Path planning method for aeronautical part on-machine measurement process and related device

Abstract

The invention discloses an aeronautical part on-machine measurement process-oriented path planning method and a related device, and relates to the technical field of path planning, the method comprises the following steps: after obtaining a measurement point set of each measurement feature of an aeronautical part three-dimensional model, determining a starting measurement point and an end measurement point of each measurement feature by using a nearest point pair algorithm, for each measurement feature, path planning is carried out by using an ant colony algorithm to obtain a collision-free local path, meanwhile, path planning is carried out by using an RRT * algorithm to obtain a collision-free connection path, and all the local paths and all the connection paths are connected according to a connection sequence to obtain a collision-free connection path; and obtaining a complete measurement path for performing on-machine measurement on the aeronautical part three-dimensional model. According to the method, three algorithms are combined, the path planning process can be optimized and completed without manual participation, so that path planning for the on-machine measurement process of aeronautical parts can be efficiently and automatically completed, and the measurement efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of path planning technology, and in particular to a path planning method and related devices for an on-machine measurement process of aviation parts. Background Art

[0002] With the popularization of digitalization in the aviation field, the detection methods of aviation parts have also ushered in innovation. Therefore, using various advanced algorithms to empower the detection process of aviation parts and achieve a higher degree of automation has become a popular research direction. Coordinate Measuring Machine (CMM) is one of the most effective methods to measure and obtain dimensional data. For the key inspection characteristics of aviation parts, it is a common technology to use CMM to perform on-machine measurement. At this time, the object to be measured is placed in the three-coordinate measurement space, and the coordinate position of each measuring point on the object to be measured can be obtained. According to the spatial coordinate values ​​of these measuring points, the geometric size, shape and position of the object to be measured are calculated.

[0003] At present, when connecting measuring points to form a measurement path, the commonly used method is: first carry out measurement point planning, manually select points or automatically arrange points according to rules to form a measurement point set, and arrange the measurement points in the measurement point set in the order of generation to obtain a measurement point sequence, and connect them according to the measurement point sequence to form the original measurement path, and then perform path obstacle avoidance. After the original measurement path is formed, the path obstacle avoidance is performed using a safety plane method to form a collision-free measurement path. However, in the above path planning process, the order of measuring points is often not paid enough attention, resulting in the final collision-free measurement path being too long and of poor quality, which leads to serious waste of measurement time. In addition, the reference plane selection and offset determination processes involved in the safety plane method require manual intervention, which cannot achieve a greater degree of automation and is inefficient. Summary of the invention

[0004] The purpose of this application is to provide a path planning method and related devices for the on-machine measurement process of aviation parts, which can efficiently and automatically complete the path planning for the on-machine measurement process of aviation parts and improve the measurement efficiency.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a path planning method for an on-machine measurement process of an aviation part, and the path planning method for an on-machine measurement process of an aviation part comprises:

[0007] Acquire a measuring point set of each measuring feature of the three-dimensional model of the aviation part; the measuring feature is a line and / or a surface of the three-dimensional model of the aviation part; the measuring point set includes a plurality of measuring points located on the measuring feature;

[0008] All measurement features are sorted according to the connection order, and the measurement point set of the i-th measurement feature and the measurement point set of the i+1-th measurement feature are used as input, and the end measurement point of the i-th measurement feature and the starting measurement point of the i+1-th measurement feature are determined by using the nearest point pair algorithm, and a measurement point other than the end measurement point of the first measurement feature is randomly selected from the measurement point set of the first measurement feature as the starting measurement point of the first measurement feature, and a measurement point other than the starting measurement point of the I-th measurement feature is randomly selected from the measurement point set of the I-th measurement feature as the ending measurement point of the I-th measurement feature, to obtain the starting measurement point and the ending measurement point of each measurement feature; wherein, i=1, 2, ..., I-1, and I is the total number of measurement features;

[0009] For each measurement feature, the ant colony algorithm is used for path planning to obtain a collision-free local path that traverses all measurement points in the measurement point set of the measurement feature and from the starting measurement point of the measurement feature to the ending measurement point of the measurement feature;

[0010] For the i-th measurement feature and the i+1-th measurement feature, the RRT* algorithm is used for path planning to obtain a collision-free connection path from the end measurement point of the i-th measurement feature to the start measurement point of the i+1-th measurement feature;

[0011] All local paths and all connection paths are connected in a connection order to obtain a complete measurement path for on-machine measurement of the three-dimensional model of the aviation part.

[0012] In a second aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned path planning method for the on-machine measurement process of aviation parts.

[0013] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned path planning method for the on-machine measurement process of aviation parts.

[0014] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above-mentioned path planning method for the on-machine measurement process of aviation parts.

[0015] According to the specific embodiments provided in this application, this application has the following technical effects:

[0016] The present application provides a path planning method and related device for the on-machine measurement process of aviation parts. After obtaining the measurement point set of each measurement feature of the three-dimensional model of the aviation part, the starting measurement point and the end measurement point of each measurement feature are determined by the nearest point pair algorithm. For each measurement feature, the ant colony algorithm is used for path planning to obtain a collision-free local path that traverses all measurement points in the measurement point set of the measurement feature and from the starting measurement point of the measurement feature to the end measurement point of the measurement feature. At the same time, for the i-th measurement feature and the i+1-th measurement feature, the RRT* algorithm is used for path planning to obtain a collision-free connection path from the end measurement point of the i-th measurement feature to the starting measurement point of the i+1-th measurement feature. All local paths and all connection paths are connected in a connection order to obtain a complete measurement path for on-machine measurement of the three-dimensional model of the aviation part. The present application combines three algorithms, and the path planning process can be completed without human intervention, and the algorithm can optimize the path planning process, so that the path planning for the on-machine measurement process of aviation parts can be completed efficiently and automatically, thereby improving the measurement efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 This is an application environment diagram of a path planning method for the on-machine measurement process of aviation parts provided in Example 1 of the present application.

[0019] Figure 2 A flow chart of a path planning method for on-machine measurement of aviation parts provided in Example 1 of the present application.

[0020] Figure 3 This is a schematic diagram of the improved RRT* algorithm flow provided in Example 1 of the present application.

[0021] Figure 4 This is a schematic diagram of local smoothing processing provided in Example 1 of the present application; wherein, Figure 4 (a) is the original path. Figure 4 (b) in the figure is the path after removing redundant points. Figure 4 (c) in the figure is the path after local smoothing.

[0022] Figure 5 This is a schematic diagram of the measurement point orientation and probe collision volume provided in Example 1 of the present application; wherein, Figure 5 (a) in the figure is processed after the measurement point orientation. Figure 5(b) in the figure is without measurement point orientation processing.

[0023] Figure 6 This is a flow chart of the collision detection algorithm provided in Example 1 of the present application.

[0024] Figure 7 A schematic diagram of the structure of a computer device provided in Example 2 of the present application. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0026] Example 1

[0027] The path planning method for the on-machine measurement process of aviation parts provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the terminal communicates with the server through the network. The data storage system can store the data that the server needs to process. The data storage system can be set up separately, integrated on the server, or placed on the cloud or other servers. The terminal can send the measurement point set of each measurement feature of the three-dimensional model of the aviation part to be processed to the server. After the server receives the measurement point set of each measurement feature of the three-dimensional model of the aviation part to be processed, for each measurement point set of the three-dimensional model of the aviation part to be processed, the server sorts all the measurement features in the connection order, and takes the measurement point set of the i-th measurement feature and the measurement point set of the i+1-th measurement feature as input, and uses the nearest point pair algorithm to determine the end measurement point of the i-th measurement feature and the starting measurement point of the i+1-th measurement feature, randomly selects a measurement point other than the end measurement point of the first measurement feature in the measurement point set of the first measurement feature as the starting measurement point of the first measurement feature, and randomly selects a measurement point in the measurement point set of the I measurement feature. The measuring points other than the starting measuring point of the I-th measuring feature are used as the end measuring point of the I-th measuring feature, and the starting measuring point and the end measuring point of each measuring feature are obtained; for each measuring feature, the ant colony algorithm is used for path planning, and a collision-free local path is obtained that traverses all measuring points in the measuring point set of the measuring feature and from the starting measuring point of the measuring feature to the end measuring point of the measuring feature; for the i-th measuring feature and the i+1-th measuring feature, the RRT* algorithm is used for path planning, and a collision-free connection path is obtained from the end measuring point of the i-th measuring feature to the starting measuring point of the i+1-th measuring feature; all local paths and all connection paths are connected in a connection order to obtain a complete measurement path for on-machine measurement of the three-dimensional model of aviation parts. The server can feed back the obtained complete measurement path to the terminal.

[0028] In addition, in some embodiments, the path planning method for the on-machine measurement process of aviation parts can also be implemented independently by a server or a terminal. For example, the terminal can directly process the measurement point set of each measurement feature of the three-dimensional model of the aviation part to be processed, or the server can obtain the measurement point set of each measurement feature of the three-dimensional model of the aviation part to be processed from the data storage system, and process the measurement point set of each measurement feature of the three-dimensional model of the aviation part to be processed.

[0029] The terminals may be, but are not limited to, various desktop computers, laptops, smart phones, tablet computers, IoT devices and portable wearable devices. IoT devices may be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. Portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server may be implemented as an independent server or a server cluster consisting of multiple servers, or a cloud server.

[0030] In an exemplary embodiment, Figure 2 As shown, a path planning method for the on-machine measurement process of aviation parts is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The following steps are used to illustrate the server in the example.

[0031] Step S1, obtaining a measuring point set of each measuring feature of the three-dimensional model of the aviation part; the measuring feature is a line and / or a surface of the three-dimensional model of the aviation part; the measuring point set includes a plurality of measuring points located on the measuring feature.

[0032] Step S2, sorting all measurement features in a connection order, and taking the measurement point set of the ith measurement feature and the measurement point set of the i+1th measurement feature as input, using the nearest point pair algorithm to determine the end measurement point of the ith measurement feature and the start measurement point of the i+1th measurement feature, randomly selecting a measurement point other than the end measurement point of the first measurement feature from the measurement point set of the first measurement feature as the start measurement point of the first measurement feature, and randomly selecting a measurement point other than the start measurement point of the Ith measurement feature from the measurement point set of the Ith measurement feature as the end measurement point of the Ith measurement feature, to obtain the start measurement point and the end measurement point of each measurement feature; wherein i=1, 2, ..., I-1, and I is the total number of measurement features.

[0033] Step S3: for each measurement feature, an ant colony algorithm is used to perform path planning to obtain a collision-free local path that traverses all measurement points in the measurement point set of the measurement feature and from the starting measurement point of the measurement feature to the ending measurement point of the measurement feature.

[0034] Step S4: for the ith measurement feature and the i+1th measurement feature, the RRT* algorithm is used for path planning to obtain a collision-free connection path from the end measurement point of the ith measurement feature to the start measurement point of the i+1th measurement feature.

[0035] Step S5, connecting all the local paths and all the connection paths in a connection order to obtain a complete measurement path for on-machine measurement of the three-dimensional model of the aviation part.

[0036] By implementing the above-mentioned steps S1 to S5, this embodiment uses the nearest point pair algorithm to determine the starting point and the end point of each measurement feature, uses the ant colony algorithm to perform path planning, and obtains a collision-free local path from the starting point of the measurement feature to the end point of the measurement feature for each measurement feature. The RRT* algorithm is used for path planning to obtain a collision-free connection path between two adjacent measurement features from the end point of the i-th measurement feature to the starting point of the i+1-th measurement feature. Finally, all local paths and all connection paths are connected to obtain a complete measurement path. The entire path planning process does not require manual participation, and can realize an efficient and automatic path planning process for the on-machine measurement process of aviation parts, thereby realizing the generation of a global efficient collision-free path.

[0037] Before using a three-dimensional coordinate measuring machine for on-machine measurement, it is necessary to generate a measurement path file through path planning in the CATIA environment. The path planning process is implemented for the MBD (Model Based Definition) model. In this embodiment, the MBD model refers to the three-dimensional model of aviation parts. In order to realize customized development of the MBD model, the component application architecture (Component Application Architecture, CAA) provided by CATIA provides interfaces for operating elements at each level, including document level (such as CATI Document), product level (such as CATI Product), topology level (such as CATCell) and geometry level (such as CAT Point), etc. In addition, a large number of interfaces are provided for operating the structure tree nodes. The path planning method of this embodiment is based on the interface provided by CAA and combined with advanced algorithms.

[0038] The path planning process is performed for the MBD model. The generation of a measurement path may involve multiple features of the MBD model. These features may include various lines, surfaces, etc. The measurement points are located on these measurement features. At this time, there is a measurement point set on one measurement feature. The idea of ​​the path planning method of this embodiment is to first determine the closest point pair between each adjacent measurement feature, and use it as the first and last measurement points of the path across the measurement feature. Then, local path planning is performed on each measurement point set on these measurement features (ensuring that the first and last measurement points remain unchanged) to form multiple local paths. Then, connection path planning is performed across the measurement features to form multiple connection paths. The two paths are combined to obtain the final complete measurement path.

[0039] In this embodiment, the measurement features, measurement point sets and connection sequence are all pre-designed according to user needs. It is only necessary to obtain the measurement point set of each measurement feature of the three-dimensional model of the aviation part. The measurement feature is the line and / or surface of the three-dimensional model of the aviation part. The measurement point set includes multiple measurement points located on the measurement feature. The connection sequence refers to the order in which each measurement feature is connected. For example, if the measurement features include A, B, and C, the connection sequence may be A connected to B first, and B connected to C, or other sequences may be used.

[0040] In this embodiment, all measurement features are sorted according to the connection order. For example, if the measurement features include A, B, and C, and the connection order is A first connected to B, and then B connected to C, then the sorting result is that A is the first measurement feature, B is the second measurement feature, and C is the third measurement feature. The measuring point set of the i-th measurement feature and the measuring point set of the i+1-th measurement feature are used as input, and the end measuring point of the i-th measurement feature and the starting measuring point of the i+1-th measurement feature are determined by using the nearest point pair algorithm. A measuring point other than the end measuring point of the first measurement feature is randomly selected from the measuring point set of the first measurement feature as the starting measuring point of the first measurement feature, and a measuring point other than the starting measuring point of the I-th measurement feature is randomly selected from the measuring point set of the I-th measurement feature as the end measuring point of the I-th measurement feature, to obtain the starting measuring point and the end measuring point of each measurement feature, where i=1, 2, ..., I-1, and I is the total number of measurement features.

[0041] The reason why the present embodiment first pre-processes the path across measurement features based on the nearest point pair algorithm is that if path planning is performed directly, the path planning algorithm will take too long to run due to the large number and wide distribution of global measurement points. In order to optimize the running time of the path planning algorithm, the nearest point pair algorithm is introduced to pre-determine the first and last measurement points of each measurement feature, and then local path planning is performed inside each measurement feature respectively. Finally, the optimized local path is combined with the connection path across measurement features to obtain a complete measurement path.

[0042] The traditional closest point pair algorithm first decomposes the input point set, sorts all the points in the point set according to the x-coordinate, and then divides the point set into two left and right subsets through the midpoint. Each subset contains half of the points. Then, the divide-and-conquer method is recursively applied to the left and right subsets to find the closest point pair in each subset and the corresponding distance. For point pairs spanning the left and right subsets, special processing is required, which involves a brute force search for points within a certain width (2 times the current minimum distance) on the midline, because these points may form a closer point pair with the points on the other side. Then, the results of the left and right subsets are merged. The closest point pair may be completely on the left, completely on the right, or one point on the left and the other on the right.

[0043] Since the input in this embodiment is two point sets instead of one point set, the nearest point pair algorithm is improved. First, the two point sets are merged into an overall point set and sorted according to the x coordinates of the points so as to perform efficient divide-and-conquer operations in subsequent steps. In the divide-and-conquer process, the sorted point set is recursively divided into left and right parts, and the shortest distance point pair is found in each part. In order to ensure that the shortest distance point pairs obtained come from two different point sets, a cross-group constraint is introduced each time the results of the left and right parts are recursively merged. To achieve this constraint, the points in the input point set are marked respectively, so that only the distance of the cross-group point pairs is calculated in the calculation of the left and right parts and near the midline. In addition, during the inspection process of the area near the midline, the point pairs whose distance to the midline is less than the current minimum distance are screened to ensure that only the shortest distance point pairs that meet the conditions are found in the cross-group point pairs to ensure the minimum cross-group distance. Through the above process, the nearest point pairs that meet the conditions and span the point set can be obtained. In the subsequent steps, each set of closest point pairs is used as the end measurement point of the previous measurement feature and the start measurement point of the next measurement feature.

[0044] At this time, in this embodiment, the measuring point set of the i-th measuring feature and the measuring point set of the i+1-th measuring feature are used as input, and the end measuring point of the i-th measuring feature and the starting measuring point of the i+1-th measuring feature are determined by using the closest point pair algorithm, which specifically includes:

[0045] (1) The measurement point set of the i-th measurement feature and the measurement point set of the i+1-th measurement feature are combined to obtain the overall point set.

[0046] (2) Under the preset constraints, the entire point set is used as input, and the closest point pair algorithm is used to determine the closest point pair. The measurement point in the measurement point set of the i-th measurement feature in the closest point pair is used as the end measurement point of the i-th measurement feature, and the measurement point in the measurement point set of the i+1-th measurement feature in the closest point pair is used as the starting measurement point of the i+1-th measurement feature. Among them, the preset constraints are that when determining the closest point pair, only the distance of the cross-group point pair is calculated. The cross-group point pair with the shortest distance is the closest point pair. The cross-group point pair consists of a measurement point in the measurement point set of the i-th measurement feature and a measurement point in the measurement point set of the i+1-th measurement feature.

[0047] After determining the starting point and the ending point of each measurement feature, this embodiment uses the ant colony algorithm to perform path planning for each measurement feature to obtain a collision-free local path that traverses all measurement points in the measurement point set of the measurement feature and from the starting point of the measurement feature to the ending point of the measurement feature.

[0048] This embodiment generates local paths based on the improved ant colony algorithm, which can achieve the shortest optimization processing of paths within the same measurement feature. To achieve this goal, the traditional ant colony algorithm is improved (mainly in the path selection part). The specific algorithm flow after the improvement is as follows.

[0049] (1) Initialize the ant colony

[0050] First, the object characteristic management interface (CATISpecObject) is called to obtain the measurement features of different measurement points. An ant colony is independently arranged on each measurement feature for calculation, and the number of ants arranged is calculated according to the number of measurement points on the measurement feature. When calculating the number of ants, the principle of more measurement points means more ants is followed. After that, the pheromone concentration is initialized to a constant.

[0051] (2) Path selection

[0052] The ant will start from the starting point and select the next point according to the heuristic rule until it reaches the end point. Specifically, the ant will make a decision on the next point based on heuristic factors such as pheromone concentration, distance, and whether a collision occurs. The calculation formula for the transfer probability is:

[0053]

[0054] Among them, p ij is the probability of transferring from measurement point i to measurement point j; τ ij is the pheromone concentration from measuring point i to measuring point j; α is the first parameter that adjusts the importance of pheromone concentration on path selection; η ij is the heuristic information from measuring point i to measuring point j; β is the second parameter for adjusting the importance of heuristic information to path selection; allowed is the set of next measuring points that do not collide, and no collision means that the line connecting measuring point i and the next measuring point does not collide with the three-dimensional model of the aviation part; τ ik is the pheromone concentration from measuring point i to measuring point k; η ik is the heuristic information from measurement point i to measurement point k; K is the total number of the next measurement points in allowed; R i,j is the probability coefficient from measuring point i to measuring point j.

[0055] η ik It is defined by the reciprocal of the distance between two measuring points, as follows:

[0056]

[0057] Among them, d ik is the distance between measuring point i and measuring point k.

[0058] R i,j Defined as:

[0059]

[0060] Among them, if a collision happens between i and j means if the line connecting measuring point i and measuring point j collides with the three-dimensional model of the aviation part.

[0061] After all ants in an ant colony complete their search, each ant generates a planned path. To improve the diversity of the ant colony and avoid falling into a local optimal solution, the idea of ​​the selection function in the genetic algorithm is borrowed. The probability of the planned path being selected is calculated based on the quality of each planned path (also known as fitness, expressed as the inverse of the path length), as follows:

[0062]

[0063] Among them, P(i) is the probability of selection of planning path i, which represents the probability of a certain planning path being selected as the next generation; fitness(i) is the fitness of planning path i; N is the size of the ant colony, that is, the total number of ants, that is, the total number of planning paths; fitness(j) is the fitness of planning path j.

[0064] According to the probability of being selected, a certain number of planned paths are selected as selected paths using roulette. Then, an even number of selected paths are selected as paths to be changed, and the remaining selected paths are recorded as unselected paths. Finally, the selected paths to be changed are crossed. Specifically, a roulette is used to select an intersection point. Next, the parts of the two paths to be changed that are located after this intersection point are exchanged to obtain two variant paths. All unselected paths and all variant paths are used as update paths for updating pheromone concentrations.

[0065] (3)Pheromone Update

[0066] The pheromone concentration on the path is updated according to the quality of the updated path. More pheromones will be retained on a better path. The pheromone update is performed using the following formula:

[0067] τ ij =(1-ρ)·τ ij +Δτ ij ;

[0068] Among them, ρ is the pheromone volatility coefficient, which is used to control the volatilization speed of pheromone; Δτ ij is the pheromone increment between measuring point i and measuring point j, which is calculated according to the following formula:

[0069]

[0070] Where Q is the pheromone enhancement coefficient; L α and L β are the total lengths of path α and path β respectively, α represents the unselected path, and β represents the mutated path obtained by crossover and mutation operations.

[0071] (4) Loop iteration and update path

[0072] The path on the measurement feature is iterated repeatedly to finally generate the optimal local path.

[0073] At this time, in this embodiment, the ant colony algorithm is used for path planning to obtain a collision-free local path that traverses all the measurement points in the measurement point set of the measurement feature and from the starting measurement point of the measurement feature to the ending measurement point of the measurement feature, specifically including:

[0074] (1) Initialize the ant colony and pheromone concentration. The ant colony includes several ants.

[0075] (2) Each ant is placed at the starting point of the measurement feature. For each ant, the next measurement point is continuously selected according to the transition probability until all the measurement points in the measurement point set of the measurement feature are traversed and the last measurement point of the measurement feature is reached, thus generating a collision-free planning path.

[0076] (3) Determine whether the first maximum number of iterations has been reached.

[0077] (4) If yes, the iteration is stopped, and the shortest planned path in the current iteration is selected as a collision-free local path that traverses all the measurement points in the measurement point set of the measurement feature and runs from the starting measurement point of the measurement feature to the ending measurement point of the measurement feature.

[0078] (5) If not, continue iterating, update the pheromone concentration according to all planned paths, and return to the step of “placing each ant at the starting point of the measured feature”.

[0079] Among them, the pheromone concentration is updated according to all planned paths, including:

[0080] (1) Calculate the fitness of each planned path respectively. For each planned path, calculate the probability of the planned path being selected based on the fitness of the planned path. The fitness is the inverse of the length of the planned path.

[0081] (2) Taking the selection probability of all planned paths as input, a roulette wheel is used to select some planned paths as the selected paths.

[0082] (3) Randomly select an even number of selected paths as the paths to be transformed, and record the remaining selected paths as unselected paths.

[0083] (4) All the paths to be transformed are combined in pairs to obtain several combinations.

[0084] (5) For each combination, the two paths to be transformed in the combination are used as input, the intersection is selected using roulette, and the parts of the two paths to be transformed in the combination located after the intersection are exchanged to obtain two variant paths.

[0085] (6) Update the pheromone concentration based on all unselected paths and all mutated paths.

[0086] In this embodiment, for the i-th measurement feature and the i+1-th measurement feature, the RRT* algorithm is used for path planning to obtain a collision-free connection path from the end measurement point of the i-th measurement feature to the starting measurement point of the i+1-th measurement feature.

[0087] This embodiment uses the improved RRT* algorithm to generate obstacle avoidance paths across measurement features. For most measurement tasks, path collision usually occurs when crossing measurement features, such as Figure 3 As shown in the figure, the improved RRT* algorithm first defines the state space, and forms nodes across measurement features by random sampling in the state space. These nodes are connected to form an initial connection path across measurement features, and then the initial connection path is locally smoothed to obtain a reasonable connection path across measurement features. The specific process is as follows.

[0088] (1) Use the improved RRT* algorithm to initially form a collision-free path

[0089] In the three-dimensional space, first build a tree T with the start node as the root node, which contains the set of new nodes generated by expansion in each iteration, set the second maximum number of iterations N and the target node q goal .

[0090] Next, in the specified feasible sampling area X = {min rand ,max rand Randomly sample nodes within}, min rand ,max rand are the minimum and maximum values ​​of the sampling nodes, respectively, as follows:

[0091]

[0092] Among them, q rand is the sampling node; p is the target node sampling rate, that is, the probability of directly selecting the target node as the next node; RandomSample(X) represents random sampling in the feasible sampling area.

[0093] Determine whether the line connecting the current node and the sampling node (including the case of directly sampling the target node) collides with the three-dimensional model of the aviation part. If so, proceed to the next iteration. If not, further find the distance sampling node q in the tree T. rand The nearest neighbor node q nearest ,as follows:

[0094]

[0095] Where q is a node in the tree T.

[0096] On this basis, a preset step size Δq is used to select neighboring nodes q nearest Along the direction of sampling node q rand Directionally expand the tree T and generate a new node q new ,as follows:

[0097]

[0098] Determine the new node q new With neighboring nodes q nearest Whether the connection line collides with the three-dimensional model of the aviation part, if so, proceed to the next iteration; if not, the new node q new Add to the tree T.

[0099] After that, check the set of neighboring nodes N = {q∈T|||qq within the search radius r new ≤r||}, find the new node q new The parent node of the new node q new The optimal path of the connection is determined to determine whether the optimal path after reselection is better, and then decide whether to replace it. The better path needs to meet the following requirements: the optimal path after reselection is shorter and no collision occurs, and the parent node of the relevant node is updated. c is a constant used to adjust the growth rate of the search radius, n is the number of nodes in the tree, and d is the spatial dimension.

[0100] New node q new The parent node of is determined as follows:

[0101]

[0102] Among them, q min is the parent node; Cost(q) is the cost of node q. To accelerate the path convergence, the Euclidean distance is used as a heuristic function to constrain the reconnection process and construct a cost function as follows:

[0103] Cost(q)=g(n)+h(n);

[0104] Wherein, g(n) is the actual cost from the starting node to the node q, and h(n) is the actual cost from the node q to the target node. This embodiment adopts the Euclidean distance, which is expressed as follows:

[0105]

[0106] h(n)=||q k -q goal ||;

[0107] Among them, k is the index of node q, q i is the i-th node in the path, q i-1 is the i-1th node in the path; q k That is the node q.

[0108] The iteration is repeated until the second maximum number of iterations N is reached, or a node is found that is close enough to the target node without colliding.

[0109] (2) Preliminary local smoothing of collision-free paths

[0110] The initial collision-free path generated above is optimized, and a smoothing function is designed to shorten the probe moving distance, such as Figure 4 As shown in the figure, firstly, the redundant points are removed, and the path after removing the intermediate nodes is cyclically checked to see if there is still no collision. If the requirement is met, the intermediate nodes are removed, and the cycle is repeated until there is no such intermediate node. Further, using local smoothing, try to use the midpoints of two adjacent nodes to replace the two adjacent nodes. If there is no collision, the midpoints are used to replace the two adjacent nodes.

[0111] At this time, in this embodiment, the RRT* algorithm is used for path planning to obtain a collision-free connection path from the end point of the i-th measurement feature to the start point of the i+1-th measurement feature, which specifically includes:

[0112] (1) Use the RRT* algorithm to perform path planning and obtain a collision-free initial connection path from the end point of the i-th measurement feature to the start point of the i+1-th measurement feature.

[0113] (2) Locally smooth the initial connection path to obtain a collision-free connection path from the end point of the i-th measurement feature to the start point of the i+1-th measurement feature.

[0114] Among them, the RRT* algorithm is used for path planning to obtain a collision-free initial connection path from the end point of the i-th measurement feature to the start point of the i+1-th measurement feature, which specifically includes:

[0115] (1) Randomly sample nodes in a preset sampling area to obtain a sampling node, and determine whether the line connecting the current node of the current iteration and the sampling node collides with the three-dimensional model of the aviation part; if so, the number of iterations is increased by 1, and the process returns to the step of "randomly sampling nodes in a preset sampling area" until the second maximum number of iterations is reached; if not, find the nearest neighboring node to the sampling node in the tree of the current iteration, and generate a new node based on the sampling node and the neighboring node; wherein the current node of the current iteration is the last node in the partial generation path of the previous iteration.

[0116] (2) Determine whether the line connecting the new node and the adjacent nodes collides with the three-dimensional model of the aviation part; if so, the number of iterations is increased by 1, and the process returns to the step of "randomly sampling nodes in a preset sampling area" until the second maximum number of iterations is reached; if not, the new node is added to the tree of the current iteration to obtain the tree of the next iteration, and the parent node of the new node is determined in the tree of the current iteration.

[0117] (3) According to the parent-child node relationship, a new partial generation path is obtained from the starting node to the new node, and it is determined whether the new partial generation path is better; if so, the new partial generation path is used as the partial generation path of the current iteration, and the new node is used as the last node in the partial generation path of the current iteration; if not, the partial generation path of the previous iteration is used as the partial generation path of the current iteration, which is equivalent to retaining the previous iteration path and taking the last node in the partial generation path of the previous iteration as the last node in the partial generation path of the current iteration; wherein the starting node is the last measurement point of the i-th measurement feature.

[0118] (4) Determine whether the line connecting the last node in the partial generation path of the current iteration and the target node collides with the three-dimensional model of the aviation part; if so, the number of iterations is increased by 1, and the process returns to the step of "randomly sampling nodes in a preset sampling area" until the second maximum number of iterations is reached; if not, the iteration is terminated, and the last node in the partial generation path of the current iteration is connected to the target node to obtain a collision-free initial connection path from the end measurement point of the i-th measurement feature to the starting measurement point of the i+1-th measurement feature; wherein the target node is the starting measurement point of the i+1-th measurement feature.

[0119] The initial connection path is locally smoothed to obtain a collision-free connection path from the end point of the i-th measurement feature to the start point of the i+1-th measurement feature, which specifically includes:

[0120] (1) Select any intermediate node in the initial connection path as the node to be judged; wherein the intermediate node is a node in the initial connection path other than the start node and the target node.

[0121] (2) Determine whether the line connecting the previous node of the node to be determined and the next node of the node to be determined collides with the three-dimensional model of the aviation part; if not, remove the node to be determined, connect the previous node of the node to be determined and the next node of the node to be determined to obtain a new initial connection path, and use the new initial connection path as the initial connection path of the next iteration, and return to the step of "selecting any intermediate node in the initial connection path as the node to be determined"; if yes, select the unselected intermediate node in the initial connection path as the node to be determined, and return to the step of "determine whether the line connecting the previous node of the node to be determined and the next node of the node to be determined collides with the three-dimensional model of the aviation part", until all the intermediate nodes have been selected and the connection path after preliminary processing is obtained.

[0122] (3) Select any two adjacent nodes in the connection path after preliminary processing as the node group to be judged.

[0123] (4) Determine the midpoint of the node group to be judged, and determine whether the line connecting the previous node of the node group to be judged and the midpoint collides with the three-dimensional model of the aviation part, or whether the line connecting the midpoint and the next node of the node group to be judged collides with the three-dimensional model of the aviation part; if not, remove the node group to be judged, connect the previous node of the node group to be judged with the midpoint, and connect the midpoint with the next node of the node group to be judged to obtain a new preliminary processed connection path, and use the new preliminary processed connection path as the preliminary processed connection path of the next iteration, and return to the step of "selecting any two adjacent nodes in the preliminary processed connection path as the node group to be judged"; if so, select any two adjacent nodes in the preliminary processed connection path that have not been selected as the node group to be judged, and return to the step of "determining the midpoint of the node group to be judged", until any two adjacent nodes have been selected, and obtain a collision-free connection path from the end measurement point of the i-th measurement feature to the starting measurement point of the i+1-th measurement feature.

[0124] It should be noted that the previous and next nodes are defined according to the connection order from the start node to the target node.

[0125] After obtaining the local paths and the connection paths, this embodiment connects all the local paths and all the connection paths in a connection order to obtain a complete measurement path for on-machine measurement of the three-dimensional model of the aviation part.

[0126] This embodiment further adjusts the measurement point orientation, such as Figure 5As shown in the figure, since the collision volume of the probe cannot be ignored, the spatial orientation of the original measuring point, that is, the entry direction of the probe, has been specified during the measuring point planning. These directions are not affected during the path planning and do not need to be adjusted. For the newly generated measuring point (that is, the intermediate node on the connecting path), the measuring point orientation is set to the normal direction of the surface of the obstacle crossed based on the model design algorithm.

[0127] At this time, in this embodiment, after obtaining the complete measurement path, the path planning method for on-machine measurement of aviation parts in this embodiment further includes: setting the orientation of the nodes other than the measuring points in the complete measurement path to the normal direction of the obstacle surface crossed.

[0128] After the above steps, a collision-free optimized path can be generated in the end.

[0129] This embodiment uses a collision detection function to determine whether a collision occurs, and specifically implements a collision detection algorithm based on the MBD model, such as Figure 6 As shown, first, the MBD model (i.e., the three-dimensional model of aviation parts) is parsed to obtain the surface information of the model, and all the surface information is stored. Subsequently, the first and last measuring points of the path to be detected (i.e., the line connecting the two points) are used to connect the lines in the three-dimensional space. Next, all the stored faces are traversed, and attempts are made to intersect with the lines to obtain the intersection points. If the intersection points are successfully obtained, in order to exclude the influence of other factors, it is also necessary to reversely determine whether the intersection points are located on the surface of the model. If this condition is met, it is determined that the path to be detected collides with the model, and in other cases it is determined that no collision occurs. It should be noted that since the position coordinates of the model and the position coordinates of the path to be detected are known, the process of obtaining the intersection points and determining whether the intersection points are located on the surface of the model can be completed directly using geometric relationships.

[0130] Among them, the path to be detected is the line between the aforementioned measuring point o and the next measuring point, the line between the current node of the current iteration and the sampling node, the line between the new node and the adjacent node, the line between the last node in the partial generated path of the current iteration and the target node, the line between the previous node of the node to be judged and the next node of the node to be judged, the line between the previous node of the node group to be judged and the midpoint, and the line between the midpoint and the next node of the node group to be judged.

[0131] This embodiment proposes a path planning method for the on-machine measurement process of aviation parts. In the CATIAV5 environment, the component application architecture CAA provided by CATIA is used for secondary development. The Ant Colony Optimization (ACO) and the improved rapidly expanding random tree algorithm (RRT*) are combined and improved to achieve the generation of global efficient collision-free paths, and its availability is verified in actual projects. After completing the preliminary measurement point planning, the measurement points across the measurement features are preprocessed based on the nearest point pair algorithm, and then ants are placed on different measurement features for tracking. The improved ACO algorithm is used for path planning to obtain local paths. Further, for the connection paths across the measurement features, the improved RRT* algorithm is used for random sampling in the legal space, and then the collision-free connection paths across the measurement features are formed after local smoothing. Finally, the orientation of the newly generated measurement points is adjusted based on the model to form the final collision-free optimized path.

[0132] This embodiment can realize the automatic generation of collision-free optimized paths for three-dimensional coordinate measuring machines. Compared with traditional path generation and obstacle avoidance methods, it has significant effects on improving generation efficiency and reducing operation complexity. When solving the path generation problem, the traditional method directly sorts the global measurement points, which has low operating efficiency and poor optimization effect. Before path optimization, this embodiment uses the nearest point pair algorithm to pre-process the measurement points across measurement features, and then places ants on each measurement feature for tracking, which greatly improves the running speed of the algorithm. When realizing path obstacle avoidance, collision detection is first realized based on the MBD model. Further, the traditional RRT* algorithm is extended to three-dimensional space, and the initially generated collision-free path is optimized using a local smoothing method that removes redundant points and smoothes local paths. Finally, the newly generated measurement points are adjusted in direction based on the MBD model to avoid collisions caused by ignoring the collision volume of the probe.

[0133] In this embodiment, the CAA secondary development tool is used in the CATIAV5 environment to achieve the generation and optimization of collision-free measurement paths. In view of the problems that the traditional path planning method of CMM has a long detection time and requires a lot of manual intervention, an advanced algorithm is used to improve it. In the past, when forming a measurement path, people often did not pay enough attention to the order of measuring points. They directly used the order planned by the measuring points or used traditional algorithms with poor results for optimization, which resulted in serious waste of measurement time. In this embodiment, the improved ACO is used to plan the measuring point sequence, forming an optimized path with a shorter length. When dealing with the problem of path collision, the safety plane method was often used in the past. The processes involved in the selection of reference planes and the determination of offsets require manual intervention and cannot achieve a greater degree of automation. In this embodiment, the RRT* algorithm is improved, and the automatic generation of obstacle-free paths is achieved by random sampling in the legal space. Finally, considering the collision volume of the probe, the orientation of the newly generated measuring points is adjusted to avoid volume collisions other than path interference. This embodiment can significantly improve the efficiency of the CMM detection process, reduce manual participation to achieve a higher degree of automation, and has a certain role in promoting the digitization of aviation detection.

[0134] The present application also provides an application scenario, which applies the above-mentioned path planning method for the on-machine measurement process of aviation parts. Specifically, the path planning method for the on-machine measurement process of aviation parts provided in this embodiment can be applied in the on-machine measurement scenario of aviation parts. The on-machine measurement scenario of aviation parts includes a data acquisition link, a path planning link and an on-machine measurement link. The data acquisition link is used to obtain the measurement point set of each measurement feature of the three-dimensional model of the aviation part. The path planning link is used to perform path planning based on the measurement point set of each measurement feature to obtain a complete measurement path. The on-machine measurement link is used to perform on-machine measurement of aviation parts using a three-coordinate measuring machine based on the complete measurement path. The path planning method for the on-machine measurement process of aviation parts provided in this embodiment belongs to the path planning link.

[0135] Example 2

[0136] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 7As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a path planning method for the on-machine measurement process of aviation parts is implemented.

[0137] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0138] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the path planning method for the on-machine measurement process of aviation parts in Example 1 is implemented.

[0139] Example 3

[0140] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, which, when executed by a processor, implements the path planning method for the on-machine measurement process of aviation parts in Example 1.

[0141] Example 4

[0142] In an exemplary embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, implements the path planning method for the on-machine measurement process of aviation parts in Embodiment 1.

[0143] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0144] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0145] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A path planning method for on-machine measurement of aviation parts, characterized in that: The path planning method for the on-machine measurement process of aviation parts includes: Acquire a measuring point set of each measuring feature of the three-dimensional model of the aviation part; the measuring feature is a line and / or a surface of the three-dimensional model of the aviation part; the measuring point set includes a plurality of measuring points located on the measuring feature; All measurement features are sorted according to the connection order, and the measurement point set of the i-th measurement feature and the measurement point set of the i+1-th measurement feature are used as input, and the end measurement point of the i-th measurement feature and the starting measurement point of the i+1-th measurement feature are determined by using the nearest point pair algorithm, and a measurement point other than the end measurement point of the first measurement feature is randomly selected from the measurement point set of the first measurement feature as the starting measurement point of the first measurement feature, and a measurement point other than the starting measurement point of the I-th measurement feature is randomly selected from the measurement point set of the I-th measurement feature as the ending measurement point of the I-th measurement feature, to obtain the starting measurement point and the ending measurement point of each measurement feature; wherein, i=1, 2, ..., I-1, and I is the total number of measurement features; For each measurement feature, the ant colony algorithm is used for path planning to obtain a collision-free local path that traverses all measurement points in the measurement point set of the measurement feature and from the starting measurement point of the measurement feature to the ending measurement point of the measurement feature; For the i-th measurement feature and the i+1-th measurement feature, the RRT* algorithm is used for path planning to obtain a collision-free connection path from the end measurement point of the i-th measurement feature to the start measurement point of the i+1-th measurement feature; All local paths and all connection paths are connected in a connection order to obtain a complete measurement path for on-machine measurement of the three-dimensional model of the aviation part.

2. The path planning method for on-machine measurement of aviation parts according to claim 1, characterized in that: Taking the measuring point set of the i-th measuring feature and the measuring point set of the i+1-th measuring feature as input, the nearest point pair algorithm is used to determine the end measuring point of the i-th measuring feature and the starting measuring point of the i+1-th measuring feature, specifically including: Merge the measurement point set of the i-th measurement feature and the measurement point set of the i+1-th measurement feature to obtain the overall point set; Under the preset constraint, the entire point set is used as input, and the nearest point pair algorithm is used to determine the nearest point pair, and the measuring point in the measuring point set belonging to the i-th measurement feature in the nearest point pair is used as the end measuring point of the i-th measurement feature, and the measuring point in the measuring point set belonging to the i+1-th measurement feature in the nearest point pair is used as the starting measuring point of the i+1-th measurement feature; wherein the preset constraint is that when determining the nearest point pair, only the distance of the cross-group point pair is calculated, and the cross-group point pair with the shortest distance is the nearest point pair, and the cross-group point pair consists of a measuring point in the measuring point set of the i-th measurement feature and a measuring point in the measuring point set of the i+1-th measurement feature.

3. The path planning method for on-machine measurement of aviation parts according to claim 1, characterized in that: The ant colony algorithm is used for path planning to obtain a collision-free local path that traverses all the measurement points in the measurement point set of the measurement feature and from the starting measurement point of the measurement feature to the end measurement point of the measurement feature, including: Initializing an ant colony and pheromone concentration; the ant colony includes a plurality of ants; Each ant is placed at the starting point of the measurement feature. For each ant, the next measurement point is continuously selected according to the transfer probability until all the measurement points in the measurement point set of the measurement feature are traversed and the last measurement point of the measurement feature is reached to generate a collision-free planning path. Determine whether the first maximum number of iterations has been reached; If yes, the iteration is stopped, and the shortest planned path in the current iteration is selected as a collision-free local path that traverses all the measuring points in the measuring point set of the measuring feature and from the starting measuring point of the measuring feature to the ending measuring point of the measuring feature; If not, continue iterating, update the pheromone concentration according to all planned paths, and return to the step of "placing each ant at the starting point of the measured feature".

4. The path planning method for on-machine measurement of aviation parts according to claim 3 is characterized in that: The calculation formula for the transition probability is: Among them, p ij is the probability of transferring from measurement point i to measurement point j; τ ij is the pheromone concentration from measuring point i to measuring point j; α is the first parameter that adjusts the importance of pheromone concentration on path selection; η ij is the heuristic information from measuring point i to measuring point j; β is the second parameter for adjusting the importance of heuristic information to path selection; allowed is the set of next measuring points that do not collide, and no collision means that the line connecting measuring point i and the next measuring point does not collide with the three-dimensional model of the aviation part; τ ik is the pheromone concentration from measuring point i to measuring point k; η ik is the heuristic information from measurement point i to measurement point k; K is the total number of the next measurement points in allowed; R i,j is the probability coefficient from measuring point i to measuring point j; Update pheromone concentrations based on all planned paths, including: Calculate the fitness of each planned path respectively, and for each planned path, calculate the probability of the planned path being selected based on the fitness of the planned path; the fitness is the inverse of the length of the planned path; Taking the selection probabilities of all planned paths as input, a roulette wheel is used to select some planned paths as the selected paths; Randomly select an even number of selected paths as the paths to be transformed, and record the remaining selected paths as unselected paths; Combine all the paths to be transformed in pairs to obtain several combinations; For each combination, the two paths to be transformed in the combination are used as input, the intersection is selected by roulette, and the parts of the two paths to be transformed in the combination located after the intersection are exchanged to obtain two variant paths; Update the pheromone concentration based on all unselected paths and all mutated paths.

5. The path planning method for on-machine measurement of aviation parts according to claim 1, characterized in that: The RRT* algorithm is used for path planning to obtain a collision-free connection path from the end point of the i-th measurement feature to the start point of the i+1-th measurement feature, including: The RRT* algorithm is used for path planning to obtain a collision-free initial connection path from the end point of the i-th measurement feature to the start point of the i+1-th measurement feature; The initial connection path is locally smoothed to obtain a collision-free connection path from the end point of the i-th measurement feature to the start point of the i+1-th measurement feature.

6. The path planning method for on-machine measurement of aviation parts according to claim 5, characterized in that: The RRT* algorithm is used for path planning to obtain a collision-free initial connection path from the end point of the i-th measurement feature to the start point of the i+1-th measurement feature, including: Randomly sample nodes in a preset sampling area to obtain a sampling node, and determine whether the line connecting the current node of the current iteration and the sampling node collides with the three-dimensional model of the aviation part; if so, the number of iterations is increased by 1, and the step of "randomly sampling nodes in the preset sampling area" is returned until the second maximum number of iterations is reached; if not, the neighboring node closest to the sampling node is found in the tree of the current iteration, and a new node is generated based on the sampling node and the neighboring node; wherein the current node of the current iteration is the last node in the partial generation path of the previous iteration; Determine whether the line connecting the new node and the adjacent nodes collides with the three-dimensional model of the aviation part; if so, increase the number of iterations by 1, and return to the step of "randomly sampling nodes in the preset sampling area" until the second maximum number of iterations is reached; if not, add the new node to the tree of the current iteration to obtain the tree of the next iteration, and determine the parent node of the new node in the tree of the current iteration; According to the parent-child node relationship, a new partial generation path from the starting node to the new node is connected, and it is judged whether the new partial generation path is better; if so, the new partial generation path is used as the partial generation path of the current iteration, and the new node is used as the last node in the partial generation path of the current iteration; if not, the partial generation path of the previous iteration is used as the partial generation path of the current iteration, and the last node in the partial generation path of the previous iteration is used as the last node in the partial generation path of the current iteration; wherein the starting node is the last measurement point of the i-th measurement feature; Determine whether the line connecting the last node in the partial generation path of the current iteration and the target node collides with the three-dimensional model of the aviation part; if so, the number of iterations is increased by 1, and the step of "randomly sampling nodes in the preset sampling area" is returned until the second maximum number of iterations is reached; if not, the iteration is terminated, and the last node in the partial generation path of the current iteration is connected to the target node to obtain a collision-free initial connection path from the end measurement point of the i-th measurement feature to the starting measurement point of the i+1-th measurement feature; wherein the target node is the starting measurement point of the i+1-th measurement feature; The initial connection path is locally smoothed to obtain a collision-free connection path from the end point of the i-th measurement feature to the start point of the i+1-th measurement feature, including: Select any intermediate node in the initial connection path as the node to be judged; wherein the intermediate node is a node in the initial connection path other than the start node and the target node; Determine whether the line connecting the previous node of the node to be determined and the next node of the node to be determined collides with the three-dimensional model of the aviation part; if not, remove the node to be determined, connect the previous node of the node to be determined and the next node of the node to be determined to obtain a new initial connection path, and use the new initial connection path as the initial connection path of the next iteration, and return to the step of "selecting any intermediate node in the initial connection path as the node to be determined"; if yes, select the unselected intermediate node in the initial connection path as the node to be determined, and return to the step of "determining whether the line connecting the previous node of the node to be determined and the next node of the node to be determined collides with the three-dimensional model of the aviation part", until all the intermediate nodes have been selected and the connection path after preliminary processing is obtained; Select any two adjacent nodes in the connection path after preliminary processing as the node group to be judged; Determine the midpoint of the node group to be determined, determine whether the line connecting the previous node of the node group to be determined and the midpoint collides with the three-dimensional model of the aviation part, or whether the line connecting the midpoint and the next node of the node group to be determined collides with the three-dimensional model of the aviation part; if not, remove the node group to be determined, connect the previous node of the node group to be determined with the midpoint, and connect the midpoint with the next node of the node group to be determined, obtain a new preliminary processed connection path, and use the new preliminary processed connection path as the preliminary processed connection path of the next iteration, and return to the step of "selecting any two adjacent nodes in the preliminary processed connection path as the node group to be determined"; if so, select any two adjacent nodes that have not been selected in the preliminary processed connection path as the node group to be determined, and return to the step of "determining the midpoint of the node group to be determined", until any two adjacent nodes have been selected, and obtain a collision-free connection path from the end measuring point of the i-th measurement feature to the starting measuring point of the i+1-th measurement feature.

7. The path planning method for on-machine measurement of aviation parts according to claim 1, characterized in that: After the complete measurement path is obtained, the path planning method for the on-machine measurement process of aviation parts further includes: setting the orientation of the nodes other than the measuring points in the complete measurement path to the normal direction of the surface of the obstacle crossed.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the path planning method for the on-machine measurement process of aviation parts according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the path planning method for the on-machine measurement process of aviation parts described in any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the path planning method for the on-machine measurement process of aviation parts described in any one of claims 1 to 7 is implemented.

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