Wheel set painting equipment and method based on visual guidance

Abstract

The invention discloses wheel set painting equipment based on visual guidance, which comprises a rotary driving device, a visual guidance module, a mechanical arm, a painting execution mechanism and a control device, and is characterized in that based on image information of the visual guidance module, a wheel set surface coordinate point set is extracted, and a painting path is generated; and controlling the mechanical arm and the painting execution mechanism to paint the wheel pair along the painting path. According to the invention, the visual guidance control system is utilized to realize accurate identification and tracking of the train wheel set, the painting path is automatically planned, the painting parameters are automatically adjusted, and the efficient and uniform painting effect is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent industrial equipment, and in particular to a wheelset painting device and a painting method based on vision guidance. Background Art

[0002] The present invention relates to a wheel set painting device and a painting method based on vision guidance, aiming to improve the automation level of wheel set painting, ensure the painting quality, reduce labor costs, and reduce environmental pollution. The device uses a vision guidance control system to accurately identify and track the wheel set, automatically plan the painting path, and automatically adjust the painting parameters to achieve efficient and uniform painting effects.

[0003] The utility model patent with application number CN216094496U discloses a wheelset painting device, including a manipulator, a brush body, a control component and a rotary drive mechanism. Painting is performed according to a preset path corresponding to the wheelset model, and then painting is achieved by driving the wheelset to rotate and contact the brush body.

[0004] However, since the structures of the hub, spokes, etc. on the wheelset surface are different and the surface is undulating, it is inefficient to set a preset path for each type of wheelset. Painting only by the brush body abutting against the wheelset and the wheelset rotating cannot control the thickness and uniformity of the paint on the surfaces of different structures. Therefore, there is an urgent need for a wheelset painting device and a painting method based on vision guidance. Summary of the invention

[0005] In order to solve the above problems, the present invention provides a wheelset painting device and a painting method based on vision guidance, which aims to solve the problems of uneven painting and low efficiency on complex surfaces of wheelsets during the painting process.

[0006] The technical solution adopted by the present invention is:

[0007] A wheelset painting device based on vision guidance comprises: a rotation drive device, a vision guidance module, a mechanical arm, a painting actuator, and a control device.

[0008] Rotation drive device: used to support and drive the wheelset to rotate so as to paint from different angles;

[0009] Vision guidance module: used to capture image information of wheelsets;

[0010] Painting actuator: equipped with painting tools, performs painting operations according to the instructions of the control device;

[0011] Robotic arm: connected to the paint actuator, driving the paint actuator to move along the paint path;

[0012] Control device: receiving image information from the visual guidance module, extracting a set of coordinate points on the surface of the wheelset, generating a painting path, and controlling the robot arm and the painting actuator to paint the wheelset along the painting path;

[0013] Each robotic arm is provided with a visual guidance module and a paint actuator. The visual guidance module can be hidden in a groove 10 at the end of the robotic arm through a folding mechanism or a mechanical rotation mechanism. When the paint actuator is performing a painting operation, the visual guidance module rotates and is hidden in the groove at the end of the robotic arm.

[0014] Preferably, the visual guidance module uses a binocular 3D camera installed on the robotic arm, and the robotic arm brings it to a designated position to collect images and obtain sampling points;

[0015] Preferably, the designated position includes a first designated position and a second designated position;

[0016] Preferably, the rotary drive device drives the wheelset to rotate, so that the wheelset captures image information of the wheelset at 0° and 180° respectively and performs painting, thereby ensuring that the entire surface of the wheelset is covered with paint while saving the number of rotations.

[0017] Preferably, the painting tool equipped with the painting actuator is a brush;

[0018] Preferably, the control device extracts a set of wheelset surface coordinate points by filtering, reducing density, and removing discrete noise points and background from the image information;

[0019] Preferably, the control device generates a painting path by generating a bounding box, and generates a painting path by intersecting parallel sections generated according to the vertices of the bounding box with the set of coordinate points on the wheelset surface; when the depth of the bounding box is greater than or equal to a first threshold, the painting path adopts an "O" type path, and when the depth of the bounding box is less than the first threshold, the painting path adopts a "Z" type path.

[0020] Preferably, the control device controls the robotic arm by setting a cylindrical obstacle model and an improved gravitational potential energy algorithm to perform path control of the robotic arm.

[0021] A wheelset painting method based on vision guidance, using any wheelset painting device as described above, characterized in that it comprises:

[0022] S1: The robot arm moves the visual guidance module at its output end to a first designated position and a second designated position;

[0023] S2: Use the visual guidance module to capture the image information of the wheelset;

[0024] S3: The control device receives the image information of the visual guidance module, extracts a set of coordinate points on the surface of the wheelset, generates a painting path, and controls the robot arm and the painting actuator to paint the wheelset along the painting path;

[0025] S4: The rotating driving device drives the wheelset to rotate by a certain angle, and steps S1, S2, and S3 are repeated until all positions of the wheelset to be painted are painted;

[0026] S5: After painting is completed, the vision guidance module inspects the wheelset surface again to ensure the quality of the painting.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] High degree of automation: greatly reduce manual operations and improve production efficiency;

[0029] Strong adaptability: suitable for wheelsets of different sizes and shapes, with good versatility;

[0030] High precision: Visual guidance eliminates the need to enter the model, size and appearance of the wheelset, ensuring the accuracy of the painting position and range;

[0031] Stable quality: Automated control ensures the consistency of coating quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0033] Figure 1 is a schematic diagram of a wheelset painting device based on vision guidance provided by an embodiment of the present invention;

[0034] Figure 2 It is a schematic diagram of painting path planning for different surfaces of a wheelset provided by an embodiment of the present invention;

[0035] Figure 3 A collision detection model for a wheelset painting device provided in an embodiment of the present invention;

[0036] Figure 4 It is a schematic diagram of the resultant force of the gravitational potential energy method provided by an embodiment of the present invention.

[0037] In the figure, 1, frame, 2, control device, 3, paint can, 4, robotic arm, 5, visual guidance module, 6, painting actuator, 7, rotation drive device, 8, second designated position, 9, first designated position, 10, groove, 11, wheelset, YZT, cylinder, JN, capsule body. DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] Example 1

[0040] Since the structures of the hub, spokes and other parts of the wheelset surface are different and the surface is undulating and changeable, it is impossible to paint the wheelset surface through a simple preset painting path. This embodiment provides a wheelset painting device based on vision guidance.

[0041] like Figure 1 As shown, the wheelset painting equipment based on vision guidance includes a rotary drive device 7, a vision guidance module 5, a robotic arm 4, a painting actuator 6, a control device 2, and also includes a frame 1 and a paint can 3.

[0042] Rotation drive device 7: used to support and drive the wheelset 11 to rotate so as to paint from different angles;

[0043] Visual guidance module 5: used to capture image information of wheelset 11;

[0044] Painting actuator 6: equipped with painting tools, performing painting operations according to the instructions of the control device;

[0045] Robotic arm 4: connected to the paint actuator, and drives the paint actuator to move along the paint path according to the instruction of the control device;

[0046] Control device 2: receives image information from the visual guidance module 5, extracts a set of coordinate points on the surface of the wheelset, generates a painting path, and controls the robot arm and the painting actuator to paint the wheelset along the painting path.

[0047] Preferably, the visual guidance module 5 uses a binocular 3D camera, which is installed on the robotic arm 4, and the robotic arm 4 brings it to a designated position to collect images and obtain sampling points;

[0048] Specifically, the visual guidance module 5 uses a binocular 3D camera or a structured light camera, and the accuracy requirement meets the requirement of ≤2% within 2m, and the working range meets the requirement of 0.1m-10m.

[0049] Specifically, a robot arm 4 is provided on both sides of the wheel set 11, and each robot arm 4 is provided with a visual guidance module 5 and a paint actuator 6. The visual guidance module 5 can be hidden in the groove 10 at the end of the robot arm through a folding mechanism or a mechanical rotation mechanism. When the paint actuator 6 is performing a painting operation, the visual guidance module 5 is rotated and hidden in the groove 10 at the end of the robot arm 4. See the attached Figure 1 The left robotic arm; when the visual guidance module 5 is performing the image acquisition operation, it rotates 90 degrees from the groove 10 to be parallel to the wheel axle (x-axis direction), and the paint actuator 6 rotates 90 degrees to be perpendicular to the wheel axle (y-axis direction), see the attached Figure 1 The right robotic arm has the effect of ensuring that the visual guidance module 5 does not interact with the paint actuator during use.

[0050] Preferably, the designated position includes a first designated position 9 and a second designated position 8, the first designated position 9 is above the outer side of the wheel, and the second designated position 8 is above the inner side of the wheel; the first designated position 9, the second designated position 8 and the center of the axle are in the same z-axis plane; their specific positions can be determined by the wheel size, camera parameters, etc., and the visual guidance module 5 must be able to clearly and completely capture the wheelset surface image at the designated position.

[0051] Specifically, first, the initial position of the wheelset is 0°, and the visual guidance module 5 obtains the image information of the wheelset at the first designated position 9 and the second designated position 8 respectively, and then performs the painting operation; then, the rotary drive device drives the wheelset to rotate, and after the wheelset rotates 180°, the visual guidance module 5 captures the image information of the wheelset at the first designated position 9 and the second designated position 8 again and performs the painting operation. The rotation operation is used to deal with the influence of the wheel axle on the occlusion of the image below, thereby ensuring that the full surface of the wheelset is covered with paint on the basis of saving the number of rotations.

[0052] Preferably, the painting tool equipped with the painting actuator 6 is a brush;

[0053] Preferably, the robotic arm 4 is a six-axis robotic arm.

[0054] Preferably, the control device 2 extracts a set of wheelset surface coordinate points by performing through-filtering, cube density reduction, and discrete noise point and background removal on the image information;

[0055] Specifically, the control device receives the image information of the visual guidance module 5. In order to reduce the amount of calculation for the later processing, the initial sampling point data needs to be preprocessed. During the processing, the density of the sampling points should be reduced on the basis of retaining the existing features to speed up the subsequent processing. Thereafter, the standardized sampling points after the preprocessing are finely processed to optimize their surface information.

[0056] Specifically, the initial sampling point data information will contain noise points and outliers due to uncertain background and production conditions. These redundant point information will affect the subsequent calculation speed and path planning process. In addition, by cooperating with the straight-through filtering algorithm and the rotary drive device, selecting a suitable range in the z direction can ensure that the paint can cover the entire wheelset. The straight-through filtering algorithm is used to eliminate these redundant information. The specific steps are as follows:

[0057] By setting the value range of x, y, and z directions, the required sampling point information can be filtered out. Suppose the sampling point data is (x i ,y i ,z i )∈P,x i ,y i ,z i Respectively represent the coordinates of the i-th point. The ranges of the sampling points in three directions are [x min ,x max ],[y min ,y max ],[z min ,z max ], the sampling point through-filter calculation is as follows:

[0058] P′={(x i ,y i ,z i )∈P|x min ≤x i ≤x max ,y min ≤y i ≤y max ,z min ≤z i ≤z max}

[0059] After the straight-through filtering is completed, due to the huge amount of sampling point data and the fact that the paint actuator often has a certain width for the painting operation, too dense sampling points increase the amount of calculation for the path planning calculation. Appropriately reducing the density of sampling points can improve the efficiency of the painting path, and also simplify the data to be processed for subsequent calculations, speeding up the operation. However, the reduction of sampling point density will also lead to the loss of some contour information. The existing technology mostly uses curvature calculation method to reduce density, and estimates the curvature of the sampling point by calculating the normal angle between the sampling point and the adjacent point. Although the operation is simple, the junction between the background and the target and some relatively isolated edge information will be over-densified.

[0060] This application proposes to use cube density reduction processing. First, the sampling points are divided into a group of small cubes, and then the center point formula is used to calculate the position of the center point of the small cube, as shown in the following formula:

[0061]

[0062] Among them, zx max ,zx min ,zy max ,zy min ,zz max ,zz min They represent the minimum and maximum values ​​of the x, y, and z coordinates of all sampling points in a small cube.

[0063] Finally, the calculated center point of the small cube replaces all the sampling points in the group of small cubes to achieve density reduction. The cube density reduction process retains edge information well and has a certain smoothing effect on the curvature, which can improve the smoothness of the painting process.

[0064] After the density reduction process is completed, the sampling point data collection is affected by equipment and environmental factors and may contain certain discrete noise points, which will have a greater impact on the subsequent sampling point processing tasks. If they are not processed, they may be included in the trajectory planning during the painting process.

[0065] Specifically, the present application adopts Gaussian filtering to form a Gaussian distribution graph by analyzing the average distance value between each sampling point in the sampling point set and the adjacent k points, calculating its mean and standard deviation, and analyzing whether the sampling point is a discrete noise point outside the standard.

[0066] Let the Nth point in the sampling point correspond to the x, y, z coordinates as X n , Y n , Z n The distance P from this point to any point m , as shown below:

[0067]

[0068] Calculate the mean μ and standard deviation σ of the distance between each point and the adjacent k points.

[0069]

[0070] A threshold M is set based on the standard deviation of the average distance of the sampling points, and sampling points with a standard deviation greater than the threshold M are deleted.

[0071] After the above-mentioned filtering, density reduction, and discrete point removal, only the part of the ground of the workbench is left with useless information. The prior art usually uses the least squares method to fit the plane, but since the distribution of the sampling points is not completely random, it will greatly affect the accuracy of the least squares method. This application uses a ground filtering algorithm to remove the background. Flip the sampling point, and assume that a piece of cloth covers the bottom of the sampling point. At this time, the area covered by the cloth is the ground background area.

[0072] The basic formula for simulating cloth covering is as follows:

[0073]

[0074] Where m is the mass parameter of the cloth particle, X is the simulated position of the cloth particle at time t, and F ext (X, t) represents the external driving factors (gravity, collision, etc.), F int (X,t) represents the internal driving factors (internal connections between particles).

[0075] The position of the cloth particle is determined by both external and internal driving factors. Assuming that only external factors exist and the internal factors are 0, the position of the next cloth particle movement can be calculated by giving the value of Δt:

[0076]

[0077] Where m is the mass parameter of the cloth particle, G is a constant, and Δt is the time step.

[0078] The reversal phenomenon of the vacant part in the sampling point plane is controlled by controlling the movement relationship of two adjacent particles. That is, if both particles can move, they move the same distance in opposite directions at the same time. The movement distance is as follows:

[0079]

[0080] Where d is the displacement of the particle; when the particle is movable, b is equal to 1; when it is immovable, b is equal to 0; p i For p o The neighboring particles of n are normalized to the unit vector in the vertical direction at the sampling point.

[0081] The specific steps of the ground filtering algorithm are as follows:

[0082] 1) Invert the sampling point data, set the mesh size of the simulated cloth according to the requirements and set it above the highest point;

[0083] 2) Project the inverted sampling points and cloth particles to the same horizontal plane, find the adjacent points of the cloth particles, and record the intersection height values.

[0084] 3) By comparing the height value of the nearest neighbor point of the cloth particle with the recorded intersection height value, determine whether the particle needs to be moved. If the height value of the nearest neighbor point is less than or equal to the intersection height value, the height value of the particle is set to the relative height value and it is set to be immovable.

[0085] 4) Calculate the applied displacement of each cloth particle around it based on the internal driving factors in the cloth simulation.

[0086] 5) Repeat (3) and (4) until the maximum height change of all cloth particles is small enough or reaches a preset iteration threshold to ensure that the position of the particles is stable.

[0087] 6) Calculate the height difference between the sampling point and the cloth particle. According to the preset threshold, judge the height difference between the sampling point and the cloth particle. If the height difference is less than the threshold, mark the point as a ground point, otherwise mark it as a non-ground point. After deleting the ground point, extract the set of wheel set surface coordinate points.

[0088] Preferably, the control device generates the painting path by generating a bounding box, and generates the painting path by intersecting the parallel sections generated by the vertices of the bounding box with the set of coordinate points on the surface of the wheelset. When the depth of the bounding box is greater than or equal to the first threshold, the painting path adopts an "O" type path, and when the depth of the bounding box is less than the first threshold, the painting path adopts a "Z" type path, see Attachment 1. Figure 2 Planning of painting paths for different surfaces of wheelsets.

[0089] Specifically, the bounding box method generates a minimum bounding box of a model, and the commonly used ones are AABB type and OBB type. The OBB type is also called a directed bounding box, which has good envelopment for different target bodies, has a higher degree of real-time performance than the AABB type, and is more suitable for wheelsets with uncertain shapes.

[0090] After determining the bounding box type, a series of slices parallel to a certain face of the bounding box can be generated according to the vertices of the bounding box. These slices are parallel to each other and equidistant. The generated intersection line is the expected trajectory line, which can be obtained by projection method or intersection method.

[0091] In view of the characteristics of the wheel set having convex and flat parts, this application adopts two different path trajectories when generating the path trajectory from the trajectory line according to the characteristics of different parts. A "Z"-type path trajectory is used for the flat part, and an "O"-type path trajectory is used for the convex part. Because the "Z"-type path trajectory consists of straight lines and broken line segments, it has a simple structure and is easy to calculate and generate. When the surface changes of the part are relatively gentle, it can reduce the travel distance and time of the paint actuator; the "O"-type path trajectory has better adaptability, the speed change of the robot arm 4 is small, it can reduce motion vibration and loss, and is more suitable for convex parts.

[0092] Specifically, when the bounding box depth is greater than or equal to the first threshold, the painting path adopts an "O" type path, and when the bounding box depth is less than the first threshold, the painting path adopts a "Z" type path. By selecting appropriate painting trajectory planning according to different surface characteristics of the parts, the work efficiency and painting effect of the painting operation can be improved.

[0093] Preferably, the control device controls the robot arm 4 by setting a cylindrical obstacle model and an improved gravitational potential energy algorithm to perform path control of the robot arm 4.

[0094] Specifically, during the operation of the robot 4, due to the increasing complexity of the working environment and the motion trajectory, the possibility of interference and collision between the robot 4 and the environment and its own connecting rods continues to increase. The occurrence of collision will not only damage the robot 4, causing property losses, but also threaten the personal safety of the operator. During the wheelset painting process, since the wheelset is mainly composed of wheels and axles, the structure is relatively fixed. Therefore, in order to reduce the complexity of the collision detection algorithm, the robot 4 and obstacles are simplified.

[0095] Since the wheels and axles are both cylindrical, the wheels and axles are wrapped in a cylindrical form as obstacles. The capsule JN conventionally used in the art is used to wrap the 4-link robot arm, and the collision detection problem between the two is converted into a collision problem between the capsule JN and the cylinder YZT. See the attached figure. Figure 3 The collision detection model of the wheelset painting equipment is shown.

[0096] Preferably, the specific coordinate position of the cylinder YZT can be obtained through the set of coordinate points on the aforementioned wheelset surface, and the size of the cylinder YZT can reserve a certain margin compared to the size of the wheelset surface to improve the reliability of the collision calculation; collision detection is performed by calculating the shortest distance between the capsule body JN surface and the cylinder YZT surface, which has the effect of simplifying the collision calculation model.

[0097] The gravitational potential energy algorithm is a classic algorithm in robot arm path planning. It realizes path planning by constructing a virtual potential field. Figure 4 The schematic diagram of the combined force of the gravitational potential energy method is shown. The motion space of the robot arm is regarded as a virtual potential field composed of a repulsive potential field and an attractive potential field. The overall repulsive potential field is composed of the repulsive field of each obstacle in the workspace. The direction of the repulsive potential field is away from the obstacle, while the attractive potential field is generated by the target position and is directed toward attracting the robot arm 4 to reach the target position. Therefore, by setting the target position as the lowest potential field, theoretically the movement of the robot arm 4 will eventually reach this position. According to the potential energy gap between the current position of the robot arm 4 and the target position, the moving direction and speed of the robot arm 4 are calculated, and the path planning of the robot arm 4 can be finally realized.

[0098] This application improves the traditional gravitational potential energy algorithm, and sets a maximum distance limit d between the robot 4 and the target position to solve the problem that the robot 4 is far away from the target position and generates a large attraction, which may cause a collision. max ,δ is the attraction factor, d(p,p g ) is the position p of the robot 4 to the target position p g The distance between them. The modified attractive potential function is:

[0099]

[0100] The attraction function is:

[0101]

[0102] In order to solve the problem that the target position and the obstacle position are close to each other and cannot be solved, the repulsive force potential field function is corrected according to the relative distance from the robot arm 4 to the target position. The improved repulsive force potential field function is:

[0103]

[0104] Where μ is the repulsive force factor, d s represents the exclusion range of each obstacle, d(p,p o ) is the minimum distance between the robot arm 4 and the obstacle, d(p,p g ) is the distance between the robot 4 and the target position, and n is a positive coefficient. The repulsive force function is to find the partial derivative along the obstacle and the target position respectively:

[0105]

[0106]

[0107] i o Represents the unit direction vector from the obstacle to the robot 4, i g Represents the unit direction vector of the robot arm 4 to the target position.

[0108] Therefore, the total force applied to the robot arm 4 is:

[0109] F to (p) = F at (p)+F re (p)

[0110] The present application completes the obstacle avoidance path planning of the robot arm 4 in the task space by setting a cylindrical obstacle model and an improved gravitational potential energy algorithm to control the path of the robot arm 4.

[0111] A wheelset painting method based on vision guidance, using any wheelset painting device as described above, characterized in that it comprises:

[0112] S1: The robot arm moves the visual guidance module at its output end to a first designated position and a second designated position;

[0113] S2: Use the visual guidance module to capture the image information of the wheelset;

[0114] S3: The control device receives the image information of the visual guidance module, extracts a set of coordinate points on the surface of the wheelset, generates a painting path, and controls the robot arm and the painting actuator to paint the wheelset along the painting path;

[0115] S4: The rotating driving device drives the wheelset to rotate by a certain angle, and steps S1, S2, and S3 are repeated until all positions of the wheelset to be painted are painted;

[0116] S5: After painting is completed, the vision guidance module inspects the wheelset surface again to ensure the quality of the painting.

[0117] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A wheelset painting device based on vision guidance, comprising: Rotary drive device, visual guidance module, robotic arm, paint actuator, control device, wherein: The rotary drive device is used to support and drive the wheelset to rotate so as to paint from different angles; The visual guidance module is used to capture image information of the wheelset; The mechanical arm is connected to the paint actuator to drive the paint actuator to move along the paint path; The painting actuator is equipped with a painting tool and performs painting operations according to the instructions of the control device; The control device receives the image information of the visual guidance module, extracts the set of coordinate points on the surface of the wheelset, generates a painting path, and controls the robot arm and the painting actuator to paint the wheelset along the painting path; The visual guidance module and the paint actuator are provided on the robotic arm. The visual guidance module can be hidden in the groove at the end of the robotic arm through a folding mechanism or a mechanical rotation mechanism. When the paint actuator is performing a painting operation, the visual guidance module rotates and is hidden in the groove at the end of the robotic arm.

2. The wheel set painting equipment based on vision guidance according to claim 1 is characterized in that: The visual guidance module uses a binocular 3D camera installed on the robotic arm, and the robotic arm brings it to a designated position for image acquisition to obtain sampling points.

3. The wheel set painting equipment based on vision guidance according to claim 2 is characterized in that: The designated positions include a first designated position and a second designated position; the rotating drive device drives the wheelset to rotate, so that the wheelset captures image information of the wheelset at 0° and 180° respectively and performs painting, corresponding to the visual guidance module at the first designated position and the second designated position respectively.

4. The wheel set painting equipment based on vision guidance according to claim 1 is characterized in that: The control device extracts a set of wheelset surface coordinate points by filtering, reducing density, and removing discrete noise points and background from image information.

5. The wheel set painting equipment based on vision guidance according to claim 4 is characterized in that: The filtering process adopts straight-through filtering, and its calculation formula is as follows: P′={(x i ,y i ,z i )∈P|x min ≤x i ≤x max ,y min ≤y i ≤y max ,z min ≤z i ≤z max } Among them, the sampling point data is (x i ,y i ,z i )∈P,x i ,y i ,z i They represent the coordinates of the i-th point respectively, and the ranges of the sampling points in the three directions are [x min ,x max ],[y min ,y max ],[z min ,z max ].

6. The wheel set painting device based on vision guidance according to claim 5 is characterized in that: The density processing adopts cube density reduction processing. First, the processed sampling points are divided into a group of small cubes, and then the center point formula is used to calculate the position of the center point of the small cube, as shown in the following formula: Among them, zx max ,zx min ,zy max ,zy min ,zz max ,zz min They respectively represent the minimum and maximum values ​​of the x, y, and z coordinates of all sampling points in a small cube. Finally, the calculated center point of the small cube replaces all the sampling points in the group of small cubes.

7. The wheelset painting device based on vision guidance according to claim 1 is characterized in that: The control device generates a painting path by generating a bounding box, and generates a painting path by intersecting the parallel sections generated by the vertices of the bounding box with the set of coordinate points on the wheelset surface; when the depth of the bounding box is greater than or equal to a first threshold, the painting path adopts an "O" type path, and when the depth of the bounding box is less than the first threshold, the painting path adopts a "Z" type path.

8. The wheelset painting device based on vision guidance according to claim 1 is characterized in that: The control device controls the robot arm by setting a cylindrical obstacle model and an improved gravitational potential energy algorithm to control the path of the robot arm.

9. The wheel set painting device based on vision guidance according to claim 8 is characterized in that: The improved gravitational potential energy algorithm is specifically: Set the maximum distance limit d between the robot arm and the target position max ,δ is the attraction factor, d(p,p g ) is the robot position p to the target position p g The distance between The attraction function is: The repulsive force function is: Where μ is the repulsive force factor, d s represents the exclusion range of each obstacle, d(p,p o ) is the minimum distance between the robot arm and the obstacle, d(p,p g ) is the distance between the robot and the target position, n is a positive coefficient, i o Represents the unit direction vector from the obstacle to the robotic arm, i g The unit direction vector representing the robot arm to the target position; The total force applied to the robot arm is: F to (p)=F at (p)+F re (p)。 10. A wheelset painting method based on vision guidance, using the wheelset painting device according to any one of claims 1 to 9, characterized in that: include: S1: The robot arm moves the visual guidance module at its output end to a first designated position and a second designated position; S2: Use the visual guidance module to capture the image information of the wheelset; S3: The control device receives the image information of the visual guidance module, extracts a set of coordinate points on the surface of the wheelset, generates a painting path, and controls the robot arm and the painting actuator to paint the wheelset along the painting path; S4: The rotating driving device drives the wheelset to rotate by a certain angle, and steps S1, S2, and S3 are repeated until all positions of the wheelset to be painted are painted; S5: After painting is completed, the vision guidance module inspects the wheelset surface again to ensure the quality of the painting.

Images