Micro-gap assembly path planning method based on high-precision interference detection

Abstract

The present application relates to a micro gap assembly path planning method based on high-precision interference detection, comprising the following steps: obtaining the actual surface shape of the mating surface of the workpiece to be assembled by a three-dimensional scanning device; generating an initial assembly path according to the relative position of the point cloud and the theoretical assembly path, and initializing the assembly path optimization algorithm based on the particle swarm; according to the key position of the assembly path, the optimal path point is found by using the assembly intelligent path optimization algorithm based on the particle swarm, and the assembly path is fitted; interference and collision detection are carried out on the assembly path; and the feasible assembly path is output. The present application combines virtual high-precision interference detection technology with assembly path planning to dynamically adjust the assembly path to adapt to the error and deformation of the actual workpiece surface. In the assembly process, the position and size of the micro gap in the assembly process are calculated in the digital space through digital twinning means, and the assembly path is adjusted according to the detection result to ensure that the micro gap is precisely controlled.

Description

Technical Field

[0001] This invention relates to the field of industrial manufacturing, specifically to high-precision assembly in large-scale equipment manufacturing. More specifically, it is a method for planning assembly paths for minute gaps based on high-precision interferometry, used in the high-precision assembly process of large equipment. It is particularly applicable to insertion path planning when the assembly datum of two components to be assembled is deformed, the assembly gap is minute, and the assembly path window is narrow. Background Technology

[0002] The manufacturing of large equipment typically requires the precise assembly of various components to ensure the performance and quality of the final product. However, due to various factors during manufacturing and transportation, such as workpiece surface manufacturing errors, deformation, and thermal expansion, controlling minute gaps during assembly becomes extremely difficult. Traditional assembly path planning methods usually only consider the assembly path under ideal conditions, without fully taking into account changes in the actual state of the workpiece. Summary of the Invention

[0003] To address the aforementioned technical problems, the present invention aims to provide a method for planning assembly paths for minute gaps based on high-precision interferometric detection. This method is primarily used for planning insertion paths when the assembly datum of two components to be assembled is deformed, the assembly gap is minute, and the assembly path window is narrow. It is used to achieve precise control of minute gaps and high-quality assembly during the high-precision assembly of large equipment.

[0004] The technical solution adopted by this invention to achieve the above objectives is: a method for planning small gap assembly paths based on high-precision interferometric detection, comprising the following steps:

[0005] Step S1: Obtain the actual surface shape of the mating surfaces of the workpieces to be assembled using a 3D scanning device, and perform preliminary point cloud data processing to ensure that the relative position of the point cloud data of the two workpieces to be assembled is consistent with the theoretical assembly position of the workpieces.

[0006] Step S2: Generate an initial assembly path based on the relative position of the point cloud and the theoretical assembly path in Step S1. Using the fixed workpiece as a reference, set several key positions of the assembly path and initialize the particle swarm-based assembly path optimization algorithm.

[0007] Step S3: Based on the key locations of the assembly path, use the particle swarm optimization algorithm to find the optimal path point and fit the optimal path point as the assembly path; if the particle swarm optimization algorithm cannot find the optimal path point at any key assembly location, it is determined that the workpiece assembly cannot be completed.

[0008] Step S4: Perform interference and collision detection on the assembly path obtained in step S3, and calculate the assembly interference and clearance between workpieces on the assembly path;

[0009] Step S5: Analyze assembly interference and gap values; if there is interference or the gap value is less than the threshold, add the location where the interference occurs as a critical location of the assembly path and repeat steps S3-S4; otherwise, output a feasible assembly path.

[0010] Step S1 includes the following steps:

[0011] Step S1.1: Denoise and smooth the 3D point cloud data of the workpiece to be assembled obtained from the 3D scanning equipment to reduce the noise of the 3D point cloud data;

[0012] Step S1.2: Register the point cloud data of the two workpieces to be assembled obtained in step S1.1 with the two assembly workpieces in the assembly CAD model, so that the relative position of the point cloud data of the two workpieces to be assembled is consistent with the theoretical assembly position of the workpieces.

[0013] Step S2 includes the following steps:

[0014] Step S2.1: Generate an initial assembly path based on the relative position of the point cloud data of the two workpieces to be assembled determined in step S1.2, the assembly direction of the workpieces during the assembly process, and the theoretical assembly path.

[0015] Step S2.2: Based on the assembly characteristics of the workpiece, set several key positions of the assembly path on the initial assembly path. The key positions of the assembly path are used as the cross-sectional cutting points of the point cloud for assembly path point optimization. The key positions include the position where the stepped hole stop begins to make contact and the position where the assembly gap accuracy is higher than the threshold.

[0016] Step S2.3: Initialize the particle swarm optimization algorithm for assembly path optimization: Each particle is the two-dimensional deviation of the non-interference assembly position along the direction perpendicular to the assembly movement during the workpiece assembly process from the theoretical assembly position. The algorithm includes the number of particles, the initial position of the particles, the range of particle values, the number of iterations, and the particle acceleration factor. The constraint condition is that the gap between the two workpiece point cloud data at the point cloud cross section cutting point is greater than a given threshold δ. The fitness function is the variance of the gap between the two workpiece point cloud data at the point cloud cross section cutting point and the convergence threshold of the fitness function.

[0017] Step S3 includes the following steps:

[0018] Step S3.1: For each critical position of the assembly path, the point cloud data of the two workpieces to be assembled is sectioned along the vertical assembly direction. The optimization objective is to make the gap between the point cloud data of the two workpieces on the cross section of the point cloud at the critical position of the assembly path uniform, that is, to minimize the fitness function. The assembly path optimization algorithm based on particle swarm optimization is used to find the optimal path point. If no particle that satisfies the constraints can be found, proceed to step S3.2; otherwise, proceed to step S3.3.

[0019] Step S3.2: If no particle that satisfies the constraints can be found, it is determined that the workpiece cannot be assembled, and the assembly path optimization algorithm is terminated.

[0020] Step S3.3: Use a particle swarm optimization-based intelligent path optimization algorithm to find the optimal critical location path point that minimizes the fitness function, determine whether the optimization algorithm meets the optimization termination condition, and record the position of the optimal path point;

[0021] Step S3.4: Repeat steps S3.1-3.3 until all critical path points of the assembly path are optimized. Use the least squares method to fit the optimal path points of the critical path points of the assembly path into a straight line to obtain the assembly path fitted with the optimal path points.

[0022] Step S4 includes the following steps:

[0023] Step S4.1: Set n point cloud cross-sectional cutting points on the fixed assembly workpiece along the direction of assembly movement, and divide the assembly path into m segments according to the accuracy requirements of the assembly path verification.

[0024] Step S4.2: Move the point cloud data of the moving assembly workpiece according to the assembly path. During the assembly movement, each segment of the assembly path is cut into a point cloud cross section once according to the point cloud cross section cutting point to obtain n point cloud data matching cross sections of the fixed assembly workpiece and the moving assembly workpiece.

[0025] Step S4.3: Repeat step S4.2 until the point cloud data of the moving assembly workpiece moves according to the assembly path to complete the assembly, and finally obtain the cross-section of the point cloud data of n*m fixed assembly workpieces and moving assembly workpieces.

[0026] Step S4.4: Calculate the interference gap values ​​between the point cloud data of the fixed assembly workpiece and the moving assembly workpiece on the n*m ​​cross-sections.

[0027] Step S5 includes the following steps:

[0028] Step S5.1: Analyze and statistically analyze the interference and gap values ​​between the point cloud data of fixed and moving assembly workpieces on n*m cross-sections. If there is interference, i.e., the gap value is less than 0, or the gap value is less than the safety threshold specified by the assembly process, proceed to step S5.2; otherwise, proceed to step S5.3.

[0029] Step S5.2: Add the locations where interference occurs (i.e., the gap value is less than 0, or the gap value is less than the safety threshold specified by the assembly process) as critical locations in the assembly path, and return to step S3;

[0030] Step S5.3: Output the assembly path obtained by the particle swarm optimization algorithm as a feasible assembly path.

[0031] A micro-gap assembly path planning system based on high-precision interferometric detection includes:

[0032] The point cloud data acquisition module is used to acquire the actual surface shape of the mating surfaces of the workpieces to be assembled through a 3D scanning device, and to perform preliminary point cloud data processing so that the relative position of the point cloud data of the two workpieces to be assembled is consistent with the theoretical assembly position of the workpieces.

[0033] The assembly path optimization initialization module is used to generate an initial assembly path based on the relative position of the point cloud and the theoretical assembly path. Taking a fixed workpiece as a reference, it sets several key positions of the assembly path and initializes the particle swarm-based assembly path optimization algorithm.

[0034] The assembly path optimization module is used to find the optimal path point based on the key positions of the assembly path using a particle swarm optimization algorithm, and fit the optimal path point as the assembly path; if the particle swarm optimization algorithm cannot find the optimal path point at any key assembly position, it is determined that the workpiece assembly cannot be completed.

[0035] The assembly path output module is used to perform interference and collision detection on the assembly path, calculate the assembly interference and clearance between workpieces on the assembly path, analyze the assembly interference and clearance values, and add the location where interference occurs as a critical location of the assembly path if there is interference or the clearance value is less than the threshold; otherwise, output a feasible assembly path.

[0036] A device for planning small gap assembly paths based on high-precision interferometric detection includes a memory and a processor; the memory is used to store a computer program; the processor is used to implement the method for planning small gap assembly paths based on high-precision interferometric detection when the computer program is executed.

[0037] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the micro-gap assembly path planning method based on high-precision interferometric detection.

[0038] The present invention has the following beneficial effects and advantages:

[0039] 1. This method takes into account workpiece surface manufacturing errors and deformation, aiming to achieve precise control of minute gaps and high-quality assembly during the assembly process. This invention is applicable to various large-scale equipment manufacturing fields, such as aerospace, automotive manufacturing, and heavy machinery, especially for assembly scenarios with extremely high precision requirements.

[0040] 2. The innovation of this invention lies in its combination of virtual high-precision interferometry detection technology with assembly path planning to dynamically adjust the assembly path to adapt to errors and deformations on the actual workpiece surface. During the assembly process, digital twin technology is used to calculate the position and size of minute gaps in digital space, and the assembly path is adjusted based on the detection results to ensure precise control of these minute gaps. This method can improve assembly accuracy and repeatability, reduce scrap rates, thereby lowering manufacturing costs and improving product quality. Attached Figure Description

[0041] Figure 1 This is the overall flowchart of the micro-gap assembly path planning method based on high-precision interferometric detection of the present invention;

[0042] Figure 2 This is a schematic diagram of two assembly workpieces and key positions of the assembly path in an embodiment of the present invention;

[0043] Figure 3 This is a schematic diagram of the cross-sectional cutting points of the point cloud set along the direction of assembly movement in an embodiment of the present invention;

[0044] Figure 4 This is a schematic diagram of the cross-sectional cutting points of the point cloud and the cross-section of the point cloud data in an embodiment of the present invention. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the embodiments. However, the scope of protection of this invention is not limited to the specific embodiments described below.

[0046] Example

[0047] This embodiment discloses a method for planning small-gap assembly paths based on high-precision interferometric detection.

[0048] like Figure 1 As shown, the assembly path planning method for small gaps based on high-precision interferometric detection includes the following steps:

[0049] Step S1: Obtain the actual surface shape of the mating surfaces of the workpieces to be assembled using a 3D scanning device, and perform preliminary point cloud data processing to ensure that the relative position of the point cloud data of the two workpieces to be assembled is consistent with the theoretical assembly position of the workpieces.

[0050] Step S2: Determine the theoretical assembly path as the initial assembly path based on the ideal CAD model of the workpiece. Using the fixed workpiece as a reference, manually define several key positions of the assembly path and initialize the particle swarm optimization algorithm.

[0051] Step S3: Based on the key locations of the assembly path, use the particle swarm optimization algorithm to find the optimal path point and fit the optimal path point as the assembly path. If the particle swarm optimization algorithm cannot find the optimal path point at any key assembly location, it is determined that the workpiece cannot be assembled.

[0052] Step S4: Perform interference and collision detection on the assembly path obtained in S3, and calculate the assembly interference and clearance between workpieces on the assembly path;

[0053] Step S5: Analyze assembly interference and gap values. If there is interference or the gap value is less than the threshold, add the location where the interference occurs as a critical location in the assembly path and repeat steps S3-S4; otherwise, output a feasible assembly path.

[0054] Specifically, step S1 includes the following steps:

[0055] Step S1.1: Denoise and smooth the 3D point cloud data of the workpiece to be assembled obtained from the 3D scanning equipment to reduce the noise of the 3D point cloud data;

[0056] Specifically, step S1.1 includes the following steps:

[0057] Step S1.1.1: Set the filter radius and threshold, and use radius filtering to remove isolated noise points in the point cloud data;

[0058] Step S1.1.2: Set the mean and variance of the filter, and use statistical filtering methods to further remove outliers from the point cloud data;

[0059] Step S1.1.3: For the point cloud data obtained in S2.2, the data is simplified by random sampling; the data is smoothed by Gaussian filtering to improve the accuracy of the 3D point cloud data.

[0060] Step S1.2: Register the point cloud data of the workpiece in S1.1 with the workpiece in the assembly CAD model, so that the relative position of the point cloud data of the two workpieces to be assembled is consistent with the theoretical assembly position of the workpiece.

[0061] Specifically, step S1.2 includes the following steps:

[0062] Step S1.2.1: First, perform coarse registration between the actual point cloud data and the ideal point cloud data. Use the local point pair feature descriptor combined with random consistency sampling to determine the initial coordinate transformation matrix and complete the coarse registration of the point cloud data acquired by the 3D scanning device.

[0063] Specifically, step S1.2.1 includes the following steps:

[0064] Step S1.2.1.1: Calculate the local point-pair feature descriptors for all points in the actual point cloud data in S2 and the ideal point cloud data in S1, respectively.

[0065] Step S1.2.1.2: Use the random consistency sampling method to identify point pairs that have a corresponding relationship between the actual point cloud data and the ideal point cloud data;

[0066] Step S1.2.1.3: Based on the point pairs obtained by the random consistency sampling method in step S3.1.2, calculate the coordinate transformation matrix from the actual point cloud data to the ideal point cloud data, and apply the matrix transformation to the actual point cloud data, transforming it from the coordinate system of the scanning device to the same coordinate system as the ideal point cloud data.

[0067] Step S1.2.2: Perform fine registration of the 3D scanning point cloud data, and use the Iterative Closest Point (ICP) algorithm to calculate the coordinate transformation matrix of the fine registration of the point cloud to obtain the finely registered 3D scanning point cloud data.

[0068] Specifically, step S1.2.2 includes the following steps:

[0069] Step S1.2.2.1: Initialize the ICP algorithm and set the convergence conditions: the error is less than a certain threshold or the number of iterations reaches a predetermined value;

[0070] Step S1.2.2.2: For each point in the actual point cloud data, find the nearest point in the ideal point cloud and establish a point pair matching relationship;

[0071] Step S1.2.2.3: Use Euclidean distance to measure the distance error between each pair of points;

[0072] Step S1.2.2.4: Assign weights to each point pair using a Gaussian weighting function based on the error value;

[0073] Step S1.2.2.5: Use numerical optimization methods to minimize the error between weighted point pairs and adjust the coordinate transformation matrix between point pairs;

[0074] Step S1.2.2.6: Check if the convergence condition is met. If the convergence condition is not met, proceed to step S1.2.2.7. If the convergence condition is met, proceed to step S1.2.2.8.

[0075] Step S1.2.2.7: Continue iterating and update the coordinate transformation matrix;

[0076] Step S1.2.2.8: Obtain the final coordinate transformation matrix, apply the matrix to the actual point cloud data, and accurately register it to the ideal point cloud data.

[0077] Step S2 includes the following steps:

[0078] Step S2.1: Determine the assembly direction and theoretical assembly path based on the ideal CAD model of the workpiece, and use them as the initial assembly path for the particle swarm optimization algorithm.

[0079] Specifically, step S2.1 includes the following steps:

[0080] Step S2.1.1: Create an ideal CAD assembly model of the workpiece to determine the assembly direction and final assembly completion position between the two workpieces to be assembled.

[0081] Step S2.1.2: Define the theoretical assembly path based on the requirements of the assembly task and the geometric features of the two workpieces to be assembled;

[0082] Step S2.2: Define several key positions on the initial assembly path based on the assembly characteristics of the workpiece, such as (the position where the stop begins to contact, the position with high fitting requirements). These key positions are used as the cross-sectional cutting points of the point cloud for assembly path point optimization; for example... Figure 2 As shown.

[0083] Specifically, step S2.2 includes the following steps:

[0084] Step S2.2.1: Analyze the geometric features and assembly requirements of the workpiece to determine which positions are more important during the assembly process (including the position where the stop begins to make contact, positions with high fitting requirements, etc.).

[0085] Step S2.2.2: Define the critical locations in step S2.2.1 on the initial assembly path as a series of critical points along the assembly path, i.e., the critical locations of the assembly path;

[0086] Step S2.3: Initialize the particle swarm optimization algorithm for assembly path optimization. Define each particle as a two-dimensional deviation value of the workpiece during assembly along the direction perpendicular to the assembly motion relative to the theoretical assembly position. Define the number of particles, the initial position of the particles, the range of particle values, the number of iterations, and the particle acceleration factor. Define the constraint condition that the gap between the two workpiece point cloud data at the point cloud cross section cutting point is greater than a given threshold δ. Define the fitness function as the variance of the gap between the two workpiece point cloud data at the point cloud cross section cutting point and the convergence threshold of the fitness function.

[0087] Step S2.3.1: Define each particle as the deviation value of the workpiece relative to the theoretical assembly position along the direction perpendicular to the assembly movement during the assembly process. Define the particle dimension as six-dimensional, which respectively represent the deviation of the axis of the moving assembly workpiece from the theoretical assembly position in six degrees of freedom in space when the assembly reaches a critical position of a certain assembly path. The deviation value along the assembly direction should be zero.

[0088] Step S2.3.2: Define the number of particles, the initial position of the particles, the range of particle values ​​(allowed values ​​that deviate from the theoretical assembly position), the number of iterations, the convergence threshold of the fitness function (the termination condition of the assembly path optimization algorithm based on particle swarm optimization), and the particle acceleration factor, etc.

[0089] Step S2.3.3: Define the fitness function used to evaluate the performance of each particle. The fitness function is defined as the variance of the fit gap between the point cloud data of two workpieces on the point cloud cross-section at all cutting points. The optimization objective is defined as achieving uniform gaps between the point cloud data of the two workpieces on the point cloud cross-section at critical locations along the assembly path (i.e., minimizing the fitness function value) to obtain the optimal assembly path.

[0090] Step S2.3.4: Randomly generate an initial particle swarm, ensuring that they are within the defined range of values;

[0091] The assembly path optimization is performed based on a path optimization algorithm. Step S3 includes the following steps:

[0092] Step S3.1: For each critical location of the assembly path, the point cloud data of the two workpieces to be assembled are sectioned along the vertical assembly direction. The optimization objective is defined as the point cloud data gap between the two workpieces on the cross section of the point cloud at the critical location of the assembly path is uniform (i.e. the fitness function is minimized). The optimal path point is found using a particle swarm-based intelligent path optimization algorithm. If no particle that satisfies the constraint conditions can be found, proceed to step S3.2; otherwise, proceed to step S3.3.

[0093] Step S3.2: If no particle that satisfies the constraints can be found, it is determined that the workpiece cannot be assembled, and the path optimization algorithm is terminated.

[0094] Step S3.3: Use a particle swarm optimization-based intelligent path optimization algorithm to find the optimal critical location path point that minimizes the fitness function, determine whether the optimization algorithm meets the optimization termination condition, and record the position of the optimal path point;

[0095] Specifically, step S3.3 includes the following steps:

[0096] Step S3.3.1: Determine whether the optimization algorithm has reached the maximum number of iterations or whether the fitness function meets the threshold requirement. If the termination condition is reached, proceed to step S3.3.2; otherwise, proceed to step S3.3.3.

[0097] Step S3.3.2: Record the position of the global optimal path point during multiple iterations to complete the optimization of the assembly path point at the key position of the assembly path;

[0098] Step S3.3.3: Update the position and velocity of the particles, calculate the fitness of each particle, and repeat step S3.3.1;

[0099] Step S3.4: Repeat steps S3.1-3.3 until all assembly path key location path points are optimized. Use the least squares method to fit the obtained optimal path points of several assembly path key locations into a straight line to obtain the assembly path fitted with the optimal path points.

[0100] Specifically, step S3.4 includes the following steps:

[0101] Step S3.4.1: Repeat steps S3.1-3.3 until all critical path points of the assembly path are optimized.

[0102] Step S3.4.2: Use the least squares method to fit all the key location path points of the assembly path into a straight line to generate the assembly path with the optimal path point fitting.

[0103] To verify the feasibility of the assembly path fitted by the optimal path points, step S4 includes the following steps:

[0104] Step S4.1: As Figure 3 As shown, n point cloud cross-sectional cutting points are set on the fixed assembly workpiece along the direction of assembly movement. The assembly path is divided into m segments according to the accuracy requirements of the assembly path verification. The larger m is, the higher the accuracy of the path verification calculation.

[0105] Specifically, step S4.1 includes the following steps:

[0106] Step S4.1.1: Along the direction of assembly movement, set n point cloud cross-sectional cutting points on the fixed assembly workpiece. These cutting points will be used to capture point cloud data at different locations to verify the assembly accuracy and calculate interference and clearance.

[0107] Step S4.1.2: Divide the assembly path into m segments according to the accuracy requirements of the assembly path verification. This is determined by the specific requirements of the application; the larger m is, the higher the accuracy of the path verification calculation. Each segment represents a step or position in the assembly.

[0108] Step S4.2: Move the point cloud data of the moving assembly workpiece according to the assembly path. During the assembly movement, each segment of the assembly path is cut into a point cloud cross section once according to the point cloud cross section cutting point to obtain n point cloud data matching cross sections of the fixed assembly workpiece and the moving assembly workpiece.

[0109] Specifically, step S4.2 includes the following steps:

[0110] Step S4.2.1: Move the point cloud data of the moving assembly workpiece according to the assembly path;

[0111] Step S4.2.2: On each assembly path, perform a point cloud cross sectioning based on the pre-set point cloud cross sectioning points to obtain point cloud data matching cross sections of n fixed assembly workpieces and moving assembly workpieces.

[0112] Step S4.3: Repeat step S4.2 until the point cloud data of the moving assembly workpiece moves according to the assembly path to complete the assembly, and finally obtain the cross-section of the point cloud data of n*m fixed assembly workpieces and moving assembly workpieces.

[0113] Step S4.4: Calculate the interference and gap values ​​between the point cloud data of the fixed and moving assembly workpieces on the n*m ​​cross-sections;

[0114] The feasibility of the assembly path is determined based on the interference and gap values. Step S5 includes the following steps:

[0115] Step S5.1: Analyze and statistically analyze the interference and gap values ​​between the point cloud data of fixed and moving assembly workpieces on n*m cross-sections. If there is interference (gap value less than 0) or the gap value is less than the safety threshold specified by the assembly process, proceed to step S5.2; otherwise, proceed to step S5.3.

[0116] Specifically, step S5.1 includes the following steps:

[0117] Step S5.1.1: For each cross section, calculate the interference or gap value between the fixed assembly workpiece and the moving assembly workpiece;

[0118] Step S5.1.2: Repeat step S5.1.1 until the calculation of the interference or gap value between the fixed assembly workpiece and the moving assembly workpiece on each cross section is completed;

[0119] Step S5.1.3: As Figure 4 As shown, the interference and gap values ​​between the point cloud data of fixed and moving assembly workpieces on n*m cross-sections are statistically analyzed. If there is interference (gap value less than 0) or the gap value is less than the safety threshold specified by the assembly process, the assembly path position corresponding to the cross-section is recorded and step S5.2 is executed; otherwise, step S5.3 is executed.

[0120] Step S5.2: Add the locations where interference occurs (gap value less than 0) or where the gap value is less than the safety threshold specified by the assembly process as critical locations in the assembly path, and repeat steps S3-S4;

[0121] Step S5.3: Output the assembly path obtained by the particle swarm optimization algorithm as a feasible assembly path.

Claims

1. A method for planning assembly paths for minute gaps based on high-precision interferometric detection, characterized in that, Includes the following steps: Step S1: Obtain the actual surface shape of the mating surfaces of the workpieces to be assembled using a 3D scanning device, and perform preliminary point cloud data processing to ensure that the relative position of the point cloud data of the two workpieces to be assembled is consistent with the theoretical assembly position of the workpieces. Step S2: Generate an initial assembly path based on the relative position of the point cloud and the theoretical assembly path in Step S1. Using the fixed workpiece as a reference, set several key positions of the assembly path and initialize the particle swarm-based assembly path optimization algorithm. Step S3: Based on the key locations of the assembly path, use the particle swarm optimization algorithm to find the optimal path point and fit the optimal path point as the assembly path; if the particle swarm optimization algorithm cannot find the optimal path point at any key assembly location, it is determined that the workpiece assembly cannot be completed. Step S4: Perform interference and collision detection on the assembly path obtained in step S3, and calculate the assembly interference clearance between workpieces on the assembly path; Step S5: Analyze assembly interference and gap values; if there is interference or the gap value is less than the threshold, add the location where the interference occurs as a critical location of the assembly path and repeat steps S3-S4; otherwise, output a feasible assembly path. Step S2 includes the following steps: Step S2.1: Generate an initial assembly path based on the relative position of the point cloud data of the two workpieces to be assembled determined in step S1.2, the assembly direction of the workpieces during the assembly process, and the theoretical assembly path. Step S2.2: Based on the assembly characteristics of the workpiece, set several key positions of the assembly path on the initial assembly path. The key positions of the assembly path are used as the cross-sectional cutting points of the point cloud for assembly path point optimization. The key positions include the position where the stepped hole stop begins to make contact and the position where the assembly gap accuracy is higher than the threshold. Step S2.3: Initialize the particle swarm optimization algorithm for assembly path optimization: Each particle is the two-dimensional deviation of the non-interference assembly position along the direction perpendicular to the assembly movement during the workpiece assembly process from the theoretical assembly position. The algorithm includes the number of particles, the initial position of the particles, the range of particle values, the number of iterations, and the particle acceleration factor. The constraint condition is that the gap between the two workpiece point cloud data at the point cloud cross section cutting point is greater than a given threshold δ. The fitness function is the variance of the gap between the two workpiece point cloud data at the point cloud cross section cutting point and the convergence threshold of the fitness function.

2. The method for planning small gap assembly paths based on high-precision interferometric detection according to claim 1, characterized in that, Step S1 includes the following steps: Step S1.1: Denoise and smooth the 3D point cloud data of the workpiece to be assembled obtained from the 3D scanning equipment to reduce the noise of the 3D point cloud data; Step S1.2: Register the point cloud data of the two workpieces to be assembled obtained in step S1.1 with the two assembly workpieces in the assembly CAD model, so that the relative position of the point cloud data of the two workpieces to be assembled is consistent with the theoretical assembly position of the workpieces.

3. The method for planning small gap assembly paths based on high-precision interferometric detection according to claim 1, characterized in that, Step S3 includes the following steps: Step S3.1: For each critical position of the assembly path, the point cloud data of the two workpieces to be assembled is sectioned along the vertical assembly direction. The optimization objective is to make the gap between the point cloud data of the two workpieces on the cross section of the point cloud at the critical position of the assembly path uniform, that is, to minimize the fitness function. The assembly path optimization algorithm based on particle swarm optimization is used to find the optimal path point. If no particle that satisfies the constraints can be found, proceed to step S3.2; otherwise, proceed to step S3.

3. Step S3.2: If no particle that satisfies the constraints can be found, it is determined that the workpiece cannot be assembled, and the assembly path optimization algorithm is terminated. Step S3.3: Use a particle swarm optimization-based intelligent path optimization algorithm to find the optimal critical location path point that minimizes the fitness function, determine whether the optimization algorithm meets the optimization termination condition, and record the position of the optimal path point; Step S3.4: Repeat steps S3.1-3.3 until all critical path points of the assembly path are optimized. Use the least squares method to fit the optimal path points of the critical path points of the assembly path into a straight line to obtain the assembly path fitted with the optimal path points.

4. The method for planning small gap assembly paths based on high-precision interferometric detection according to claim 1, characterized in that, Step S4 includes the following steps: Step S4.1: Set n point cloud cross-sectional cutting points on the fixed assembly workpiece along the direction of assembly movement, and divide the assembly path into m segments according to the accuracy requirements of the assembly path verification. Step S4.2: Move the point cloud data of the moving assembly workpiece according to the assembly path. During the assembly movement, each segment of the assembly path is cut into a point cloud cross section once according to the point cloud cross section cutting point to obtain n point cloud data matching cross sections of the fixed assembly workpiece and the moving assembly workpiece. Step S4.3: Repeat step S4.2 until the point cloud data of the moving assembly workpiece moves along the assembly path to complete the assembly, and finally obtain n. Point cloud data of m fixed assembly workpieces and moving assembly workpieces combined with cross-sections; Step S4.4: Calculate n Interference gap values ​​between point cloud data of fixed and moving assembly workpieces on m cross-sections.

5. The method for planning small gap assembly paths based on high-precision interferometric detection according to claim 1, characterized in that: Step S5 includes the following steps: Step S5.1: Analyze and statistically analyze n If there is interference between the point cloud data of the fixed assembly workpiece and the moving assembly workpiece on m cross-sections, and the gap value is calculated, if there is interference (i.e., the gap value is less than 0 or the gap value is less than the safety threshold specified by the assembly process), proceed to step S5.2; otherwise, proceed to step S5.

3. Step S5.2: Add the locations where interference occurs (i.e., the gap value is less than 0, or the gap value is less than the safety threshold specified by the assembly process) as critical locations in the assembly path, and return to step S3; Step S5.3: Output the assembly path obtained by the particle swarm optimization algorithm as a feasible assembly path.

6. A micro-gap assembly path planning system based on high-precision interferometric detection, characterized in that, include: The point cloud data acquisition module is used to acquire the actual surface shape of the mating surfaces of the workpieces to be assembled through a 3D scanning device, and to perform preliminary point cloud data processing so that the relative position of the point cloud data of the two workpieces to be assembled is consistent with the theoretical assembly position of the workpieces. The assembly path optimization initialization module is used to generate an initial assembly path based on the relative position of the point cloud and the theoretical assembly path. Taking a fixed workpiece as a reference, it sets several key positions of the assembly path and initializes the particle swarm-based assembly path optimization algorithm. The assembly path optimization initialization module is configured to execute: Step S2.1: Generate an initial assembly path based on the relative position of the point cloud data of the two workpieces to be assembled determined in step S1.2, the assembly direction of the workpieces during the assembly process, and the theoretical assembly path. Step S2.2: Based on the assembly characteristics of the workpiece, set several key positions of the assembly path on the initial assembly path. The key positions of the assembly path are used as the cross-sectional cutting points of the point cloud for assembly path point optimization. The key positions include the position where the stepped hole stop begins to make contact and the position where the assembly gap accuracy is higher than the threshold. Step S2.3: Initialize the particle swarm optimization algorithm for assembly path optimization: Each particle is the two-dimensional deviation of the non-interference assembly position along the direction perpendicular to the assembly movement from the theoretical assembly position during the workpiece assembly process. The parameters include the number of particles, the initial position of the particles, the range of particle values, the number of iterations, and the particle acceleration factor. The constraint condition is that the gap between the two workpiece point cloud data at the point cloud cross section cutting point is greater than a given threshold δ. The fitness function is the variance of the gap between the two workpiece point cloud data at the point cloud cross section cutting point and the convergence threshold of the fitness function. The assembly path optimization module is used to find the optimal path point based on the key positions of the assembly path using a particle swarm optimization algorithm, and fit the optimal path point as the assembly path; if the particle swarm optimization algorithm cannot find the optimal path point at any key assembly position, it is determined that the workpiece assembly cannot be completed. The assembly path output module is used to perform interference and collision detection on the assembly path, calculate the assembly interference and clearance between workpieces on the assembly path, analyze the assembly interference and clearance values, and add the location where interference occurs as a critical location of the assembly path if there is interference or the clearance value is less than the threshold; otherwise, output a feasible assembly path.

7. A micro-gap assembly path planning device based on high-precision interferometric detection, characterized in that, It includes a memory and a processor; the memory is used to store a computer program; the processor is used to implement, when executing the computer program, the method for planning small gap assembly paths based on high-precision interferometric detection as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the micro-gap assembly path planning method based on high-precision interferometric detection as described in any one of claims 1-5.

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