Traffic sign line marking robot and method based on visual identification

Abstract

The invention provides a traffic sign line marking robot and method based on visual identification. The traffic sign line marking robot comprises a frame, a driving mechanism, a navigation and positioning assembly, a spraying assembly, a power supply assembly, a camera, an auxiliary marking mechanism and a control system. The driving mechanism is installed in the frame, and the navigation and positioning assembly is installed on the top of the frame and located in the front. Part of the spraying assembly is installed above the frame, and part of the spraying assembly is installed below the frame. The power supply assembly is mounted at the top of the frame; the camera is installed in front of the navigation and positioning assembly, the control system conducts visual recognition through images shot by the camera, receives position information sent by the navigation and positioning assembly at the same time, and obtains an optimal advancing route by analyzing the image information and the position information in front of the robot. And the driving mechanism is controlled to advance according to the planned path. The robot can realize automatic marking of traffic sign lines.

Description

Technical Field

[0001] The present invention belongs to the field of road construction robots, and in particular relates to a traffic sign line marking robot and method based on visual recognition. Background Art

[0002] In modern road construction and maintenance, traditional traffic marking methods usually rely on manual construction, involving manual operation of paint and tools. This method has the following problems: (1) High labor intensity: Manual marking requires a lot of physical labor, especially when constructing large-scale roads.

[0003] (2) Low efficiency: Traditional manual marking is slow and requires high operating skills from personnel, resulting in a long construction period.

[0004] (3) Unstable quality: Manual operation makes it difficult to ensure the uniformity, accuracy and durability of each marking line. The marking lines may have problems such as inconsistent width, inconsistent spacing, and uneven coating.

[0005] (4) Safety risks: When operating manually on the road, construction workers are exposed to areas with heavy traffic, which poses a high safety hazard. Summary of the invention

[0006] The invention aims to provide a traffic sign line marking robot based on visual recognition technology, which can realize automatic marking of traffic sign lines.

[0007] In order to realize the above-mentioned technical features, the purpose of the present invention is realized as follows: A traffic sign line marking robot based on visual recognition comprises a frame, a driving mechanism, a navigation and positioning component, a spraying component, a power supply component, a camera, an auxiliary marking mechanism and a control system; the driving mechanism is installed inside the frame, and the navigation and positioning component is installed on the top of the frame and located in the front; part of the spraying component is installed above the frame, and part is installed below the frame; the power supply component is installed on the top of the frame; the camera is installed in front of the navigation and positioning component, the control system performs visual recognition through the image captured by the camera, and at the same time receives the position information sent by the navigation and positioning component, and obtains the optimal forward route by analyzing the image information and position information in front of the robot, and controls the driving mechanism to move forward according to the planned path.

[0008] The frame comprises a bottom plate and an upper cover, and the driving mechanism is installed inside a cavity formed by the bottom plate and the upper cover.

[0009] The driving mechanism includes a front wheel steering mechanism and a rear wheel driving mechanism; the front wheel steering mechanism includes a front wheel, a steering link, a steering gear, a stepper motor, a first gear and a second gear, the two front wheels are symmetrically installed on the left and right sides of the chassis, one end of the two steering links is connected to the front wheel, and the other end is connected to the steering gear, the output shaft of the steering gear is connected to the first gear through a pin, the output shaft of the stepper motor is connected to the second gear through a pin, and the stepper motor and the steering gear are driven by gear meshing between the first gear and the second gear.

[0010] The rear wheel drive mechanism includes a rear wheel, a transmission shaft, a bearing, a third gear, a fourth gear and a DC motor; the two rear wheels are installed on the left and right sides of the chassis through bearings, the two ends of the transmission shaft are respectively connected to the two rear wheels and provide power for the rear wheels, the third gear is fixed to the transmission shaft through a pin, the fourth gear is fixed to the output shaft of the DC motor through a pin, the third gear is meshed with the fourth gear, the DC motor transmits the rotational force to the transmission shaft through gear transmission, and the transmission shaft then transmits the rotational force to the two rear wheels.

[0011] The navigation and positioning component includes a laser radar and a GPS positioning system; the GPS positioning system is installed above the upper cover and is used for the robot to determine the exact position within a large area, and the laser radar is installed in front of the GPS positioning system.

[0012] The spraying assembly includes a paint box, a pumping system, a direct head, a heating system and a nozzle; the paint box is installed above the upper cover and is located below the power supply assembly and behind the navigation and positioning assembly, and the fixing frame of the paint box is fixed to the upper cover by bolts; the pumping system includes a right-angle joint and a water pipe, one end of the right-angle joint is threadedly connected to the discharge port of the paint box, and the other end is threadedly connected to the water pipe, and the other end of the water pipe is threadedly connected to the direct head, one end of the heating system is threadedly connected to the other end of the direct head, and the other end of the heating system is threadedly connected to the nozzle; the heating system is fixed to the chassis by bolts, and the nozzle can adjust the width of the sprayed marking line by adjusting the distance between it and the heating system.

[0013] The power supply assembly includes a solar panel, a pillar and a battery; the four pillars are respectively fixed on the top of the upper cover by bolt connection, and the solar panel is installed on the top of the four pillars and is fixed to the four pillars by bolt connection; the battery is installed on the top of the upper cover and located behind the navigation and positioning assembly; the solar panel is connected to the battery and charges the battery; the battery is respectively connected to the driving mechanism, navigation and positioning assembly, spray assembly, camera, auxiliary marking mechanism and control system, and provides electrical energy therefor respectively.

[0014] The auxiliary marking mechanism includes a DC motor fixing frame and multiple groups of electric brush assemblies; the electric brush assembly includes a DC motor and a brush; the DC motor fixing frame is installed above the chassis and is located in front of the navigation and positioning assembly and below the camera; the DC motor is fixedly installed above the branch motor fixing frame by bolt connection, and the output shaft of the DC motor and the brush are fixedly connected by a pin.

[0015] The number of the electric brush assemblies is at least 3 groups.

[0016] A method for marking traffic sign lines using a traffic sign marking robot based on visual recognition, comprising the following steps: S1. Turn on the robot and it will enter standby mode; S2. Adjust the nozzle height of the robot and input the range of the working area into the robot's control system. The robot will automatically calculate and plan the route. S3. Inject paint into the robot paint box and send a start work instruction to the robot. After the robot receives the work instruction, the drive mechanism, navigation and positioning components, power supply components and camera start working and go to the starting point of the preset route; S4. After the robot reaches the preset starting point, the spraying assembly starts working, and the robot control system controls the robot to move along the preset path; S5. During the operation of the robot, the camera captures the image information on the road ahead of the robot in real time and sends it to the robot control system, which determines whether there is an obstacle ahead of the path; if there is no obstacle, the robot is controlled to continue moving forward; if there is an obstacle, the robot's auxiliary marking mechanism is controlled to start and sweep the obstacle out of the original path; S6. After the robot completes its work, it goes to the standby area and enters the standby state.

[0017] The present invention has the following beneficial effects: 1. Efficiency: Through fully automated control, the robot can complete road marking construction on a large scale and efficiently, significantly reducing construction time and labor costs.

[0018] 2. Accuracy: The robot is equipped with high-precision sensors, navigation systems and automated control algorithms, which can ensure that the width, position, spacing, etc. of the markings meet the requirements and can operate accurately under complex road conditions.

[0019] 3. Flexibility: Modern road marking robots are highly adaptable and can handle different road types, weather conditions and construction environments.

[0020] 4. Safety: It reduces manual involvement and reduces the safety risks of construction workers exposed to traffic environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0022] Figure 1 It is a schematic diagram of the three-dimensional structure of the present invention.

[0023] Figure 2 It is a schematic diagram of the three-dimensional structure from another viewing angle of the present invention.

[0024] Figure 3 It is a schematic diagram of the structure of the garbage collection mechanism and the garbage conveying mechanism of the present invention.

[0025] Figure 4 It is a schematic diagram of the structure of the propulsion mechanism of the present invention.

[0026] Figure 5 It is a schematic diagram of the collection bin of the present invention.

[0027] In the figure: frame 100, chassis 101, upper cover 102, cavity 103; Driving mechanism 200, front wheel steering mechanism 201, rear wheel driving mechanism 202, front wheel 203, steering link 204, steering gear 205, stepping motor 206, first gear 207, second gear 208, rear wheel 209, transmission shaft 210, bearing 211, third gear 212, fourth gear 213, DC motor 214; Navigation and positioning component 300, laser radar 301, GPS positioning system 302; Spraying assembly 400, paint box 401, pumping system 402, straight connector 403, heating system 404, nozzle 405, right angle connector 406, water pipe 407; Power supply assembly 500, solar panel 501, support 502, battery 503; Camera 600; Auxiliary marking mechanism 700, DC motor fixing frame 701, electric brush assembly 702, DC motor 703, brush 704. DETAILED DESCRIPTION

[0028] The embodiments of the present invention are further described below in conjunction with the accompanying drawings.

[0029] Embodiment 1: See also Figure 1-5A traffic sign line marking robot based on visual recognition technology includes a frame 100, a driving mechanism 200, a navigation and positioning component 300, a spraying component 400, a power supply component 500, a camera 600, an auxiliary marking mechanism 700 and a control system. The driving mechanism 200 is installed inside the frame 100, and the navigation and positioning component 300 is installed above the frame 100 and in front of the power supply component 500. The spraying component 400 is installed above and below the frame 100, and the power supply component 500 is installed above the frame 100. The camera 600 is installed in front of the navigation and positioning component 300. The control system performs visual recognition through the image captured by the camera 600, and receives the position information sent by the navigation and positioning component 300 at the same time. By analyzing the image information and position information in front of the robot, the optimal forward route is obtained, and the driving mechanism 200 is controlled to move forward according to the planned path. Through fully automated control, the robot can complete the marking construction on a large scale and efficiently, significantly reducing construction time and labor costs.

[0030] Preferably, the control system adopts the CVM multifunctional visual motion control application platform.

[0031] See also Figure 3-4 The frame 100 includes a chassis 101 and an upper cover 102, and the driving mechanism 200 is installed inside a cavity 103 formed by the chassis 101 and the upper cover 102. The frame 100 can be used to carry and install the entire robot.

[0032] See also Figure 3-4 , the driving mechanism 200 includes a front wheel steering mechanism 201 and a rear wheel driving mechanism 202. The front wheel steering mechanism 201 includes a front wheel 203, a steering link 204, a steering gear 205, a stepper motor 206, a first gear 207 and a second gear 208. The two front wheels 203 are symmetrically mounted on the left and right sides of the chassis 101, one end of the two steering links 204 is connected to the front wheel 203, and the other end is connected to the steering gear 205. The output shaft of the steering gear 205 is connected to the first gear 207 by a pin, the output shaft of the stepper motor 206 is connected to the second gear 208 by a pin, and the stepper motor 206 and the steering gear 205 are driven by the gear meshing of the first gear 207 and the second gear 208. Specifically, the stepper motor 206 can be replaced by other motors. The steering control of the robot can be achieved by the above-mentioned front wheel steering mechanism 201.

[0033] See also Figure 3-4The rear wheel drive mechanism 202 includes a rear wheel 209, a transmission shaft 210, a bearing 211, a third gear 212, a fourth gear 213 and a DC motor 214. The two rear wheels 209 are mounted on the left and right sides of the chassis 101 through the bearing 211, and the two ends of the transmission shaft 210 are respectively connected to the two rear wheels 209 to provide power to the rear wheels 209. The third gear 212 is fixed to the transmission shaft 210 through a pin, and the fourth gear 213 is fixed to the output shaft of the DC motor 214 through a pin. The DC motor 214 transmits the rotational force to the transmission shaft 210 through gear transmission, and the transmission shaft 210 then transmits the rotational force to the two rear wheels 209. The above-mentioned rear wheel drive mechanism 202 can provide the walking power of the robot.

[0034] Specifically, the driving mechanism 200 is powered by an electric motor and a fuel engine, and is regulated and managed by a control system to ensure that the robot can stably travel along a predetermined trajectory.

[0035] See also Figure 3-4 The navigation and positioning component 300 includes a laser radar 301 and a GPS positioning system 302. The GPS positioning system 302 is installed above the upper cover 102 to help the robot determine the exact position in a large area, especially in large road construction or highway projects. The laser radar 301 is installed in front of the GPS positioning system 302 to help the robot determine the exact position in a large area, especially in large road construction or highway projects.

[0036] Furthermore, the laser radar 301 and the GPS positioning system 302 work together to ensure that the robot can accurately travel along the predetermined path and adjust the route according to environmental changes.

[0037] See also Figure 5The spraying assembly 400 includes a paint box 401, a pumping system 402, a direct head 403, a heating system 404 and a nozzle 405. The paint box 401 is installed above the upper cover 102, below the power supply assembly 500, and behind the navigation and positioning assembly 300. The fixing frame of the paint box 401 is fixed to the upper cover 102 by bolt connection. The pumping system 402 includes a right-angle joint 406 and a water pipe 407. One end of the right-angle joint 406 is threadedly connected to the discharge port of the paint box 401, and the other end is threadedly connected to the water pipe 407. The other end of the water pipe 407 is threadedly connected to the direct head 403, one end of the heating system 404 is threadedly connected to the other end of the direct head 403, and the other end of the heating system 404 is threadedly connected to the nozzle 405. The heating system 404 is fixed to the chassis 101 by bolt connection, and the nozzle 405 can determine the width of the sprayed marking line by adjusting the distance between the nozzle 405 and the heating system 404. By heating the paint system, it can better adhere to the road surface during spraying and accelerate the drying process.

[0038] See also Figure 1 The power supply assembly 500 includes a solar panel 501, a support 502 and a battery 503. The four support pillars 502 are respectively fixed on the top of the upper cover 102 by bolt connection, and the solar panel 501 is installed on the four support pillars 502 and is fixed to the four support pillars by bolt connection. The battery 503 is installed on the top of the upper cover 102 and behind the navigation and positioning assembly 300. The solar panel 501 is connected to the battery 503 to charge the battery 503; the battery 503 is respectively connected to the driving mechanism 200, the navigation and positioning assembly 300, the spray assembly 400, the camera 600, the auxiliary marking mechanism 700 and the control system to provide power.

[0039] See also Figure 1-4 The auxiliary marking mechanism 700 includes a DC motor fixing frame 701 and three sets of electric brush assemblies 702. The electric brush assembly 702 includes a DC motor 703 and a brush 704. The DC motor fixing frame 701 is installed above the chassis 101, in front of the navigation and positioning assembly 300, and below the camera 600. The DC motor 703 is fixedly installed above the branch motor fixing frame 701 by bolt connection, and the output shaft of the DC motor 703 and the brush 702 are fixedly connected by a pin.

[0040] The mechanical principle of the traffic marking robot combines precise drive, control, spraying and navigation systems, enabling it to efficiently and accurately complete the marking work in complex environments. The application of this technology not only improves construction efficiency, but also greatly improves the quality and safety of road markings.

[0041] Embodiment 2: In this paper, a visual recognition system based on the YOLOv8 architecture is proposed, whose core component is a deep convolutional neural network (CNN). The network design aims to achieve efficient and accurate target detection, making full use of the advantages of CNN in image feature extraction.

[0042] First, the input image received by the system undergoes a preprocessing step, including resizing and normalization, to adapt to the network's input requirements. The preprocessed image is fed into the initial convolutional layer, which typically uses a 3×3 convolution kernel, a stride of 1, and uses the "same" padding method to maintain the spatial size of the feature map. Through these convolution operations, the network can effectively capture basic edges, textures, and other low-level features in the image, laying the foundation for subsequent deep feature extraction.

[0043] After the initial convolutional layer, the network introduces a batch normalization layer, which not only speeds up the model training process, but also improves the model's stability and generalization ability. Next, the linear rectification function (ReLU) is used as the activation function to give the network nonlinear characteristics, enabling it to learn more complex and diverse feature representations.

[0044] The network structure of YOLOv8 contains multiple repeated convolution modules, each of which consists of multiple convolution layers, activation functions, and normalization layers. These modules are interconnected through residual connections to form a deep network structure. The introduction of residual connections effectively alleviates the common gradient vanishing and gradient exploding problems in deep networks, ensures that information can be smoothly transmitted in the network, and enhances the multi-level expression capabilities of features.

[0045] In order to further optimize network performance, YOLOv8 adopts the Cross Stage Partial (CSP) structure. The CSP structure divides the feature map into two parts, one part is processed by multiple convolutional layers, and the other part is directly fused with the processed part. This design not only reduces the number of network parameters and computational complexity, but also maintains the richness and diversity of feature expression, improving the computational efficiency and generalization ability of the model.

[0046] In addition, YOLOv8 introduces the Depthwise Separable Convolution technology, which decomposes the standard convolution into two steps: depthwise convolution and point-by-point convolution. This method significantly reduces the amount of calculation and model parameters while maintaining the effect of feature extraction, allowing YOLOv8 to have stronger real-time processing capabilities while ensuring high detection accuracy.

[0047] In terms of feature fusion, YOLOv8 adopts a multi-scale feature fusion mechanism. By fusing feature maps of different levels, the network can simultaneously utilize shallow detail information and deep semantic information. This multi-scale fusion strategy effectively improves the accuracy and robustness of target detection, especially when detecting targets of different sizes. Shallow feature maps provide rich spatial information, which helps to identify small targets; while deep feature maps contain higher-level semantic information, which helps to identify large targets.

[0048] Finally, the feature map after multi-layer convolution and feature fusion processing is input into the detection head module, which is responsible for generating the final detection result. The detection head module usually includes a series of convolutional layers and fully connected layers to predict key information such as the location, category and confidence of the target.

[0049] In summary, the YOLOv8 visual recognition system in the present invention achieves efficient and accurate image feature extraction and target detection by integrating advanced technologies such as multi-layer convolution structure, residual connection, CSP structure, deep separable convolution and multi-scale feature fusion. This innovative design not only improves the overall performance of the system, but also provides strong technical support for real-time visual recognition applications.

[0050] Embodiment 3: A traffic marking line marking robot based on visual recognition technology is used for marking road marking lines, comprising the following steps: S1. Turn on the robot and it enters standby mode.

[0051] S2. Adjust the height of the robot nozzle 405, input the range of the working area into the robot's control system, and the robot will automatically calculate and plan the route.

[0052] S3. Inject paint into the robot paint box 401 and send a start work instruction to the robot. After the robot receives the work instruction, the drive mechanism 200, the navigation and positioning component 300, the power supply component 500 and the camera 600 start working and go to the starting point of the preset route.

[0053] S4. After the robot reaches the preset starting point, the spraying assembly 400 starts working, and the robot control system controls the robot to move forward along the preset path.

[0054] S5. During the robot's operation, the camera 600 captures the image information on the road ahead of the robot in real time and sends it to the robot control system, which determines whether there is an obstacle ahead of the path. If there is no obstacle, the robot is controlled to continue moving forward; if there is an obstacle, the robot's auxiliary marking mechanism 700 is controlled to start and sweep the obstacle out of the original path.

[0055] S6. After the robot completes its work, it goes to the standby area and enters the standby state.

Claims

1. A traffic sign marking robot based on visual recognition, characterized in that: The robot comprises a frame (100), a driving mechanism (200), a navigation and positioning component (300), a spraying component (400), a power supply component (500), a camera (600), an auxiliary marking mechanism (700), and a control system; the driving mechanism (200) is installed inside the frame (100); the navigation and positioning component (300) is installed on the top of the frame (100) and located in the front; part of the spraying component (400) is installed above the frame (100) and part of it is installed below the frame (100); the power supply component (500) is installed on the top of the frame (100); the camera (600) is installed in front of the navigation and positioning component (300); the control system performs visual recognition through the image captured by the camera (600), and simultaneously receives the position information sent by the navigation and positioning component (300); by analyzing the image information and position information in front of the robot, the optimal forward route is obtained, and the driving mechanism (200) is controlled to move forward according to the planned path.

2. A traffic sign marking robot based on visual recognition according to claim 1, characterized in that: The frame (100) comprises a bottom plate (101) and an upper cover (102), and the driving mechanism (200) is installed inside a cavity (103) formed by the bottom plate (101) and the upper cover (102).

3. The traffic sign marking robot based on visual recognition according to claim 2, characterized in that: The driving mechanism (200) comprises a front wheel steering mechanism (201) and a rear wheel driving mechanism (202); the front wheel steering mechanism (201) comprises a front wheel (203), a steering link (204), a steering gear (205), a stepping motor (206), a first gear (207) and a second gear (208); the two front wheels (203) are symmetrically mounted on the left and right sides of the chassis (101); one end of the two steering links (204) is connected to the front wheel (203) and the other end is connected to the steering gear (205); the output shaft of the steering gear (205) is connected to the first gear (207) via a pin; the output shaft of the stepping motor (206) is connected to the second gear (208) via a pin; the stepping motor (206) and the steering gear (205) are connected via gear meshing transmission between the first gear (207) and the second gear (208).

4. The traffic sign marking robot based on visual recognition according to claim 3, characterized in that: The rear wheel drive mechanism (202) comprises a rear wheel (209), a transmission shaft (210), a bearing (211), a third gear (212), a fourth gear (213) and a DC motor (214); the two rear wheels (209) are mounted on the left and right sides of the chassis (101) via the bearings (211); the two ends of the transmission shaft (210) are respectively connected to the two rear wheels (209) and provide power for the rear wheels (209); the third gear (212) is fixed to the transmission shaft (210) via a pin; the fourth gear (213) and the output shaft of the DC motor (214) are fixed via a pin; the third gear (212) and the fourth gear (213) are meshed; the DC motor (214) transmits the rotational force to the transmission shaft (210) via gear transmission; and the transmission shaft (210) transmits the rotational force to the two rear wheels (209).

5. The traffic sign marking robot based on visual recognition according to claim 3, characterized in that: The navigation and positioning component (300) comprises a laser radar (301) and a GPS positioning system (302); the GPS positioning system (302) is installed above the upper cover (102) and is used for the robot to determine the accurate position within a large area, and the laser radar (301) is installed in front of the GPS positioning system (302).

6. The traffic sign marking robot based on visual recognition according to claim 3, characterized in that: The spraying assembly (400) comprises a paint box (401), a pumping system (402), a direct connector (403), a heating system (404) and a nozzle (405); the paint box (401) is installed above the upper cover (102), and is located below the power supply assembly (500) and behind the navigation and positioning assembly (300); the fixing frame of the paint box (401) is fixed to the upper cover (102) by bolt connection; the pumping system (402) comprises a right-angle joint (406) and a water pipe (407); one end of the right-angle joint (406) is connected to the water pipe (407); The discharge port of the paint box (401) is connected by a thread, and the other end is connected to the water pipe (407) by a thread, and the other end of the water pipe (407) is connected to the direct connector (403) by a thread, and one end of the heating system (404) is connected to the other end of the direct connector (403) by a thread, and the other end of the heating system (404) is connected to the nozzle (405) by a thread; the heating system (404) is fixed to the chassis (101) by bolts, and the nozzle (405) can adjust the width of the sprayed marking line by adjusting the distance between the nozzle (405) and the heating system (404).

7. The traffic sign marking robot based on visual recognition according to claim 3, characterized in that: The power supply assembly (500) comprises a solar panel (501), a support (502) and a battery (503); the four support columns (502) are respectively fixed on the top of the upper cover (102) by bolt connection; the solar panel (501) is installed on the top of the four support columns (502) and is fixed to the four support columns by bolt connection; the battery (503) is installed on the top of the upper cover (102) and is located behind the navigation and positioning assembly (300); the solar panel (501) is connected to the battery (503) and charges the battery (503); the battery (503) is respectively connected to the driving mechanism (200), the navigation and positioning assembly (300), the spraying assembly (400), the camera (600), the auxiliary marking mechanism (700) and the control system, and provides electrical energy thereto respectively.

8. The traffic sign marking robot based on visual recognition according to claim 3, characterized in that: The auxiliary marking mechanism (700) comprises a DC motor fixing frame (701) and a plurality of electric brush assemblies (702); the electric brush assembly (702) comprises a DC motor (703) and a brush (704); the DC motor fixing frame (701) is mounted above the chassis (101) and is located in front of the navigation and positioning assembly (300) and below the camera (600); the DC motor (703) is fixedly mounted above the branch motor fixing frame (701) by bolt connection, and the output shaft of the DC motor (703) and the brush (702) are fixedly connected by a pin.

9. The traffic sign marking robot based on visual recognition according to claim 8, characterized in that: The number of the electric brush assemblies (702) is at least 3 groups.

10. A method for marking traffic signs using a traffic sign marking robot based on visual recognition as described in any one of claims 3 to 9, characterized in that: The following steps are involved: S1. Turn on the robot and it will enter standby mode; S2. Adjust the height of the robot's nozzle (405), input the range of the working area into the robot's control system, and the robot will automatically calculate and plan the route; S3. Inject paint into the robot paint box (401), send a start work instruction to the robot, and after the robot receives the work instruction, the drive mechanism (200), the navigation and positioning component (300), the power supply component (500) and the camera (600) start working and go to the starting point of the preset route; S4. After the robot reaches the preset starting point, the spraying assembly (400) starts working, and the robot control system controls the robot to move along the preset path; S5. During the operation of the robot, the camera (600) captures image information on the road ahead of the robot in real time and sends it to the robot control system, which determines whether there is an obstacle in front of the path; If there is no obstacle, the robot is controlled to continue moving forward; if there is an obstacle, the robot auxiliary marking mechanism (700) is controlled to start and sweep the obstacle out of the original path; S6. After the robot completes its work, it goes to the standby area and enters the standby state.

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