Visual identification paint spraying robot system and operation method thereof

Abstract

The invention discloses a visual recognition paint spraying robot system and an operation method thereof, and relates to the technical field of automatic paint spraying, and the technical scheme is that the visual recognition paint spraying robot system comprises a robot system module which comprises a hardware module and a software module; the hardware module comprises a visual acquisition unit module, a spraying execution module, a paint supply module, a safety protection unit module and a control terminal module, and the software module comprises a visual identification subsystem module, a path planning subsystem module, a monitoring and alarm subsystem module and a safety control subsystem module. The high-precision paint spraying system has the beneficial effects that through the high-resolution industrial camera, the 3D laser scanner and an advanced visual recognition algorithm, information such as grooves, defects and sizes of workpieces can be precisely recognized, high-precision paint spraying of complex workpieces is achieved in combination with a self-adaptive path generation algorithm, the paint spraying quality and the product percent of pass are greatly improved, the production efficiency is improved, and the production cost is reduced. And the whole paint spraying process is smoother and more efficient.

Description

Technical Field

[0001] The present invention relates to the technical field of automated painting, and in particular to a visual recognition painting robot system and an operation method thereof. Background Art

[0002] In industrial production, painting operations are an important part of protecting and decorating product surfaces. Traditional painting methods, such as manual painting, are not only inefficient and labor-intensive, but also difficult to ensure the consistency of paint quality. They are easily affected by human factors, resulting in problems such as uneven paint thickness and missed spraying. Although early automated painting equipment has improved efficiency to a certain extent, it has obvious shortcomings in dealing with complex-shaped workpieces, diverse production needs, and safety protection.

[0003] Existing painting operations cannot accurately identify the position and depth of grooves on workpieces with groove structures, resulting in poor painting effects in the grooved areas. The equipment lacks effective real-time monitoring and alarm functions, making it difficult to respond promptly to abnormal situations during the painting process. Summary of the Invention

[0004] To this end, the present invention provides a visual recognition painting robot system and its operation method to solve the problem that when painting a workpiece with a groove structure, the position and depth of the groove cannot be accurately identified, resulting in poor painting effect on the groove part, and the equipment lacks effective real-time monitoring and alarm functions, making it difficult to respond promptly to abnormal situations during the painting process.

[0005] In order to achieve the above-mentioned objectives, the present invention provides the following technical solutions: a visual recognition painting robot system, including a robot system module, the robot system module including a hardware module and a software module, the hardware module including a visual acquisition unit module, a spraying execution module, a paint supply module, a safety protection unit module and a control terminal module, the software module including a visual recognition subsystem module, a path planning subsystem module, a monitoring and alarm subsystem module and a safety control subsystem module.

[0006] Preferably, the connection ends of the visual acquisition unit module are respectively provided with a workpiece image acquisition module and an image analysis and processing module, and the visual acquisition unit module is respectively data-connected with the workpiece image acquisition module and the image analysis and processing module, and the workpiece image acquisition module and the image analysis and processing module are both data-connected with the software module.

[0007] Preferably, a painting parameter adjustment module is provided at the connection end of the painting execution module, and the painting parameter adjustment module is data-connected to the software module.

[0008] Preferably, the visual recognition subsystem module includes a groove detection module, a defect detection module and a size measurement module, and the visual recognition subsystem module is data-connected with the groove detection module, the defect detection module and the size measurement module respectively.

[0009] Preferably, the path planning subsystem module includes an adaptive path generation module and a memory function module, and the path planning subsystem module is data-connected with the adaptive path generation module and the memory function module respectively.

[0010] Preferably, the monitoring and alarm subsystem module includes a real-time quality detection module and an abnormality alarm module, and the monitoring and alarm subsystem module is data-connected with the real-time quality detection module and the abnormality alarm module respectively.

[0011] Preferably, a dynamic obstacle avoidance module is provided at the connection end of the safety control subsystem module, and the safety control subsystem module is data-connected with the dynamic obstacle avoidance module.

[0012] Preferably, the hardware module is respectively connected to the visual acquisition unit module, the spray execution module, the paint supply module, the safety protection unit module and the control terminal module, and the software module is respectively connected to the visual recognition subsystem module, the path planning subsystem module, the monitoring and alarm subsystem module and the safety control subsystem module.

[0013] A method for operating a visual recognition painting robot system, the specific steps are as follows: S1. Workpiece loading: The workpiece is accurately fixed in the specified position by a conveyor belt or fixture. The high-resolution industrial camera and 3D laser scanner in the visual acquisition unit module immediately scan the workpiece in all directions. The workpiece image acquisition module obtains the workpiece's RGB image and 3D point cloud data. The image analysis and processing module performs image processing and transmits this data to the industrial PC of the control terminal module for processing to generate an accurate 3D model of the workpiece. S2. Defect Detection: The industrial PC compares and analyzes the generated 3D model of the workpiece with the standard model stored in the database. The defect detection module in the visual recognition subsystem module uses a defect detection algorithm to quickly identify various defects such as scratches, deformation, and bubbles on the workpiece surface. Once an unqualified workpiece is found, the alarm device is immediately triggered, and the control system controls the conveyor belt or fixture to remove the unqualified workpiece to prevent it from entering the subsequent painting process. S3. Path Planning: For workpieces being painted for the first time, the path planning subsystem uses the adaptive path generation module to generate a new painting path using an adaptive path generation algorithm based on information such as groove location, depth, and overall workpiece contour provided by the groove detection and dimension measurement modules of the visual recognition subsystem. This path is then displayed on the HMI interface for operator verification. For workpieces of the same type that already have a stored painting path, the system automatically matches and calls the corresponding path from the SQLite database. S4. Spraying execution: The multi-degree-of-freedom robotic arm precisely controls the motion trajectory of the spray gun according to the painting path generated by the path planning subsystem module. At the same time, paint is supplied through the paint supply module, and the painting parameters are adjusted by the painting parameter adjustment module of the spray execution module. Under the action of the pressure regulation and flow monitoring device, the paint is delivered to the spray gun at a constant pressure and accurate flow rate to achieve uniform spraying of the workpiece; S5. Real-time monitoring: During the painting process, the infrared thickness gauge monitors the paint film thickness in real time and feeds the data back to the monitoring and alarm subsystem module. Once an abnormal paint film thickness is detected, the system automatically adjusts the painting parameters and performs additional spraying to ensure the painting quality; S6. Safety protection: The ultrasonic sensor continuously scans the working area and movement path of the painting robot. If an obstacle is detected, the safety control subsystem module responds quickly, triggering the dynamic obstacle avoidance module to avoid the obstacle, causing the painting robot to stop moving immediately to prevent a collision accident. At the same time, the fault point information is displayed on the HMI human-computer interaction interface.

[0014] The embodiments of the present invention have the following advantages: Through high-resolution industrial cameras, 3D laser scanners, and advanced visual recognition algorithms, it can accurately identify workpiece grooves, defects, dimensions, and other information. Combined with an adaptive path generation algorithm, it achieves high-precision painting of complex workpieces, greatly improving painting quality and product qualification rate. The system's memory function can automatically match the spray path of the same workpiece, reducing path planning time and improving production efficiency. At the same time, fast visual recognition and response speed make the entire painting process smoother and more efficient. With the help of real-time monitoring and closed-loop control of the infrared thickness gauge, as well as real-time detection and alarm of workpiece defects and dimensions, problems in the painting process can be discovered and corrected in a timely manner to ensure the stability and consistency of product quality. The safety protection unit composed of ultrasonic sensors and emergency stop modules can effectively prevent collision accidents and ensure the safety of equipment and personnel. The automatic cleaning device ensures the normal operation of the print head, reduces equipment failures, and improves the reliability and stability of the system. It can adapt to the painting needs of workpieces of various shapes, sizes and materials, and achieves rapid response to diversified production tasks through flexible parameter settings and intelligent path planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and those skilled in the art can derive other implementation drawings based on the provided drawings without inventive effort.

[0016] The structures, proportions, sizes, etc. illustrated in this specification are intended only to complement the contents disclosed herein and to facilitate understanding and reading by persons familiar with the art. They are not intended to limit the conditions under which the present invention may be implemented and therefore have no substantive technical significance. Any structural modifications, changes in proportions, or adjustments in sizes, without affecting the efficacy and objectives of the present invention, shall still fall within the scope of the technical contents disclosed herein.

[0017] Figure 1 A schematic diagram of the overall system structure provided by the present invention; Figure 2 A schematic diagram of the structure of the visual recognition subsystem module provided by the present invention; Figure 3 This is a schematic diagram of the module structure of the path planning subsystem provided by the present invention.

[0018] In the figure: 1. Robot system module; 2. Hardware module; 3. Software module; 4. Workpiece image acquisition module; 5. Image analysis and processing module; 6. Visual acquisition unit module; 7. Spraying execution module; 8. Paint supply module; 9. Safety protection unit module; 10. Control terminal module; 11. Paint parameter adjustment module; 12. Visual recognition subsystem module; 13. Path planning subsystem module; 14. Monitoring and alarm subsystem module; 15. Safety control subsystem module; 16. Dynamic obstacle avoidance module; 17. Real-time quality detection module; 18. Abnormal alarm module; 19. Groove detection module; 20. Defect detection module; 21. Dimension measurement module; 22. Adaptive path generation module; 23. Memory function module. DETAILED DESCRIPTION

[0019] The following describes the implementation of the present invention using specific embodiments. Those skilled in the art will readily understand the other advantages and benefits of the present invention from the disclosure herein. Obviously, the embodiments described are only a portion of the present invention, not all of it. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are intended to fall within the scope of protection of the present invention.

[0020] Refer to the attached Figure 1 -Attached Figure 3 The present invention provides a visual recognition painting robot system, including a robot system module 1, the robot system module 1 including a hardware module 2 and a software module 3, the hardware module 2 including a visual acquisition unit module 6, a spraying execution module 7, a paint supply module 8, a safety protection unit module 9 and a control terminal module 10, the software module 3 including a visual recognition subsystem module 12, a path planning subsystem module 13, a monitoring and alarm subsystem module 14 and a safety control subsystem module 15, the hardware module 2 is respectively connected to the visual acquisition unit module 6, the spraying execution module 7, the paint supply module 8, the safety protection unit module 9 and the control terminal module 10, and the software module 3 is respectively connected to the visual recognition subsystem module 12, the path planning subsystem module 13, the monitoring and alarm subsystem module 14 and the safety control subsystem module 15; In this embodiment, a high-resolution industrial camera is used through the visual acquisition unit module 6. The camera supports RGB+depth information acquisition and can obtain rich visual information of the workpiece. Whether it is the surface color characteristics or three-dimensional spatial information of the workpiece, it can be accurately captured, providing a high-quality data foundation for subsequent visual recognition and analysis. Equipped with a ring fill light, the brightness and angle can be adjusted according to the reflective conditions of different workpiece surfaces, ensuring that the camera can capture clear, reflection-free workpiece images in various lighting environments, improving the accuracy and stability of visual acquisition; The introduction of a 3D laser scanner is specifically used to accurately model the grooves and overall contours of the workpiece. By emitting a laser beam and receiving reflected light, it can quickly and accurately obtain the three-dimensional coordinate information of the workpiece surface, construct a high-precision workpiece model, and provide precise geometric data for subsequent path planning and spray control. The spray execution module 7 uses a multi-degree-of-freedom robotic arm with high flexibility and motion precision. It can move freely in three-dimensional space and is equipped with an anti-drip nozzle to ensure that the paint is sprayed evenly and stably during the painting process, avoiding waste caused by paint dripping and pollution to the working environment, while ensuring the quality of the paint spraying; The paint supply module 8 includes a paint storage tank, a paint delivery pipeline, a paint pump, and a paint flow control valve. The paint storage tank is used to store paint. The paint pump draws paint from the storage tank and delivers it to the spray gun through the paint delivery pipeline. The paint flow control valve precisely adjusts the paint delivery flow according to the control system's instructions to control the thickness and uniformity of the paint spray. The safety protection unit module 9 is equipped with ultrasonic sensors distributed around the working area of ​​the painting robot and on the movement path of the robot arm. These sensors can monitor in real time whether there are obstacles intruding on the path. When foreign objects are detected, the signal is immediately transmitted to the control system; Equipped with an emergency stop module. Once the emergency stop signal is triggered, the module can quickly cut off the power supply of the painting robot within 0.5 seconds, causing the robotic arm to stop moving immediately, avoiding safety accidents such as collisions and ensuring the safety of personnel and equipment to the greatest extent; The control terminal module 10 uses an industrial PC as the core control device. It is equipped with a GPU to accelerate visual algorithms. Its powerful computing power enables the industrial PC to quickly process the large amount of image data and point cloud data obtained by the visual acquisition unit. By running advanced visual recognition algorithms, it can achieve rapid and accurate identification and analysis of workpieces. Equipped with an HMI human-machine interaction interface, the operator can easily set various parameters such as paint thickness, spraying speed, paint flow, etc., and at the same time display the operating status of the painting robot system, workpiece information, alarm prompts, etc. in real time, realizing efficient interaction between man and machine; Among them, in order to achieve the purpose of image acquisition and processing, the present device is implemented by the following technical solutions: the connection ends of the visual acquisition unit module 6 are respectively provided with a workpiece image acquisition module 4 and an image analysis and processing module 5, and the visual acquisition unit module 6 is respectively data-connected with the workpiece image acquisition module 4 and the image analysis and processing module 5, and the workpiece image acquisition module 4 and the image analysis and processing module 5 are both data-connected with the software module 3. When the workpiece enters the painting working area, multiple high-definition industrial cameras simultaneously collect image information of the workpiece from different angles, and the workpiece image acquisition module 4 collects the image, and the image analysis and processing module 5 pre-processes the collected image, including denoising, grayscale, edge detection and other operations, and then uses a deep learning algorithm to extract and analyze the features of the pre-processed image, identify the contour, edge, hole and other key features of the workpiece, and match and compare them with the preset workpiece model to calculate the actual position, posture and size deviation of the workpiece relative to the painting robot; Among them, in order to achieve the purpose of parameter adjustment, the present device adopts the following technical solution: the connection end of the spray execution module 7 is provided with a spray parameter adjustment module 11, and the spray parameter adjustment module 11 is data-connected with the software module 3. According to the material, surface shape and spray process requirements of the workpiece, the spray parameter adjustment module 11 automatically adjusts the spray parameters of the paint flow control valve and the spray gun of the paint supply module; Among them, in order to achieve the purpose of visual recognition, the present device adopts the following technical solutions: the visual recognition subsystem module 12 includes a groove detection module 19, a defect detection module 20 and a size measurement module 21, and the visual recognition subsystem module 12 is respectively connected with the groove detection module 19, the defect detection module 20 and the size measurement module 21. The groove detection module 19 is based on a deep learning algorithm, such as the advanced YOLOv8 algorithm, to analyze and process the workpiece image. By learning and training a large number of workpiece samples with grooves, the algorithm can accurately identify the position, shape and depth information of the workpiece groove, providing a key basis for subsequent paint path planning. The defect detection module 20 utilizes the OpenCV image processing library combined with deep neural network (DNN) technology to compare and analyze the collected workpiece image with the standard template pre-stored in the database. This can accurately mark various defects such as scratches, deformations, and bubbles on the workpiece surface, promptly detect unqualified products, and improve the accuracy and efficiency of product quality inspection. The dimension measurement module 21 uses the point cloud library PCL to process and analyze the workpiece point cloud data obtained by the 3D laser scanner. Through a series of algorithm calculations, it can accurately obtain the key dimensional parameters of the workpiece, such as length, width, height, aperture, etc., providing data support for quality control and production process monitoring. Among them, in order to achieve the purpose of path planning, the present device is implemented by the following technical solutions: the path planning subsystem module 13 includes an adaptive path generation module 22 and a memory function module 23, and the path planning subsystem module 13 is respectively connected to the adaptive path generation module 22 and the memory function module 23. The adaptive path generation module 22 combines the groove information obtained by the visual recognition subsystem and the overall contour data of the workpiece, and adopts the optimized RRT* algorithm to dynamically adjust the painting trajectory. The algorithm can quickly search for an optimal painting path under a complex workpiece surface environment, ensuring that the spray gun always maintains a suitable distance and angle with the workpiece surface during the spraying process, so as to achieve a uniform and high-quality painting effect; the memory function module 23 is a powerful storage function of the system, which can store the spray path of the first workpiece in detail in the SQLite database. When the same type of workpiece is subsequently sprayed, the system can automatically match and call the corresponding spray path from the database without the need for re-path planning, which greatly improves production efficiency and ensures the consistency of paint quality. Among them, in order to achieve the purpose of monitoring and alarm, this device is implemented by the following technical solutions: the monitoring and alarm subsystem module 14 includes a real-time quality detection module 17 and an abnormal alarm module 18, and the monitoring and alarm subsystem module 14 is respectively connected to the real-time quality detection module 17 and the abnormal alarm module 18 for data. The real-time quality detection module 17 uses an infrared thickness gauge to detect the thickness of the paint film in the painting process in real time, and feeds back the detection data to the control system to form a closed-loop control. When it is detected that the paint film thickness exceeds the preset tolerance range, the control system immediately adjusts the painting parameters, such as the movement speed of the spray gun, the paint flow rate, etc., and automatically performs additional spraying or reduces the spraying amount to ensure that the final paint thickness meets the process requirements; when the abnormal alarm module 18 detects abnormal conditions such as workpiece placement offset, defects or dimensional deviation, it immediately triggers the sound and light alarm device. At the same time, the alarm information and fault point location are clearly displayed on the HMI human-computer interaction interface to remind the operator to deal with it in time to avoid the production of unqualified products and the interruption of the production process; To achieve safety control, the device utilizes the following technical solutions: a dynamic obstacle avoidance module 16 is provided at the connection end of the safety control subsystem module 15. The safety control subsystem module 15 is data-connected to the dynamic obstacle avoidance module 16. When the ultrasonic sensor detects a foreign object intruding on the path, the dynamic obstacle avoidance module 16 of the safety control subsystem module 15 responds quickly, controlling the painting robot to stop moving within a very short time of ≤0.3 seconds, effectively preventing the robot arm from colliding with obstacles and ensuring the safety of equipment and personnel. Once the obstacle is removed, the system automatically resumes the painting operation according to a preset program. A method for operating a visual recognition painting robot system, the specific steps are as follows: S1. Workpiece loading: The workpiece is accurately fixed in the specified position by a conveyor belt or fixture. The high-resolution industrial camera and 3D laser scanner in the visual acquisition unit module 6 immediately scan the workpiece in all directions. The workpiece image acquisition module 4 obtains the RGB image and 3D point cloud data of the workpiece. The image analysis and processing module 5 performs image processing and transmits this data to the industrial PC of the control terminal module 10 for processing to generate an accurate 3D model of the workpiece. S2. Defect Detection: The industrial PC compares and analyzes the generated 3D model of the workpiece with the standard model stored in the database, and uses the defect detection module 20 in the visual recognition subsystem module 12 to perform a defect detection algorithm to quickly identify various defects such as scratches, deformation, and bubbles on the workpiece surface. Once an unqualified workpiece is found, the alarm device is immediately triggered, and the control system controls the conveyor belt or fixture to remove the unqualified workpiece to prevent it from entering the subsequent painting process; S3. Path Planning: For workpieces being painted for the first time, the path planning subsystem module 13 uses the adaptive path generation module 22 to generate a new painting path using an adaptive path generation algorithm based on information such as the groove location, depth, and overall workpiece profile provided by the groove detection module 19 and the dimension measurement module 21 of the visual recognition subsystem module 12. The path is then displayed on the HMI interface for manual verification by the operator. For workpieces of the same type that already have a stored painting path, the system automatically matches and calls the corresponding path from the SQLite database. S4. Spraying execution: The multi-degree-of-freedom robotic arm precisely controls the motion trajectory of the spray gun according to the painting path generated by the path planning subsystem module 13. At the same time, paint is supplied through the paint supply module 8, and the painting parameters are adjusted by the painting parameter adjustment module 11 of the spray execution module 7. Under the action of the pressure regulation and flow monitoring device, the paint is delivered to the spray gun at a constant pressure and accurate flow rate to achieve uniform spraying of the workpiece; S5. Real-time monitoring: During the painting process, the infrared thickness gauge monitors the paint film thickness in real time and feeds the data back to the monitoring and alarm subsystem module 14. Once an abnormal paint film thickness is detected, the system automatically adjusts the painting parameters and performs additional spraying to ensure the painting quality; S6. Safety protection: The ultrasonic sensor continuously scans the working area and movement path of the painting robot. If an obstacle is detected, the safety control subsystem module 15 responds quickly and triggers the dynamic obstacle avoidance module 16 to avoid the obstacle, causing the painting robot to stop moving immediately to prevent a collision accident. At the same time, the fault point information is displayed on the HMI human-machine interaction interface.

[0021] The above description is merely a preferred embodiment of the present invention. Anyone skilled in the art may utilize the above-described technical solutions to modify the present invention or modify it into an equivalent technical solution. Therefore, any simple modification or equivalent replacement based on the technical solution of the present invention falls within the scope of protection claimed by the present invention.

Claims

1. A visual recognition painting robot system, comprising a robot system module (1), characterized in that: The robot system module (1) includes a hardware module (2) and a software module (3), wherein the hardware module (2) includes a visual acquisition unit module (6), a spray execution module (7), a paint supply module (8), a safety protection unit module (9) and a control terminal module (10), and the software module (3) includes a visual recognition subsystem module (12), a path planning subsystem module (13), a monitoring and alarm subsystem module (14) and a safety control subsystem module (15).

2. The visual recognition painting robot system according to claim 1, characterized in that: The connection ends of the visual acquisition unit module (6) are respectively provided with a workpiece image acquisition module (4) and an image analysis and processing module (5); the visual acquisition unit module (6) is respectively data-connected to the workpiece image acquisition module (4) and the image analysis and processing module (5); and the workpiece image acquisition module (4) and the image analysis and processing module (5) are both data-connected to the software module (3).

3. The visual recognition painting robot system according to claim 1, characterized in that: The connection end of the spraying execution module (7) is provided with a spraying parameter adjustment module (11), and the spraying parameter adjustment module (11) is data-connected to the software module (3).

4. The visual recognition painting robot system according to claim 1, characterized in that: The visual recognition subsystem module (12) includes a groove detection module (19), a defect detection module (20), and a dimension measurement module (21), and the visual recognition subsystem module (12) is data-connected to the groove detection module (19), the defect detection module (20), and the dimension measurement module (21), respectively.

5. The visual recognition painting robot system according to claim 1, characterized in that: The path planning subsystem module (13) comprises an adaptive path generation module (22) and a memory function module (23), and the path planning subsystem module (13) is data-connected to the adaptive path generation module (22) and the memory function module (23), respectively.

6. The visual recognition painting robot system according to claim 1, characterized in that: The monitoring and alarm subsystem module (14) includes a real-time quality detection module (17) and an abnormality alarm module (18), and the monitoring and alarm subsystem module (14) is data-connected to the real-time quality detection module (17) and the abnormality alarm module (18), respectively.

7. The visual recognition painting robot system according to claim 1, characterized in that: A dynamic obstacle avoidance module (16) is provided at the connection end of the safety control subsystem module (15), and the safety control subsystem module (15) is data-connected to the dynamic obstacle avoidance module (16).

8. The visual recognition painting robot system according to claim 1, characterized in that: The hardware module (2) is respectively connected to the visual acquisition unit module (6), the spray execution module (7), the paint supply module (8), the safety protection unit module (9) and the control terminal module (10), and the software module (3) is respectively connected to the visual recognition subsystem module (12), the path planning subsystem module (13), the monitoring and alarm subsystem module (14) and the safety control subsystem module (15).

9. A method for operating a visual recognition painting robot system, based on the visual recognition painting robot system according to any one of claims 1 to 8, characterized in that: The specific steps are as follows: S1. Workpiece loading: The workpiece is accurately fixed at the designated position by a conveyor belt or a fixture. The high-resolution industrial camera and 3D laser scanner in the visual acquisition unit module (6) immediately scan the workpiece in all directions. The workpiece image acquisition module (4) obtains the RGB image and 3D point cloud data of the workpiece. The image analysis and processing module (5) performs image processing and transmits the data to the industrial PC of the control terminal module (10) for processing to generate an accurate 3D model of the workpiece. S2. Defect detection: The industrial PC compares and analyzes the generated 3D model of the workpiece with the standard model stored in the database, and uses the defect detection module (20) in the visual recognition subsystem module (12) to perform a defect detection algorithm to quickly identify various defects such as scratches, deformation, bubbles, etc. on the workpiece surface. Once an unqualified workpiece is found, the alarm device is immediately triggered, and the control system controls the conveyor belt or fixture to remove the unqualified workpiece to prevent it from entering the subsequent painting process; S3. Path planning: For workpieces that are being painted for the first time, the path planning subsystem module (13) generates a new painting path using an adaptive path generation algorithm through the adaptive path generation module (22) based on the groove position, depth, and overall contour of the workpiece provided by the groove detection module (19) and the dimension measurement module (21) of the visual recognition subsystem module (12). The path generation algorithm is then displayed on the HMI human-machine interface for manual verification by the operator. For workpieces of the same type that have already stored a painting path, the system automatically matches and calls the corresponding path from the SQLite database. S4, spraying execution: The multi-degree-of-freedom robotic arm accurately controls the motion trajectory of the spray gun according to the spraying path generated by the path planning subsystem module (13). At the same time, the paint is supplied through the paint supply module (8), and the spraying parameters are adjusted through the spraying parameter adjustment module (11) of the spraying execution module (7). Under the action of the pressure regulation and flow monitoring device, the paint is delivered to the spray gun at a constant pressure and accurate flow rate to achieve uniform spraying of the workpiece; S5. Real-time monitoring: During the painting process, the infrared thickness gauge monitors the paint film thickness in real time and feeds the data back to the monitoring and alarm subsystem module (14). Once an abnormal paint film thickness is found, the system automatically adjusts the painting parameters and performs additional spraying to ensure the quality of the painting. S6. Safety protection: The ultrasonic sensor continuously scans the working area and movement path of the painting robot. If an obstacle is detected, the safety control subsystem module (15) responds quickly and triggers the dynamic obstacle avoidance module (16) to avoid the obstacle, causing the painting robot to stop moving immediately to prevent a collision accident. At the same time, the fault point information is displayed on the HMI human-computer interaction interface.

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