A coating processing method, device, terminal and storage medium

Abstract

The application relates to the technical field of coating processing, and discloses a coating processing method, a coating processing device, a terminal and a storage medium, the method comprising the following steps: obtaining three-dimensional point cloud data of a target workpiece; based on the three-dimensional point cloud data, all workpiece effective surfaces of the target workpiece are recognized and segmented, and the surface types of each workpiece effective surface are determined; for each workpiece effective surface, a corresponding grid model is generated, and geometric attribute information of each grid model is extracted; according to the surface types and the geometric attribute information, coating process parameters corresponding to each workpiece effective surface are determined; based on the coating process parameters and the grid model of each workpiece effective surface, a spraying operation area covering each workpiece effective surface is generated, and a coating execution track in the spraying operation area is determined; and the coating execution device is controlled to perform a coating operation based on the coating execution track. The application can realize coating operation of non-standard workpieces without relying on original three-dimensional drawings of the workpieces.

Description

Technical Field

[0001] This application relates to the field of coating processing technology, and in particular to a coating processing method, apparatus, terminal and storage medium. Background Technology

[0002] In the context of automated coating of workpieces in intelligent manufacturing, the operation of multi-axis industrial robots relies heavily on the original 3D drawing model of the target workpiece. Key aspects such as trajectory planning and posture adjustment all require standard digital models as the basis for parameter configuration. However, issues such as non-standard customized workpieces, missing drawings, equipment refurbishment, and shape deviations are common in the field, making it impossible to obtain a complete workpiece model that matches the actual working conditions. Standard models are unable to support accurate calibration and reliable execution, severely restricting the automation of coating operations for non-standard workpieces. Summary of the Invention

[0003] In view of this, embodiments of this application provide a coating process method, apparatus, terminal, and storage medium.

[0004] In a first aspect, embodiments of this application provide a coating process method, including: Acquire the 3D point cloud data of the target workpiece; Based on the three-dimensional point cloud data, all effective surfaces of the target workpiece are identified and segmented, and the surface type of each effective surface is determined. For each effective surface of the workpiece, a corresponding mesh model is generated, and the geometric attribute information of each mesh model is extracted; Based on the surface type and the geometric attribute information, determine the coating process parameters corresponding to each effective surface of the workpiece; Based on the coating process parameters and the mesh model of each effective surface of the workpiece, a spraying operation area covering each effective surface of the workpiece is generated, and the coating execution trajectory is determined within the spraying operation area; The coating execution device is controlled to perform coating operations based on the coating execution trajectory.

[0005] Secondly, embodiments of this application provide a coating processing apparatus, comprising: The acquisition module is used to acquire the three-dimensional point cloud data of the target workpiece; The surface type determination module is used to identify and segment all effective surfaces of the target workpiece based on the three-dimensional point cloud data, and determine the surface type of each effective surface of the workpiece. The extraction module is used to generate a corresponding mesh model for each effective surface of the workpiece and extract the geometric attribute information of each mesh model; The process parameter determination module is used to determine the coating process parameters corresponding to each effective surface of the workpiece based on the surface type and the geometric attribute information. The execution trajectory determination module is used to generate a spraying operation area covering each effective surface of the workpiece based on the coating process parameters and the mesh model of each effective surface of the workpiece, and to determine the coating execution trajectory within the spraying operation area. The control module is used to control the coating execution device to perform coating operations based on the coating execution trajectory.

[0006] Thirdly, embodiments of this application provide a terminal device, the terminal device including a processor and a memory, the memory storing a computer program, and the processor executing the computer program to implement the above-described coating processing method.

[0007] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed on a processor, performs the above-described coating process.

[0008] The embodiments of this application have the following beneficial effects: This application identifies the effective surfaces of a workpiece and determines its surface type based on 3D point cloud data, thereby generating a mesh model, extracting geometric attribute information, automatically matching coating process parameters with surface type and geometric attributes, and generating the spraying operation area and coating execution trajectory accordingly. Finally, it controls the coating execution device to complete the operation. This method can achieve end-to-end semantic connectivity from the original point cloud to controllable coating actions. This method abandons the rigid dependence on the original 3D drawings of the workpiece, enabling the system to directly model and process real physical workpieces; using the effective surface of the workpiece as the smallest process decision unit, it combines shape features, spatial relationships, and process requirements, thereby ensuring accurate exclusion of no-spray areas, precise activation of anti-sag strategies, and uniform coverage of curved surfaces; the entire process has strong adaptability and repeatability, which can significantly improve the automation level and process consistency of non-standard workpiece coating operations. Attached Figure Description

[0009] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 A first flow diagram of the coating process according to an embodiment of this application is shown; Figure 2 A second flow diagram of the coating process according to an embodiment of this application is shown; Figure 3 This paper illustrates a schematic diagram of the workpiece acquisition pose according to an embodiment of this application. Figure 4 A schematic diagram of the third process of the coating process according to an embodiment of this application is shown; Figure 5 A schematic diagram of the fourth process of the coating process according to an embodiment of this application is shown; Figure 6 A schematic diagram of a workpiece according to an embodiment of this application is shown; Figure 7 A schematic diagram of the fifth process of the coating process according to an embodiment of this application is shown; Figure 8 A bias schematic diagram of an embodiment of this application is shown; Figure 9 A sixth process diagram of the coating process according to an embodiment of this application is shown; Figure 10 A schematic diagram of the equipment for a coating process according to an embodiment of this application is shown; Figure 11 A schematic diagram of a coating processing apparatus according to an embodiment of this application is shown. Detailed Implementation

[0011] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0012] The components of the embodiments of this application described and illustrated in the accompanying drawings can be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of this application provided in the drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0013] In the following text, the terms "comprising," "having," and their cognates, which may be used in various embodiments of this application, are intended only to indicate a particular feature, number, step, operation, element, component, or combination thereof, and should not be construed as primarily excluding the presence of one or more other features, numbers, steps, operations, elements, components, or combinations thereof, or adding the possibility of one or more combinations thereof. Furthermore, the terms "first," "second," "third," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance.

[0014] Unless otherwise specified, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of this application pertain. Terms (such as those defined in commonly used dictionaries) shall be interpreted as having the same meaning as in their contextual meaning in the relevant technical field and shall not be construed as having an idealized or overly formal meaning, unless clearly defined in the various embodiments of this application.

[0015] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0016] In intelligent manufacturing scenarios involving automated workpiece coating, multi-axis industrial robots are core equipment for achieving process automation and improving production efficiency and operational accuracy, and are widely used in workpiece coating, grinding, and assembly processes. However, in actual on-site production, existing robot operation solutions face two key constraints: First, traditional operations heavily rely on the original 3D drawings and models of the workpiece. Core processes such as trajectory planning, point calibration, and attitude adjustment all require standard digital models as the basis for parameter configuration. However, due to factors such as non-standard customized workpieces, refurbishment of old equipment, lack of drawings and data, and changes in on-site working conditions, it is often impossible to obtain a complete workpiece model that matches the actual shape and size deviations on-site. This makes it difficult for robots to accurately calibrate their operating parameters and stably execute automated processing tasks for non-standard workpieces. Second, industrial sites have complex layouts of equipment, tooling, and materials, with numerous fixed obstacles as well as dynamic obstacles such as personnel and transportation equipment. Currently, robot motion trajectories are mostly planned offline based on ideal open environments, lacking the ability to perceive and respond to the real working space. During operation, interference and collisions easily occur between the robot body, end effector, or the model swept across the spraying area and obstacles, causing work interruptions, workpiece damage, or equipment downtime, seriously affecting continuity and safety.

[0017] Based on this, the embodiments of this application construct a set of implementable, verifiable and reusable paperless workpiece coating processing methods by acquiring and reconstructing three-dimensional point clouds without relying on original drawings, automatically matching process parameters driven by surface type, adaptive constraints of the spraying area based on a two-dimensional parametric mapping model, and dynamic environment collaborative obstacle avoidance guarantee that integrates offline planning and online response, so as to improve the automation level and operational stability of on-site operations.

[0018] The coating process method will be explained below with reference to some specific embodiments.

[0019] Figure 1 A schematic flowchart of a coating process according to an embodiment of this application is shown. Exemplarily, the coating process includes steps S100-S600: Step S100: Obtain the three-dimensional point cloud data of the target workpiece.

[0020] The purpose of this step is to acquire complete point cloud data representing the true geometric shape and spatial dimensions of the target workpiece through non-contact 3D visual perception without relying on the original 3D drawing model of the target workpiece, so as to provide a data foundation for subsequent semantic modeling and process-driven trajectory generation.

[0021] The specific methods to achieve this goal include two scanning modes: one is a handheld scanning mode, in which the operator holds the vision acquisition device and scans the target workpiece from multiple angles; the other is a self-planning scanning mode, in which the vision acquisition device is fixedly installed at the end of the coating execution process, and the control system drives the end of the coating execution process to move along a pre-planned path, thereby enabling the vision acquisition device to complete a full-coverage scan of the target workpiece. Whether to enable the autonomous planning scanning mode depends on the assessment of the structural similarity between the target workpiece and historical workpieces. When the control system determines that the structural similarity between the target workpiece and historical workpieces is greater than or equal to a preset similarity threshold, it indicates that the two have a high degree of consistency in overall outline, main feature distribution, and spatial topological relationship, and the autonomous planning scanning process is initiated; otherwise, the handheld scanning mode is used to complete the initial point cloud acquisition.

[0022] When the autonomous planning scanning mode is enabled, the overall geometric contour features of the target workpiece are first analyzed and extracted based on the point cloud data of historical workpieces. Then, a convex hull geometric model of the target workpiece is constructed based on these geometric contour features. Next, acquisition poses are set up at the edge corners of the convex hull geometric model. Then, combined with the field of view parameters of the vision acquisition device, the visible point cloud that can be captured under each acquisition pose is calculated, and all visible point clouds are deduplicated and summarized to obtain the coverage point cloud. The proportion of the coverage point cloud to the total point cloud of the target workpiece is then calculated as the point cloud coverage rate. If this proportion is less than the preset coverage rate threshold, new acquisition poses are inserted equidistantly along the line connecting two adjacent acquisition poses to increase the scanning density and recalculate the point cloud coverage rate until the proportion is greater than or equal to the preset coverage rate threshold. Finally, the vision acquisition device is controlled to complete the scan at all determined acquisition poses to obtain three-dimensional point cloud data that meets the integrity requirements. Throughout the autonomous scanning process, the control system synchronously performs collision detection, taking the motion trajectory of the coating execution end, the spatial pose of the vision acquisition device, and the point cloud model of the target workpiece as inputs, and verifying in real time whether there is spatial interference between the coating execution end or the vision acquisition device and the target workpiece, ensuring that the planned scanning path can be executed safely.

[0023] In some implementations, such as Figure 2As shown, step S100 specifically includes steps S110-S130: Step S110: In response to the fact that the structural similarity between the target workpiece and the historical workpiece is greater than or equal to a preset similarity threshold, the geometric contour features of the target workpiece are determined based on the point cloud data of the historical workpiece, and the convex hull geometric model of the target workpiece is constructed based on the geometric contour features.

[0024] In this step, if the structural similarity between the target workpiece and the historical workpiece is greater than or equal to a preset similarity threshold (e.g., 70%, which can be set as needed), the autonomous planning scanning mode is activated. At this time, the point cloud data of the historical workpiece can be retrieved to analyze its overall shape trend, main protrusion distribution, and outer enclosure shape, thereby determining the geometric contour features of the target workpiece. Subsequently, based on the geometric contour features, the system generates a convex hull geometric model of the target workpiece. This convex hull geometric model is the boundary of the smallest convex polyhedron containing all the point cloud data of the target workpiece, used to characterize its spatial envelope range.

[0025] Step S120: Based on the field of view parameters of the visual acquisition device, at least one acquisition pose is set at the edge corner position of the convex hull geometric model.

[0026] Among them, the field of view parameters include, but are not limited to, the Z-axis field of view height and the effective field of view radius. The field of view parameters are mainly used to determine the spatial area that can be covered by a single static acquisition.

[0027] In this step, after determining the field of view parameters of the vision acquisition device, multiple acquisition poses can be initially set up at the edge corners of the target workpiece, based on the characteristics of the target workpiece. The purpose of setting up multiple acquisition poses is to make the theoretical visible areas corresponding to each position connect with each other, thereby enabling the scanning of the entire target workpiece.

[0028] Figure 3 A schematic diagram of the acquisition pose setup for a target workpiece is shown. Figure 3 In the image, 'a' represents the target workpiece and 'b' represents the acquired pose. Figure 3 As shown, multiple acquisition poses (e.g., 4) can be preset. These acquisition poses should ideally cover all areas of the target workpiece. It should be noted that... Figure 3 For ease of understanding, a relatively simple workpiece is given. In actual use, it may be a workpiece with a more complex structure, but the pose acquisition can be set using the method of this embodiment.

[0029] It should be noted that only one vision acquisition device can be used during the entire scanning process. After all the final acquisition poses are determined, the vision acquisition devices can be moved to the corresponding acquisition poses in the set order to scan the corresponding areas. However, multiple vision acquisition devices can also be used. If multiple vision acquisition devices are used, the area to be acquired by each vision acquisition device can be preset and then each vision acquisition device can be moved in sequence until all acquisition work is completed.

[0030] Step S130: If the proportion of the visible point cloud that the visual acquisition device can cover to the total point cloud of the target workpiece is greater than or equal to the preset coverage threshold in all acquisition poses, then control the visual acquisition device to perform three-dimensional scanning of the target workpiece in all acquisition poses to obtain three-dimensional point cloud data.

[0031] Specifically, such as Figure 4 As shown, step S130 specifically includes steps S131-S134: Step S131: Calculate the visible point cloud that the visual acquisition device can cover under each acquisition pose, and summarize and deduplicate to obtain the covered point cloud.

[0032] In this step, the control system performs a spatial intersection operation between the spatial region defined by the field of view parameters of the vision acquisition device, namely the theoretical visible spatial region jointly defined by the Z-axis field of view height and the effective field of view radius, and the point cloud data of the target workpiece. All point cloud data located within this spatial region are extracted as the visible point cloud of the corresponding initial acquisition position. Then, the visible point clouds extracted from each initial acquisition position are compared point by point according to the three-dimensional rectangular coordinates. Duplicate points with identical spatial coordinates are removed to obtain the deduplicated point cloud set, which is the covered point cloud.

[0033] Step S132: Calculate the proportion of the covered point cloud to the total point cloud of the target workpiece, and use it as the point cloud coverage rate.

[0034] In this step, the total number of covered point clouds is counted, and the total number of point clouds of the target workpiece is counted simultaneously. Then, the quotient obtained by dividing the total number of covered point clouds by the total number of point clouds of the target workpiece is calculated to obtain the point cloud coverage rate.

[0035] Step S133: If the point cloud coverage rate is less than the preset coverage rate threshold, a new acquisition pose is added between adjacent acquisition poses, and the point cloud coverage rate is recalculated based on all updated acquisition poses and the field of view parameters of the visual acquisition device until the point cloud coverage rate is greater than or equal to the preset coverage rate threshold.

[0036] In this step, the preset coverage threshold is a pre-set value, such as 80%. When the point cloud coverage rate is less than the preset coverage rate threshold, it indicates that the point cloud acquired by the current set of acquisition positions fails to represent all the geometric information of the outer surface of the target workpiece, and there are point cloud areas that have not been captured by the vision acquisition device. At this time, it is necessary to increase the acquisition density to enhance the coverage of the overall shape of the target workpiece. Therefore, in this embodiment, one or more new acquisition positions can be inserted at equal straight-line distances on the straight line between every two adjacent initial acquisition positions. After the insertion is completed, all initial acquisition positions and the newly added acquisition positions are used together as the updated acquisition position set. Subsequently, for the updated acquisition position set, steps S131 and S132 are repeated. That is, for each new acquisition position, the corresponding visible point cloud is recalculated based on the spatial region defined by the field of view parameters of the vision acquisition device. All visible point clouds are summarized and three-dimensional coordinates are deduplicated to obtain a new covered point cloud. Then, the new point cloud coverage rate is recalculated. This process is continuously cyclically executed until the calculated point cloud coverage rate is greater than or equal to the preset coverage rate threshold. At this time, the interpolation operation is stopped, and the current updated acquisition position set is determined as the final acquisition position set.

[0037] Step S134: Control the vision acquisition device to perform three-dimensional scanning of the target workpiece at all updated acquisition poses to obtain three-dimensional point cloud data.

[0038] In this step, the driving coating execution end moves sequentially to each position in the final acquisition position set determined in step S133; at each position, the control system triggers the vision acquisition device to perform a static three-dimensional scan to acquire local point cloud data from that perspective; finally, the control system stitches and fuses all local point cloud data through a spatial coordinate registration algorithm to generate complete three-dimensional point cloud data covering the entire outer surface of the target workpiece.

[0039] Step S200: Based on the three-dimensional point cloud data, identify and segment all effective surfaces of the target workpiece, and determine the surface type of each effective surface.

[0040] Among them, the surface types include suspended structural surfaces identified based on spatial adjacency relationships, locally protruding feature surfaces identified based on normal consistency and height deviation, and one or more of planes and curved surfaces.

[0041] In this step, after acquiring the 3D point cloud data, noise reduction and filtering are first performed on the 3D point cloud data. Then, based on the degree of change of the point cloud normal and the degree of change of the local point cloud curvature, the 3D point cloud data is clustered and segmented to obtain the point cloud cluster set of each workpiece's effective surface. Finally, based on the outer envelope shape, normal consistency, area parameters, and adjacent point cloud distribution characteristics of the point cloud cluster set, the surface type of each workpiece's effective surface is determined, and geometric completion processing is performed on the point cloud cluster set of the specific type.

[0042] In some implementations, such as Figure 5 As shown, step S200 includes steps S210-S220: Step S210: Based on the degree of change of the point cloud normal and the degree of change of the local point cloud curvature, the three-dimensional point cloud data is clustered and segmented to obtain the point cloud cluster set of each workpiece effective surface.

[0043] In this step, the normal vector of each point in the 3D point cloud data and its changing trend are calculated, and the curvature of the continuous point array in the local area of ​​the point cloud is analyzed. Then, based on the degree of change of the point cloud normal and the degree of change of the local point cloud curvature as a joint criterion, points that are spatially close and have similar trends of change of normal and curvature are grouped into the same group to form multiple non-overlapping point cloud cluster sets. Each point cloud cluster set corresponds to an effective surface of the target workpiece.

[0044] Step S220: Determine the surface type of each workpiece's effective surface based on the outer envelope shape, normal consistency, area parameters, and distribution characteristics of adjacent point clouds of the point cloud cluster set.

[0045] In this step, for each point cloud cluster, its outer envelope shape, the concentration of all point normal vectors (as a normal consistency index), and the coverage area (as an area parameter) are calculated, and its relative distribution relationship with other point cloud clusters in 3D space is analyzed (as the distribution characteristics of adjacent point clouds); then, the surface type is determined according to the following rules: If a point cloud cluster has neighboring point cloud clusters only on the side indicated by its normal vector within a preset search radius, and no neighboring point cloud clusters on the opposite side, then this point cloud cluster is defined as a suspended structure surface. This situation is common in scenarios with incomplete scan coverage, such as in single-view scanning scenarios where the visual acquisition device only captures a single isolated point cloud cluster, which is a suspended structure surface. Figure 5 Suspended flange.

[0046] in, Figure 6 A schematic diagram of a workpiece is given. Figure 6In the example, the workpiece is a cuboid (this workpiece is just an example, taking a simple cuboid as an example; in reality, the shape of the workpiece may vary). It has a top flange e on the top and a suspended flange f on the side.

[0047] If there exist two point cloud clusters whose normal consistency indices both satisfy the preset consistency conditions, and the projection distance between the centroids of the two clusters on their respective normal vector directions is less than or equal to a preset height tolerance range, and the ratio of their area parameters is greater than a preset area ratio threshold, then the point cloud cluster with the smaller area parameter is determined as a local convex feature surface; for example... Figure 6 The top partial protrusion feature shown is essentially an installation structure that protrudes relative to the adjacent large surface.

[0048] For other point cloud clusters, if their outer envelope shape is flat and the normal consistency index is higher than the preset plane determination threshold, they are determined to be planes; if their outer envelope shape is continuously curved and the curvature change exceeds the preset curved surface determination threshold, they are determined to be curved surfaces.

[0049] After determining the face type, the following geometric completion process must be performed: For a point cloud cluster set identified as a suspended structural surface, the normal vector of the point cloud cluster set is used as the extension direction, and the length corresponding to the preset lug thickness parameter is extended in the opposite direction to generate a symmetrically distributed complete point cloud. This complete point cloud is then merged with the original point cloud cluster set to form complete point cloud data of the suspended structural surface; for example... Figure 6 The side overhang structure shown is an example of achieving geometric integrity completion using this method; for the point cloud cluster set identified as a local convex feature surface, the normal vector of the point cloud cluster set is used as the extension direction, and the length corresponding to the preset flange height parameter is extended along its positive direction to generate the top surface completion point cloud. This completion point cloud is then merged with the original point cloud cluster set to form complete local convex feature surface point cloud data; for example... Figure 6 The top partial convex feature shown is an example of how geometric integrity is completed.

[0050] Step S300: For each effective surface of the workpiece, generate a corresponding mesh model and extract the geometric attribute information of each mesh model.

[0051] This step transforms the classified and completed point cloud clusters in S220 into a digital mesh model with accurate geometric description capabilities, providing a structured data foundation for subsequent matching of coating process parameters. Achieving this requires sequentially completing two steps: mesh surface generation and geometric attribute extraction.

[0052] For each effective surface of a workpiece, the control system uses its corresponding point cloud cluster set as input to generate a mesh model for that effective surface. Specifically, this includes: if the outer envelope boundary of the point cloud cluster set corresponding to the effective surface of the workpiece is a closed polygon, and its point cloud normal consistency index is higher than a preset plane determination threshold, then the effective surface of the workpiece is determined to be planar; the control system uses the outer envelope boundary of the point cloud cluster set as the contour and uses a triangulation algorithm to generate a continuously covered planar mesh model; if the outer envelope boundary of the point cloud cluster set corresponding to the effective surface of the workpiece is a closed curve, and its curvature change exceeds a preset curved surface determination threshold, then the effective surface of the workpiece is determined to be curved surface; the control system arranges the points on its outer envelope boundary in an orderly manner along the principal curvature direction of the point cloud cluster set, and then connects adjacent boundary points in the order of arrangement to generate a mesh surface model with continuous curvature transition; for other effective surfaces of the workpiece, the control system generates triangular patches using three non-collinear point cloud data as vertices, and gradually covers the entire point cloud cluster set by iteratively adding patches, finally forming a closed mesh model.

[0053] After generating the mesh model corresponding to the effective surface of each workpiece, the control system extracts geometric attribute information based on the mesh model; wherein, the geometric attribute information includes, but is not limited to, one or more of the following: normal direction, radius of curvature, area, spacing between adjacent regions, and included angle between adjacent regions.

[0054] The specific extraction methods are as follows: the normal direction is obtained by calculating the weighted average of the normal vectors of all triangular facets in the mesh model; the radius of curvature is obtained by fitting the radius of curvature of the arc or sphere of a local region in the mesh model; the area is obtained by accumulating the areas of all triangular facets in the mesh model; the distance between adjacent regions is obtained by calculating the Euclidean distance between the centroid of the current mesh model and the centroid of the neighboring mesh model; and the angle between adjacent regions is obtained by calculating the minimum angle between the normal direction of the current mesh model and the normal direction of the neighboring mesh model.

[0055] Step S400: Determine the coating process parameters corresponding to the effective surfaces of each workpiece based on the surface type and geometric attribute information.

[0056] This step transforms the surface type and attribute information of each surface obtained above into specific process instructions that can drive the final action of the coating process, thereby achieving differentiated control of the coating operation. Specifically, based on the surface type and geometric attribute information of each workpiece's effective surface, corresponding coating process parameters can be matched from the process knowledge base. The process knowledge base includes, but is not limited to, surface type, geometric attribute conditions, and corresponding coating process parameters; the coating process parameters include, but are not limited to, one or more of the following: the feed speed of the coating execution device, paint flow rate, spraying distance, spray overlap rate, and spray gun attitude adjustment.

[0057] If the effective surface of the workpiece is a suspended structure surface, then the current effective surface of the workpiece is determined as a no-spray area and no-spray process parameters are matched; such no-spray process parameters may include sending a spray gun shut-off signal to the end of the coating execution and excluding the spatial area covered by the mesh model during the trajectory planning stage. If the surface type of the effective surface of the workpiece is a locally raised feature surface, and the angle between the normal direction of the current effective surface of the workpiece and the direction of gravity is less than the preset anti-sagging angle threshold, then the anti-sagging process parameters are matched; for example, the anti-sagging process parameters may include reducing the paint flow rate to 60%-80% of the normal flow rate, increasing the feed speed of the coating execution device, shortening the spraying distance, etc.

[0058] If the effective surface of the workpiece is a curved surface and the radius of curvature of the current effective surface of the workpiece is less than the preset curvature threshold, then the curved surface spraying process parameters are matched. For example, the curved surface spraying process parameters may include: increasing the spray overlap rate, using two-layer superimposed trajectory for spraying, and adjusting the posture of the coating execution end in real time to maintain a constant distance from the nozzle center point to the curved surface normal.

[0059] If the effective surface of the workpiece is a plane, or its surface type is a curved surface but the radius of curvature is greater than or equal to the preset curvature threshold, or its surface type is a locally raised feature surface but the angle between the normal direction and the gravity direction is greater than or equal to the preset anti-sag angle threshold, then the conventional coating process parameters are matched; the conventional coating process parameters may include standard feed speed, standard paint flow rate, standard spraying distance, standard spray overlap rate, etc.

[0060] Step S500: Based on the coating process parameters and the mesh model of each workpiece's effective surface, generate a spraying operation area covering each workpiece's effective surface, and determine the coating execution trajectory within the spraying operation area.

[0061] This step transforms the process semantics into physically executable spatial constraints, avoiding ineffective spraying, overspraying, and missed spraying, thereby improving paint utilization and coating consistency.

[0062] In some implementations, such as Figure 7 As shown, based on the mesh model of the effective surface of each workpiece according to the coating process parameters, a spraying operation area covering the effective surface of each workpiece is generated, including steps S510-S540: Step S510: Establish a two-dimensional parametric mapping model for the mesh model corresponding to the effective surface of each workpiece.

[0063] In this step, the mesh model corresponding to the effective surface of each workpiece is used as input, and the texture coordinate mapping method is used to map the three-dimensional surface single value to the two-dimensional plane to generate a one-to-one corresponding two-dimensional parametric mapping model. The two-dimensional parametric mapping model retains the topological relationship and geometric scale characteristics of the original mesh model, so as to be used for efficient region calculation in the two-dimensional domain in the future.

[0064] Step S520: In the two-dimensional parametric mapping model, perform an inward bias operation on the boundary contour and an outward bias operation on the hole contour to obtain the biased region.

[0065] In this step, the control system extracts the outer boundary contour and the internal hole contour from the two-dimensional parametric mapping model; then, the outer boundary contour is offset inward by a preset offset distance along its normal direction to form an inwardly narrowed boundary; the internal hole contour is offset outward by the same preset offset distance along its normal direction (e.g., ...). Figure 8 As shown: the dashed rectangle is obtained by offsetting the outer boundary contour inward by a preset offset distance along its normal direction, and the dashed circle is obtained by offsetting the inner hole contour outward by the same preset offset distance along its normal direction, forming an outer expansion boundary; a minimum size threshold for the hole contour is set, and for hole contours with a size smaller than this threshold, the outward offset operation is not performed; finally, the area enclosed by the inner contraction boundary and the outer expansion boundary is determined as the offset region.

[0066] Step S530: Based on the spray pattern sweep model at the end of the coating execution and the offset region, spatial intersection is performed to obtain the intersection point cloud set, and the proportion of the intersection point cloud set to the point cloud set corresponding to the local convex feature surface is calculated.

[0067] In this step, the spray pattern sweep model is the three-dimensional envelope formed by the spray medium covering the space at the end of the coating execution in a static spraying state. The control system projects the spray pattern sweep model onto the plane of the two-dimensional parametric mapping model to obtain its two-dimensional projection area. Then, the spatial intersection of the two-dimensional projection area and the offset area is calculated, and the discrete point cloud data in the intersection area is extracted to form the intersection point cloud set. Simultaneously, the control system retrieves the point cloud set corresponding to the local convex feature surface determined in S220. Finally, the proportion of the number of intersection point cloud sets to the number of point cloud sets corresponding to the local convex feature surface is calculated.

[0068] In step S540, if the ratio is greater than the preset tolerance threshold, the offset distance is reduced, and the inward and outward offset operations are re-executed until the ratio is less than or equal to the tolerance threshold. The offset area that meets the tolerance threshold is then determined as the spraying operation area.

[0069] If the ratio is greater than the preset tolerance threshold, the bias distance is reduced, and steps S520 and S530 are re-executed until the ratio is less than or equal to the preset tolerance threshold.

[0070] Exemplary, if the ratio calculated in step S530 is greater than a preset tolerance threshold, the current offset distance is reduced by a fixed step size. Then, the control system re-executes the offset operation in step S520 and the intersection calculation in step S530 with the updated offset distance. This process is repeated until the calculated ratio is less than or equal to the preset tolerance threshold. The control system then uses the finally offset region as the spraying operation area corresponding to the effective surface of the workpiece. This region is a closed connected region in the two-dimensional parametric mapping model. After the boundary is offset, the risk that the spray gun spray medium may cover the local raised feature surface has been eliminated, so as to ensure the safety and compliance of the coating operation.

[0071] In some implementations, such as Figure 9 As shown, the coating execution trajectory is determined within the spraying operation area, including steps S550-S570: Step S550: Generate an initial coating trajectory point sequence based on the spraying operation area.

[0072] Exemplary, this step requires mapping the spraying operation area determined in step S550 back to three-dimensional space to obtain its actual coverage area on the workpiece surface; then, along the main scanning direction of the area, sampling is performed uniformly at a preset trajectory point spacing to generate a series of discrete spatial coordinate points; then, according to the normal direction of the mesh model at each coordinate point, corresponding attitude parameters are assigned to form an initial coating trajectory point sequence containing position and attitude information.

[0073] Step S560: Perform trajectory smoothing processing on the initial coating trajectory point sequence to obtain a coating execution trajectory with continuous position and attitude.

[0074] During the process of generating the painting execution trajectory, kinematic feasibility verification and basic collision constraint verification are performed simultaneously.

[0075] As an example, the control system performs interpolation and filtering on the initial painting trajectory point sequence to keep the rate of change of position coordinates and the rate of change of attitude Euler angle between adjacent trajectory points continuous, eliminating abrupt changes and obtaining a painting execution trajectory that is both positionally and attitudeally continuous. Kinematic feasibility verification is used to eliminate pose abrupt changes that would prevent the robot from achieving its goals. Specifically, it includes: taking the optimal pose of the end effector for painting, input by the operator via the teach pendant, as a reference, where the optimal pose is a numerical combination of the angles of the six joint axes; the control system uses a numerical iterative method based on the pseudo-inverse of the Jacobian matrix to solve for the robot joint angle solution set that satisfies the position and attitude constraints of each point on the painting trajectory; then, it calculates the Euclidean distance between each joint angle combination and the optimal pose in this solution set, and selects the joint angle combination with the smallest distance as the final inverse solution of the current trajectory point.

[0076] Basic collision constraint verification is used to eliminate trajectory segments that cause interference between the end effector of the painting process and the target workpiece. Specifically, it includes: performing spatial interference detection on the geometric models of each link of the robot and the end effector model corresponding to the final inverse solution, the point cloud model of the target workpiece, and the virtual spraying area model constructed based on the spraying area sweep model. When any detection result indicates the existence of interference, while keeping the normal direction of the painting process trajectory unchanged, the inverse solution that satisfies the collision constraint is regenerated in one of the following ways: adjusting the rotation angle around the normal direction; or adjusting the displacement of the external axis, such as the X-axis, Y-axis, and Z-axis translation mechanism of the gantry truss, or the rotation axis of the positioner.

[0077] The kinematic feasibility verification and the basic collision constraint verification are performed simultaneously. That is, for each trajectory point, the inverse kinematics solution, similarity screening, interference detection, and obstacle avoidance adjustment are performed in sequence until the joint angle solution that satisfies both kinematic feasibility and collision safety is obtained.

[0078] Step S570: The painting execution trajectory that has passed the kinematic feasibility check and the basic collision constraint check is determined as the final painting execution trajectory.

[0079] In this step, the joint angle sequence obtained after double verification of all trajectory points is integrated into a complete robot motion command sequence. This command sequence ensures that each pose can be realized by the physical structure of the coating execution device, and that there is no spatial interference between any part and the target workpiece or virtual spraying area throughout the process. The control system sends this command sequence as the final coating execution trajectory to the coating execution device controller for execution.

[0080] Step S600: The coating execution device is controlled to perform the coating operation based on the coating execution trajectory.

[0081] In this step, the determined coating execution trajectory is converted into a sequence of motion commands that the coating execution device controller can parse, including the position coordinates, attitude parameters and joint angle values ​​at each moment. Subsequently, the control system drives the coating execution end to move strictly according to the command sequence, and synchronously controls the spray gun to output paint according to the coating process parameters matched by S400, so as to complete the coating operation of the target workpiece.

[0082] Furthermore, during the process of controlling the movement of the end effector along the coating execution trajectory, dynamic environment collaborative obstacle avoidance is implemented. This includes: periodically collecting on-site environmental point cloud data during the movement of the end effector along the coating execution trajectory; identifying dynamic obstacles and updating the spatial pose of the end effector based on the latest environmental point cloud data; when a dynamic obstacle is detected intruding into the safe area of ​​the coating sweep model or body model of the end effector, a local obstacle avoidance trajectory segment is generated in real time based on the current pose and target pose of the end effector, while satisfying the normal direction constraint of the coating execution trajectory and the kinematic continuity constraint of the robot; and performing temporal pose continuity fitting between the local obstacle avoidance trajectory segment and two adjacent trajectory points in the coating execution trajectory to form a complete trajectory that ensures continuous coating operations.

[0083] The dynamic environment collaborative obstacle avoidance mechanism can be understood as follows: During the movement of the coating execution end along the coating execution trajectory, the on-site environmental point cloud data is periodically triggered at fixed time intervals; then, based on the on-site environmental point cloud data obtained from two consecutive acquisitions, differential processing is performed to extract point cloud clusters whose spatial positions have changed, and these clusters are identified as dynamic obstacles; subsequently, the three-dimensional spatial pose of the dynamic obstacle is updated according to its spatial distribution in the latest frame of point cloud data; when the point cloud data of the dynamic obstacle is detected to intrude into the safe area of ​​the spraying area sweep model of the coating execution end, or into the safe area of ​​the body model of the coating execution end, a local obstacle avoidance response is triggered; wherein, the safe area of ​​the spraying area sweep model is a three-dimensional envelope space formed by extending a preset safe distance outward from the outer surface of the model, and the safe area of ​​the body model can be the same safe distance extended outward from the outer surface of the body model of the coating execution end. A three-dimensional envelope space is defined by a safety distance. Upon triggering, the control system, based on the current pose of the end of the painting process and the pose of the next target, generates a local obstacle avoidance trajectory segment containing at least three intermediate poses by adjusting the rotation angle around the normal or the displacement of the external axis, while maintaining the normal direction of the painting trajectory unchanged. After collision detection confirms no interference, this local obstacle avoidance trajectory segment is inserted into the original painting trajectory. Finally, the control system can use a polynomial interpolation algorithm to perform time-pose continuity fitting between the local obstacle avoidance trajectory segment and two adjacent original trajectory points in the painting trajectory. The fitting result ensures that the position, linear velocity, linear acceleration, attitude angle, angular velocity, and angular acceleration of the insertion segment's starting point and the previous original trajectory point, and the ending point and the next original trajectory point are all continuous. The resulting complete trajectory can both avoid dynamic obstacles and ensure the continuity and stability of the painting process.

[0084] It should be noted that the coating process method in this embodiment can be executed by a dedicated hardware system, such as... Figure 10 As shown ( Figure 10(where 'c' represents the gantry truss and 'd' represents the industrial robot) This system includes: a two-axis XY gantry truss traveling mechanism, a first six-axis industrial robot, a second six-axis industrial robot, a vision acquisition device, a painting execution device, and a central control system. The two-axis XY gantry truss traveling mechanism serves as the load-bearing base of the entire system. Its X and Y axes are arranged in mutually orthogonal directions in the horizontal plane to provide a large-scale, high-precision spatial positioning reference. The vision acquisition device is fixedly installed on the end flange of the first six-axis industrial robot, forming a scanning unit. The painting execution device is fixedly installed on the end flange of the second six-axis industrial robot, forming a work execution unit. The central control system is communicatively connected to the gantry truss, the two six-axis industrial robots, the vision acquisition device, and the painting execution device, and is used to uniformly schedule scanning and painting tasks. During the operation, the central control system first drives the first six-axis industrial robot to move according to the determined acquisition posture under the cooperative positioning of the gantry gantry, completing the acquisition of three-dimensional point cloud data of the target workpiece. After the point cloud data is processed by S200 to S400 to generate coating process parameters, the central control system then drives the second six-axis industrial robot to execute the coating execution trajectory under the cooperative positioning of the gantry gantry, and synchronously controls the coating execution device to perform the spraying operation according to the matched process parameters. This dual-machine collaborative architecture can ensure complete decoupling and high-precision connection between the scanning process and the coating process in space, time and coordinate system, avoiding repetitive positioning errors and system downtime losses caused by frequent changes of the end effector of a single robot.

[0085] This embodiment automatically activates a scanning path planning method based on a convex hull geometric model and the field of view parameters of the visual acquisition device by determining whether the structural similarity between the target workpiece and historical workpieces is greater than or equal to a preset similarity threshold. The iteration termination condition is based on whether the point cloud coverage is greater than or equal to a preset coverage threshold, ensuring the acquisition of complete and reliable 3D point cloud data and effectively addressing issues such as missing drawings, workpiece shape deviations, and on-site layout limitations. In the surface recognition and process matching stage, clustering and segmentation are performed based on the degree of change in point cloud normal and the degree of change in local point cloud curvature. This accurately identifies surface types such as suspended structural surfaces, locally protruding feature surfaces, planes, and curved surfaces. Combined with geometric attribute information such as normal direction, radius of curvature, area, distance between adjacent areas, and included angle, differentiated process parameters such as no-spray, anti-sagging, curved surface spraying, and conventional coating are matched from the process knowledge base, thereby improving the consistency of coating quality. Furthermore, the spraying area is generated with inward and outward biases using a two-dimensional parametric mapping model. The bias distance is adjusted based on the proportion of the intersection point cloud between the spraying area sweep model and local raised feature surfaces, precisely controlling the spraying boundary and reducing paint waste. Moreover, while satisfying normal constraints and kinematic feasibility, the coating execution trajectory simultaneously completes basic collision constraint verification and dynamic environment collaborative obstacle avoidance, ensuring continuous and stable operation and significantly reducing manual debugging workload.

[0086] Figure 11 A schematic diagram of a coating processing apparatus according to an embodiment of this application is shown. Exemplarily, the coating processing apparatus includes: The acquisition module 100 is used to acquire the three-dimensional point cloud data of the target workpiece.

[0087] The surface type determination module 200 is used to identify and segment all effective surfaces of the target workpiece based on three-dimensional point cloud data, and determine the surface type of each effective surface.

[0088] The extraction module 300 is used to generate a corresponding mesh model for each effective surface of the workpiece and extract the geometric attribute information of each mesh model.

[0089] The process parameter determination module 400 is used to determine the coating process parameters corresponding to the effective surfaces of each workpiece based on the surface type and geometric attribute information.

[0090] The execution trajectory determination module 500 is used to generate a spraying operation area covering the effective surface of each workpiece based on the coating process parameters and the mesh model of each workpiece's effective surface, and to determine the coating execution trajectory within the spraying operation area.

[0091] The control module 600 is used to control the coating execution device to perform coating operations based on the coating execution trajectory.

[0092] In some implementations, acquiring 3D point cloud data of the target workpiece includes: in response to the structural similarity between the target workpiece and historical workpieces being greater than or equal to a preset similarity threshold, determining the geometric contour features of the target workpiece based on the point cloud data of the historical workpieces, and constructing a convex hull geometric model of the target workpiece based on the geometric contour features; deploying at least one acquisition pose at the edge corner positions of the convex hull geometric model according to the field of view parameters of the vision acquisition device; if the proportion of the visible point cloud that the vision acquisition device can cover to the total point cloud of the target workpiece under all acquisition poses is greater than or equal to a preset coverage threshold, then controlling the vision acquisition device to perform 3D scanning of the target workpiece at all acquisition poses to obtain 3D point cloud data.

[0093] In some implementations, if the proportion of the visible point cloud covered by the visual acquisition device to the total point cloud of the target workpiece in all acquisition poses is greater than or equal to a preset coverage threshold, then the visual acquisition device is controlled to perform a three-dimensional scan of the target workpiece at all acquisition poses to obtain three-dimensional point cloud data. This includes: calculating the proportion of the covered point cloud to the total point cloud of the target workpiece as the point cloud coverage rate; if the point cloud coverage rate is less than the preset coverage threshold, then adding a new acquisition pose between adjacent acquisition poses, and recalculating the point cloud coverage rate based on the updated total acquisition poses and the field of view parameters of the visual acquisition device, until the point cloud coverage rate is greater than or equal to the preset coverage threshold; and controlling the visual acquisition device to perform a three-dimensional scan of the target workpiece at all updated acquisition poses to obtain three-dimensional point cloud data.

[0094] In some implementations, based on 3D point cloud data, all effective surfaces of the target workpiece are identified and segmented, and the surface type of each effective surface is determined, including: Based on the degree of change in the normal and the degree of change in the curvature of the local point cloud data, the 3D point cloud data is clustered and segmented to obtain a point cloud cluster set for each workpiece's effective surface. According to the outer envelope shape, normal consistency, area parameter, and distribution characteristics of adjacent point clouds of the point cloud cluster set, the surface type of each workpiece's effective surface is determined. Among them, the surface type includes suspended structural surfaces identified based on spatial adjacency, locally protruding feature surfaces identified based on normal consistency and height deviation, and one or more of planes and curved surfaces.

[0095] In some implementations, the geometric attribute information includes one or more of the following: normal direction, radius of curvature, area, distance between adjacent regions, and angle between adjacent regions. Based on the surface type and geometric attribute information, the coating process parameters corresponding to each workpiece's effective surface are determined, including: matching the corresponding coating process parameters from a process knowledge base based on the surface type and geometric attribute information of each workpiece's effective surface; wherein, if the surface type of the workpiece's effective surface is a suspended structure surface, the current workpiece's effective surface is determined as a no-spray area, and no-spray process parameters are matched; if the surface type of the workpiece's effective surface is a locally protruding feature surface, and the angle between the normal direction and the gravity direction of the current workpiece's effective surface is less than a preset anti-sagging angle threshold, then anti-sagging process parameters are matched; if the surface type of the workpiece's effective surface is a curved surface, and the radius of curvature of the current workpiece's effective surface is less than a preset curvature threshold, then curved surface spraying process parameters are matched.

[0096] In some implementations, a spraying operation area covering each workpiece's effective surface is generated based on the mesh model of each workpiece's effective surface according to the coating process parameters. This includes: establishing a two-dimensional parametric mapping model for the mesh model corresponding to each workpiece's effective surface; performing an inward offset operation on the boundary contour and an outward offset operation on the hole contour in the two-dimensional parametric mapping model to obtain the offset region; performing spatial intersection between the spraying area sweep model at the end of the coating execution and the offset region to obtain an intersection point cloud set, and calculating the proportion of the intersection point cloud set to the point cloud set corresponding to the local protruding feature surface; if the proportion is greater than a preset tolerance threshold, reducing the offset distance and re-performing the inward and outward offset operations until the proportion is less than or equal to the tolerance threshold; and determining the offset region that meets the tolerance threshold as the spraying operation area.

[0097] In some implementations, determining the coating execution trajectory within the spraying operation area includes: generating an initial coating trajectory point sequence based on the spraying operation area; performing trajectory smoothing on the initial coating trajectory point sequence to obtain a coating execution trajectory that is both positionally and orientationally continuous; wherein, during the generation of the coating execution trajectory, kinematic feasibility verification and basic collision constraint verification are performed simultaneously; kinematic feasibility verification is used to eliminate pose abrupt changes that would prevent the robot from achieving its goal; basic collision constraint verification is used to eliminate trajectory segments that would cause interference between the coating execution device and the target workpiece body; and the coating execution trajectory that has passed the kinematic feasibility verification and basic collision constraint verification is determined as the final coating execution trajectory.

[0098] In some implementations, the method further includes: implementing dynamic environment collaborative obstacle avoidance during the process of controlling the end effector to move along the coating execution trajectory; the dynamic environment collaborative obstacle avoidance includes: periodically collecting on-site environmental point cloud data during the movement of the end effector along the coating execution trajectory; identifying dynamic obstacles and updating the spatial pose of the end effector based on the latest environmental point cloud data; when a dynamic obstacle is detected to intrude into the safe area of ​​the spraying area sweep model or body model of the end effector, based on the current pose and target pose of the end effector, generating a local obstacle avoidance trajectory segment in real time, under the premise of satisfying the normal direction constraint of the coating execution trajectory and the kinematic continuity constraint of the robot; and fitting the local obstacle avoidance trajectory segment with the temporal pose continuity between two adjacent trajectory points in the coating execution trajectory to form a complete trajectory that ensures continuous coating operation.

[0099] It is understood that the apparatus in this embodiment corresponds to the coating process method in the above embodiments, and the options in the above embodiments are also applicable to this embodiment, so they will not be described again here.

[0100] This application also provides a terminal device, exemplary of which includes a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to enable the terminal device to perform the functions of the various modules in the above-described coating processing method or the above-described coating processing apparatus.

[0101] The processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, including at least one of a Central Processing Unit (CPU), Graphics Processing Unit (GPU), Network Processor (NP), Digital Signal Processor (DSP), Application-Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this application.

[0102] The memory can be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc. The memory is used to store computer programs, and the processor can execute the computer programs accordingly after receiving execution instructions.

[0103] This application also provides a computer-readable storage medium for storing the computer program used in the aforementioned terminal device. For example, the computer-readable storage medium may include, but is not limited to, various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0104] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that, in alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0105] In addition, the functional modules or units in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0106] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a smartphone, personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0107] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A coating process method, characterized in that, include: Acquire the 3D point cloud data of the target workpiece; Based on the three-dimensional point cloud data, all effective surfaces of the target workpiece are identified and segmented, and the surface type of each effective surface is determined. For each effective surface of the workpiece, a corresponding mesh model is generated, and the geometric attribute information of each mesh model is extracted; Based on the surface type and the geometric attribute information, determine the coating process parameters corresponding to each effective surface of the workpiece; Based on the coating process parameters and the mesh model of each effective surface of the workpiece, a spraying operation area covering each effective surface of the workpiece is generated, and the coating execution trajectory is determined within the spraying operation area; The coating execution device is controlled to perform coating operations based on the coating execution trajectory.

2. The coating process method according to claim 1, characterized in that, The acquisition of the three-dimensional point cloud data of the target workpiece includes: In response to the fact that the structural similarity between the target workpiece and the historical workpiece is greater than or equal to a preset similarity threshold, the geometric contour features of the target workpiece are determined based on the point cloud data of the historical workpiece, and the convex hull geometric model of the target workpiece is constructed based on the geometric contour features. Based on the field of view parameters of the visual acquisition device, at least one acquisition pose is set at the edge corner position of the convex hull geometric model. If the proportion of the visible point cloud that the visual acquisition device can cover to the total point cloud of the target workpiece in all the acquisition poses is greater than or equal to the preset coverage threshold, then the visual acquisition device is controlled to perform a three-dimensional scan of the target workpiece in all the acquisition poses to obtain the three-dimensional point cloud data.

3. The coating process method according to claim 2, characterized in that, If the proportion of the visible point cloud covered by the visual acquisition device to the total point cloud of the target workpiece in all the acquisition poses is greater than or equal to the preset coverage threshold, then the visual acquisition device is controlled to perform a three-dimensional scan of the target workpiece at all the acquisition poses to obtain the three-dimensional point cloud data, including: Calculate the visible point cloud that the visual acquisition device can cover under each acquisition pose, and obtain the covered point cloud after summarizing and deduplicating. The proportion of the covered point cloud to the total point cloud of the target workpiece is calculated as the point cloud coverage rate. If the point cloud coverage is less than a preset coverage threshold, a new acquisition pose is added between adjacent acquisition poses, and the point cloud coverage is recalculated based on all updated acquisition poses and the field of view parameters of the visual acquisition device, until the point cloud coverage is greater than or equal to the preset coverage threshold. The vision acquisition device is controlled to perform three-dimensional scanning of the target workpiece at all updated acquisition poses to obtain the three-dimensional point cloud data.

4. The coating process method according to claim 1, characterized in that, The process of identifying and segmenting all effective surfaces of the target workpiece based on the 3D point cloud data, and determining the surface type of each effective surface, includes: Based on the degree of change in the point cloud normal and the degree of change in the local point cloud curvature of the three-dimensional point cloud data, the three-dimensional point cloud data is clustered and segmented to obtain a point cloud cluster set for each effective surface of the workpiece. Based on the outer envelope shape, normal consistency, area parameters, and adjacent point cloud distribution characteristics of the point cloud cluster set, the surface type of each effective surface of the workpiece is determined; wherein, the surface type includes suspended structure surfaces identified based on spatial adjacency relationships, locally protruding feature surfaces identified based on normal consistency and height deviation, and one or more of planes and curved surfaces.

5. The coating process method according to claim 4, characterized in that, The geometric attribute information includes one or more of the following: normal direction, radius of curvature, area, distance between adjacent regions, and angle between adjacent regions; The step of determining the coating process parameters corresponding to each effective surface of the workpiece based on the surface type and the geometric attribute information includes: Based on the surface type and geometric attribute information of each effective surface of the workpiece, the corresponding coating process parameters are matched from the process knowledge base; If the surface type of the effective surface of the workpiece is the suspended structure surface, then the current effective surface of the workpiece is determined as a no-spray area, and no-spray process parameters are matched. If the surface type of the effective surface of the workpiece is the local protrusion feature surface, and the angle between the normal direction and the gravity direction of the current effective surface of the workpiece is less than the preset anti-sagging angle threshold, then the anti-sagging process parameters are matched. If the surface type of the effective surface of the workpiece is the curved surface, and the radius of curvature of the current effective surface of the workpiece is less than the preset curvature threshold, then the curved surface spraying process parameters are matched.

6. The coating process method according to claim 5, characterized in that, The process of generating a spraying area covering each effective surface of a workpiece based on the coating process parameters and the mesh model of each workpiece includes: A two-dimensional parametric mapping model is established for the mesh model corresponding to each effective surface of the workpiece; In the two-dimensional parametric mapping model, an inward bias operation is performed on the boundary contour, and an outward bias operation is performed on the hole contour to obtain the biased region. Based on the spatial intersection of the spraying area sweep model at the end of the coating execution and the offset region, an intersection point cloud set is obtained, and the proportion of the intersection point cloud set to the point cloud set corresponding to the local protruding feature surface is calculated. If the ratio is greater than the preset tolerance threshold, the bias distance is reduced, and the inward and outward bias operations are re-executed until the ratio is less than or equal to the tolerance threshold. The biased region that meets the tolerance threshold is determined as the spraying operation area.

7. The coating process method according to claim 6, characterized in that, Determining the coating execution trajectory within the spraying operation area includes: An initial coating trajectory point sequence is generated based on the spraying operation area; The initial painting trajectory point sequence is subjected to trajectory smoothing processing to obtain a painting execution trajectory that is both positionally and orientationally continuous; wherein, during the generation of the painting execution trajectory, kinematic feasibility verification and basic collision constraint verification are performed simultaneously. The painting execution trajectory that passes the kinematic feasibility check and the basic collision constraint check is determined as the final painting execution trajectory.

8. The coating process method according to claim 1, characterized in that, Also includes: During the process of controlling the movement of the coating execution end along the coating execution trajectory, dynamic environmental collaborative obstacle avoidance is implemented; The dynamic environment collaborative obstacle avoidance guarantee includes: During the movement of the coating execution end along the coating execution trajectory, point cloud data of the on-site environment is periodically collected; Based on the latest environmental point cloud data, dynamic obstacles are identified and the spatial pose of the end effector of the painting process is updated. When a dynamic obstacle is detected to intrude into the safe area of ​​the spraying area sweep model or the body model of the coating execution end, a local obstacle avoidance trajectory segment is generated in real time based on the current pose and target pose of the coating execution end, under the premise of satisfying the coating execution trajectory normal direction constraint and the robot kinematic continuity constraint. The local obstacle avoidance trajectory segment is fitted with the time pose continuity of two adjacent trajectory points in the painting execution trajectory to form a complete trajectory that ensures continuous painting operation.

9. A coating processing apparatus, characterized in that, include: The acquisition module is used to acquire the three-dimensional point cloud data of the target workpiece; The surface type determination module is used to identify and segment all effective surfaces of the target workpiece based on the three-dimensional point cloud data, and determine the surface type of each effective surface of the workpiece. The extraction module is used to generate a corresponding mesh model for each effective surface of the workpiece and extract the geometric attribute information of each mesh model; The process parameter determination module is used to determine the coating process parameters corresponding to each effective surface of the workpiece based on the surface type and the geometric attribute information. The execution trajectory determination module is used to generate a spraying operation area covering each effective surface of the workpiece based on the coating process parameters and the mesh model of each effective surface of the workpiece, and to determine the coating execution trajectory within the spraying operation area. The control module is used to control the coating execution device to perform coating operations based on the coating execution trajectory.

10. A terminal device, characterized in that, The terminal device includes a processor and a memory, the memory storing a computer program, and the processor executing the computer program to implement the coating process method according to any one of claims 1-8.

11. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed on a processor, implements the coating process method according to any one of claims 1-8.

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