Measurement and calculation method for large-size precision component

Abstract

The invention relates to a measuring and calculating method for a large-size precise component, and belongs to the technical field of contour measurement. The method comprises the following steps: arranging a laser tracker and a first target ball, establishing a measurement coordinate system, and realizing station transfer from the measurement coordinate system to a global coordinate system by using the target ball; the original point of each measurement coordinate system is optimized; arranging a scanner and a second target ball, and respectively establishing coordinate systems; calculating a conversion matrix of a scanner coordinate system and a target ball coordinate system by using the first target ball; then removing the first target ball and arranging the precision component, and simulating the precision component; selecting feature points on the simulation precision component to calculate a scanning path; finally, a trolley is used for carrying a scanner to scan along the path to obtain global coordinate point clouds of the component, the point clouds are spliced to restore the component, and the size is measured and calculated. According to the method, the defect that a traditional tracking type three-dimensional scanning measurement and calculation technology is large in error is overcome, the pose of the mechanical arm is inversely solved through the calculation path, and the accuracy is improved while automatic measurement and calculation are achieved.

Description

Technical Field

[0001] The invention relates to a method for measuring a large-size precision component, and belongs to the technical field of measuring contours (G01B11 / 24). Background Art

[0002] At present, my country's large-scale on-site measurement engineering technology is constantly developing, and related technologies and equipment have been widely used in the fields of aviation, aerospace, and shipbuilding. Taking the 15-meter-scale aerospace products as an example, the dimensional measurement requirements of the contour of its components are diverse: the coaxiality error needs to be controlled within 0.4 mm, and the contour error needs to be controlled within 0.5 mm; and the small size (200~300 mm) of the local key interface The measurement accuracy requirement must reach 0.08 mm or more. In the traditional large-scale measurement process, a large number of manual operations such as point sticking and calibration are usually required to adapt to the measurement of large-scale precision components of different size ranges and shapes. However, this method cannot meet the cross-scale high-precision measurement requirements of the above-mentioned large-scale products, and is prone to errors.

[0003] In recent years, tracking three-dimensional scanning and measurement technology has emerged. For example, in the patent with publication number CN112964196A, it is proposed to use a trolley carrying a tracking head to scan an object, obtain the point cloud data of the object, and convert it into a global coordinate system for splicing, and finally obtain complete point cloud data. This method effectively improves the measurement accuracy, but there are still some problems. In particular, for large-scale precision components, multiple point cloud data conversions may introduce errors; and if the trolley's moving path planning is not accurate enough, it may lead to point cloud redundancy and omission of key features, thereby affecting the final measurement accuracy.

[0004] Therefore, a more accurate method for measuring large-scale precision components is urgently needed to meet the growing high-precision requirements of modern manufacturing. Summary of the invention

[0005] The technical problem to be solved by the present invention is: how to improve the measurement accuracy of large-sized precision components.

[0006] The technical solution proposed by the present invention to solve the above technical problems is: a method for measuring large-size precision components, comprising the following steps:

[0007] Step 1: Arrange at least four first target balls and at least four laser trackers, and establish measurement coordinate systems with the laser trackers as origins respectively;

[0008] Take any measurement coordinate system as the global coordinate system, and take the coordinates in the global coordinate system as the global coordinates;

[0009] Step 2: Transfer the measurement coordinate system to the global coordinate system, and obtain the global coordinate representation of the coordinates of all points in the measurement coordinate system in the global coordinate system;

[0010] Step 3: Measure the distance from the first target sphere to the origin of the measurement coordinate system ;

[0011] The Bancroft algorithm is used to calculate the following formula (1) to obtain the two global coordinates of the first target sphere:

[0012] (1);

[0013] In formula (1), , and are the global x-axis coordinate, global y-axis coordinate, and global z-axis coordinate of the origin of the i-th measurement coordinate system; , and are the global x-axis coordinate, global y-axis coordinate and global z-axis coordinate of the kth first target ball respectively;

[0014] Step 4: Remove the one global coordinate that obviously does not conform to the actual layout position from the two global coordinates of the first target sphere, substitute the remaining global coordinate of the first target sphere into the following formula (2), and use the Levenberg-Marquardt algorithm to iteratively update the global coordinate of the origin of the measurement coordinate system until the error sum of squares is When the value is the minimum, the iteration is stopped to obtain the optimized global coordinates of the origin of the measurement coordinate system;

[0015] (2);

[0016] In formula (2), m is the total number of laser trackers; n is the total number of first target balls; is the known weight of the distance from the kth first target ball to the origin of the i-th measurement coordinate system; is the distance error value from the kth first target ball to the origin of the i-th measurement coordinate system;

[0017] Step 5: Arrange at least one 3D scanner including a tracker and a scanner and at least two AGVs, one of which has a robotic arm; install the scanner on the AGV with the robotic arm and the tracker on the other AGV; place the AGV with the tracker 2 to 6 meters behind the rear of the AGV with the scanner.

[0018] Install at least one second target ball on each of the trackers, and construct a target ball coordinate system with the second target ball as the origin; and construct a scanning coordinate system with the tracker as the origin;

[0019] Step 6: For the scanning coordinate system, use the singular value decomposition method to solve the target value in the following equation (3): The minimum rotation matrix R and translation vector t

[0020] (3);

[0021] In formula (3), is the three-dimensional coordinate of the kth first target ball in the measurement coordinate system; is the three-dimensional coordinate of the kth first target ball in the scanning coordinate system;

[0022] Thus, the first transformation matrix from the scanning coordinate system to the measurement coordinate system is obtained: ;

[0023] Step 6.1: Use the laser tracker to directly measure the three-dimensional coordinates of the second target sphere, thereby obtaining a second transformation matrix from the target sphere coordinate system to the measurement coordinate system.

[0024] Step 6.2: Calculate the third transformation matrix from the scanning coordinate system to the target sphere coordinate system according to formula (4):

[0025] (4);

[0026] Step 7: remove the first target ball and place a large-sized precision component;

[0027] Step 8: Simulate the large-size precision component to generate a simulated large-size precision component, and select feature points in sequence on the simulated large-size precision component;

[0028] Step 8.1: Calculate the normal of the feature point according to formula (5)

[0029] (5);

[0030] In formula (5), is the parallel tangent vector of the feature point; is the feature point and Tangent vectors are not collinear;

[0031] Step 8.2: Generate the path from the first feature point to the last feature point according to formula (6)

[0032] (6);

[0033] In formula (6), d is the path direction vector; i, j are constants; d1 and d2 are direction vectors; P(t) is the position of the feature point at time t; P0 is the first feature point;

[0034] The B-spline method is used to smooth the curved path between two adjacent feature points;

[0035] Step 8.3 uniformly select trajectory points on the path, and calculate the robot arm joint angle corresponding to each trajectory point through inverse kinematics;

[0036] Step 9: Mapping the trajectory points into station points around the large-size precision component;

[0037] Start the AGV to move forward and backward along the path; each time the AGV equipped with a scanner reaches a station, the robotic arm will adjust the joint angle according to the robotic arm joint angle obtained in step 8.4. After the adjustment is completed, the scanner scans the large-size precision component to obtain the point cloud of the large-size precision component;

[0038] The fourth transformation matrix from the scanner coordinate system to the measurement coordinate system is calculated according to the following formula (7): , thereby converting the coordinates of the point cloud in the three-dimensional scanning coordinate system into the coordinates in the measurement coordinate system, and then obtaining the global coordinates in the global coordinate system;

[0039] (7);

[0040] In formula (7), l is the lth station location; is the fifth transformation matrix from the second target ball to the global coordinate system directly measured by the AGV at the lth station position;

[0041] Step 10: Stitch the point cloud in the global coordinate system to restore the size of the large-scale precision component.

[0042] Furthermore, the simulation of large-scale precision components in step 8 is a digital twin simulation.

[0043] Furthermore, the starting of the AGV in step 9 is to place the AGV with the scanner installed at different stations and start it.

[0044] The beneficial effects of the present invention are as follows: by integrating multiple types and quantities of measurement scanning equipment, a large-scale spatial high-precision multi-modal measurement field is constructed, which meets the diversified measurement needs of large-scale precision components with large size spans and multiple size categories; at the same time, the errors caused by multiple station transfers are compensated, effectively improving the measurement accuracy. In addition, the accurately calculated scanning path and robot arm angle effectively avoid the problem of low measurement accuracy caused by point cloud redundancy and omission of key features. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The measurement method of large-size precision components of the present invention is further described below in conjunction with the accompanying drawings.

[0046] Figure 1 This is the layout diagram of the first target ball and laser tracker in the measurement method of large-size precision components in Example 1.

[0047] Figure 2 This is a large-size precision component scanning scene diagram in the large-size precision component measurement method of Example 1.

[0048] Figure 3 This is a large-size precision component scanning scene diagram in the large-size precision component measurement method of Example 2. DETAILED DESCRIPTION

[0049] Example 1

[0050] The method for measuring large-size precision components of this embodiment includes the following steps:

[0051] Step 1: Arrange at least four first target balls and at least four laser trackers, and establish measurement coordinate systems with the laser trackers as origins;

[0052] Take any measurement coordinate system as the global coordinate system, and take the coordinates in the global coordinate system as the global coordinates;

[0053] like Figure 1 As shown, in this embodiment, four laser trackers 1 and four first target balls 2 are arranged, and measurement coordinate systems L1, L2, L3, and L4 are established with the laser trackers as origins, respectively, and the measurement coordinate system L1 is taken as the global coordinate system.

[0054] Step 2: Transfer the measurement coordinate system to the global coordinate system, and obtain the global coordinate representation of the coordinates of all points in the measurement coordinate system in the global coordinate system;

[0055] Converting the measurement coordinate system to the global coordinate system is a common existing technology in the fields of geographic information systems (GIS), building surveying, engineering positioning, etc. Matrix operations or coordinate transformation formulas are usually used to provide accurate location information for engineering positioning, equipment installation, structure monitoring, etc.

[0056] Step 3: Measure the distance from the first target sphere to the origin of the measurement coordinate system ;

[0057] The Bancroft algorithm is used to calculate the following formula (1) to obtain the two global coordinates of the first target ball:

[0058] (1);

[0059] In formula (1), , and are the global x-axis coordinate, global y-axis coordinate, and global z-axis coordinate of the origin of the i-th measurement coordinate system; , and are the global x-axis coordinate, global y-axis coordinate and global z-axis coordinate of the kth first target ball respectively;

[0060] This embodiment uses the Bancroft algorithm to transform equation (1) into a quadratic equation, and the two solutions obtained are the two global coordinates of the first target ball. Taking one of the first target balls as an example, the specific calculation process is as follows:

[0061] According to the distance from the first target ball to the origin of the measurement coordinate system, the following formula (1.1) is obtained:

[0062] (1.1);

[0063] make The following formula (1.2) is obtained

[0064] (1.2);

[0065] make The following formula (1.3) is obtained

[0066] (1.3);

[0067] Subtracting equation (1.3) from equation (1.2) yields equation (1.4)

[0068] (1.4);

[0069] Formula (1.4) can be expressed as a matrix:

[0070] ;

[0071] make get ;

[0072] Re-order

[0073] ;

[0074] ;

[0075] The following formula (1.5) is obtained

[0076] (1.5);

[0077] Substituting equation (1.5) into equation a 2 +b 2 +c 2 =d 2 , we get the following formula (1.6)

[0078] (1.6);

[0079] make Solved

[0080] Right now

[0081]

[0082] The final two solutions are the two global coordinates of the first target sphere.

[0083] Step 4: Remove the one global coordinate that obviously does not conform to the actual layout position from the two global coordinates of the first target ball, substitute the remaining global coordinate of the first target ball into the following formula (2), and use the Levenberg-Marquardt algorithm to iteratively update the global coordinate of the origin of the measurement coordinate system until the error sum of squares is Stop the iteration when the value is minimum, and get the optimized global coordinates of the origin of the measurement coordinate system;

[0084] (2);

[0085] In formula (2), m is the total number of laser trackers; n is the total number of first target balls; is the known weight of the distance from the kth first target ball to the origin of the i-th measurement coordinate system; is the distance error value from the kth first target ball to the origin of the i-th measurement coordinate system;

[0086] This embodiment uses the Levenberg-Marquard algorithm to solve the optimized global coordinates of the origin of the measurement coordinate system, compensates for the errors caused by multiple station transfers in traditional practices, and effectively improves the dimensional measurement accuracy of large-size precision components in the later stage.

[0087] Step 5: Arrange at least one 3D scanner including a tracker and a scanner and at least two AGVs, one of which has a robotic arm; install the scanner on the AGV with the robotic arm, and install the tracker on the other AGV; place the AGV with the tracker 2 to 6 meters behind the rear of the AGV with the scanner;

[0088] At least one second target ball is installed on each tracker, and a target ball coordinate system is constructed with the second target ball as the origin; a scanning coordinate system is constructed with the tracker as the origin;

[0089] like Figure 2 As shown, this embodiment arranges a three-dimensional scanner including a tracker 3 and a scanner 4 and two AGV vehicles 5; the AGV vehicle equipped with the tracker is placed 3 meters behind the rear of the AGV vehicle equipped with the scanner;

[0090] In this embodiment, two second target balls 6 are installed on the tracker, and target ball coordinate systems Z1 and Z2 are respectively constructed with the second target balls as origins; and a scanning coordinate system S1 is constructed with the tracker as origin.

[0091] Step 6: For the scanning coordinate system, use the singular value decomposition method to solve the target value in the following equation (3): The minimum rotation matrix R and translation vector t

[0092] (3);

[0093] In formula (3), is the three-dimensional coordinate of the kth first target ball in the measurement coordinate system; is the three-dimensional coordinate of the kth first target ball in the scanning coordinate system;

[0094] Thus, the first transformation matrix from the scanning coordinate system to the measurement coordinate system is obtained ;

[0095] The singular value decomposition method used in this embodiment is a mathematical tool widely used in coordinate system transformation, point cloud registration and three-dimensional geometry problems. It has been used in the prior art to solve the least squares solution of the rotation matrix and the translation vector. Because the three-dimensional coordinates of the origin of the coordinate system cannot be accurately scanned, the second target ball is used as a common point, and the different three-dimensional coordinates of the second target ball in the scanning coordinate system and the measurement coordinate system are combined and obtained by the singular value decomposition method. The laser tracker can directly measure the three-dimensional coordinates of the second target ball, so the spatial position relationship between the measurement coordinate system and the target ball coordinate system can be directly obtained.

[0096] Step 6.1: Use the laser tracker to directly measure the three-dimensional coordinates of the second target sphere, thereby obtaining the second transformation matrix from the target sphere coordinate system to the measurement coordinate system ;

[0097] Step 6.2: Calculate the third transformation matrix from the scanning coordinate system to the target sphere coordinate system according to formula (4).

[0098] (4);

[0099] Step 7: Remove the first target ball and place large-sized precision components;

[0100] The large-size precision components 7 placed in this embodiment are as follows Figure 2 shown.

[0101] Step 8: Simulate the large-size precision component to generate a simulated large-size precision component, and select feature points in sequence on the simulated large-size precision component;

[0102] This embodiment adopts digital twin simulation, which is based on the principle of combining existing physical models with virtual models, and is widely used in real-time monitoring simulation in the fields of industry, construction, transportation, etc.

[0103] Step 8.1: Calculate the normal of the feature point according to formula (5)

[0104] (5);

[0105] In formula (5), is the parallel tangent vector of the feature point; is the feature point and Tangent vectors are not collinear;

[0106] Step 8.2: Generate the path from the first feature point to the last feature point according to formula (6)

[0107] (6);

[0108] In formula (6), d is the path direction vector; i, j are constants; d1 and d2 are direction vectors; P(t) is the position of the feature point at time t; P0 is the first feature point;

[0109] In this embodiment, Parallel to the path direction vector d, two linearly independent direction vectors d1 and d2 are selected to determine the plane where the scanning path is located;

[0110] The B-spline method is used to smooth the curved path between two adjacent feature points;

[0111] The B-Spline method used in this embodiment is a mathematical tool widely used in curve smoothing, path fitting and data interpolation, especially in computer graphics, robot path planning and geographic information system (GIS). The B-Spline method can effectively smooth the path between adjacent feature points, ensure the continuity and smoothness of the smooth curve, and is suitable for path modeling of complex shapes.

[0112] Step 8.3 uniformly select trajectory points on the path, and calculate the robot arm joint angle corresponding to each trajectory point through inverse kinematics;

[0113] The inverse kinematics used in this embodiment is an existing technology widely used in robot control and path planning, and aims to calculate the corresponding robot arm joint angles according to the desired end effector position and posture.

[0114] Step 9: Map the trajectory points into station points around the large-scale precision components;

[0115] Start the AGV and move it forward and backward along the path. When the AGV equipped with the scanner reaches a station, the robot arm will adjust the joint angle according to the robot arm joint angle obtained in step 8.4. After the adjustment is completed, the scanner scans the large-size precision component to obtain the point cloud of the large-size precision component.

[0116] The fourth transformation matrix from the scanner coordinate system to the measurement coordinate system is calculated according to formula (7): , thereby converting the coordinates of the point cloud in the 3D scanning coordinate system into the coordinates in the measurement coordinate system, and then obtaining the global coordinates in the global coordinate system;

[0117] (7);

[0118] In formula (7), l is the lth station location; It is the fifth transformation matrix from the second target ball to the global coordinate system directly measured by the AGV at the lth station position;

[0119] In this embodiment, the second target ball is arranged on the tracker, so that during the movement of the AGV, the second target ball always remains stationary relative to the tracker. Therefore, at different station locations, the laser tracker only needs to measure the three-dimensional coordinates of the second target ball to obtain the transformation matrix from the target ball coordinate system to the measurement coordinate system, and then directly calculate the fourth transformation matrix from the scanner coordinate system to the measurement coordinate system according to formula (7).

[0120] Step 10: Stitch the point cloud in the global coordinate system to restore the size of the large-scale precision component.

[0121] Example 2

[0122] The steps of this embodiment 2 are basically the same as those of embodiment 1. Figure 3As shown, the difference is that in step 5, three three-dimensional scanners including trackers 3 and scanners 4 and six AGVs 5 are arranged; two second target balls 6 are installed on the tracker, and the target ball coordinate systems Z1, Z2, Z3, ..., Z6 are constructed with the second target balls as the origin; and the scanning coordinate systems S1, S2 and S3 are constructed with the tracker as the origin. The three AGVs with scanners installed are placed at three different stations and started. Each AGV with scanners installed is followed by an AGV with trackers installed 3 meters behind the rear of the AGV, which is conducive to improving the speed of scanning and acquiring point clouds in subsequent actual scanning and improving work efficiency.

[0123] The above description is only a preferred embodiment of the present invention, but the present invention is not limited thereto. All equivalent replacements or equivalent changes of the concepts and technical solutions according to the present invention should be included in the protection scope of the present invention.

Claims

1. A method for measuring large-size precision components, characterized in that The steps include: Step 1: Arrange at least four first target balls and at least four laser trackers, and establish measurement coordinate systems with the laser trackers as origins respectively; Take any measurement coordinate system as the global coordinate system, and take the coordinates in the global coordinate system as the global coordinates; Step 2: Transfer the measurement coordinate system to the global coordinate system, and obtain the global coordinate representation of the coordinates of all points in the measurement coordinate system in the global coordinate system; Step 3: Measure the distance from the first target sphere to the origin of the measurement coordinate system ; The Bancroft algorithm is used to calculate the following formula (1) to obtain the two global coordinates of the first target sphere: （1）； In formula (1), , and are the global x-axis coordinate, global y-axis coordinate, and global z-axis coordinate of the origin of the i-th measurement coordinate system; , and are the global x-axis coordinate, global y-axis coordinate and global z-axis coordinate of the kth first target ball respectively; Step 4: Remove the one global coordinate that obviously does not conform to the actual layout position from the two global coordinates of the first target sphere, substitute the remaining global coordinate of the first target sphere into the following formula (2), and use the Levenberg-Marquardt algorithm to iteratively update the global coordinate of the origin of the measurement coordinate system until the error sum of squares is When the value is the minimum, the iteration is stopped to obtain the optimized global coordinates of the origin of the measurement coordinate system; （2）； In formula (2), m is the total number of laser trackers; n is the total number of first target balls; is the known weight of the distance from the kth first target ball to the origin of the i-th measurement coordinate system; is the distance error value from the kth first target ball to the origin of the i-th measurement coordinate system; Step 5: Arrange at least one 3D scanner including a tracker and a scanner and at least two AGVs, one of which has a robotic arm; install the scanner on the AGV with the robotic arm and the tracker on the other AGV; place the AGV with the tracker 2 to 6 meters behind the rear of the AGV with the scanner. Install at least one second target ball on each of the trackers, and construct a target ball coordinate system with the second target ball as the origin; and construct a scanning coordinate system with the tracker as the origin; Step 6: For the scanning coordinate system, use the singular value decomposition method to solve the target value in the following equation (3): The minimum rotation matrix R and translation vector t （3）； In formula (3), is the three-dimensional coordinate of the kth first target ball in the measurement coordinate system; is the three-dimensional coordinate of the kth first target ball in the scanning coordinate system; Thus, the first transformation matrix from the scanning coordinate system to the measurement coordinate system is obtained: ; Step 6.1: Use the laser tracker to directly measure the three-dimensional coordinates of the second target sphere, thereby obtaining a second transformation matrix from the target sphere coordinate system to the measurement coordinate system. Step 6.2: Calculate the third transformation matrix from the scanning coordinate system to the target sphere coordinate system according to formula (4): （4）； Step 7: remove the first target ball and place a large-sized precision component; Step 8: Simulate the large-size precision component to generate a simulated large-size precision component, and select feature points in sequence on the simulated large-size precision component; Step 8.1: Calculate the normal of the feature point according to formula (5) （5）； In formula (5), is the parallel tangent vector of the feature point; is the feature point and Non-collinear tangent vectors; Step 8.2: Generate the path from the first feature point to the last feature point according to formula (6) （6）； In formula (6), d is the path direction vector; i, j are constants; d1 and d2 are direction vectors; P(t) is the position of the feature point at time t; P0 is the first feature point; The B-spline method is used to smooth the curved path between two adjacent feature points; Step 8.3 uniformly select trajectory points on the path, and calculate the robot arm joint angle corresponding to each trajectory point through inverse kinematics; Step 9: Mapping the trajectory points into station points around the large-size precision component; Start the AGV to move forward and backward along the path; each time the AGV equipped with a scanner reaches a station, the robotic arm will adjust the joint angle according to the robotic arm joint angle obtained in step 8.

4. After the adjustment is completed, the scanner scans the large-size precision component to obtain the point cloud of the large-size precision component; The fourth transformation matrix from the scanner coordinate system to the measurement coordinate system is calculated according to the following formula (7): , thereby converting the coordinates of the point cloud in the three-dimensional scanning coordinate system into the coordinates in the measurement coordinate system, and then obtaining the global coordinates in the global coordinate system; （7）； In formula (7), l is the lth station location; is the fifth transformation matrix from the second target ball to the global coordinate system directly measured by the AGV at the lth station position; Step 10: Stitch the point cloud in the global coordinate system to restore the size of the large-scale precision component.

2. The method for measuring large-size precision components according to claim 1, characterized in that: The simulation of large-scale precision components in step 8 is digital twin simulation.

3. The method for measuring large-size precision components according to claim 1, characterized in that: The starting of the AGV trolley in step 9 is to place the AGV trolley equipped with the scanner at different station locations and start it.

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