Automatic cleaning method and system based on plasma and track control

Abstract

The invention provides an automatic cleaning method and system based on plasma and track control, and relates to the technical field of control, and the method comprises the steps: obtaining a three-dimensional model of a workpiece, extracting depth information, dividing the surface of the workpiece into a plane area, a convex area and a concave area based on a principal curvature value, setting differentiated track density parameters for different areas, and setting a track transition zone; and the attitude angle of the robot is calculated in combination with the normal vector, and finally track data containing pose information and plasma cleaning process parameters are generated. According to the cleaning device, precise cleaning of a complex surface is achieved, and the cleaning efficiency and quality are improved.

Description

Technical Field

[0001] The present invention relates to the field of control technology, and particularly to an automatic cleaning method and system based on plasma and trajectory control. Background Art

[0002] With the development of industrial automation, robot-assisted cleaning technology has gradually replaced traditional manual cleaning methods, improving cleaning efficiency and quality consistency. As an environmentally friendly and efficient cleaning method, plasma cleaning is widely used in fields such as aerospace and precision manufacturing, and can effectively remove contaminants on the surface of workpieces without damaging the substrate.

[0003] Traditional robot cleaning systems usually adopt simple trajectory planning methods, such as equidistant parallel scanning or preset trajectory templates. However, when the workpiece has a complex geometric shape, these methods are difficult to adapt to the curvature changes of the workpiece surface, resulting in uneven cleaning effects. At the same time, most existing systems fail to perform intelligent zoning processing according to the geometric characteristics of the workpiece surface, and often use unified cleaning parameters, unable to perform differential processing for different regional characteristics, thus affecting the cleaning effect. Summary of the Invention

[0004] Embodiments of the present invention provide an automatic cleaning method and system based on plasma and trajectory control, which can solve the problems in the prior art.

[0005] In the first aspect of the embodiments of the present invention, an automatic cleaning method based on plasma and trajectory control is provided, including: Obtain a three-dimensional digital model of the workpiece to be cleaned, perform mesh division on the three-dimensional digital model to obtain mesh division data on the surface of the workpiece to be cleaned, and extract depth information on the surface of the workpiece to be cleaned based on the mesh division data; Determine the principal curvature value based on the gradient information corresponding to the depth information, use the difference between the principal curvature value and the current curvature data as the curvature segmentation degree, and divide the surface of the workpiece to be cleaned into a flat area, a convex area, and a concave area according to the comparison result between the curvature segmentation degree and a preset threshold; According to the area division result of the surface of the workpiece to be cleaned, set differential trajectory density parameters for different types of areas, generate an initial cleaning trajectory of the robot, where the trajectory density parameter of the flat area is set as a reference value, the trajectory density parameter of the convex area increases linearly according to the curvature value, and the trajectory density parameter of the concave area increases exponentially according to the curvature value; at the same time, set a trajectory transition zone between adjacent areas, and the density parameter of the trajectory transition zone is determined by the weighted average of the density parameters of adjacent areas; Based on the initial cleaning trajectory of the robot, the attitude angle of the robot at each trajectory point is calculated by combining the normal vectors of each region, and the robot motion trajectory data including pose information is generated. And the plasma cleaning process parameters corresponding to each trajectory point are set according to the material characteristics of the workpiece to be cleaned.

[0006] Obtain the three-dimensional digital model of the workpiece to be cleaned, perform mesh division on the three-dimensional digital model to obtain the mesh division data of the surface of the workpiece to be cleaned. The depth information of the surface of the workpiece to be cleaned extracted based on the mesh division data includes: Calculate the local curvature values of each point on the surface of the three-dimensional digital model, and construct a mesh size function according to the local curvature values. The mesh size function adopts an exponential decay form, and the mesh size decreases as the curvature value increases, and the mesh division is encrypted at the curvature mutation position; perform adaptive mesh dissection on the workpiece to be cleaned based on the mesh size function to obtain the mesh division data of the surface of the workpiece to be cleaned; Establish a local coordinate system on the surface of the workpiece to be cleaned, project the vertex coordinates in the mesh division data to the local coordinate system, and calculate the depth values of each vertex; Construct a depth feature map of the surface of the workpiece to be cleaned based on the depth values of each vertex, calculate the gradient distribution of the depth feature map, and determine the depth mutation region according to the gradient distribution; Extract the principal curvature information of the surface of the workpiece to be cleaned, calculate the shape index based on the principal curvature information, cluster the shape indices according to the numerical values, obtain the shape clustering regions, and establish a transition zone between the shape clustering regions. The width of the transition zone is proportional to the difference in shape indices between adjacent regions.

[0007] Determine the principal curvature value based on the gradient information corresponding to the depth information, take the difference between the principal curvature value and the current curvature data as the curvature segmentation degree, and divide the surface of the workpiece to be cleaned into a flat region, a convex region and a concave region according to the comparison result between the curvature segmentation degree and the preset threshold, including: Calculate the mean curvature and Gaussian curvature of the surface of the workpiece to be cleaned based on the first-order gradient and second-order gradient corresponding to the depth information, and determine the principal curvature value based on the mean curvature and the Gaussian curvature; Continuously collect the curvature data of the surface of the workpiece to be cleaned within a preset sampling period, establish a time-series curvature data set, and take the difference between the curvature data in the time-series curvature data set and the principal curvature value as the curvature segmentation degree; Divide the surface of the workpiece to be cleaned according to the curvature segmentation degree: when the curvature segmentation degree is less than the first preset threshold, the area where the current curvature data is located is regarded as a planar area, and the current curvature value is updated using the neighborhood average value based on distance weights; when the curvature segmentation degree is between the first preset threshold and the second preset threshold, the area where the current curvature data is located is regarded as a convex area, and the current curvature value is updated using the weighted average value of exponentially decaying time-series data; when the curvature segmentation degree is greater than the second preset threshold, the area where the current curvature data is located is regarded as a concave area, and the current curvature value is maintained; Calculate the adjacent curvature gradient between adjacent areas, determine the width distribution of the transition area according to the adjacent curvature gradient, and construct a smooth transition boundary using a B-spline curve in the transition area.

[0008] According to the area division result of the surface of the workpiece to be cleaned, set different trajectory density parameters for different types of areas to generate the initial cleaning trajectory of the robot. Among them, the trajectory density parameter of the planar area is set to the reference value, the trajectory density parameter of the convex area increases linearly according to the curvature value, and the trajectory density parameter of the concave area increases exponentially according to the curvature value, including: Collect the curvature data of each area on the surface of the workpiece to be cleaned, calculate the average curvature value of all sampling points in each area, calculate the curvature change amount between adjacent sampling points, and statistically obtain the area curvature dispersion value of the discrete degree of the average curvature value; Determine the reference trajectory density value of the planar area according to the preset minimum trajectory spacing, and perform weighted combination of the reference trajectory density value with the average curvature value and the curvature change amount respectively. Among them, the weight of the average curvature value is proportional to the shape complexity value, and the weight of the curvature change amount is proportional to the curvature dispersion value; Calculate the trajectory density parameter of each area: for the convex area, divide the maximum curvature value of the area by the preset reference curvature value and perform a quadratic power operation to obtain the convex trajectory density parameter; for the concave area, divide the minimum curvature value of the area by the preset reference curvature value and perform a quadratic power operation to obtain the concave trajectory density parameter; Generate the initial cleaning trajectory of the robot according to the trajectory density parameters of each area.

[0009] Based on the initial cleaning trajectory of the robot, combine the normal vectors of each area to calculate the attitude angle of the robot at each trajectory point, and generate the robot motion trajectory data including pose information, including: Based on the discrete point position coordinate sequence of the initial cleaning trajectory of the robot, calculate the position difference vector between adjacent trajectory points in the position coordinate sequence, and unitize the position difference vector to obtain the trajectory tangent vector sequence; Extract the corresponding surface normal vector at each trajectory point position, use the surface normal vector as the Z-axis direction vector of the robot tool coordinate system, and use the sequence of trajectory tangent vectors at the corresponding position as the Y-axis direction vector of the tool coordinate system; obtain the X-axis direction vector of the tool coordinate system through the cross product operation of the Z-axis direction vector and the Y-axis direction vector, construct an attitude rotation matrix based on the X-axis direction vector, Y-axis direction vector and Z-axis direction vector, and decompose the attitude rotation matrix to obtain the Euler angles of the tool coordinate system relative to the base coordinate system. Substitute the position coordinate sequence and the attitude rotation matrix sequence into the robot kinematic equation, solve the joint angle solution set at each trajectory point, and calculate the joint motion amount and the joint median deviation amount of each group of solutions in the joint angle solution set.

[0010] Set the plasma cleaning process parameters corresponding to each trajectory point according to the material characteristics of the workpiece to be cleaned, including: The material characteristics of the workpiece to be cleaned include local hardness value and local roughness value; Calculate the plasma cleaning power based on the local hardness value and the local roughness value: multiply the local hardness value by the first weighting coefficient to obtain the first power component, multiply the local roughness value by the second weighting coefficient to obtain the second power component, and add the first power component and the second power component to obtain the plasma cleaning power value corresponding to the trajectory point; Obtain the local curvature value at each trajectory point, use the weighted sum of the local curvature value and the local hardness value as the process parameter modulation coefficient, and multiply the process parameter modulation coefficient by the preset reference gas flow rate to obtain the actual gas flow rate value corresponding to the trajectory point; Calculate the angle between the Z-axis direction of the tool coordinate system and the surface normal vector of the workpiece to be cleaned, determine the actual working distance of the plasma jet according to the angle, and convert the actual working distance, the plasma cleaning power value and the actual gas flow rate value into the ion concentration value corresponding to each trajectory point.

[0011] The method further includes: When the difference in plasma cleaning power between adjacent trajectory points is greater than the preset power threshold, insert a transition point between the adjacent trajectory points; when the difference in actual gas flow rate between adjacent trajectory points is greater than the preset flow threshold, use linear interpolation for transition; Form a process parameter group with the plasma cleaning power value, the actual gas flow rate value and the ion concentration value of each trajectory point, and establish a mapping relationship between the process parameter group and the trajectory point position.

[0012] In the second aspect of the embodiments of the present invention, an automatic cleaning system based on plasma and trajectory control is provided, including: The first unit is used to obtain the three-dimensional digital model of the workpiece to be cleaned, perform mesh division on the three-dimensional digital model to obtain the mesh division data of the surface of the workpiece to be cleaned, and extract the depth information of the surface of the workpiece to be cleaned based on the mesh division data; The second unit is used to determine the principal curvature value based on the gradient information corresponding to the depth information, use the difference between the principal curvature value and the current curvature data as the curvature segmentation degree, and divide the surface of the workpiece to be cleaned into a flat area, a convex area, and a concave area according to the comparison result between the curvature segmentation degree and a preset threshold; The third unit is used to, according to the area division result of the surface of the workpiece to be cleaned, set different trajectory density parameters for different types of areas, and generate the initial cleaning trajectory of the robot, wherein the trajectory density parameter of the flat area is set to a reference value, the trajectory density parameter of the convex area increases linearly according to the curvature value, and the trajectory density parameter of the concave area increases exponentially according to the curvature value; at the same time, a trajectory transition zone is set between adjacent areas, and the density parameter of the trajectory transition zone is determined by the weighted average of the density parameters of the adjacent areas; The fourth unit is used to, based on the initial cleaning trajectory of the robot, combine the normal vectors of each area to calculate the attitude angle of the robot at each trajectory point, generate the robot motion trajectory data including pose information, and set the plasma cleaning process parameters corresponding to each trajectory point according to the material characteristics of the workpiece to be cleaned.

[0013] In a third aspect of the embodiments of the present invention, an electronic device is provided, including: A processor; A memory for storing instructions executable by the processor; Wherein, the processor is configured to call the instructions stored in the memory to execute the method described above.

[0014] In a fourth aspect of the embodiments of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the method described above is implemented.

[0015] The beneficial effects of this application are as follows: By performing curvature analysis and area division on the surface of the workpiece, the present invention realizes a differential cleaning strategy for different surface features, effectively improving the cleaning efficiency and quality. Especially for complex uneven surfaces, by dynamically adjusting the trajectory density parameters, the uniformity and thoroughness of cleaning are ensured.

[0016] The present invention adopts the design of a trajectory transition zone, solves the problems of trajectory breakage and discontinuous cleaning between adjacent areas, makes the cleaning process transition more smoothly, avoids the generation of cleaning dead corners and missed areas, and at the same time reduces the number of acceleration and deceleration times during the movement of the robot, extending the service life of the equipment.

[0017] The present invention customizes the plasma cleaning process parameters in combination with the workpiece material characteristics, realizes the precise control of the cleaning process, not only ensures the cleaning effect, but also avoids the damage to the workpiece surface caused by over-cleaning, improves the product quality and production efficiency, and reduces the production cost and energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic flow chart of the automatic cleaning method based on plasma and trajectory control according to an embodiment of the present invention; Figure 2 It is a schematic flow chart of the workpiece surface area division based on the curvature segmentation degree of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0020] The technical solutions of the present invention will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

[0021] Figure 1 It is a schematic flow chart of the automatic cleaning method based on plasma and trajectory control according to an embodiment of the present invention, as Figure 1 shown, the method includes: Obtain the three-dimensional digital model of the workpiece to be cleaned, perform mesh division on the three-dimensional digital model to obtain the mesh division data of the surface of the workpiece to be cleaned, and extract the depth information of the surface of the workpiece to be cleaned based on the mesh division data; Determine the principal curvature value based on the gradient information corresponding to the depth information, use the difference between the principal curvature value and the current curvature data as the curvature segmentation degree, and divide the surface of the workpiece to be cleaned into a flat area, a convex area and a concave area according to the comparison result between the curvature segmentation degree and a preset threshold; According to the area division result of the surface of the workpiece to be cleaned, set different trajectory density parameters for different types of areas to generate the initial cleaning trajectory of the robot, wherein the trajectory density parameter of the flat area is set as the reference value, the trajectory density parameter of the convex area increases linearly according to the curvature value, and the trajectory density parameter of the concave area increases exponentially according to the curvature value; at the same time, set a trajectory transition zone between adjacent areas, and the density parameter of the trajectory transition zone is determined by the weighted average of the density parameters of the adjacent areas; Based on the initial cleaning trajectory of the robot, the attitude angle of the robot at each trajectory point is calculated by combining the normal vectors of each region, and robot motion trajectory data including pose information is generated. The plasma cleaning process parameters corresponding to each trajectory point are set according to the material characteristics of the workpiece to be cleaned.

[0022] In an alternative embodiment, a three-dimensional digital model of the workpiece to be cleaned is obtained, and the three-dimensional digital model is meshed to obtain mesh division data on the surface of the workpiece to be cleaned. Extracting the depth information on the surface of the workpiece to be cleaned based on the mesh division data includes: Calculating the local curvature value of each point on the surface of the three-dimensional digital model, and constructing a mesh size function according to the local curvature value. The mesh size function adopts an exponential decay form, and the mesh size decreases as the curvature value increases, and the mesh division is encrypted at the curvature mutation position; performing adaptive mesh dissection on the workpiece to be cleaned based on the mesh size function to obtain mesh division data on the surface of the workpiece to be cleaned; Establishing a local coordinate system on the surface of the workpiece to be cleaned, projecting the vertex coordinates in the mesh division data to the local coordinate system, and calculating the depth value of each vertex; Constructing a depth feature map on the surface of the workpiece to be cleaned based on the depth values of each vertex, calculating the gradient distribution of the depth feature map, and determining the depth mutation region according to the gradient distribution; Extracting the principal curvature information on the surface of the workpiece to be cleaned, calculating the shape index based on the principal curvature information, clustering the shape indices according to the numerical size to obtain shape clustering regions, and establishing a transition zone between the shape clustering regions, where the width of the transition zone is proportional to the difference in shape indices of adjacent regions.

[0023] The present invention provides a method for obtaining a three-dimensional digital model of a workpiece to be cleaned and extracting surface depth information. The method first obtains a three-dimensional digital model of the workpiece to be cleaned, for example, obtaining point cloud data by scanning the workpiece with a three-dimensional scanner, or obtaining a three-dimensional model by modeling with CAD software. After obtaining the three-dimensional digital model, the model is meshed and the surface depth information is extracted.

[0024] When meshing the surface of the three-dimensional digital model, first calculate the local curvature value of each point on the model surface. Specifically, for each point on the surface of the three-dimensional model, by fitting the local surface around the point, the mean curvature and Gaussian curvature at the point are calculated. For example, for a point P on the surface, a point set within a radius of 3 mm around it is selected, and a quadratic surface is fitted based on these points, and then the local curvature value at point P is obtained.

[0025] Based on the calculated curvature values, a mesh size function is constructed. This function adopts an exponential decay form, such that the mesh size decreases as the curvature value increases. The specific implementation is as follows: For a point with a curvature value of K, the corresponding mesh size S is calculated as S = Smin + (Smax - Smin) × exp(-αK), where Smin is the minimum mesh size (e.g., 0.5 mm), Smax is the maximum mesh size (e.g., 5 mm), and α is the adjustment coefficient (e.g., 10). At the positions of curvature mutations, such as at edges, corners, etc., the mesh is refined by reducing the mesh size in this area. For example, when it is detected that the change rate of curvature between two adjacent points exceeds 30%, the mesh size in this area is reduced to 1 / 3 of the original size.

[0026] The adaptive mesh generation process starts from a rough initial mesh and, guided by the mesh size function, subdivides and optimizes the mesh. For example, initially, the mesh size of the entire workpiece surface is set to 4 mm. Then, the curvature values at the center points of each mesh cell are iteratively calculated, and based on the mesh size function, it is determined whether the cell needs to be further subdivided. After multiple iterations (e.g., 5 times), the mesh division data of the workpiece surface is obtained, including vertex coordinates and connection relationships.

[0027] To extract the depth information of the workpiece surface, a local coordinate system of the workpiece surface needs to be established. For each area on the workpiece surface, the center point of this area is selected as the origin, the surface normal vector at this point is used as the positive direction of the z-axis, and the two principal tangent vectors are used as the directions of the x-axis and y-axis, thereby establishing a local coordinate system. In practical applications, the workpiece surface can be divided into several areas. For example, the surface is divided into local areas with an area of approximately 25 square centimeters, and a local coordinate system is established for each area.

[0028] Specifically, for each vertex in the mesh, first determine the local area to which it belongs, and then convert its coordinates from the global coordinate system to the corresponding local coordinate system. The z-coordinate of the vertex after conversion is the depth value of this point. For example, for a vertex with a global coordinate of (10 mm, 15 mm, 5 mm), if the origin of the local coordinate system to which it belongs is (8 mm, 12 mm, 3 mm) and the coordinate axis rotation matrix is R, then the coordinate of this vertex in the local coordinate system is R × (2 mm, 3 mm, 2 mm)^T, and its depth value is the converted z-coordinate.

[0029] The depth feature map is a two-dimensional image, and its grayscale value corresponds to the depth value of the vertex. To construct the depth feature map, the xy plane of the local area is divided into grids (such as a 100×100 grid), and each grid cell corresponds to a pixel in the depth feature map. For each grid cell, the average depth value of all vertices falling within the cell is calculated as the grayscale value of the corresponding pixel. If there are no vertices in a certain grid cell, the depth value of the cell is obtained by interpolation through the surrounding cells with values.

[0030] Calculate the gradient distribution of the constructed depth feature map. The finite difference method is used to calculate the gradient of the depth feature map. That is, for each pixel (i,j) in the depth feature map, calculate its x-direction gradient Gx(i,j) = D(i+1,j) - D(i-1,j) and y-direction gradient Gy(i,j) = D(i,j+1) - D(i,j-1), where D(i,j) represents the depth value at pixel (i,j). The gradient magnitude G(i,j) = sqrt(Gx(i,j)^2 + Gy(i,j)^2) reflects the severity of the depth change. Set a gradient threshold (such as a depth change rate exceeding 20%), and mark the area where the gradient magnitude is greater than the threshold as the depth mutation area.

[0031] Extract the principal curvature information of the workpiece surface, including calculating the maximum principal curvature and the minimum principal curvature at each point on the surface. Calculate the shape index based on the principal curvature information. The shape index is defined as SI = (2 / π) × arctan((κ1+κ2) / (κ1-κ2)), where κ1 and κ2 are the maximum and minimum principal curvatures respectively (κ1≥κ2). The value range of the shape index is [-1,1], and different values correspond to different surface shape types.

[0032] Cluster the calculated shape indices according to the numerical size. For example, the shape indices can be divided into 5 intervals: [-1,-0.6), [-0.6,-0.2), [-0.2,0.2), [0.2,0.6) and [0.6,1], corresponding to concave regions, groove regions, saddle regions, convex groove regions and convex regions respectively. For each point on the workpiece surface, classify it into the corresponding region according to its shape index, so as to obtain the shape clustering region.

[0033] Establish a transition zone between the shape clustering regions. The width of the transition zone is proportional to the difference in the shape indices of adjacent regions. For example, if the central values of the shape indices of two adjacent regions are 0.8 and 0.4 respectively, and the difference is 0.4, then set the width of the transition zone to 8mm; if the difference is 0.2, then the width of the transition zone is 4mm. The points in the transition zone linearly mix the characteristics of the two regions according to the distance ratio to the boundaries of the two regions, ensuring the continuity of the entire surface features.

[0034] In an alternative embodiment, the principal curvature value is determined based on the gradient information corresponding to the depth information, the difference between the principal curvature value and the current curvature data is used as the curvature segmentation degree, and the surface of the workpiece to be cleaned is divided into a flat region, a convex region, and a concave region according to the comparison result between the curvature segmentation degree and a preset threshold, including: Calculate the mean curvature and Gaussian curvature of the surface of the workpiece to be cleaned based on the first-order gradient and second-order gradient corresponding to the depth information, and determine the principal curvature value based on the mean curvature and the Gaussian curvature; Continuously collect the curvature data of the surface of the workpiece to be cleaned within a preset sampling period, establish a time-series curvature data set, and use the difference between the curvature data of the time-series curvature data set and the principal curvature value as the curvature segmentation degree; Divide the surface of the workpiece to be cleaned according to the curvature segmentation degree: when the curvature segmentation degree is less than the first preset threshold, the region where the current curvature data is located is used as a flat region, and the current curvature value is updated using the neighborhood average value based on distance weights; when the curvature segmentation degree is between the first preset threshold and the second preset threshold, the region where the current curvature data is located is used as a convex region, and the current curvature value is updated using the weighted average value of exponentially decaying time-series data; when the curvature segmentation degree is greater than the second preset threshold, the region where the current curvature data is located is used as a concave region, and the current curvature value is maintained; Calculate the adjacent curvature gradient between adjacent regions, determine the width distribution of the transition region according to the adjacent curvature gradient, and construct a smooth transition boundary using a B-spline curve in the transition region.

[0035] In a method for dividing the surface region of a workpiece to be cleaned, the depth information of the surface of the workpiece to be cleaned is obtained, and the depth information can be collected by devices such as a depth camera and a lidar. The corresponding gradient information, including the first-order gradient and the second-order gradient, is calculated based on the obtained depth information. The first-order gradient reflects the change rate of the surface of the workpiece to be cleaned in each direction, and the second-order gradient represents the change degree of the change rate.

[0036] Figure 2 This is a schematic flow chart of the workpiece surface region division based on the curvature segmentation degree of the present invention. For each pixel point in the depth image, calculate the depth change in the horizontal and vertical directions to obtain the first-order gradient; then calculate the change rate of the first-order gradient to obtain the second-order gradient. For example, for three consecutive points with depth values of 10mm, 12mm, and 15mm, the first-order gradient from the first point to the second point is 2mm, and the first-order gradient from the second point to the third point is 3mm, then the second-order gradient of the middle point is 1mm.

[0037] Using the calculated first-order gradient and second-order gradient, further calculate the mean curvature and Gaussian curvature of the surface of the workpiece to be cleaned. The mean curvature is the arithmetic mean of the principal curvatures, and the Gaussian curvature is the product of the principal curvatures. For example, when the first-order gradient in the x-direction and y-direction of a region is 0.05 and 0.03 respectively, and the second-order gradients in the xx, yy, and xy directions are 0.02, 0.01, and 0.005 respectively, the mean curvature of this region can be calculated as 0.015, and the Gaussian curvature as 0.0002.

[0038] The principal curvature is an important index to describe the degree of curvature of a surface at a certain point. The two principal curvature values can be inversely solved through the mean curvature and Gaussian curvature. Continuing with the above example, when the mean curvature is 0.015 and the Gaussian curvature is 0.0002, the two principal curvatures can be calculated as 0.02 and 0.01 respectively.

[0039] To enhance the stability and anti-interference ability of the system, continuously collect the curvature data of the surface of the workpiece to be cleaned within the preset sampling period, and establish a time-series curvature data set. For example, collect 10 curvature data within every 500 milliseconds to form a time-series curvature data set containing 10 data points. Compare these curvature data with the principal curvature values calculated previously, calculate the difference between them, and define this difference as the curvature segmentation degree.

[0040] Specifically, set two preset thresholds, the first preset threshold is 0.005, and the second preset threshold is 0.02. When the curvature segmentation degree is less than the first preset threshold of 0.005, the region where the current curvature data is located is divided into a planar region. For example, the curvature segmentation degree of a certain region is 0.003, which is less than 0.005, so this region is divided into a planar region. For the planar region, update the current curvature value using the neighborhood average value based on distance weights, that is, consider the curvature values of the surrounding points, assign different weights according to the distance, and calculate the weighted average value as the updated curvature value. For example, if the curvature value of the center point of a certain planar region is 0.01, and the curvature values of the surrounding four points are 0.012, 0.009, 0.011, and 0.008 respectively, and the distances are 1, 2, 1.5, and 2.5 units respectively, then the updated curvature value can be calculated as 0.0102.

[0041] When the curvature segmentation degree is between the first preset threshold value 0.005 and the second preset threshold value 0.02, the area where the current curvature data is located is divided into a convex area. For example, the curvature segmentation degree of a certain area is 0.01, which is greater than 0.005 but less than 0.02, so the area is divided into a convex area. For convex areas, the current curvature value is updated using the weighted average of exponentially decaying time series data, that is, considering historical curvature data, giving more recent data a larger weight and more distant data a smaller weight, and calculating the weighted average as the updated curvature value. For example, the curvature values ​​of the last five measurements of a convex area are 0.025, 0.028, 0.027, 0.026, and 0.029, respectively. Using a decay factor of 0.8, the updated curvature value is 0.0267.

[0042] When the curvature segmentation degree is greater than the second preset threshold value of 0.02, the area where the current curvature data is located is divided into a concave area. For example, the curvature segmentation degree of a certain area is 0.03, which is greater than 0.02, so the area is divided into a concave area. For the concave area, the current curvature value is kept unchanged, because the concave area usually has a complex geometric shape, and simple smoothing may cause the loss of important features.

[0043] After completing the area division, calculate the adjacent curvature gradient between adjacent areas, that is, the rate of change of the curvature values ​​of two adjacent areas. For example, at the junction of the plane area and the convex area, the curvature value of the plane area is 0.005, the curvature value of the convex area is 0.015, and the spatial distance between the two areas is 10 units, then the adjacent curvature gradient is 0.001 / unit.

[0044] The width distribution of the transition area is determined based on the calculated adjacent curvature gradient. The larger the adjacent curvature gradient, the more dramatic the curvature change, and a narrower transition area needs to be set; the smaller the adjacent curvature gradient, the more gradual the curvature change, and a wider transition area can be set. For example, when the adjacent curvature gradient is 0.001 / unit, the width of the transition area can be set to 15 units; when the adjacent curvature gradient is 0.005 / unit, the width of the transition area can be set to 5 units.

[0045] In the determined transition region, a B-spline curve is used to construct a smooth transition boundary. B-spline curves have the characteristics of good local controllability and high computational efficiency, and are suitable for constructing smooth transition boundaries. By selecting appropriate control points in the transition region, a B-spline curve that smoothly connects adjacent regions is generated. For example, in a transition region with a width of 10 units, 5 control points are selected to generate a 3rd-order B-spline curve as a smooth transition boundary.

[0046] In an alternative embodiment, according to the regional division result of the surface of the workpiece to be cleaned, different trajectory density parameters are set for different types of regions to generate the initial cleaning trajectory of the robot. Among them, the trajectory density parameter of the planar region is set as the reference value, the trajectory density parameter of the convex region increases linearly according to the curvature value, and the trajectory density parameter of the concave region increases exponentially according to the curvature value, including: Collect the curvature data of each region on the surface of the workpiece to be cleaned, calculate the average curvature value of all sampling points in each region, calculate the curvature change amount between adjacent sampling points, and statistically obtain the regional curvature dispersion value of the discrete degree of the average curvature value; Determine the reference trajectory density value of the planar region according to the preset minimum trajectory spacing, and perform weighted combination of the reference trajectory density value with the average curvature value and the curvature change amount respectively, where the weight of the average curvature value is proportional to the shape complexity value, and the weight of the curvature change amount is proportional to the curvature dispersion value; Calculate the trajectory density parameters of each region: for the convex region, divide the maximum curvature value of the region by the preset reference curvature value and perform a quadratic power operation to obtain the convex trajectory density parameter; for the concave region, divide the minimum curvature value of the region by the preset reference curvature value and perform a quadratic power operation to obtain the concave trajectory density parameter; Generate the initial cleaning trajectory of the robot according to the trajectory density parameters of each region.

[0047] In this embodiment, the system first performs three-dimensional scanning on the surface of the workpiece to be cleaned to obtain the point cloud data of the workpiece surface. Through the point cloud data processing algorithm, the system divides the surface region of the workpiece into a planar region, a convex region, and a concave region. After the region division is completed, the system collects the curvature data of each region to prepare for the subsequent setting of different trajectory density parameters.

[0048] The system uses a high-precision laser scanner to scan the surface of the workpiece, and the sampling spacing is set to 0.5 mm to ensure that sufficient dense surface information is collected. For each point collected, the system fits the local surface of its surrounding neighborhood points by the least squares method and calculates the principal curvature value of the point. The system groups the curvature data of all points according to regions and calculates the average curvature value of all sampling points in each region. For example, the calculated average curvature value of a certain convex region is 0.025, indicating the degree of curvature of the surface of this region.

[0049] The system further calculates the curvature change amount between adjacent sampling points in each region. Based on the grid adjacent points, calculate the absolute value of the curvature difference between each pair of adjacent points, and then average all the curvature differences in the entire region to obtain the average curvature change amount of the region. If the average curvature change amount of a certain region is 0.003, it means that the curvature change in this region is relatively gentle.

[0050] To evaluate the discreteness of regional curvature, the system calculates the standard deviation of the curvature values of all points within the region to obtain the regional curvature discreteness value. The larger the curvature discreteness value, the more uneven the curvature distribution within the region and the more complex the regional shape. For example, the curvature discreteness value of a complex convex surface region is 0.018, while that of a simple convex surface region is only 0.004.

[0051] The system presets the minimum trajectory spacing to 10 millimeters as the reference trajectory density value for the planar region. For each region, the system combines the reference trajectory density value with the average curvature value and the curvature change amount of the region through weighted combination. During the weighting process, the weight of the average curvature value is proportional to the shape complexity value of the region, and the weight of the curvature change amount is proportional to the curvature discreteness value. The shape complexity value is comprehensively calculated based on the complexity of the regional boundary and the unevenness of the curvature distribution within the region.

[0052] For example, if the shape complexity value of a region is 0.75 and the curvature discreteness value is 0.012, the weight of the average curvature value can be set to 0.75, and the weight of the curvature change amount can be set to 0.6. Through this weighted combination method, the system can more accurately reflect the influence of regional characteristics on the trajectory density.

[0053] For the convex surface region, the system divides the maximum curvature value of the region by the preset reference curvature value of 0.01 and then performs a quadratic power operation to obtain the convex surface trajectory density parameter. For example, if the maximum curvature value of a convex surface region is 0.035, the trajectory density parameter is calculated as (0.035 / 0.01)² = 12.25, that is, the trajectory density of this region is 12.25 times the reference density, and the actual trajectory spacing is approximately 0.82 millimeters. This calculation method makes the convex surface region with a larger curvature have a higher trajectory density and more thorough cleaning.

[0054] For the concave surface region, the system takes the absolute value of the minimum curvature value (negative value) of the region, divides it by the preset reference curvature value of 0.01, and then performs a quadratic power operation to obtain the concave surface trajectory density parameter. For example, if the minimum curvature value of a concave surface region is -0.042, the trajectory density parameter is calculated as (0.042 / 0.01)² = 17.64, that is, the trajectory density of this region is 17.64 times the reference density, and the actual trajectory spacing is approximately 0.57 millimeters. Since dirt is likely to accumulate in the concave surface region, by exponentially increasing the trajectory density, it ensures more thorough cleaning.

[0055] The system generates the initial cleaning trajectory of the robot based on the trajectory density parameters calculated for each region. The trajectory generation process uses an improved ZigZag algorithm. First, the main trajectory direction is determined, and then parallel trajectory lines are generated according to the calculated trajectory spacing. To ensure the continuity of the trajectory, the system performs trajectory smoothing at the regional boundary, using a cubic spline curve to fit the trajectory to eliminate the abrupt points of the trajectory.

[0056] In practical applications, for an industrial part with various surface morphologies, the system divides it into 5 planar regions, 3 convex regions, and 2 concave regions. After calculation, the track spacing of the planar regions remains 10 mm, the track spacings of the convex regions are 1.25 mm, 0.82 mm, and 1.67 mm respectively, and the track spacings of the concave regions are 0.57 mm and 0.63 mm respectively. The total length of the initial cleaning track generated by the system is 4.73 m, and the cleaning coverage rate reaches 99.8%.

[0057] Through the above method for setting differential track density parameters, the system can adaptively adjust the cleaning track density according to the geometric characteristics of the workpiece surface, ensuring both cleaning quality and improving cleaning efficiency, and is particularly suitable for the precision cleaning scenarios of industrial parts with complex shapes.

[0058] In an alternative embodiment, based on the initial cleaning track of the robot, the attitude angle of the robot at each track point is calculated by combining the normal vectors of each region, and the robot motion track data including pose information is generated as follows: Based on the discrete point position coordinate sequence of the initial cleaning track of the robot, the position difference vector between adjacent track points in the position coordinate sequence is calculated, and the position difference vector is unitized to obtain a track tangent vector sequence; At each track point position, the corresponding surface normal vector is extracted. The surface normal vector is used as the Z-axis direction vector of the robot tool coordinate system, and the track tangent vector sequence at the corresponding position is used as the Y-axis direction vector of the tool coordinate system; the X-axis direction vector of the tool coordinate system is obtained through the cross product operation of the Z-axis direction vector and the Y-axis direction vector. Based on the X-axis direction vector, Y-axis direction vector, and Z-axis direction vector, an attitude rotation matrix is constructed, and the attitude rotation matrix is decomposed to obtain the Euler angles of the tool coordinate system relative to the base coordinate system; The position coordinate sequence and the attitude rotation matrix sequence are substituted into the robot kinematic equation to solve the joint angle solution set at each track point, and the joint motion amount and joint median deviation amount of each group of solutions in the joint angle solution set are calculated.

[0059] This embodiment provides a method for calculating the attitude angle of a robot based on the initial cleaning track of the robot. This method is applicable to the motion planning of the robot when performing cleaning operations on complex curved surfaces, ensuring that the robot can complete the cleaning task with an appropriate attitude.

[0060] In this embodiment, the initial cleaning trajectory of the robot usually consists of a series of discrete position coordinate points, which can be obtained through a pre - path planning algorithm or manual teaching. These position coordinate points are arranged in sequence to form the spatial distribution of the cleaning trajectory. To calculate the attitude angle of the robot during the cleaning process, it is necessary to calculate in combination with the surface normal vectors of each area.

[0061] The initial cleaning trajectory of the robot can be represented as a sequence of discrete points in a three - dimensional space. For example, when the robot needs to clean a spherical surface, the cleaning trajectory can contain 100 points. For instance, the coordinates of the first point may be (100, 200, 300), and the coordinates of the second point may be (105, 202, 302), and so on.

[0062] To calculate the sequence of tangent vectors of the trajectory, it is necessary to perform a difference calculation on the positions of adjacent trajectory points. Specifically, for the i - th point and the (i + 1)-th point, the position difference vector is calculated as (x_{i + 1}-x_i, y_{i + 1}-y_i, z_{i + 1}-z_i). For example, for the first point and the second point in the above example, the position difference vector is (5, 2, 2).

[0063] Next, the position difference vector is unitized, that is, its length is normalized to 1 to obtain the trajectory tangent vector. Taking the above example, the length of the position difference vector is √(5² + 2²+2²)=√33. Therefore, the unitized trajectory tangent vector is (5 / √33, 2 / √33, 2 / √33), approximately equal to (0.87, 0.35, 0.35). Such calculations are applicable to each point on the trajectory, and finally a complete sequence of trajectory tangent vectors is obtained.

[0064] After determining the trajectory tangent vector, the next step is to extract the corresponding surface normal vector at each trajectory point. The surface normal vector is usually perpendicular to the surface to be cleaned and can be calculated through the geometric model of the surface or point cloud data. For example, for a spherical surface, the normal vector at any point points to the center of the sphere. Suppose the surface normal vector extracted at the first trajectory point is (0.2, 0.3, 0.93), and this vector is already a unit vector.

[0065] When constructing the robot tool coordinate system, the surface normal vector is used as the Z - axis direction vector of the tool coordinate system, and the trajectory tangent vector is used as the Y - axis direction vector of the tool coordinate system. Taking the first point in the above example as an example, the Z - axis direction vector is (0.2, 0.3, 0.93), and the Y - axis direction vector is (0.87, 0.35, 0.35).

[0066] To ensure that the tool coordinate system is orthogonal, the X-axis direction vector needs to be calculated through the cross product operation. The X-axis direction vector is equal to the cross product of the Z-axis direction vector and the Y-axis direction vector, i.e., X = Z × Y. For the above example, the X-axis direction vector is calculated as follows: X = (0.2, 0.3, 0.93) × (0.87, 0.35, 0.35), and the result is approximately (-0.06, 0.76, -0.65).

[0067] At this time, the Y-axis direction vector needs to be corrected to be orthogonal to the X-axis and the Z-axis. The new Y-axis direction vector is equal to the cross product of the Z-axis and the X-axis, i.e., Y' = Z × X. For the above example, the corrected Y-axis direction vector is (0.2, 0.3, 0.93) × (-0.06, 0.76, -0.65), and the result is approximately (-0.98, 0.14, 0.11).

[0068] Based on the X-axis, the corrected Y-axis, and the Z-axis direction vectors, an attitude rotation matrix can be constructed. The three columns of this matrix are composed of the X-axis, Y-axis, and Z-axis direction vectors respectively. For the above example, the rotation matrix is: [[-0.06, -0.98, 0.2], [0.76, 0.14, 0.3], [-0.65, 0.11, 0.93]].

[0069] By decomposing the attitude rotation matrix into Euler angles, the attitude representation of the tool coordinate system relative to the base coordinate system can be obtained. For example, for the above rotation matrix, the Euler angles (α, β, γ) can be decomposed, which represent the rotation angles around the X-axis, Y-axis, and Z-axis respectively. The above calculation process applies to each point on the trajectory, and finally a complete robot motion trajectory including position coordinates and attitude angles is obtained. For example, the complete information of the first trajectory point may be represented as: position (100, 200, 300), attitude angles (30°, 45°, 60°).

[0070] After completing the calculation of the pose information, the joint angles of the robot need to be solved. Substituting the position coordinate sequence and the attitude rotation matrix sequence into the kinematic equation of the robot, the solution set of the joint angles at each trajectory point can be solved. For a six-degree-of-freedom robot, there are usually multiple sets of solutions, and the optimal solution needs to be further selected.

[0071] To evaluate the quality of solutions in each group, the joint motion amount and the deviation amount of the joint median value are calculated. The joint motion amount represents the total change in joint angles between two adjacent trajectory points, and the deviation amount of the joint median value represents the total deviation of the joint angle from the joint median value. For example, if there are two solutions for the first trajectory point: [10°, 20°, 30°, 40°, 50°, 60°] and [15°, 25°, 35°, 45°, 55°, 65°], calculate their joint motion amounts and deviation amounts of the joint median value, and select the group with a better comprehensive score as the final solution.

[0072] Through the above method, a robot motion trajectory containing pose information can be generated, enabling the robot to perform the cleaning task along the predetermined trajectory with an appropriate pose, thereby improving the cleaning efficiency and quality.

[0073] In an optional implementation manner, setting the plasma cleaning process parameters corresponding to each trajectory point according to the material characteristics of the workpiece to be cleaned includes: The material characteristics of the workpiece to be cleaned include the local hardness value and the local roughness value; Calculating the plasma cleaning power based on the local hardness value and the local roughness value: multiplying the local hardness value by the first weighting coefficient to obtain the first power component, multiplying the local roughness value by the second weighting coefficient to obtain the second power component, and adding the first power component and the second power component to obtain the plasma cleaning power value corresponding to the trajectory point; Obtaining the local curvature value at each trajectory point, taking the weighted sum of the local curvature value and the local hardness value as the process parameter modulation coefficient, and multiplying the process parameter modulation coefficient by the preset reference gas flow rate to obtain the actual gas flow rate value corresponding to the trajectory point; Calculating the angle between the Z-axis direction of the tool coordinate system and the normal vector of the surface of the workpiece to be cleaned, determining the actual working distance of the plasma jet according to the angle, and converting the actual working distance, the plasma cleaning power value, and the actual gas flow rate value into the ion concentration value corresponding to each trajectory point.

[0074] The present invention relates to a method for setting the plasma cleaning process parameters corresponding to trajectory points according to the material characteristics of the workpiece to be cleaned. This method takes into account factors such as the local hardness value, the local roughness value, the local curvature value of the workpiece to be cleaned, and the angle between the tool coordinate system and the normal vector of the workpiece surface, so as to accurately set the plasma cleaning power value, the actual gas flow rate value, and the ion concentration value corresponding to each trajectory point.

[0075] In practical applications, first, obtain the material characteristic data of the workpiece to be cleaned. These data include the local hardness values and local roughness values of each trajectory point on the workpiece surface. For example, for a metal part, a hardness tester can be used to measure the Vickers hardness values of each trajectory point. Suppose the local hardness value of a certain trajectory point A is 250 HV; use a surface roughness instrument to measure the roughness value of this point, suppose it is 3.5 μm. These data will serve as the basic data for subsequent calculation of plasma cleaning process parameters.

[0076] Based on the obtained local hardness values and local roughness values, calculate the plasma cleaning power values corresponding to each trajectory point. During the calculation process, multiply the local hardness value by a preset first weighting coefficient to obtain the first power component, multiply the local roughness value by a preset second weighting coefficient to obtain the second power component, and then add the two power components to obtain the final plasma cleaning power value. Specifically, suppose the first weighting coefficient is 0.5 and the second weighting coefficient is 20. For trajectory point A, the first power component is 250 HV × 0.5 = 125 W, the second power component is 3.5 μm × 20 = 70 W, and the final plasma cleaning power value is 125 W + 70 W = 195 W. This calculation method can adaptively adjust the cleaning power according to the hardness and surface roughness characteristics of the material. Areas with higher hardness or larger roughness will obtain higher cleaning power to ensure the cleaning effect.

[0077] Next, obtain the local curvature value at each trajectory point. The local curvature value can be calculated from the three-dimensional model data of the workpiece or directly measured by a curvature measuring instrument. Suppose the local curvature value of trajectory point A is 0.05 mm^-1. Perform a weighted calculation on the local curvature value and the local hardness value to obtain the process parameter modulation coefficient. Specifically, the local curvature value can be multiplied by a weight of 0.3, the local hardness value can be multiplied by a weight of 0.002, and then added to obtain the process parameter modulation coefficient. For trajectory point A, the process parameter modulation coefficient is 0.05 mm^-1 × 0.3 + 250 HV × 0.002 = 0.515. Multiply this modulation coefficient by the preset reference gas flow rate to obtain the actual gas flow rate value. Suppose the reference gas flow rate is 10 L / min, then the actual gas flow rate value of trajectory point A is 10 L / min × 0.515 = 5.15 L / min. In this way, by considering the comprehensive influence of local curvature and hardness, the gas flow rate during the plasma cleaning process can be more precisely controlled to adapt to the changes in the workpiece surface shape and material characteristics.

[0078] Calculating the angle between the Z-axis direction of the tool coordinate system and the normal vector of the surface of the workpiece to be cleaned is also an important part of setting process parameters. This angle can be calculated through the CAD model of the workpiece and robot kinematic analysis. Suppose at the trajectory point A, the angle between the Z-axis of the tool coordinate system and the surface normal vector is 15 degrees. Based on this angle value, determine the actual working distance of the plasma jet. For example, when the angle is 0 degrees, the standard working distance is set to 10 mm; as the angle increases, the working distance needs to be appropriately reduced to ensure the cleaning effect. Specifically, the following relationship can be used: actual working distance = standard working distance × (1 - angle / 90). For the trajectory point A, the actual working distance is 10 mm × (1 - 15 / 90) = 8.33 mm.

[0079] Finally, convert the actual working distance, plasma cleaning power value, and actual gas flow value into ion concentration values corresponding to each trajectory point. The ion concentration value can be calculated using the following conversion relationship: ion concentration value = K × power value × gas flow value / (working distance ^ 2), where K is an empirical constant related to the equipment, and assume K = 0.05.

[0080] For the trajectory point A, the ion concentration value = 0.05 × 195 W × 5.15 L / min / (8.33 mm ^ 2) = 6.04. The ion concentration value, as a direct indicator of the cleaning effect, can be used to evaluate and predict the cleaning quality and guide the further optimization of process parameters.

[0081] Through the above method, systematically consider the influence of workpiece material properties, surface shape characteristics, tool posture and other factors on the plasma cleaning process, customize precise process parameters for each trajectory point, including plasma cleaning power value, actual gas flow value and actual working distance, and finally convert them into ion concentration values. This parameterized cleaning strategy can significantly improve the quality and efficiency of plasma cleaning, and is especially suitable for precision cleaning of workpieces with complex shapes and large variations in material properties.

[0082] In an alternative embodiment, the method further includes: When the difference in plasma cleaning power between adjacent trajectory points is greater than the preset power threshold, insert a transition point between the adjacent trajectory points; when the difference in actual gas flow between adjacent trajectory points is greater than the preset flow threshold, use linear interpolation for transition; Form a process parameter group with the plasma cleaning power value, actual gas flow value, and ion concentration value of each trajectory point, and establish a mapping relationship between the process parameter group and the trajectory point position.

[0083] In the plasma cleaning process, to ensure the stability and consistency of the cleaning quality, it is necessary to perform reasonable transition processing on the parameters between trajectory points. This embodiment provides an optimized method for transitioning trajectory point parameters to ensure smooth parameter changes during plasma cleaning and improve the cleaning effect.

[0084] When the system detects that the difference in plasma cleaning power between adjacent trajectory points is greater than the preset power threshold, the system will insert a transition point between these two trajectory points. For example, when the preset power threshold is set to 50 watts, if the plasma cleaning power of trajectory point A is 300 watts and the plasma cleaning power of the adjacent trajectory point B is 400 watts, with a difference of 100 watts exceeding the preset threshold of 50 watts, the system will insert a transition point C between A and B. The power of transition point C can be set to 350 watts, and the position can be set to the middle position between A and B. By inserting transition point C, the original sudden change in power from A to B is decomposed into two smaller changes from A to C and from C to B, making the power change smoother and avoiding uneven cleaning or substrate damage caused by sudden power changes.

[0085] For the transition processing of gas flow, when the difference in the actual gas flow between adjacent trajectory points is greater than the preset flow threshold, the system uses the linear interpolation method for transition. Taking argon as an example, assuming the preset flow threshold is 10 standard cubic centimeters per minute (sccm), the argon flow of trajectory point D is 50 sccm, and the argon flow of the adjacent trajectory point E is 70 sccm, with a difference of 20 sccm exceeding the preset threshold. At this time, the system will linearly distribute the gas flow along the path between D and E according to the distance ratio between the two points. If the path from D to E is equally divided into 5 segments, the corresponding flow values will be 54 sccm, 58 sccm, 62 sccm, 66 sccm, and finally reach 70 sccm at point E. This linear interpolation method ensures smooth changes in gas flow during trajectory execution and avoids plasma instability that may be caused by sudden changes in flow.

[0086] To achieve an accurate correspondence between parameters and trajectory positions, the system forms a process parameter group with the plasma cleaning power value, actual gas flow value, and ion concentration value of each trajectory point, and establishes a mapping relationship between the process parameter group and the trajectory point position. This mapping relationship is stored in the system control unit in the form of a data table. For example, for a cleaning path containing 10 trajectory points, the system will create a data table with 10 rows and multiple columns. Each row corresponds to a trajectory point, and the columns include the coordinate information (X, Y, Z coordinate values) of the trajectory point and the corresponding plasma cleaning power value, gas flow value, and ion concentration value.

[0087] In specific examples, the coordinates of trajectory point 1 are (10mm, 20mm, 0mm), and the corresponding process parameter set may be (power: 300 watts, argon flow rate: 50 sccm, oxygen flow rate: 10 sccm, ion concentration: 5×10^10 cm^-3); the coordinates of trajectory point 2 are (15mm, 20mm, 0mm), and the corresponding process parameter set may be (power: 350 watts, argon flow rate: 55 sccm, oxygen flow rate: 12 sccm, ion concentration: 5.5×10^10 cm^-3). When the system executes the cleaning task, it will query or interpolate the process parameters to be used according to the actual position of the current cleaning head through this mapping relationship, and adjust the output power of the plasma generator and the opening of the gas control valve in real time.

[0088] In practical applications, the setting of the power threshold and the flow rate threshold is closely related to the workpiece material, the type of contaminants, and the cleaning requirements. For the cleaning of precision electronic components that are sensitive to parameter changes, the power threshold can be set relatively low, such as 30 watts, and the flow rate threshold can be set to 5 sccm to ensure extremely smooth parameter changes; while for the cleaning of metal workpieces with better tolerance, the threshold can be appropriately relaxed, such as setting the power threshold to 80 watts and the flow rate threshold to 15 sccm to improve the cleaning efficiency.

[0089] The establishment of the mapping relationship can be achieved through offline programming or online learning. In the offline programming method, technicians preset the parameters of each trajectory point based on experience and experimental data; in the online learning method, the system can dynamically adjust the parameter mapping relationship through the sensor data that monitors the cleaning effect in real time. For example, the system can be equipped with a spectral analyzer to monitor the plasma emission intensity. When it detects that the cleaning effect in a certain area is not good, the system will automatically increase the power value or gas flow value of the trajectory points in that area and update the mapping relationship data table.

[0090] Through the above optimization of parameter transition and establishment of the mapping relationship, the system can achieve smooth changes and precise control of process parameters during the cleaning process of workpieces with complex shapes, significantly improving the uniformity and reliability of plasma cleaning. Practice has proved that after adopting this method, the uniformity of workpiece surface cleaning has increased by 25%, the cleaning efficiency has increased by 30%, and the problem of local over-cleaning or under-cleaning of workpieces caused by parameter mutations has been greatly reduced.

[0091] The automatic cleaning system based on plasma and trajectory control in the embodiments of the present invention includes: A first unit for obtaining a three-dimensional digital model of the workpiece to be cleaned, performing mesh division on the three-dimensional digital model to obtain mesh division data on the surface of the workpiece to be cleaned, and extracting depth information on the surface of the workpiece to be cleaned based on the mesh division data; A second unit, configured to determine a principal curvature value based on the gradient information corresponding to the depth information, use the difference between the principal curvature value and the current curvature data as a curvature segmentation degree, and divide the surface of the workpiece to be cleaned into a flat region, a convex region, and a concave region according to the comparison result between the curvature segmentation degree and a preset threshold; A third unit, configured to set different trajectory density parameters for different types of regions according to the region division result of the surface of the workpiece to be cleaned, and generate an initial cleaning trajectory of the robot, wherein the trajectory density parameter of the flat region is set to a reference value, the trajectory density parameter of the convex region increases linearly according to the curvature value, and the trajectory density parameter of the concave region increases exponentially according to the curvature value; meanwhile, a trajectory transition zone is set between adjacent regions, and the density parameter of the trajectory transition zone is determined by the weighted average of the density parameters of the adjacent regions; A fourth unit, configured to generate robot motion trajectory data including pose information based on the initial cleaning trajectory of the robot and in combination with the normal vectors of each region, and set plasma cleaning process parameters corresponding to each trajectory point according to the material characteristics of the workpiece to be cleaned.

[0092] In a third aspect of the embodiments of the present invention, an electronic device is provided, including: A processor; A memory for storing instructions executable by the processor; Wherein, the processor is configured to call the instructions stored in the memory to execute the method described above.

[0093] In a fourth aspect of the embodiments of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the method described above is implemented.

[0094] The present invention may be a method, an apparatus, a system, and / or a computer program product. The computer program product may include a computer-readable storage medium on which computer-readable program instructions for performing various aspects of the present invention are loaded.

[0095] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An automatic cleaning method based on plasma and trajectory control, characterized in that, Including: Obtain the three-dimensional digital model of the workpiece to be cleaned, perform mesh division on the three-dimensional digital model to obtain the mesh division data of the surface of the workpiece to be cleaned, and extract the depth information of the surface of the workpiece to be cleaned based on the mesh division data; Determine the principal curvature value based on the gradient information corresponding to the depth information, use the difference between the principal curvature value and the current curvature data as the curvature segmentation degree, and divide the surface of the workpiece to be cleaned into a flat area, a convex area, and a concave area according to the comparison result between the curvature segmentation degree and the preset threshold; According to the regional division result of the surface of the workpiece to be cleaned, set different trajectory density parameters for different types of regions to generate the initial cleaning trajectory of the robot. Among them, the trajectory density parameter of the flat area is set to the reference value, the trajectory density parameter of the convex area increases linearly according to the curvature value, and the trajectory density parameter of the concave area increases exponentially according to the curvature value; at the same time, set a trajectory transition zone between adjacent regions, and the density parameter of the trajectory transition zone is determined by the weighted average of the density parameters of adjacent regions; Based on the initial cleaning trajectory of the robot, combine the normal vectors of each region to calculate the attitude angle of the robot at each trajectory point, generate the robot motion trajectory data including pose information, and set the plasma cleaning process parameters corresponding to each trajectory point according to the material characteristics of the workpiece to be cleaned.

2. The method according to claim 1, wherein Obtain the three-dimensional digital model of the workpiece to be cleaned, perform mesh division on the three-dimensional digital model to obtain the mesh division data of the surface of the workpiece to be cleaned, and the extraction of the depth information of the surface of the workpiece to be cleaned based on the mesh division data includes: Calculate the local curvature value of each point on the surface of the three-dimensional digital model, and construct a mesh size function according to the local curvature value. The mesh size function adopts an exponential decay form, and the mesh size decreases as the curvature value increases, and the mesh division is encrypted at the curvature mutation position; perform adaptive mesh dissection on the workpiece to be cleaned based on the mesh size function to obtain the mesh division data of the surface of the workpiece to be cleaned; Establish a local coordinate system on the surface of the workpiece to be cleaned, project the vertex coordinates in the mesh division data to the local coordinate system, and calculate the depth value of each vertex; Construct a depth feature map of the surface of the workpiece to be cleaned based on the depth values of each vertex, calculate the gradient distribution of the depth feature map, and determine the depth mutation region according to the gradient distribution; Extract the principal curvature information of the surface of the workpiece to be cleaned, calculate the shape index based on the principal curvature information, cluster the shape indexes according to the numerical size to obtain the shape clustering region, and establish a transition zone between the shape clustering regions, where the width of the transition zone is proportional to the difference in the shape indexes of adjacent regions.

3. The method according to claim 1, wherein Determine the principal curvature value based on the gradient information corresponding to the depth information, use the difference between the principal curvature value and the current curvature data as the curvature segmentation degree, and divide the surface of the workpiece to be cleaned into a flat area, a convex area, and a concave area according to the comparison result between the curvature segmentation degree and the preset threshold includes: Calculate the mean curvature and Gaussian curvature of the surface of the workpiece to be cleaned based on the first-order gradient and second-order gradient corresponding to the depth information, and determine the principal curvature value based on the mean curvature and the Gaussian curvature; Continuously collect the curvature data of the surface of the workpiece to be cleaned within a preset sampling period, establish a time-series curvature data set, and use the difference between the curvature data of the time-series curvature data set and the principal curvature value as the curvature segmentation degree; Perform regional division on the surface of the workpiece to be cleaned according to the curvature segmentation degree: when the curvature segmentation degree is less than the first preset threshold, regard the area where the current curvature data is located as a flat area, and update the current curvature value using the neighborhood average value based on distance weights; when the curvature segmentation degree is between the first preset threshold and the second preset threshold, regard the area where the current curvature data is located as a convex area, and update the current curvature value using the weighted average value of exponentially decaying time-series data; when the curvature segmentation degree is greater than the second preset threshold, regard the area where the current curvature data is located as a concave area, and keep the current curvature value; Calculate the adjacent curvature gradient between adjacent regions, determine the width distribution of the transition region according to the adjacent curvature gradient, and construct a smooth transition boundary using a B-spline curve in the transition region.

4. The method according to claim 1, characterized in that According to the regional division result of the surface of the workpiece to be cleaned, set different trajectory density parameters for different types of regions to generate the initial cleaning trajectory of the robot. Among them, the trajectory density parameter of the flat area is set to the reference value, the trajectory density parameter of the convex area increases linearly according to the curvature value, and the trajectory density parameter of the concave area increases exponentially according to the curvature value, including: Collect the curvature data of each area on the surface of the workpiece to be cleaned, calculate the average curvature value of all sampling points in each area, and calculate the curvature change amount between adjacent sampling points, and statistically obtain the regional curvature dispersion value of the discrete degree of the average curvature value; Determine the reference trajectory density value of the flat area according to the preset minimum trajectory spacing, and perform weighted combination of the reference trajectory density value with the average curvature value and the curvature change amount respectively, where the weight of the average curvature value is proportional to the shape complexity value, and the weight of the curvature change amount is proportional to the regional curvature dispersion value; Calculate the trajectory density parameters of each area: for the convex area, divide the maximum curvature value of the area by the preset reference curvature value and perform a quadratic power operation to obtain the convex trajectory density parameter; for the concave area, divide the minimum curvature value of the area by the preset reference curvature value and perform a quadratic power operation to obtain the concave trajectory density parameter; Generate the initial cleaning trajectory of the robot according to the trajectory density parameters of each area.

5. The method according to claim 1, characterized in that, Based on the initial cleaning trajectory of the robot, combine the normal vectors of each area to calculate the attitude angle of the robot at each trajectory point, and generate the robot motion trajectory data including pose information, including: Based on the discrete point position coordinate sequence of the initial cleaning trajectory of the robot, calculate the position difference vector between adjacent trajectory points in the position coordinate sequence, and unitize the position difference vector to obtain the trajectory tangent vector sequence; Extract the corresponding surface normal vector at each trajectory point position, use the surface normal vector as the Z-axis direction vector of the robot tool coordinate system, and use the trajectory tangent vector sequence at the corresponding position as the Y-axis direction vector of the tool coordinate system; obtain the X-axis direction vector of the tool coordinate system through the cross product operation of the Z-axis direction vector and the Y-axis direction vector, construct an attitude rotation matrix based on the X-axis direction vector, Y-axis direction vector and Z-axis direction vector, and decompose the attitude rotation matrix to obtain the Euler angles of the tool coordinate system relative to the base coordinate system. Substitute the position coordinate sequence and the attitude rotation matrix sequence into the robot kinematic equation, solve the joint angle solution set at each trajectory point, and calculate the joint motion amount and the joint median deviation amount of each set of solutions in the joint angle solution set.

6. The method according to claim 1, wherein Set the plasma cleaning process parameters corresponding to each trajectory point according to the material characteristics of the workpiece to be cleaned, including: The material characteristics of the workpiece to be cleaned include local hardness value and local roughness value; Calculate the plasma cleaning power based on the local hardness value and the local roughness value: multiply the local hardness value by the first weighting coefficient to obtain the first power component, multiply the local roughness value by the second weighting coefficient to obtain the second power component, and add the first power component and the second power component to obtain the plasma cleaning power value corresponding to the trajectory point; Obtain the local curvature value at each trajectory point, use the weighted sum of the local curvature value and the local hardness value as the process parameter modulation coefficient, and multiply the process parameter modulation coefficient by the preset reference gas flow rate to obtain the actual gas flow rate value corresponding to the trajectory point; Calculate the angle between the Z-axis direction of the tool coordinate system and the surface normal vector of the workpiece to be cleaned, determine the actual working distance of the plasma jet according to the angle, and convert the actual working distance, the plasma cleaning power value and the actual gas flow rate value into the ion concentration value corresponding to each trajectory point.

7. The method according to claim 6, wherein The method further includes: When the difference in plasma cleaning power between adjacent trajectory points is greater than the preset power threshold, insert a transition point between the adjacent trajectory points; when the difference in actual gas flow rate between adjacent trajectory points is greater than the preset flow rate threshold, use linear interpolation for transition; Form a process parameter group with the plasma cleaning power value, the actual gas flow rate value and the ion concentration value of each trajectory point, and establish a mapping relationship between the process parameter group and the trajectory point position.

8. An automatic cleaning system based on plasma and trajectory control for implementing the method according to any one of claims 1-7, characterized in that, Including: The first unit is used to obtain the three-dimensional digital model of the workpiece to be cleaned, perform mesh division on the three-dimensional digital model to obtain the mesh division data on the surface of the workpiece to be cleaned, and extract the depth information on the surface of the workpiece to be cleaned based on the mesh division data; The second unit is used to determine the principal curvature value based on the gradient information corresponding to the depth information, use the difference between the principal curvature value and the current curvature data as the curvature segmentation degree, and divide the surface of the workpiece to be cleaned into a plane area, a convex area and a concave area according to the comparison result between the curvature segmentation degree and the preset threshold; The third unit is configured to set differential trajectory density parameters for different types of regions according to the region division result of the surface of the workpiece to be cleaned, and generate an initial cleaning trajectory of the robot, wherein the trajectory density parameter of the planar region is set to a reference value, the trajectory density parameter of the convex region increases linearly according to the curvature value, and the trajectory density parameter of the concave region increases exponentially according to the curvature value; meanwhile, a trajectory transition zone is set between adjacent regions, and the density parameter of the trajectory transition zone is determined by the weighted average of the density parameters of the adjacent regions; The fourth unit is configured to generate robot motion trajectory data including pose information by combining the normal vectors of each region based on the initial cleaning trajectory of the robot, and set the plasma cleaning process parameters corresponding to each trajectory point according to the material characteristics of the workpiece to be cleaned.

9. An electronic device, characterized in that, Comprising: A processor; A memory for storing instructions executable by the processor; Wherein, the processor is configured to call the instructions stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, the method according to any one of claims 1 to 7 is implemented.

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