An automated precision measurement method for large components

Abstract

The application belongs to the technical field of precision measurement, and discloses an automatic precision measurement method for large components. According to the measurement scene and the distribution of the points to be measured, a plurality of measurement stations of laser trackers are generated as constraints of measurement distance and measurement angle; according to the generated plurality of measurement stations, the shortest moving path between the measurement stations is planned to ensure that the laser tracker moves to each measurement station as fast as possible without colliding with obstacles; the laser tracker executes movement and measurement tasks and stably measures at the plurality of measurement stations in turn; and the measured multi-station measurement data is unified into a global coordinate system for subsequent data analysis. The application meets the precision measurement requirements of large components, is simple to operate, has high automation, avoids the phenomenon of manually "dragging" the measuring instrument and the need for multiple instruments to measure, can reduce the required operating personnel, and improves the measurement efficiency.

Description

Technical Field

[0001] This invention relates to the field of precision measurement technology, and in particular to an automated precision measurement method for large components. Background Technology

[0002] In recent years, many challenges remain in the measurement and inspection of large components in actual production and manufacturing sites. Laser trackers, as precision measuring instruments for large-scale spatial geometry, are prone to problems when measuring large components, such as light obstruction and excessively long measurement distances, leading to incomplete or incomplete measurements. For example, when measuring the spatial coordinates of an entire aircraft, the complex structure and large size of the aircraft prevent the laser tracker from measuring all the coordinates at a single location. Therefore, it is necessary to measure the aircraft's coordinates at multiple stations and then convert the data from each station to a unified coordinate system. Another example is satellite cabin structures, which are typically welded from several large integral panels. These panels are generally manufactured by milling grids from a single piece of aluminum sheet and then rolling them. Due to residual stress during the manufacturing process, macroscopic warping and localized deformation at different grid locations occur after the panels are formed. Therefore, there is an urgent need for high-precision measuring equipment such as laser trackers to perform overall measurement and inspection of the formed panels. However, due to the large size and high curvature of the overall wall panel, the laser tracker cannot complete the measurement task in a single location. This necessitates moving the laser tracker to multiple stations for measurement. In summary, when inspecting large components, laser trackers require moving them to multiple stations for measurement. Existing multi-station measurement methods rely on manual, arbitrary station setting, and the process involves multiple people dragging the tracker, resulting in cumbersome operation and low efficiency. This is increasingly failing to meet the high-precision and high-efficiency measurement requirements of large component manufacturing. Therefore, an automated precision measurement method is of great significance for the measurement process of large components.

[0003] Chinese patent CN113296112A discloses a mobile measuring trolley device for a laser tracker. In this device, the laser tracker is mounted on a manually operated wheeled trolley, and a lifting mechanism is included to adjust the height of the laser tracker. The laser tracker is moved by manually pulling the trolley, thus enabling it to be moved between different stations. However, the operation of this device still requires manual pulling of the instrument and power cord, and the placement of the measuring station is random or inaccurate. It has a limited range of motion, is complex to operate, has a low degree of automation, and is difficult to meet the measurement needs of large components. Summary of the Invention

[0004] This invention addresses the shortcomings of existing technologies by proposing an automated precision measurement method for large components. The proposed method can generate multiple measurement positions for a laser tracker and plan the shortest movement path between stations, improving station transfer efficiency and efficiently completing multi-station measurements. This enhances overall measurement efficiency and provides theoretical and technical support for the automated precision measurement of large components.

[0005] The technical solution of this invention is as follows: An automated precision measurement method for large components, which generates multiple measurement stations for laser trackers based on the measurement scenario and the distribution of points to be measured, with measurement distance and measurement angle as constraints; according to the generated multiple measurement stations, the shortest movement path between the measurement stations is planned to ensure that the laser tracker moves to each measurement station as quickly as possible without colliding with obstacles; the laser tracker performs motion and measurement tasks, and measures stably at multiple measurement stations in sequence; based on the measured multi-station measurement data, it is unified into a global coordinate system for subsequent data analysis.

[0006] The specific measurement stations for generating multiple laser trackers are as follows:

[0007] Based on the three-dimensional model of the large component and the location distribution of the points to be measured, a station planning algorithm is used to determine multiple measurement stations of the laser tracker at the same height.

[0008] The constraints of the station planning algorithm include the farthest distance from the measurement station to the point to be measured and the range of the vertical angle between the point to be measured and the measurement station; the farthest distance is controlled within L, and the vertical angle is less than α, where L is 10m, and α is limited to a maximum of 145° according to the vertical angle limit of the laser tracker. The constraint formula is as follows:

[0009]

[0010] In the formula, (X l ,Y l (x, h) represents the coordinates of the measurement station to be planned, where h is the height of the mobile laser tracker, and (x, h) is the coordinates of the station. i ,y i ,z i Let be the coordinates of the point to be measured. The constraints m1 and m2 represent the farthest distance L from the measurement station to the point and the maximum perpendicular angle α of the point relative to the measurement station, respectively. t represents the number of measurement stations to be planned, and s represents the number of points to be measured. This indicates that multiple measurement location coordinates are generated based on the constraints: Station 1: (X1, Y1, h), Station 2: (X2, Y2, h)... Station t: (X... t ,Y t ,h).

[0011] The shortest travel path between the planned measurement stations is as follows:

[0012] First, a two-dimensional grid map of the measurement scene is created. Based on the distribution of obstacles, the two-dimensional grid map is divided into feasible and infeasible areas. The laser tracker only moves within the feasible area. Based on the generated laser tracker measurement station information, the measurement station order is set as follows: (X1,Y1,h)→(X2,Y2,h)→(X3,Y3,h)…(X… t ,Y t The A* algorithm is used to plan the shortest movement path between two measurement stations (h).

[0013] The A* algorithm is a heuristic search algorithm that uses the minimum total cost function as the evaluation metric for selecting a search path. It calculates the cost value of each node around the starting point as its parent node, selects the node with the minimum cost value as its parent node, and repeats this process for its surrounding nodes until the destination is reached. The cost function is expressed as:

[0014] f(n)=g(n)+h(n))(2)

[0015] Where: f(n) is the total cost of node n, g(n) is the cost of node n from the starting point, and h(n) is the expected cost of node n from the ending point, which is also the heuristic function; after the path search is completed, the moving path is denoted as: Path{path1, path2, path3…}.

[0016] The laser tracker performs motion and measurement tasks as follows: the planned path information is sent to the laser tracker via the central control platform, and the laser tracker moves to the first measurement station according to the planned path, where it performs the measurement task; the laser tracker measures the points to be measured on the upper part of the large component and the target points in the calibration area used for station transfer; the coordinates of the target points in the calibration area in the global coordinate system are known in advance, and after the measurement is completed, the data is uploaded to the host computer. After verification, the laser tracker moves to the second station according to the planned path information and performs the measurement task; it then proceeds to the remaining stations in sequence for measurement.

[0017] The subsequent data analysis, based on the multi-station measurement data, is unified to a global coordinate system as follows: After the entire measurement process is completed, the transformation relationship between the measurement coordinate system of the laser tracker at a station and the global coordinate system is obtained by using the SVD method from the target points in the calibration area to obtain the transformation matrix. w 1T; The target points in the calibration area under the measurement coordinate system W1 are Q = {q1,q2,q3...}, and the corresponding target point 7 in the calibration area is known in the global coordinate system, denoted as Q' = {q1',q2',q3'...}. Solve for the rotation matrix R and translation vector t to minimize the error function E(R,t) of the transformation between the two coordinate systems:

[0018]

[0019] The rotation matrix R and translation vector t of the transformation between the two coordinate systems can be calculated using the singular value decomposition method, thus obtaining the transformation matrix. Similarly, the transformation matrices between the other measurement coordinate systems and the global coordinate system can be obtained. The coordinates of all target points in the calibration area of ​​large components under various measurement coordinate systems are transformed to the global coordinate system to complete the precision measurement task of large components.

[0020] The beneficial effects of this invention are as follows: Based on the actual measurement scenario and requirements of large components, and considering factors such as on-site obstruction and measurement angles, the overall measurement task is generated by the central control platform, which plans the laser tracker station positions and their relocation paths. Then, the laser tracker moves sequentially to each station to perform the measurement task. Finally, the measurement data from each station are aligned to the global coordinate system using the calibration area target point 7, achieving precise measurement of the large components. This method, while meeting the accuracy requirements for large component measurement, is simple to operate, highly automated, avoids the need for manual "dragging" of measuring instruments during relocation, and eliminates the need for multiple instruments. It also reduces the number of operators required and improves measurement efficiency. Attached Figure Description

[0021] Figure 1 A scene diagram for measuring large components.

[0022] Figure 2 This is a site location planning map.

[0023] Figure 3 This is a path planning diagram.

[0024] Figure 4 This is a diagram showing the data transformation relationship of large components.

[0025] Figure 5 This is a diagram showing the relationship between the host computer and the mobile laser tracker.

[0026] Figure 6 This is a flowchart of an automated precision measurement method for large components.

[0027] In the diagram: 1-Reflector, 2-LiDAR, 3-Laser tracker, 4-Intelligent mobile platform, 5-Satellite panel, 6-Target point on the panel, 7-Target point in the calibration area, 8-Panel support. Detailed Implementation

[0028] The specific implementation of the present invention will be described in detail below with reference to the accompanying drawings and technical solutions.

[0029] An automated precision measurement method for large components first generates multiple measurement positions for a laser tracker based on the measurement scenario and the distribution of measurement points, constrained by measurement distance and angle, thus making the laser tracking positions more rationally placed. Second, based on the generated multiple measurement positions, the shortest movement path between the stations is planned, ensuring that the mobile laser tracker moves to each measurement position as quickly as possible without colliding with obstacles, greatly improving station transfer efficiency. Then, the mobile laser tracker performs motion and measurement tasks, stably measuring at multiple stations sequentially. Finally, based on the measured multi-station data, the SVD method is used to unify it into a global coordinate system for subsequent data analysis. This method is effective, simple to operate, and has good stability.

[0030] The specific steps of the method of the present invention are as follows:

[0031] Step 1: Generate multiple measurement stations for the laser tracker.

[0032] Based on the 3D model of the large component and the location distribution of the target points, a station planning algorithm is used to determine multiple measurement stations of the laser tracker at the same height. The constraints of the station planning algorithm include the farthest distance from the station to the measurement point and the range of the vertical angle of the measurement point relative to the station. Considering the characteristics of the laser tracker used, to prevent single-point deviations or measurement unreachability, the farthest measurement distance is controlled within L, and the vertical rotation angle is less than α. L is generally selected as 10m based on accuracy requirements, and α is limited to a maximum of 145° based on the vertical rotation angle limit of the laser tracker. The constraint formulas are as follows:

[0033]

[0034]

[0035]

[0036] In the formula, (X l ,Y l (x, h) represents the coordinates of the measurement station to be planned, where h is the height of the mobile laser tracker, and (x, h) represents the coordinates of the station. i ,y i ,z i Let be the coordinates of the target point to be measured. The constraints m1 and m2 represent the farthest distance L from the measurement station to the target point and the maximum perpendicular angle α of the target point relative to the measurement station, respectively. t represents the number of measurement stations to be planned, and s represents the number of points to be measured. Multiple measurement position coordinates are generated based on the constraints: Station 1: (X1, Y1, h), Station 2: (X2, Y2, h)... Station t: (X... t ,Y t ,h).

[0037] Step 2: Plan the shortest movement path for the mobile laser tracker.

[0038] First, a two-dimensional grid map of the measurement scene is created. Based on the obstacle distribution, the grid map is divided into feasible and infeasible areas. The mobile laser tracker can only move within the feasible area. Based on the generated laser tracker measurement station information, the measurement station sequence is set: (X1,Y1,h)→(X2,Y2,h)→(X3,Y3,h)…(X… t ,Y t The A* algorithm is used to plan the shortest path between two stations, ensuring the mobile laser tracker can move quickly and safely between stations. The A* algorithm is a heuristic search algorithm that uses the minimum total cost function as the evaluation metric for path searching. First, the cost value of the starting point as the parent node and its surrounding nodes is calculated. Then, the node with the minimum cost value is selected as the parent node, and the cost value of its surrounding nodes is calculated again, until the destination is reached. The cost function is expressed as:

[0039] f(n) = g(n) + h(n)

[0040] Where: f(n) is the total cost of node n, g(n) is the cost of node n from the starting point, and h(n) is the expected cost of node n from the ending point, i.e., the heuristic function. After the path search is completed, the movement path is denoted as: Path{path1, path2, path3…}

[0041] Step 3: Issue the movement path and execute the multi-station measurement task.

[0042] The planned path information is sent from the central control platform to the mobile laser tracker. The mobile laser tracker moves to station one according to the path and uses its lifting module to lift the entire device off the ground, ensuring stable measurement conditions. Then, it performs the measurement task, using the laser tracker to measure the target points on the upper part of the large component and the calibration area target point 7 used for station transfer. The coordinates of the calibration area target point 7 in the global coordinate system are known in advance. After the measurement is completed, the data is uploaded to the host computer. After verification, the entire device is lowered to the ground. Then, it moves to station two according to the path information, lifts the entire device off the ground again, and uses the laser tracker to measure the target points on the upper part of the large component and the calibration area target point 7 used for station transfer. After the measurement is completed, the data is uploaded to the host computer. After verification, the entire device is lowered to the ground and then proceeds to the remaining stations for measurement.

[0043] Step 4: Utilize multi-station measurement data to achieve precise measurement of large components.

[0044] After the entire measurement process is completed, the transformation relationship between the measurement coordinate system 1 at the laser tracker station and the global coordinate system is obtained from the target point 7 in the calibration area using the SVD method to obtain the transformation matrix. w Similarly, the transformation matrices between the remaining measurement coordinate systems and the global coordinate system can be calculated for 1T. w 2T w 3T… w t T, which allows all target point coordinates on large components under various measurement coordinate systems to be transformed to the global coordinate system, thus completing the precision measurement task of large components.

[0045] The mobile laser tracker used in this embodiment consists of a laser tracker 3 and an intelligent mobile platform 4. The target points are 1.5-inch target balls placed on a target ball holder. Taking the measurement of target points arranged on the outer surface of the satellite panel 5 as an example, the implementation process of this invention is explained.

[0046] Figure 1 This is a scene diagram for measuring large components. Station 1 and Station 2 are the planned measurement stations, as shown below. Figure 2 As shown, target point area 1 and target point area 2 are the target points to be measured at stations one and two, respectively. The calibration area is located on the wall panel support 8, and the coordinates of each point are known in the global coordinate system.

[0047] Figure 3 This is the path planning map. The black grid represents the infeasible areas of the mobile laser tracker 3, the white grid represents the feasible areas, and the gray grid represents the path search area. The black grid is obtained based on the obstacle plane projection.

[0048] Figure 4 This is a diagram showing the data transformation relationship for large components. W0 - Global coordinate system, W1 - Measurement coordinate system one, W2 - Measurement coordinate system two, M0 - AGV motion coordinate system.

[0049] Figure 5 This is a diagram showing the relationship between the host computer and the mobile laser tracker. After receiving the task, the mobile laser tracker executes the movement command, moves to the designated station, and lifts off the ground via the lifting device. It then sends a signal to the onboard PC indicating that it has reached its position. The laser tracker then begins measurement. Once the measurement is complete, the data is transmitted to the host computer for verification. After confirming that the data is correct, the host computer sends a signal to the onboard PC indicating that the measurement is complete. Finally, the entire device is lowered to the ground and continues to the next station.

[0050] Figure 6 Here is a flowchart for measuring large components, and the specific steps of the method are as follows:

[0051] Step 1: Generate the measurement station location for the laser tracker.

[0052] Based on the 3D model of the wall panel and the distribution of the target points to be measured, the positions of the laser tracker are planned. According to the position planning algorithm, two measurement positions are set. The two planned positions are as follows: Figure 2 The first station (X1, Y1, h), the second station (X2, Y2, h), and the target points to be measured at both stations are shown.

[0053] Step 2: Plan the shortest movement path for the mobile laser tracker.

[0054] Before path planning, a motion coordinate system M0 for the intelligent AGV needs to be established to achieve real-time positioning of the measuring device, thereby ensuring that the measuring device accurately reaches the designated station. Establishing the coordinate system requires the vehicle-mounted LiDAR 2 to scan the reflectors 1 on the surrounding walls, ensuring that at least three reflectors 1 are scanned simultaneously at any position, thus preventing position loss during the movement of the mobile laser tracker. After establishing the motion coordinate system, a two-dimensional grid map is created by measuring the obstacle distribution of the scene. Then, the shortest path is planned between the first and second stations using the A* algorithm, as described above. Figure 3 As shown.

[0055] Step 3: Issue the motion path and execute the multi-station measurement task.

[0056] The central control platform sends the movement path information to the mobile laser tracker. The mobile laser tracker, guided accurately by lidar 2, moves to the first station. Then, a lifting module lifts the entire device off the ground. The laser tracker 3 then begins measuring the target points 6 on the wall panel, denoted as P1 = {p1, p2...p...}. i Next, the target points 7 in the calibration area of ​​the upper part of the wall panel support 8 are measured and recorded as Q = {q1, q2, q3...}. After the measurement at the first station is completed, the data is transmitted to the host computer for verification. Then, the entire device is lowered to the ground and guided to the second station. Similarly, the lifting module is used to lift the entire device off the ground, and then the laser tracker 3 is used to measure the target points 6 on the wall panel and recorded as P2 = {p...}. i+1 ,p i+2 ...p n Next, the target point 7 in the calibration area is measured and denoted as S = {s1, s2, s3...}. After all measurements at the second station are completed, the data is uploaded to the host computer for verification. Then, the entire device is lowered to the ground to complete the measurement process.

[0057] Step 4: Using measurement data from two stations, achieve precise measurement of large wall panels.

[0058] It is known that the target points in the calibration area of ​​the laser tracker at the first station's measurement coordinate system W1 are Q = {q1,q2,q3...}, and the corresponding target point 7 in the calibration area is known in the global coordinate system, denoted as Q' = {q1',q2',q3'...}. Solve for the rotation matrix R and translation vector t to minimize the error function E(R,t) of the coordinate system transformation:

[0059]

[0060] The rotation matrix R and translation vector t of the transformation between the two coordinate systems can be calculated using the singular value decomposition method, thus obtaining the transformation matrix. Furthermore, the measurement data of target point 6 on the large wall panel in the measurement coordinate system W1 can be transformed to the global coordinate system W0, that is:

[0061]

[0062] Similarly, the data of target point 6 on the large wall panel measured in coordinate system W2 can also be transformed to the global coordinate system W0, that is:

[0063]

[0064] This unifies the coordinate values ​​measured at both stations into the global coordinate system, thus completing the measurement task for the large wall panel.

[0065] In summary, this invention addresses the challenges of measuring large components by proposing an automated precision measurement method for them. Based on planned measurement stations and movement paths, a mobile laser tracker can flexibly move between stations and perform rapid measurements, ultimately achieving high-precision and efficient measurement of large components. This method has significant practical application value for measuring large components and other measurement tasks within large spaces.

[0066] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should be considered within the scope of protection of the present invention.

Claims

1. An automated precision measurement method for large components, characterized in that, Based on the measurement scenario and the distribution of the points to be measured, multiple measurement stations for the laser tracker are generated with measurement distance and measurement angle as constraints. Based on these multiple measurement stations, the shortest movement path between them is planned to ensure that the laser tracker moves to each measurement station as quickly as possible without colliding with obstacles. The laser tracker performs motion and measurement tasks, sequentially and stably measuring at multiple measurement stations. Based on the measured data from multiple stations, the data is unified into a global coordinate system for subsequent data analysis. The specific measurement stations for generating multiple laser trackers are as follows: Based on the three-dimensional model of the large component and the location distribution of the points to be measured, the station planning algorithm is used to determine multiple measurement stations of the laser tracker at the same height; The constraints of the station planning algorithm include the farthest distance from the measurement station to the point to be measured and the range of the vertical angle between the point to be measured and the measurement station; the farthest distance is controlled within L, and the vertical angle is less than α, where L is 10m, and α is limited to a maximum of 145º based on the vertical angle limit of the laser tracker. The constraint formula is as follows: In the formula, Let h be the coordinates of the measurement station to be planned, where h is the height of the mobile laser tracker. Given the coordinates of the point to be measured, and the constraints... , represents the farthest distance L from the measurement station to the point to be measured, and the maximum vertical angle α of the point to be measured relative to the measurement station, respectively. t represents the number of measurement stations to be planned, and s represents the number of points to be measured. This indicates that multiple measurement location coordinates are generated based on constraints. Station 1: Position 2: …positioning : ; The shortest travel path between the planned measurement stations is as follows: First, a two-dimensional grid map of the measurement scene is created. Based on the distribution of obstacles, the two-dimensional grid map is divided into feasible and infeasible areas. The laser tracker only moves within the feasible areas. Based on the generated laser tracker measurement station information, the measurement station order is set. → → … The A* algorithm is used to plan the shortest path between two measurement stations.

2. The automated precision measurement method for large components according to claim 1, characterized in that, The A* algorithm is a heuristic search algorithm that uses the minimum total cost function as the evaluation metric for selecting a search path. It calculates the cost value of each node around the starting point as its parent node, selects the node with the minimum cost value as its parent node, and repeats this process for its surrounding nodes until the destination is reached. The cost function is expressed as: in: It is the total cost of node n. It is the cost of node n's distance from the starting point. It is the expected cost of node n from the destination, i.e., the heuristic function; after the path search is completed, the moving path is denoted as: Path{path1, path2, path3...}.

3. The automated precision measurement method for large components according to claim 1 or 2, characterized in that, The laser tracker performs motion and measurement tasks as follows: the planned path information is sent to the laser tracker via the central control platform, and the laser tracker moves to the first measurement station according to the planned path, where it performs the measurement task; the laser tracker measures the points to be measured on the upper part of the large component and the target points in the calibration area used for station transfer; the coordinates of the target points in the calibration area in the global coordinate system are known in advance, and after the measurement is completed, the data is uploaded to the host computer. After verification, the laser tracker moves to the second station according to the planned path information and performs the measurement task; it then proceeds to the remaining stations in sequence for measurement.

4. The automated precision measurement method for large components according to claim 3, characterized in that, The subsequent data analysis, based on the multi-station measurement data, is unified to a global coordinate system as follows: After the entire measurement process is completed, the transformation relationship between the measurement coordinate system of the laser tracker at a station and the global coordinate system is obtained by using the SVD method from the target points in the calibration area to obtain the transformation matrix. Measurement coordinate system The subscripted target area is the partial target point. The target points in the corresponding calibration area are known in the global coordinate system, denoted as . Solve for the rotation matrix R and translation vector t to find the error function of the transformation between the two coordinate systems. Minimum: The rotation matrix R and translation vector t of the transformation between the two coordinate systems can be calculated using the singular value decomposition method, thus obtaining the transformation matrix. ; Similarly, the transformation matrices between the other measurement coordinate systems and the global coordinate system can be obtained. , … The coordinates of all target points in the calibration area of ​​large components under various measurement coordinate systems are transformed to the global coordinate system to complete the precision measurement task of large components.

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