A LiDAR station planning method for automated scanning of aircraft shape

Abstract

The present invention relates to the technical field of station planning for automated scanning of aircraft shapes, and solves the problems of low efficiency, over-scanning, missed scanning, and the addition of redundant stations caused by relying on experience in the deployment process of laser radars during aircraft shape scanning. In particular, it relates to a laser radar station planning method for automated scanning of aircraft shapes, comprising analyzing the relationship between the laser radar scanning angle and distance and the scanning error respectively; classifying the scanning points and obtaining the initial station based on an improved region growing algorithm; cyclically optimizing the scanning station and scanning area according to the scanning error and the scanning occlusion rate; and re-dividing the unreasonable scanning points and areas to form the final laser radar station. The present invention optimizes the laser radar scanning station and scanning area through scanning error analysis. While ensuring scanning accuracy and scanning integrity, the number and redundancy of scanning stations are reduced, thereby facilitating the automation of aircraft shape scanning.

Description

Technical Field

[0001] The present invention relates to the technical field of station planning for automated scanning of aircraft shapes, and in particular to a laser radar station planning method for automated scanning of aircraft shapes. Background Art

[0002] Aircraft shape scanning is an important means of aircraft inspection. By scanning point clouds, the aircraft's assembly errors, surface defects, skin gaps and step differences are detected, thereby identifying the aircraft's surface defects and ensuring the aircraft's aerodynamic performance, stealth performance, and safety performance during flight.

[0003] LiDAR, as a 3D measurement device, offers advantages such as non-contact measurement, high accuracy, and a wide scanning range, making it widely used in aircraft shape scanning operations. However, manually deploying LiDAR stations is not only inefficient, but also relies solely on empirical data for irrational station allocation, leading to low scanning accuracy, overscans, and missed scans. Furthermore, increasing the number of LiDAR deployments results in voluminous data, making it difficult to process. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides a laser radar station planning method for automated scanning of aircraft shape, which solves the problems of low efficiency, multiple scans and missed scans, and the addition of unnecessary stations when deploying laser radars during aircraft shape scanning. It greatly improves the efficiency of aircraft shape scanning and realizes automated scanning of aircraft shape.

[0005] To solve the above technical problems, the present invention provides the following technical solution: a laser radar station planning method for automatic scanning of aircraft shape, the method comprising the following steps:

[0006] S1. Measure the scanning error of the laser radar at different scanning distances and scanning angles, and obtain the relationship between the scanning error and the scanning distance and scanning angle respectively;

[0007] S2. Adaptively adjust the sampling density of scanning points according to the curvature to form a set of scanning points , use the improved region growing algorithm to classify the scanning points to obtain multiple scanning areas, and generate an initial scanning station corresponding to each scanning area to form a scanning station set S ;

[0008] S3. Calculate the total error of each scanning point under all initial scanning positions based on the scanning error relationship, redivide the scanning points to obtain a new scanning area and update it to the scanning position set. S In the scanning area, the scanning station is optimized and the scanning points are re-divided according to the new scanning area and updated to the scanning point set. middle;

[0009] S4. Place the scanning points that cannot be scanned by each scanning station into a temporary group and for temporary groups Secondary planning is performed to obtain the final planning result, which is used to control the automatic scanning equipment to reach the designated position to scan the aircraft's shape.

[0010] Furthermore, in step S1, the specific process includes the following steps:

[0011] S11, use laser radar to scan the distance A Scan the standard part with a scanning angle of 0° and use the obtained scanning data as the benchmark;

[0012] S12. Place the standard part at a distance from the laser radar A+2k The scanning data with a scanning angle of 0° is used as the first set of experimental data, where ;

[0013] S13, then place the standard part at a distance from the laser radar A meters, with the laser incident angle of (0+5 f )° scanning data is used as the second set of experimental data, where ;

[0014] S14. Analyze the first set of experimental data to obtain the scanning distance error Scanning distance The relationship between them is:

[0015] ;

[0016] in, and These are all known parameters obtained from actual experiments;

[0017] S15. Analyze the second set of experimental data to obtain the scanning angle error and scanning angle The relationship between them is:

[0018] ;

[0019] in, is the scanning error of the standard part when it is A meters away from the lidar and the scanning angle is 0°.

[0020] Furthermore, in step S2, the specific process includes the following steps:

[0021] S21. Determine the row spacing parameter of the sampling path of the row cutting method based on the curvature K of the aircraft digital model surface, and obtain the sampling density used to control the sampling scanning points on the aircraft surface. The calculation formula is:

[0022] ;

[0023] in, Indicates that at the sampling point The sampling density at For the sampling points; and are the minimum and maximum sampling densities, respectively; and are the minimum and maximum curvatures of the aircraft digital model surface respectively; is the curvature influence coefficient; is a natural constant; The digital model surface of the aircraft at the sampling point The curvature at

[0024] S22. Obtain the three-dimensional coordinates and unit normal information of all scanning points and put them into the scanning point set , and number each scanning point;

[0025] S23. Classify the scanning points using an improved region growing algorithm, and divide the scanning area according to the normal of the scanning point and the curvature of the scanning point;

[0026] S24. Calculate the position center of each scanning area and normal center , the calculation formula is:

[0027] ;

[0028] ;

[0029] in, is the scanning point coordinate, , is the number of scanning points in the current scanning area; For the Normal unit vector of each scan point;

[0030] S25. Generate a laser tracker station space based on the position that the laser tracker can reach in the workspace , and along the center of the scan area Normal to center Offset until it reaches the laser tracker station space Generate the initial scanning position within the range;

[0031] S26, obtain the three-dimensional coordinates of all initial scanning stations and number them, and store the data in the scanning station set S middle.

[0032] Furthermore, in step S23, the specific process includes the following steps:

[0033] S231, select the scanning point with the lowest curvature as the initial seed point, and determine whether the angle between the normal of the neighboring points around the initial seed point and the normal of the initial seed point is less than the threshold ;

[0034] If it is less than, the adjacent point is merged into the current area, otherwise it is not merged;

[0035] S232, determine the curvature of the neighboring points in the current area, if the curvature is less than the threshold , then use this point as the new seed point;

[0036] S233, repeat the above growth process, and name the scan points merged into the current area during the growth process , is the number of the current region, named according to the order of region growth. For the current region Next points to be added to the current region;

[0037] S234, real-time determination of the number of adjacent points With threshold size;

[0038] If the number of neighboring points in the growth process Always less than the threshold , then continue the current growth process until no new seed points are generated;

[0039] If the number of neighboring points in the growth process Exceeding the threshold , then the scanning points of the current area are projected onto a plane perpendicular to the average normal of the area, and an OBB bounding rectangle is generated;

[0040] S235, determine the area of ​​the rectangle enclosing the OBB;

[0041] If the area of ​​the rectangle exceeds the maximum allowed area , then growth stops;

[0042] If it does not exceed, the growth direction is made to grow in the direction of the shorter side of the rectangle until the area of ​​the rectangle reaches the maximum allowed area. Stop growing.

[0043] Furthermore, in step S3, the specific process includes the following steps:

[0044] S31. Calculate the distance between the scanning point and the current scanning position , the calculation formula is:

[0045] ;

[0046] in, is the coordinate of the scanning point, , is the number of scanning points; are the coordinates of the initial scanning station, , is the number of initial scanning stations;

[0047] S32, calculate the scanning incident angle between the scanning point and the initial scanning position , the calculation formula is:

[0048] ;

[0049] ;

[0050] in, For the Normal unit vector of each scan point;

[0051] S33, according to distance and scanning incident angle , the total error of the scanning point at the current initial scanning position is obtained through the scanning error relationship ,Right now:

[0052] ;

[0053] Where, is the scanning angle error; is the scanning distance error;

[0054] S34, based on the total error of the scanning point at each initial scanning position , redivide the scanning point to make the total error of the point Under the minimum initial scanning position, a new scanning area is obtained;

[0055] S35. Calculate a new scanning position that minimizes the sum of the total errors of the scanning points and the occlusion ratio under the scanning position while keeping the scanning position within the laser radar working space, and then re-divide the scanning points according to the new scanning position;

[0056] S36, repeating steps S34 to S35 until the scanning area and scanning position no longer change, and then completing the optimization of the scanning position;

[0057] S37. Update the new scanning point to the scanning point set and add the serial number of the scan station to which the scan point belongs; update the new scan station to the scan station set S and add all the serial numbers of the scan points under the scan station.

[0058] Furthermore, in step S35, the optimization function can be expressed as follows:

[0059] ;

[0060] in, is the sum of the total errors of the scanning points at the new scanning position; and are the weight of the sum of the total error and the weight of the occlusion ratio respectively; It is the number of scanning points that cannot be scanned at the new scanning position.

[0061] Furthermore, in step S4, the specific process includes the following steps:

[0062] S41. Place the scanning points that will be blocked at each scanning station into a temporary group. Calculate the occlusion ratio of each scanning point under each scanning station, and find the points whose occlusion ratio is greater than the threshold ε Area ;

[0063] The area Cancel the scanning station point in the The scan points are placed in a temporary group middle;

[0064] S42. For temporary groups Perform scanning cone analysis on the scanning points in the image, that is, calculate the position range of the laser radar that can scan the current scanning point;

[0065] S43, through temporary group The effective scanning cone range of all scanning points in the image is determined, and the overlapping area between the effective scanning cone range and the lidar working space is determined. The optimal intersection space that can cover the most points to be scanned is selected from the optimal intersection space, and a point position that minimizes the sum of the total errors is selected in the optimal intersection space as the scanning position of these scanning points.

[0066] And these scan points from the temporary group Remove; put the scanning station into the scanning station collection SThe corresponding scanning points are from the temporary group Remove and put the scan point set middle;

[0067] S44. For temporary groups Repeat steps S42-S43 for the remaining scan points in the temporary group until The number of scan points is lower than the threshold , then stop repeating the steps, and choose to ignore or manually re-measure these scanning points based on their importance and quantity;

[0068] S45, with the final scanning point set Assemble with scanning station S As a result of the planning of the scanning area and scanning station, the automatic scanning equipment is controlled to reach the designated position to scan the aircraft's shape.

[0069] Furthermore, in step S42, the constraints of the scanning cone can be obtained according to the scanning range of the laser radar:

[0070] ;

[0071] in, is the lidar pitch angle; is the horizontal angle; The maximum distance allowed for scanning.

[0072] By means of the above technical solution, the present invention provides a laser radar station planning method for automatic scanning of aircraft shape, which has at least the following beneficial effects:

[0073] 1. The present invention solves the problems of low efficiency, multiple scans and missed scans, and redundant positions in the process of aircraft shape scanning, which occurs when the laser radar position relies on workers' experience. It effectively improves the efficiency and automation capability of aircraft shape scanning.

[0074] 2. This invention uses an improved region growing algorithm to ensure a more reasonable division of scanning points, preventing irregular or overly large areas that could lead to redundant or incomplete generated positions. It also provides a reasonable initial scanning position, thereby reducing the number of optimization cycles and invalid scanning positions.

[0075] 3. This invention establishes a scanning area and scanning station optimization function, and uses this optimization function to find the optimal scanning station and corresponding scanning area through cyclic optimization. Secondary programming is then used to ensure scanning coverage and reduce the occlusion rate of the scanning station, facilitating the automation of aircraft shape scanning. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0077] Figure 1 This is a flow chart of the laser radar station planning method of the present invention;

[0078] Figure 2 This is the partition result diagram using the conventional region growing algorithm in the present invention;

[0079] Figure 3 Flowchart for optimizing the scanning area and scanning position in the present invention. DETAILED DESCRIPTION

[0080] To make the above-mentioned objectives, features, and advantages of the present invention more clearly understood, the present invention is further described below in detail with reference to the accompanying drawings and specific embodiments. This will enable a full understanding of how this application uses technical means to solve technical problems and achieve technical effects, and to implement the invention accordingly.

[0081] This embodiment proposes a LiDAR position planning method for automated scanning of aircraft shapes. Based on the analysis of the relationship between LiDAR scanning angle, scanning distance, and scanning error, the aircraft digital model is first adaptively sampled, and an improved region growing algorithm is used to classify and generate initial scanning positions. Subsequently, the position layout is dynamically adjusted using an iterative optimization algorithm that comprehensively considers scanning error and occlusion rate indicators. Finally, secondary planning is performed on abnormal scanning areas to output the optimal LiDAR scanning position solution, effectively improving the efficiency and automation capability of aircraft shape scanning. Figure 1-Figure 3 As shown, the method includes the following steps:

[0082] S1. Experimentally measure the scanning error of the LiDAR at different scanning distances and scanning angles, and obtain the relationship between the scanning error and the scanning distance and scanning angle. The specific process includes the following steps:

[0083] S11, use laser radar to scan the distance A Scan the standard part with a scanning angle of 0° and use the obtained scanning data as the benchmark;

[0084] S12. Place the standard part at a distance from the laser radar A+2k The scanning data with a scanning angle of 0° is used as the first set of experimental data, where ;

[0085] S13, then place the standard part at a distance from the laser radar A meters, with the laser incident angle of (0+5 f)° scanning data is used as the second set of experimental data, where ;

[0086] S14. Analyze the first set of experimental data to obtain the scanning distance error Scanning distance The relationship between them is:

[0087] ;

[0088] in, and These are all known parameters obtained from actual experiments;

[0089] S15. Analyze the second set of experimental data to obtain the scanning angle error and scanning angle The relationship between them is:

[0090] ;

[0091] in, is the scanning error of the standard part when it is A meters away from the lidar and the scanning angle is 0°.

[0092] S2. Adaptively adjust the sampling density of scanning points according to the curvature to form a set of scanning points , use the improved region growing algorithm to classify the scanning points to obtain multiple scanning areas, and generate an initial scanning station corresponding to each scanning area to form a scanning station set S The specific process includes the following steps:

[0093] S21. Determine the row spacing parameter of the sampling path of the row cutting method based on the curvature K of the aircraft digital model surface, and obtain the sampling density used to control the sampling scanning points on the aircraft surface. The calculation formula is:

[0094] ;

[0095] in, Indicates that at the sampling point The sampling density at For the sampling points; and are the minimum and maximum sampling densities, respectively; and are the minimum and maximum curvatures of the aircraft digital model surface respectively; is the curvature influence coefficient; is a natural constant; The digital model surface of the aircraft at the sampling point The curvature of the .

[0096] S22. Obtain the three-dimensional coordinates and unit normal information of all scanning points and put them into the scanning point set , and number each scanning point.

[0097] S23, using the improved region growing algorithm to classify the scan points, and divide the scan area according to the normal of the scan point and the curvature of the scan point. When the traditional region growing algorithm is used to divide the aircraft surface into regions, the following will occur: Figure 2 The problems are: the random growth will lead to irregular shapes in some areas; the small change in the curvature of the upper surface will lead to too large areas in some areas.

[0098] To solve this problem, this embodiment improves the region growing algorithm. By using the improved region growing algorithm to classify the scanning points, the scanning area is divided according to the normal line of the scanning point and the curvature at that point. The specific process includes the following steps:

[0099] S231, select the scanning point with the lowest curvature as the initial seed point, and determine whether the angle between the normal of the neighboring points around the initial seed point and the normal of the initial seed point is less than the threshold ;

[0100] If it is less than, the adjacent point is merged into the current area, otherwise it is not merged;

[0101] S232, determine the curvature of the neighboring points in the current area, if the curvature is less than the threshold , then use this point as the new seed point;

[0102] S233, repeat the above growth process, and name the scan points merged into the current area during the growth process , is the number of the current region, named according to the order of region growth. For the current region Next points to be added to the current region;

[0103] S234, real-time determination of the number of adjacent points With threshold size;

[0104] If the number of neighboring points in the growth process Always less than the threshold , then continue the current growth process until no new seed points are generated;

[0105] If the number of neighboring points in the growth process Exceeding the threshold , the scanning points of the current area are projected onto a plane perpendicular to the average normal of the area, and an OBB bounding rectangle is generated.

[0106] S235, determine the area of ​​the rectangle enclosing the OBB;

[0107] If the area of ​​the rectangle exceeds the maximum allowed area , then growth stops;

[0108] If it does not exceed, the growth direction is made to grow in the direction of the shorter side of the rectangle until the area of ​​the rectangle reaches the maximum allowed area. Stop growing.

[0109] This invention uses an improved region growing algorithm to ensure a more reasonable division of scanning points, preventing irregular or overly large regions that could lead to redundant or incompletely covered stations. It also provides reasonable initial scanning positions, thereby reducing the number of optimization cycles and invalid scanning positions.

[0110] S24. Calculate the position center of each scanning area and normal center , the calculation formula is:

[0111] ;

[0112] ;

[0113] in, is the scanning point coordinate, , is the number of scanning points in the current scanning area; For the Normal unit vector of each scan point;

[0114] S25. Generate a laser tracker station space based on the position that the laser tracker can reach in the workspace , and along the center of the scan area Normal to center Offset until it reaches the laser tracker station space Generate the initial scanning position within the range;

[0115] S26, obtain the three-dimensional coordinates of all initial scanning stations and number them, and store the data in the scanning station set S middle.

[0116] S3. Calculate the total error of each scanning point under all initial scanning positions based on the scanning error relationship, redivide the scanning points to obtain a new scanning area and update it to the scanning position set. S In the scanning area, the scanning station is optimized and the scanning points are re-divided according to the new scanning area and updated to the scanning point set. middle.

[0117] This embodiment calculates the total error of each scanning point under all initial scanning positions based on the scanning error relationship, and then re-divides the scanning points to the scanning position with the smallest total error to obtain a new scanning area. Then, based on the new scanning point area, a new scanning position is calculated that minimizes the combined sum of the total error and the occlusion ratio of the current scanning point category. The scanning points are then re-divided based on the new scanning position, and this step is repeated until the scanning point area and scanning position remain unchanged. The specific process includes the following steps:

[0118] S31. Calculate the distance between the scanning point and the current scanning position , the calculation formula is:

[0119] ;

[0120] in, is the coordinate of the scanning point, , is the number of scanning points; are the coordinates of the initial scanning station, , is the number of initial scanning stations.

[0121] S32, calculate the scanning incident angle between the scanning point and the initial scanning position , the calculation formula is:

[0122] ;

[0123] ;

[0124] in, For the The normal unit vector of each scan point.

[0125] S33, according to distance and scanning incident angle , the total error of the scanning point at the current initial scanning position is obtained through the scanning error relationship ,Right now:

[0126] ;

[0127] Where, is the scanning angle error; is the scanning distance error.

[0128] S34, based on the total error of the scanning point at each initial scanning position , redivide the scanning point to make the total error of the point Below the minimum initial scanning position, a new scanning area is obtained.

[0129] S35. Calculate a new scanning position that minimizes the sum of the total errors of the scanning points and the occlusion ratio under the scanning position while keeping the scanning position within the laser radar working space. Then, re-divide the scanning points according to the new scanning position. The optimization function can be expressed as follows:

[0130] ;

[0131] in, is the sum of the total errors of the scanning points at the new scanning position; and are the weight of the sum of the total error and the weight of the occlusion ratio respectively; It is the number of scanning points that cannot be scanned at the new scanning position.

[0132] S36. Repeat steps S34 to S35 until the scanning area and the scanning position no longer change, and the optimization of the scanning position is completed.

[0133] S37. Update the new scanning point to the scanning point set and add the serial number of the scan station to which the scan point belongs; update the new scan station to the scan station set S and add all the serial numbers of the scan points under the scan station.

[0134] S4. Place the scanning points that cannot be scanned by each scanning station into a temporary group and for temporary groups Secondary planning is performed to obtain the final planning result, which is used to control the automatic scanning equipment to reach the designated position to scan the aircraft's shape.

[0135] This embodiment places the scanning points that cannot be scanned by each scanning station into a temporary group. By calculating the occlusion ratio of each scanning station, the scanning station with an occlusion ratio exceeding a threshold is removed and the scanning points under this scanning station are placed in the temporary group. Then, secondary planning is performed on the temporary group to obtain the final scanning station. The specific process includes the following steps:

[0136] S41. Place the scanning points that will be blocked at each scanning station into a temporary group. Calculate the occlusion ratio of each scanning point under each scanning station, and find the points whose occlusion ratio is greater than the threshold ε Area , The occlusion ratio is greater than the threshold ε The number of regions. Cancel the scanning station point in the The scan points are placed in a temporary group middle.

[0137] S42. For temporary groups The scanning cone analysis is performed on the scanning points in , that is, the range of the LiDAR station that can scan the current scanning point is calculated. The constraints of the scanning cone can be obtained based on the scanning range of the LiDAR:

[0138] ;

[0139] in, is the lidar pitch angle; is the horizontal angle; The maximum distance allowed for scanning.

[0140] S43, through temporary group The effective scanning cone range of all scanning points in the image is determined, and the overlapping area between the effective scanning cone range and the lidar working space is determined. The optimal intersection space that can cover the most points to be scanned is selected from the optimal intersection space, and a point position that minimizes the sum of the total errors is selected in the optimal intersection space as the scanning position of these scanning points.

[0141] And these scan points from the temporary group Remove; put the scanning station into the scanning station collection S The corresponding scanning points are from the temporary group Remove and put the scan point set middle.

[0142] S44. For temporary groups Repeat steps S42-S43 for the remaining scan points in the temporary group until The number of scan points is lower than the threshold , then stop repeating the steps, and then choose to ignore or manually re-measure based on the importance and number of these scanning points.

[0143] S45, with the final scanning point set Assemble with scanning station S As a result of the planning of the scanning area and scanning station, the automatic scanning equipment is controlled to reach the designated position to scan the aircraft's shape.

[0144] This invention establishes a scanning area and scanning station optimization function, and uses this optimization function to find the optimal scanning station and corresponding scanning area through cyclic optimization. Secondary programming is then used to ensure scanning coverage and reduce the occlusion rate of scanning stations, effectively improving the efficiency and automation of aircraft shape scanning.

[0145] The present invention optimizes the laser radar scanning stations and scanning area through scanning error analysis, thereby reducing the number and redundancy of scanning stations while ensuring scanning accuracy and scanning integrity, thereby facilitating the automation of aircraft shape scanning.

[0146] Those skilled in the art will appreciate that all or part of the steps in the above-mentioned embodiment methods can be accomplished by instructing the relevant hardware through a program. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0147] The above embodiments provide a detailed introduction to the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. At the same time, for those skilled in the art, according to the ideas of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

Claims

1. A laser radar station planning method for automatic scanning of aircraft shape, characterized in that: The method comprises the following steps: S1. Measure the scanning error of the laser radar at different scanning distances and scanning angles, and obtain the relationship between the scanning error and the scanning distance and scanning angle respectively; S2. Adaptively adjust the sampling density of scanning points according to the curvature to form a set of scanning points , use the improved region growing algorithm to classify the scanning points to obtain multiple scanning areas, and generate an initial scanning station corresponding to each scanning area to form a scanning station set S , the classifying of scanning points using the improved region growing algorithm includes: S231, select the scanning point with the lowest curvature as the initial seed point, and determine whether the angle between the normal of the neighboring points around the initial seed point and the normal of the initial seed point is less than the threshold ; If it is less than, the adjacent point is merged into the current area, otherwise it is not merged; S232, determine the curvature of the neighboring points in the current area, if the curvature is less than the threshold , then use this point as the new seed point; S233, repeat the above growth process, and name the scan points merged into the current area during the growth process , is the number of the current region, named according to the order of region growth. For the current region Next points to be added to the current region; S234, real-time determination of the number of adjacent points With threshold size; If the number of neighboring points in the growth process Always less than the threshold , then continue the current growth process until no new seed points are generated; If the number of neighboring points in the growth process Exceeding the threshold , then the scanning points of the current area are projected onto a plane perpendicular to the average normal of the area, and an OBB bounding rectangle is generated; S235, determine the area of ​​the rectangle enclosing the OBB; If the area of ​​the rectangle exceeds the maximum allowed area , then growth stops; If it does not exceed, the growth direction is made to grow in the direction of the shorter side of the rectangle until the area of ​​the rectangle reaches the maximum allowed area. Stop growing; S3. Calculate the total error of each scanning point under all initial scanning positions based on the scanning error relationship, redivide the scanning points to obtain a new scanning area and update it to the scanning position set. S In the scanning area, the scanning station is optimized and the scanning points are re-divided according to the new scanning area and updated to the scanning point set. middle; S4. Place the scanning points that cannot be scanned by each scanning station into a temporary group and for temporary groups Secondary planning is performed to obtain the final planning result, which is used to control the automatic scanning equipment to reach the designated position to scan the aircraft's shape.

2. The laser radar station planning method according to claim 1, characterized in that: In step S1, the specific process includes the following steps: S11, use laser radar to scan the distance A Scan the standard part with a scanning angle of 0° and use the obtained scanning data as the benchmark; S12. Place the standard part at a distance from the laser radar A+2k The scanning data with a scanning angle of 0° is used as the first set of experimental data, where ; S13, then place the standard part at a distance from the laser radar A meters, with the laser incident angle of (0+5 f )° scanning data is used as the second set of experimental data, where ; S14. Analyze the first set of experimental data to obtain the scanning distance error Scanning distance The relationship between them is: ； in, and These are all known parameters obtained from actual experiments; S15. Analyze the second set of experimental data to obtain the scanning angle error and scanning angle The relationship between them is: ； in, is the scanning error of the standard part when it is A meters away from the lidar and the scanning angle is 0°.

3. The laser radar station planning method according to claim 1, characterized in that: In step S2, the specific process includes the following steps: S21. Determine the row spacing parameter of the sampling path of the row cutting method based on the curvature K of the aircraft digital model surface, and obtain the sampling density for regulating the sampling scanning points on the aircraft surface. The calculation formula is: ； in, Indicates that at the sampling point The sampling density at For the sampling points; and are the minimum and maximum sampling densities, respectively; and are the minimum and maximum curvatures of the aircraft digital model surface respectively; is the curvature influence coefficient; is a natural constant; The digital model surface of the aircraft at the sampling point The curvature at S22. Obtain the three-dimensional coordinates and unit normal information of all scanning points and put them into the scanning point set , and number each scanning point; S23, using an improved region growing algorithm to classify the scanning points, and dividing the scanning area according to the normal of the scanning point and the curvature of the scanning point; S24. Calculate the position center of each scanning area and normal center , the calculation formula is: ； ； in, is the scanning point coordinate, , is the number of scanning points in the current scanning area; For the Normal unit vector of each scan point; S25. Generate a laser tracker station space based on the position that the laser tracker can reach in the workspace , and along the center of the scan area Normal to center Offset until it reaches the laser tracker station space Generate the initial scanning position within the range; S26, obtain the three-dimensional coordinates of all initial scanning stations and number them, and store the data in the scanning station set S middle.

4. The laser radar station planning method according to claim 1, characterized in that: In step S3, the specific process includes the following steps: S31. Calculate the distance between the scanning point and the current scanning position , the calculation formula is: ； in, is the coordinate of the scanning point, , is the number of scanning points; are the coordinates of the initial scanning station, , is the number of initial scanning stations; S32, calculate the scanning incident angle between the scanning point and the initial scanning position , the calculation formula is: ； ； in, For the Normal unit vector of each scan point; S33, according to distance and scanning incident angle , the total error of the scanning point at the current initial scanning position is obtained through the scanning error relationship ,Right now: ； Where, is the scanning angle error; is the scanning distance error; S34, based on the total error of the scanning point at each initial scanning position , redivide the scanning point to make the total error of the point Under the minimum initial scanning position, a new scanning area is obtained; S35. Calculate a new scanning position that minimizes the sum of the total errors of the scanning points and the occlusion ratio under the scanning position while keeping the scanning position within the laser radar working space, and then re-divide the scanning points according to the new scanning position; S36, repeating steps S34 to S35 until the scanning area and scanning position no longer change, and then completing the optimization of the scanning position; S37. Update the new scanning point to the scanning point set and add the serial number of the scan station to which the scan point belongs; update the new scan station to the scan station set S and add all the serial numbers of the scan points under the scan station.

5. The laser radar station planning method according to claim 4, characterized in that: In step S35, the optimization function can be expressed as follows: ； in, is the sum of the total errors of the scanning points at the new scanning position; and are the weight of the sum of the total error and the weight of the occlusion ratio respectively; It is the number of scanning points that cannot be scanned at the new scanning position.

6. The laser radar station planning method according to claim 1, characterized in that: In step S4, the specific process includes the following steps: S41. Place the scanning points that will be blocked at each scanning station into a temporary group. Calculate the occlusion ratio of each scanning point under each scanning station, and find the points whose occlusion ratio is greater than the threshold ε Area ; The area Cancel the scanning station point in the The scan points are placed in a temporary group middle; S42. For temporary groups Perform scanning cone analysis on the scanning points in the image, that is, calculate the position range of the laser radar that can scan the current scanning point; S43, through temporary group The effective scanning cone range of all scanning points in the image is determined, and the overlapping area between the effective scanning cone range and the lidar working space is determined. The optimal intersection space that can cover the most points to be scanned is selected from the optimal intersection space, and a point position that minimizes the sum of the total errors is selected in the optimal intersection space as the scanning position of these scanning points. And these scan points from the temporary group Remove; put the scanning station into the scanning station collection S The corresponding scanning points are from the temporary group Remove and put the scan point set middle; S44. For temporary groups Repeat steps S42-S43 for the remaining scan points in the temporary group until The number of scan points is lower than the threshold , then stop repeating the steps, and choose to ignore or manually re-measure these scanning points based on their importance and quantity; S45, with the final scanning point set Assemble with scanning station S As a result of the planning of the scanning area and scanning station, the automatic scanning equipment is controlled to reach the designated position to scan the aircraft's shape.

7. The laser radar station planning method according to claim 6, characterized in that: In step S42, the constraints of the scanning cone can be obtained according to the scanning range of the laser radar: ； in, is the lidar pitch angle; is the horizontal angle; The maximum distance allowed for scanning.

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