A robot path generation method for automatic gluing in aircraft assembly

Abstract

The present invention relates to a robot path generation method for automatic gluing in aircraft assembly. The method comprises: preprocessing point cloud data, performing multi-line / curve fitting based on a distance threshold on the cross-sectional profile of a workpiece to be coated, segmenting and extracting actual point clouds of the surface to be coated and other surfaces, determining the positions of characteristic points, offsetting the characteristic points according to coating process requirements, and obtaining actual coating path points and corresponding postures; controlling a tool coordinate system to rotate as the pitch angle of the surface to be coated changes, and adjusting the posture with the main motion direction of the coating process as the axis; re-segmenting the path points based on the characteristic points, using an interpolation method and the change trend of each characteristic point within the interpolation segment to calculate the coating path; when performing offline coating, performing high-order curve fitting on the path points, re-segmenting the path according to the required speed based on the high-order curve expression, calculating the coordinates of each path point at the corresponding speed, and guiding the coating movement.

Description

Technical Field

[0001] The invention belongs to the technical field of sealant coating methods for aircraft assembly, and in particular relates to a robot path generation method for automatic gluing in aircraft assembly. Background Art

[0002] The sealing performance of an aircraft has a significant impact on safe flight and the lifespan of the fuselage. With the increasing use of composite materials in the aviation sector, a large number of components, such as fuselage panels, wing frames, cockpits, and window frames, are now bonded together using adhesives. Consequently, the application of sealants to aircraft components plays a crucial role in the aircraft assembly and manufacturing process. However, due to the diverse structural features of most aircraft components, applying sealants according to their actual contours is difficult, leading OEMs to rely on manual application. However, manual application presents challenges such as unstable quality, poor operability, and the potential for harmful effects of adhesive materials on human health, severely hindering the development of my country's aircraft manufacturing industry.

[0003] Industrial robots are used in a variety of tasks, including welding, grinding, cutting, and painting. This has led to the development of automated sealant coating systems based on these robots. Four types of systems have been developed to date: general-purpose gluing robots, mechanical gluing robots, soft contour-based gluing robots, and intelligent gluing robots. Intelligent gluing robots, which utilize highly flexible and intelligent technologies such as machine vision and intelligent control, have become the mainstream technology for automated coating systems. However, currently used specialized coating robots are limited to applications such as automotive production lines, where the structural features of the workpiece being coated are relatively simple, due to limitations in automation, accuracy, coating quality, and cost. These applications are not directly applicable to aircraft sealant coating. Most currently proposed aircraft sealant coating systems still suffer from issues such as unsuitability for large components, unsuitable for spatially curved surfaces, large positioning errors, poor coating quality, low path generation and coating efficiency, and an inability to perform online conformal coating. Summary of the Invention

[0004] To overcome the aforementioned shortcomings of existing sealant coating systems for aircraft assembly, this paper proposes a novel robotic path generation method for automated glue application in aircraft assembly. This method ensures sealant coating quality and improves coating efficiency, meeting the design and manufacturing standards of next-generation aircraft. Furthermore, the sealant coating system developed based on this method supports highly automated, high-precision, and high-efficiency in-line conformal coating.

[0005] In general, the method of the present invention includes the following key designs:

[0006] (1) Point cloud image feature extraction

[0007] According to the coating process, the path of aircraft sealant coating is mainly based on some characteristic structures of the workpiece frame to be coated, such as the surface to be coated and its edges. Therefore, after collecting the laser line point cloud data, the system first pre-processes the point cloud data, such as removing noise and cropping the ROI area. Then, the cross-sectional contour of the workpiece to be coated described by the line laser data is subjected to multi-line / curve fitting based on the distance threshold, and the actual point clouds of different surfaces (surface to be coated and surface not to be coated) are segmented and extracted to distinguish the surface to be coated from other surfaces, and the position of the feature points is determined by calculating the endpoints or the intersection points of different surfaces. Finally, according to the coating process requirements, the feature points are offset in combination with parameters such as the coating end head model to obtain the actual coating path points and corresponding postures.

[0008] (2) Point cloud pose calculation based on time series

[0009] During the coating process, the sealant coating module needs to adjust its posture according to the surface to be coated. According to the process information, the surface to be coated has a pitch angle change. Therefore, the tool coordinate system established on the main axis of the coating end needs to rotate with the change of the pitch angle under the control of the system. After adjusting the base coordinate system and the tool coordinate system to a reasonable posture, the system will adjust the posture with the main motion direction of the coating process as the axis. In the online coating mode, since the coating process has a high demand for real-time performance, the system will directly use the original image obtained by the line laser scanning to perform relative posture calculation based on time series.

[0010] (3) Path generation based on feature points

[0011] After acquiring the characteristic points, the system generates a path based on this series of characteristic points. The system re-segments the path points based on the characteristic points and uses interpolation to calculate the coating path, leveraging the changing trends of each characteristic point within the interpolated segment. To streamline the coating process, the system divides it into three phases: pre-sequence, mid-sequence, and post-sequence. Each phase employs a different path generation strategy, ensuring a relatively stable transition throughout the entire process.

[0012] (IV) Coating path fitting and re-segmentation

[0013] The above-mentioned feature point-based path generation function is mainly designed to meet the high real-time system processing requirements and is mostly used in online coating modes. On this basis, the offline coating mode has added the coating path fitting and re-segmentation functions to further improve the accuracy and rationality of path generation. Through the path generation function, the system obtains a series of path points calculated based on the feature points, and fits the path points with high-order curves to form a high-order curve expression in space, thereby unifying the path points. Based on this high-order curve expression, the system will then re-segment the path according to the required speed, calculate the coordinates of each path point at the corresponding speed, and guide the movement of the sealant coating module at the end of the robotic arm.

[0014] Specifically, the present invention provides a robot path generation method for automatic gluing of aircraft assembly, the method comprising:

[0015] S1. Point cloud image feature extraction: After the line laser sensor collects the point cloud data of the workpiece to be coated, it first pre-processes the point cloud data. Then, a multi-line / curve fitting based on a distance threshold is performed on the cross-sectional profile of the workpiece to be coated described by the point cloud data. The actual point clouds of the coated and non-coated surfaces are segmented and extracted. The locations of the feature points are determined by calculating the endpoints or intersection points of different surfaces. The feature points are then offset according to the coating process requirements and the coating tip model to obtain the actual coating path points and corresponding postures.

[0016] S2. Time-series-based point cloud pose calculation: Control the tool coordinate system established on the coating end spindle, causing it to rotate as the pitch angle of the coated surface changes. After adjusting the base coordinate system and tool coordinate system to a reasonable position, the pose is adjusted based on the main motion direction of the coating process.

[0017] S3. Path generation based on feature points: After obtaining the feature points, the path points are re-segmented based on the feature points. The interpolation method is used to calculate the coating path by using the changing trend of each feature point within the interpolation segment;

[0018] S4. Coating path fitting and re-segmentation: When performing offline coating, a series of path points calculated based on the feature points are obtained in the previous step, and high-order curve fitting is performed on the obtained path points to form a high-order curve expression in space. Then, the path is re-segmented according to the required speed based on the high-order curve expression, and the coordinates of each path point at the corresponding speed are calculated to guide the coating movement.

[0019] Furthermore, the reference datum for sealant coating in the robot path generation method for automatic gluing in aircraft assembly of the present invention is the surface to be coated itself, the boundary of the surface to be coated, and the junction between the surface to be coated and other surfaces.

[0020] Furthermore, the preprocessing described in step S1 of the robot path generation method for automatic gluing in aircraft assembly of the present invention includes noise removal and ROI region cropping.

[0021] Furthermore, the time-series-based point cloud posture calculation described in step S2 of the robot path generation method for automatic gluing in aircraft assembly of the present invention directly uses the original image obtained by line laser scanning to perform time-series-based relative posture calculation when online coating is performed.

[0022] Furthermore, the time-series-based point cloud posture calculation described in step S2 of the robot path generation method for automatic gluing in aircraft assembly of the present invention includes:

[0023] The movement direction of the tool coordinate system is set to coincide with the main movement direction of the base coordinate system and the actual coating process, reducing the degree of freedom of posture transformation that needs to be adjusted;

[0024] By utilizing the consistency and continuity of the structure of the workpiece to be coated during the coating process, the posture change is transformed into a feature-based continuous change process, that is, the absolute posture calculation at an independent moment is transformed into the relative posture calculation in continuous time;

[0025] The time-series-based point cloud relative pose calculation method uses point cloud features at adjacent moments to compare the pose changes between the two and obtain the relative pose relationship of the tool coordinate system at the corresponding moments. After feature point extraction and feature structure segmentation, the feature is fitted using the RANSAC method to obtain the point-direction formula of the line on which it lies, and the unit plane vector of the visual coordinate system in that direction is obtained. Based on the point cloud image at the previous moment, the feature structure unit vector at that moment is extracted, as shown in the following formula:

[0026]

[0027] in, Indicates T i-1 The feature vector at time, Indicates T i The feature vector at time, Indicates T i-1 Always T i The tool coordinate system posture transformation at the moment can be obtained by calculating the posture transformation angle of the end of the robot arm;

[0028] Then calculate the angle θ between the current eigenvector and the previous eigenvector using the angle calculation formula:

[0029]

[0030] in, Indicates T i-1 The preceding eigenvector of the moment;

[0031] Determine the direction of rotation by taking the cross product of the vectors:

[0032]

[0033] Furthermore, the path generation based on feature points described in step S3 of the robot path generation method for automatic gluing of aircraft assembly according to the present invention includes:

[0034] Optimize and fit the feature points used to generate the path;

[0035] Based on the original path points, the point cloud smoothing filtering method is adopted, and the path points are iteratively processed using the mean filtering method to obtain a point cloud path with stronger correlation and consistency, as shown in the following formula:

[0036]

[0037] That is, the current target point P i is the center, i represents the order of the current target point in the entire path, and the average processing is performed by combining n points within a certain range in the neighborhood, where n is set to 3;

[0038] The path points are re-segmented and processed using the interpolation method based on the known interval period, as shown in the following formula:

[0039]

[0040] Where i = 0, 1, 2..., 7, 8.

[0041] Furthermore, the coating path fitting and re-segmentation described in step S4 of the robot path generation method for automatic gluing of aircraft assembly of the present invention includes:

[0042] When performing offline coating, a spatial high-order curve fitting based on the path points is performed. The final curve parameters are obtained by solving the overdetermined equation using the least squares method. For the N-order curve polynomial, it is shown as follows:

[0043]

[0044] Among them, y is the y-axis coordinate value of the high-order curve, x is the x-axis coordinate value of the independent variable, i is the specific order of different order terms, and k is the parameter of each order term. The above formula is further constructed into a system of equations:

[0045] Y = XK;

[0046] Among them, Y is the [M×1] dimensional matrix composed of parameter points, X is the [M×6] dimensional matrix composed of parameter points, X Tis the transpose of the [M×6]-dimensional matrix X composed of parameter points, M represents the number of coordinate points involved in the fitting, and K is the [6×1]-dimensional coefficient matrix to be determined. By the least squares solution theorem of overdetermined equations, we can obtain:

[0047] K=(X T X) -1 X T Y;

[0048] Then the coefficient matrix K is obtained through the matrix operation;

[0049] The high-order fitting curve corresponding to the set of feature points is obtained by the above method, and the obtained path is proportionally divided by the quantitative interception method to achieve the re-segmentation of the path points and provide coating path guidance for the coating system.

[0050] Furthermore, the robot path generation method for automatic gluing in aircraft assembly of the present invention is applicable to the working conditions of long stringers, skin lap joints and window frames.

[0051] In summary, the present invention's robot path generation method for automated glue application in aircraft assembly can ensure sealant coating quality and improve coating efficiency, meeting the design and manufacturing standards of next-generation aircraft. Furthermore, the sealant coating system constructed based on this method can support highly automated, high-precision, and high-efficiency online conformal coating. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments of the present invention. Obviously, the following drawings are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0053] Figure 1 Schematic diagram of the working condition of a long stringer coated with aircraft sealant.

[0054] Figure 2 Schematic diagram of the working condition of the aircraft skin lap joint coated with sealant.

[0055] Figure 3 Schematic diagram of the window frame working condition for aircraft sealant coating.

[0056] Figure 4 This is a schematic diagram of the results of fitting multiple lines and extracting the actual point cloud of each section under the long stringer working condition (the red point cloud in the figure is the reference point cloud for coating, and the green point cloud and blue point cloud are non-reference point clouds).

[0057] Figure 5This is a schematic diagram of the results of fitting multiple lines and extracting the actual point clouds of each segment under the skin lap seam working condition (the red point cloud in the figure is the reference point cloud for coating, and the green point cloud and blue point cloud are non-reference point clouds).

[0058] Figure 6 Schematic diagram of the results of fitting multiple lines and extracting each segment of actual point cloud under the window frame working condition (the red point cloud in the figure is the reference point cloud for coating, and the green and blue point clouds are non-reference point clouds).

[0059] Figure 7 Schematic diagram of characteristic points and coating posture under long string working condition.

[0060] Figure 8 Schematic diagram of characteristic points and coating posture under skin lap joint conditions.

[0061] Figure 9 Schematic diagram of feature points and coating posture under window frame working conditions.

[0062] Figure 10 Figure 3 is the actual point cloud data obtained by the method of the present invention and a schematic diagram of the final path after offset (the red path in the figure is the reference path composed of the actual feature points before offset, and the green path is the actual coating path after optimization based on parameters such as the inclination angle of the surface to be coated and the offset distance of the coating process).

[0063] Figure 11 The figure is a flow chart of the overall implementation of the method of the present invention. DETAILED DESCRIPTION

[0064] To make the objectives, technical solutions, and advantages of the present invention more clearly apparent, the technical solutions of the present invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The present invention may also be implemented or applied through different specific implementation methods, and the details in this specification may be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention.

[0065] At the same time, it should be understood that the scope of protection of the present invention is not limited to the specific embodiments described below; it should also be understood that the terms used in the embodiments of the present invention are for describing specific embodiments rather than for limiting the scope of protection of the present invention.

[0066] Example: A robot path generation method and sealant coating system for automatic gluing in aircraft assembly

[0067] In response to the sealant coating needs of large aircraft, typical sealant models and scenarios are selected, key sealant coating technologies are researched and verified, and a robotic sealant coating system is developed to prepare for aircraft mass production.

[0068] This sealant coating system is primarily software-based, and a hardware platform is built to validate it. The software system primarily comprises the data analysis and processing system located on the host computer; the hardware platform primarily consists of a 3D line laser sensor, a KUKA robot, a sealant coating module, a laser tracker, and the necessary components for system assembly and functionality. Through the integration of the hardware platform and software system, the system enables precise coating benchmark extraction and real-time coating path generation in both online and offline modes.

[0069] The reference base for sealant coating is the surface to be coated, as well as the boundary of the surface to be coated or the junction with other surfaces. The working conditions of aircraft sealant coating are roughly divided into long stringers, skin lap joints and window frames (such as Figure 1-Figure 3 The above three working conditions have different benchmark structural characteristics, so the recognition accuracy and computational efficiency of the visual algorithm are required to be high.

[0070] The collected line laser point cloud data is processed using the RANSAC point cloud spatial linear fitting algorithm based on discrete point clouds. Point cloud fitting algorithms are a method for fitting three-dimensional point cloud data into specific geometric shapes. This technology has wide applications in fields such as machine vision, robotics, and computer graphics. The core concept of point cloud fitting algorithms is to use mathematical models to fit point cloud data, thereby obtaining the quantitative geometric shape of the object represented by the point cloud. Common point cloud fitting algorithms include the least squares method, the RANSAC algorithm, and deep learning-based methods.

[0071] The least squares method is a common point cloud fitting algorithm that solves for model parameters by minimizing the error between the point cloud data and the fitted model. The least squares method represents the point cloud data as a vector and the fitted model as a function. The method solves for model parameters by minimizing the difference between the point cloud data and the model function. This method is commonly used for tasks such as line fitting, curve fitting, plane fitting, and surface fitting.

[0072] It is known that there are n points in the point cloud data, that is, the point set (x0, y0), (x1, y1), (x2, y2)… (x n ,y n ), based on the point set, a straight line Ax+By+C=0 is fitted. The parameters that need to be obtained by fitting the straight line using the least squares method are A, B, and C. The spatial distance d from the point (x0, y0) in the point set to the straight line is:

[0073]

[0074] The RANSAC algorithm is a statistical point cloud fitting algorithm that estimates model parameters through random sampling and iteration. The algorithm randomly selects a set of point cloud data as inliers, then fits the model using these inliers. The error between the inliers and outliers is calculated, and the final fitting result is the model that minimizes the error by repeating (iterating) these steps multiple times. This method is commonly used for fitting lines and planes.

[0075] The basic idea of ​​the RANSAC algorithm is to continuously randomly extract sample sets from the data set to seek model parameters that support more inliers; use the model residual set to test the obtained model parameters; after a certain number of iterations, when the probability of consistency between the sample set and the reasonable solution is maximized, the sample set is used as the sample set of the reasonable solution, and the correctness of the parameter solution is supported by the sample residual set test. The data set contains correct data (inliers) and abnormal data (outliers).

[0076] Assume that the proportion of "inliers" in the data is t, and the number of inliers is n inliers , the outer point is n outliers ,but:

[0077]

[0078] Then, when the model uses N points each time, the selected points have at least one external point:

[0079] 1-t N ;

[0080] In the case of k iterations, (1-t n ) k That is, the probability of the model being calculated is calculated by sampling at least one external point in the k-iteration calculation model. Therefore, the probability of being able to sample the correct N points to calculate the correct model is:

[0081] P=1-(1-t n ) k ;

[0082] The above formula can be obtained:

[0083]

[0084] Among them, n is known, t can be calculated, and the number of iterations k can be obtained by simply setting the value of P.

[0085] Since the coating reference data scanned under this working condition does not present a strictly straight line, both the least squares method and the RANSAC algorithm are used to fit the data. This allows the user to pre-select the fitting algorithm function or combine the fitting results of the two methods in the algorithm, thereby maximizing the adaptability of the algorithm and the accuracy of the results.

[0086] The implementation process of this method is as follows ( Figure 11 shown):

[0087] Unlike a simple single line / curve fitting algorithm, this method uses a distance threshold to fit multiple lines, divides the point cloud data into multiple approximate lines / curves, and selects the actual point cloud selected by the corresponding line instead of the fitted mathematical model. The above method can approximately achieve the effect of regional division or deep learning semantic division, and does not require pre-collection of data sets for model training. When used, the calculation speed is fast, the calculation process is simple, and computing power is saved. The following is the result of fitting multiple lines and extracting the actual point cloud of each segment (such as Figure 4-Figure 6 (As shown in the figure), the red point cloud represents the reference point cloud used for coating, while the green and blue point clouds represent non-reference point clouds. The coating process for stringers and window frames requires the coating to be perpendicular to the surface to be coated, while the coating process for skin lap joints requires the coating to be tilted approximately 45° to the surface to be coated.

[0088] After extracting the actual point cloud of the reference part for coating, it is necessary to extract feature points of the actual point cloud of the reference part to obtain the key position points for guiding the robot end to coat (guiding the robot end to move to this position) and the end coating posture.

[0089] By means of the endpoints of the regional segments and the intersection of the fitting lines of the multi-region segments, the characteristic points (yellow points) and coating postures (yellow arrows) under different working conditions are calculated as follows (e.g. Figure 7-Figure 9 As shown in the figure). A flat-tip coating head is used for coating long stringers and window frames. Therefore, it is necessary to offset the original feature point by a specified distance (orange point) in the opposite direction of the edge to be coated.

[0090] The actual point cloud data and the final offset path are as follows (such as Figure 10 The red path is the reference path composed of the actual feature points before the offset, and the green path is the actual coating path after optimization based on parameters such as the inclination angle of the surface to be coated and the offset distance of the coating process.

[0091] It can be seen that features such as holes on the coated surface will not affect feature point extraction and path generation, which proves that this visual algorithm can complete the tasks of feature recognition of the surface to be coated and coating path generation with high redundancy, high robustness and high flexibility.

[0092] During the actual coating process, the system needs to adjust its posture according to the inclination angle characteristics of the coating surface of the workpiece to be coated, so as to control the tool coordinate system set in the sealant coating module, and then drive the coating end to adjust to achieve more accurate conformal coating. The traditional posture solution method is to calculate the inclination angle in the visual coordinate system through the line point cloud plane structure obtained by the line laser scanner, and then convert it to the tool coordinate system and the robot base coordinate system. After that, its posture state in space is calculated, and the coating spindle corresponding to the tool coordinate system is coincided with it, and the tool coordinate system solution and the corresponding robot arm posture state are inversely solved at each moment. However, this process is often accompanied by more coordinate transformation processes, longer coordinate transformation time and lower precision solution results. Therefore, while ensuring the coating process, this system combines the actual coating process and the control method to propose a relatively efficient and high-precision relative posture calculation method.

[0093] This system sets the tool coordinate system's motion direction to coincide with the base coordinate system and the main motion direction of the actual coating process, minimizing the number of degrees of freedom required for posture transformations and limiting them to pitch angle adjustments. The system then leverages the consistency and continuity of the coated workpiece's structure during the coating process to transform complex posture changes into a feature-based continuous process. This translates absolute posture calculations at individual moments into relative posture calculations over continuous time.

[0094] The time-series-based point cloud relative posture calculation method mainly utilizes the point cloud features of adjacent moments, and obtains the relative posture relationship of the tool coordinate system at the corresponding moment by comparing the posture changes of the two. At adjacent measurement moments, the visual measurement coordinate system will produce posture changes in the base coordinate system as the posture of the tool coordinate system at the end of the current robot arm changes. Therefore, for the measurement datum at adjacent moments, although the datum to be measured remains basically unchanged due to its structural consistency and continuity, its plane posture will change significantly in the visual measurement coordinate plane. The features corresponding to the plane structure will directly determine the direction of the main axis of the coating end. In the above method, after feature point extraction and feature structure segmentation, the system fits the feature using the RANSAC method to obtain the point-to-point formula of the straight line where it is located, and obtains the unit plane vector of the visual coordinate system in that direction. Similarly, based on the point cloud image at the previous moment, the system can also extract the unit vector of the feature structure at that moment. As shown in the following formula:

[0095]

[0096] in, Indicates T i-1 The feature vector at time, Indicates T i The feature vector at time, Indicates T i-1 Always Ti The tool coordinate system posture transformation at the moment can be obtained by calculating the posture transformation angle of the robot end.

[0097] Then calculate the angle θ between the current eigenvector and the previous eigenvector using the angle calculation formula:

[0098]

[0099] in, Indicates T i-1 The preceding eigenvector of the moment;

[0100] The direction of rotation can be determined by the cross product of the vectors:

[0101]

[0102] After extracting and optimizing feature points based on point cloud images, the feature points can more accurately describe the path, but there is still a certain degree of fluctuation. Therefore, this system will optimize and fit the feature points used to generate the path to ensure the accuracy and smoothness of the final path points in both online and offline modes.

[0103] Based on the original path points, this system adopts the point cloud smoothing filtering method and uses the mean filtering method to iteratively process the path points to obtain a point cloud path with stronger correlation and consistency, as shown in the following formula:

[0104]

[0105] That is, the current target point P i is the center, i represents the order of the current target point in the entire path, and the averaging process is performed on n points within a certain range in the neighborhood, where n is initially set to 3.

[0106] The above method can achieve the optimization of the path points to a great extent and can describe the coating path more accurately. Since the interval processing method is used in sampling and point cloud processing to increase the adaptability of the system, and redundant cycles are used to avoid response delays caused by unstable processing time, the path point cannot be directly used for coating path guidance, and the system needs to re-segment the path point. When the interval period is known, the system will use the interpolation method to process the path points. Under the above premise, the following formula is obtained:

[0107]

[0108] Where i = 0, 1, 2..., 7, 8.

[0109] In the offline coating mode, the system will perform spatial high-order curve fitting based on the path points, that is, using the least squares method to obtain the final curve parameters by solving the overdetermined equation. As can be seen from the following formula, for the N-order curve polynomial:

[0110]

[0111] Among them, y is the y-axis coordinate value of the high-order curve, x is the x-axis coordinate value of the independent variable, i is the specific order of different order terms, and k is the parameter of each order term. The above formula can be constructed into a system of equations:

[0112] Y = XK;

[0113] Among them, Y is the [M×1] dimensional matrix composed of parameter points, X is the [M×6] dimensional matrix composed of parameter points, X T is the transpose of the [M×6]-dimensional matrix X composed of parameter points, M represents the number of coordinate points involved in the fitting, and K is the [6×1]-dimensional coefficient matrix to be determined. By the least squares solution theorem of overdetermined equations, we can obtain:

[0114] K=(X T X) -1 X T Y;

[0115] Then, the coefficient matrix K is obtained through this matrix operation.

[0116] Through the above method, the system will obtain the high-order fitting curve corresponding to the set of feature points, and proportionally divide the acquired path through the quantitative interception method, thereby realizing the re-segmentation of the path points and providing the system with accurate coating path guidance.

[0117] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any technician familiar with the profession can make some changes or modifications to the technical content disclosed above without departing from the scope of the technical solution of the present invention to obtain equivalent embodiments with equivalent changes. However, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention should be included in the scope of protection of the claims of the present invention.

Claims

1. A robot path generation method for automatic gluing in aircraft assembly, characterized in that: The method comprises: S1. Point cloud image feature extraction: After the line laser sensor collects the point cloud data of the workpiece to be coated, it first pre-processes the point cloud data. Then, a multi-line / curve fitting based on a distance threshold is performed on the cross-sectional profile of the workpiece to be coated described by the point cloud data. The actual point clouds of the coated and non-coated surfaces are segmented and extracted. The locations of the feature points are determined by calculating the endpoints or intersection points of different surfaces. The feature points are then offset according to the coating process requirements and the coating tip model to obtain the actual coating path points and corresponding postures. S2. Time-series-based point cloud pose calculation: Control the tool coordinate system established on the coating end spindle, causing it to rotate as the pitch angle of the coated surface changes. After adjusting the base coordinate system and tool coordinate system to a reasonable position, the pose is adjusted based on the main motion direction of the coating process. S3. Path generation based on feature points: After obtaining the feature points, the path points are re-segmented based on the feature points. The interpolation method is used to calculate the coating path by using the changing trend of each feature point within the interpolation segment; S4. Coating path fitting and re-segmentation: When performing offline coating, a series of path points calculated based on the feature points are obtained in the previous step, and high-order curve fitting is performed on the obtained path points to form a high-order curve expression in space. Then, the path is re-segmented according to the required speed based on the high-order curve expression, and the coordinates of each path point at the corresponding speed are calculated to guide the coating movement.

2. The robot path generation method for automatic gluing of aircraft assembly according to claim 1 is characterized in that: The reference datum for coating is the surface to be coated, the boundary of the surface to be coated, and the interface between the surface to be coated and other surfaces.

3. The robot path generation method for automatic gluing of aircraft assembly according to claim 1 is characterized in that: The preprocessing described in step S1 includes noise removal and ROI region cropping.

4. The robot path generation method for automatic gluing of aircraft assembly according to claim 1 is characterized in that: The time-series-based point cloud pose calculation described in step S2, when performing online coating, directly uses the original image obtained by line laser scanning to perform time-series-based relative pose calculation.

5. The robot path generation method for automatic gluing of aircraft assembly according to claim 1 is characterized in that: The time-series-based point cloud pose calculation described in step S2 includes: The movement direction of the tool coordinate system is set to coincide with the main movement direction of the base coordinate system and the actual coating process, reducing the degree of freedom of posture transformation that needs to be adjusted; By utilizing the consistency and continuity of the structure of the workpiece to be coated during the coating process, the posture change is transformed into a feature-based continuous change process, that is, the absolute posture calculation at an independent moment is transformed into the relative posture calculation in continuous time; The time-series-based point cloud relative pose calculation method uses point cloud features at adjacent moments to compare the pose changes between the two and obtain the relative pose relationship of the tool coordinate system at the corresponding moments. After feature point extraction and feature structure segmentation, the feature is fitted using the RANSAC method to obtain the point-direction formula of the line on which it lies, and the unit plane vector of the visual coordinate system in that direction is obtained. Based on the point cloud image at the previous moment, the feature structure unit vector at that moment is extracted, as shown in the following formula: in, Indicates T i-1 The feature vector at time, Indicates T i The feature vector at time, Indicates T i-1 Always T i The tool coordinate system posture transformation at the moment can be obtained by calculating the posture transformation angle of the end of the robot arm; Then calculate the angle θ between the current eigenvector and the previous eigenvector using the angle calculation formula: in, Indicates T i-1 The preceding eigenvector of the moment; Determine the direction of rotation by taking the cross product of the vectors:

6. The robot path generation method for automatic gluing of aircraft assembly according to claim 1 is characterized in that: The path generation based on feature points in step S3 includes: Optimize and fit the feature points used to generate the path; Based on the original path points, the point cloud smoothing filtering method is adopted, and the path points are iteratively processed using the mean filtering method to obtain a point cloud path with stronger correlation and consistency, as shown in the following formula: That is, the current target point P i is the center, i represents the order of the current target point in the entire path, and the average processing is performed by combining n points within a certain range in the neighborhood, where n is set to 3; The path points are re-segmented and processed using the interpolation method based on the known interval period, as shown in the following formula: Where i = 0, 1, 2..., 7, 8.

7. The robot path generation method for automatic gluing of aircraft assembly according to claim 1 is characterized in that: The coating path fitting and re-segmentation in step S4 includes: When performing offline coating, a spatial high-order curve fitting based on the path points is performed. The final curve parameters are obtained by solving the overdetermined equation using the least squares method. For the N-order curve polynomial, it is shown as follows: Among them, y is the y-axis coordinate value of the high-order curve, x is the x-axis coordinate value of the independent variable, i is the specific order of different order terms, and k is the parameter of each order term. The above formula is further constructed into a system of equations: Y = XK; Among them, Y is the [M×1] dimensional matrix composed of parameter points, X is the [M×6] dimensional matrix composed of parameter points, X T is the transpose of the [M×6]-dimensional matrix X composed of parameter points, M represents the number of coordinate points involved in the fitting, and K is the [6×1]-dimensional coefficient matrix to be determined. By the least squares solution theorem of overdetermined equations, we can obtain: K=(X T X) -1 X T Y; Then the coefficient matrix K is obtained through the matrix operation; The high-order fitting curve corresponding to the set of feature points is obtained by the above method, and the obtained path is proportionally divided by the quantitative interception method to achieve the re-segmentation of the path points and provide coating path guidance for the coating system.

8. The robot path generation method for automatic gluing in aircraft assembly according to any one of claims 1 to 7, characterized in that: The robot path generation method is applicable to the working conditions of long stringers, skin lap joints and window frames.

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