 are respectively connected to the bus 54. The memory 52 stores program data, and the processor 51 is used to execute the program data to implement the zero-point calibration method described in any of the above embodiments.
[0144] In this embodiment, processor 51 can also be referred to as a CPU (Central Processing Unit). Processor 51 may be an integrated circuit chip with signal processing capabilities. Processor 51 can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor, or processor 51 can be any conventional processor.
[0145] This application also provides a computer storage medium; please refer to the following: Figure 8 , Figure 8 This is a schematic diagram of a computer storage medium according to an embodiment of the present application. The computer storage medium 600 stores program data 61, which is used to implement the zero-point calibration method of any of the above embodiments when executed by the processor.
[0146] When the embodiments of this application are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0147] The above description is merely an embodiment of this application and does not limit the patent scope of this application. Equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.
Claims
1. A method for zero-point calibration of a robot, characterized in that, The zero-point calibration method includes: The robot is moved to several first teaching points, and from each first teaching point it is moved to a standard point. The robot end coordinates corresponding to the movement from different first teaching points to the standard point are recorded. The standard point is the position of the robot when the center of the marker point is aligned with the main point of the camera. Using the robot end-effector coordinates, the offset estimate of the tool coordinate system is calculated; The robot is moved to several second teaching points, and from each second teaching point it is moved to the standard point, and the robot joint positions corresponding to the movement from different second teaching points to the standard point are recorded. Based on the invariance of the coordinates of the center of the marker point in the robot's base coordinate system, a nonlinear optimization model is constructed using the robot's joint positions. Using the offset estimate as an initial value, the nonlinear optimization model is solved to obtain the zero-point parameters, wherein the zero-point parameters include the robot's zero-point offset value and the tool offset value.
2. The zero-point calibration method according to claim 1, characterized in that, The zero-point offset value of the robot includes the zero-point offset values between the joints of the robot.
3. The zero-point calibration method according to claim 2, characterized in that, After obtaining the zero-point parameters, the zero-point calibration method further includes: Obtain the first homogeneous transformation matrix from the tool coordinate system to the robot end effector coordinate system, and the second homogeneous transformation matrix between the joints of the robot; Based on the first homogeneous transformation matrix and the second homogeneous transformation matrix, a third homogeneous transformation matrix is constructed for the center of the marker point in the robot base coordinate system; The zero-point parameters are used to solve the third homogeneous transformation matrix to obtain the zero-point coordinates of the center of the marker point in the robot's base coordinate system. The error of the zero-point parameter is calculated based on the zero-point coordinates.
4. The zero-point calibration method according to claim 1, characterized in that, The nonlinear optimization model constructed using the robot joint positions, based on the invariant coordinates of the center of the marker point in the robot's base coordinate system, includes: Obtain the original posture of each teaching point; The attitude angle between each pair of teaching points is obtained based on the original attitude of each teaching point; The attitude angle is used to perform attitude interpolation on the pairwise teaching points to obtain the interpolated attitude between the pairwise teaching points. Based on the invariance of the coordinates of the center of the marker point in the robot's base coordinate system, the nonlinear optimization model is constructed using the robot joint positions, the original pose, and the interpolated pose.
5. The zero-point calibration method according to claim 4, characterized in that, The process of obtaining the attitude angle between each pair of teaching points based on the original attitude of each teaching point includes: Convert the attitude angle of the original attitude of each teaching point into a unit quaternion; Based on the unit quaternions of the pairwise teaching points, the attitude angle of the short attitude path is obtained.
6. The zero-point calibration method according to claim 1, characterized in that, The step of calculating the offset estimate of the tool coordinate system based on the robot end-effector coordinates includes: Based on the robot end-effector coordinates, construct a homogeneous transformation matrix from the robot end-effector coordinate system to the robot base coordinate system; Construct the homogeneous transformation matrix from the tool coordinate system to the robot end effector coordinate system; The offset estimate of the tool coordinate system is calculated using the homogeneous transformation matrix from the robot end-effector coordinate system to the robot base coordinate system and the homogeneous transformation matrix from the tool coordinate system to the robot end-effector coordinate system.
7. The zero-point calibration method according to claim 1, characterized in that, The recording of the robot end-effector coordinates corresponding to the movement from different first teaching points to the standard point includes: Obtain the coordinates of the camera principal point and the pixel coordinates of the marker point corresponding to each first teaching point; Based on the camera principal point coordinates and the marker point pixel coordinates, the required offset of the robot end effector is obtained; Based on the required offset and the original robot end coordinates at the first teaching point, the robot end coordinates at the standard point are obtained.
8. The zero-point calibration method according to claim 7, characterized in that, The step of obtaining the required offset of the robot's end effector based on the camera principal point coordinates and the marker point pixel coordinates includes: Obtain the deviation between the coordinates of the camera principal point and the pixel coordinates of the marker point; Based on the camera's pixel equivalent and the deviation value, the required offset of the robot's end effector is obtained; Before obtaining the required offset of the robot end effector based on the camera's pixel equivalent and the deviation value, the zero-point calibration method further includes: Take a picture at any teaching point to obtain the coordinates of the first teaching pixel; The robot end effector moves according to a preset step size and preset direction, and takes a picture to obtain the coordinates of the second teaching pixel; Based on the first taught pixel coordinates and the second taught pixel coordinates, the rotation angle between the robot base coordinate system and the camera coordinate system is obtained; A free vector transformation equation is constructed using the first taught pixel coordinates, the second taught pixel coordinates, and the rotation angle. The pixel equivalent of the camera is obtained by jointly calculating using multiple sets of free vector transformation equations.
9. A zero-point calibration device for a robot, characterized in that, The zero-point calibration device includes a memory and a processor coupled to the memory; The memory is used to store program data, and the processor is used to execute the program data to implement the zero-point calibration method as described in any one of claims 1 to 8.
10. A computer storage medium, characterized in that, The computer storage medium is used to store program data, which, when executed by the computer, is used to implement the zero-point calibration method as described in any one of claims 1 to 8.