Method for switching laser navigation into two-dimensional code navigation

Abstract

The invention relates to a method for switching laser navigation to two-dimensional code navigation, and belongs to the technical field of navigation of automatic guided vehicles or autonomous mobile robots. The method comprises the following steps: periodically detecting the ground reflectivity around a navigation switching point through a multi-line laser radar, calculating the reflectivity variation, presetting a sudden change threshold to judge whether the reflectivity sudden change occurs, and further evaluating whether the current environment is suitable for navigation switching. And after the environment is confirmed to be stable, the autonomous mobile carrier continues to move towards the navigation switching point and scans a ground two-dimensional code mark through a two-dimensional code sensor when approaching, and laser navigation is switched to two-dimensional code navigation after successful recognition. And after switching, pre-judging the visual integrity of the two-dimensional code label by adopting a convolutional neural network model, and determining whether to trigger a detouring instruction or continue to run according to a preset path according to an identification result. The method effectively improves the stability and reliability of switching laser navigation to two-dimensional code navigation, and is suitable for complex and changeable industrial environments.

Description

Technical Field

[0001] The present application relates to the field of automatic guided vehicle or autonomous mobile robot navigation technology, and in particular to a method for switching from laser navigation to QR code navigation. Background Art

[0002] An automated guided vehicle (AGV) is an intelligent wheeled mobile robot that uses electromagnetic or optical automatic guidance devices and travels along a specified guidance path to complete handling tasks. It has the advantages of fast movement, strong controllability and good safety. It is widely used in material transportation in factory automated production lines, warehousing logistics, airports and ports.

[0003] Laser SLAM (Simultaneous Localization and Mapping) navigation is a technology used by robots to autonomously locate and build maps in unknown environments. The word "SLAM" itself reflects the characteristics of simultaneous positioning and map building. Among them, laser refers to the use of laser radar (LiDAR) as the main sensor to obtain environmental information. LiDAR determines the distance of surrounding objects by emitting laser beams and measuring the time of reflected light, thereby generating point cloud data of the environment.

[0004] QR code navigation is achieved by laying QR codes on the ground along the AGV's driving path. The AGV scans these QR codes through a camera to obtain the current location information. The specific process is as follows: The camera on the AGV reads the QR code image laid on the ground and passes the image coordinate information of the QR code to the AGV controller. The controller calculates these coordinate data, determines the location of the QR code on the map, and sends navigation path instructions to the AGV. The AGV navigates according to the received path instructions, and finally scans the destination QR code to confirm arrival when it arrives at the destination.

[0005] Laser + QR code composite navigation is a composite navigation method that combines the advantages of laser navigation and QR code navigation technology. Laser navigation provides high-precision environmental perception and positioning capabilities, and QR code provides a clear position reference point for the robot. The combination of the two can further improve positioning accuracy, reaching millimeter-level or even higher accuracy requirements; at the same time, laser navigation can perceive changes in the surrounding environment in real time and adjust path planning in time. QR code, as an auxiliary positioning method, provides additional position calibration when laser navigation is disturbed or errors occur, thereby improving navigation stability and reliability.

[0006] However, when the laser + QR code composite navigation is in the laser cutting QR code navigation mode, the repeated positioning accuracy of the laser navigation will be deviated due to the influence of the external environment. At the same time, the scanning range of the QR code sensor is too small, resulting in the laser navigation at the navigation switching point. The QR code sensor cannot scan the QR code at the navigation switching point, causing the system to shut down and fail, affecting the stability of the entire system.

[0007] Therefore, how to improve the stability and reliability of switching from laser navigation to QR code navigation is an urgent problem to be solved. Summary of the invention

[0008] The main purpose of the present application is to provide a method for switching from laser navigation to QR code navigation, which aims to solve the problem of unstable navigation caused by sudden environmental changes or damage to the QR code label when laser navigation is switched to QR code navigation in the prior art.

[0009] To achieve the above purpose, the present application provides a method for switching from laser navigation to QR code navigation, comprising the following steps: S1, in the laser navigation stage, periodically detecting the ground reflectivity within a radius of 5m around the navigation switching point by a multi-line laser radar, wherein the navigation switching point is a key position point pre-set on the laser navigation path and connected to the QR code navigation area; S2, according to the ground reflectivity obtained in step S1, recorded as According to formula (1) The calculated reflectivity change of adjacent sampling points within a radius of 5m around the navigation switching point during the laser navigation phase is ; in, It is expressed as the reflectivity value of the i-th sampling point, and the reflectivity change It is represented by the absolute value of the reflectivity difference between each sampling point and the next point; S3, preset the mutation threshold M, and compare the mutation threshold M with the reflectivity change within a detection cycle calculated in step S2 In contrast, when the mutation threshold M is greater than the reflectivity change , then it is considered that at the sampling point and There was a sudden change in reflectivity between Calculate the reflectivity mutation rate ;in, represents the reflectivity mutation rate based on the mutation threshold M; is the total number of reflectivity data points collected by the LiDAR in one detection cycle; is the mutation indicator function, which is defined as: ;when hour, ; In other cases ; S4, according to the reflectivity mutation rate calculated in step S3, determine whether the current environment is suitable for navigation switching; if the reflectivity mutation rate If the reflectivity mutation rate is lower than the preset stability threshold, the environment is considered stable and suitable for navigation switching. If the value is higher than the stability threshold, the environment is considered unstable, the navigation switch is delayed and laser navigation is continued until the reflectivity mutation rate drops below the stability threshold; S5. After confirming that the environment is stable, the autonomous mobile vehicle continues to move towards the navigation switching point, and when the autonomous mobile vehicle approaches the navigation switching point, the QR code sensor starts to scan the QR code mark on the ground; if the QR code sensor successfully recognizes the QR code mark, the navigation mode is immediately switched from laser navigation to QR code navigation; S6. After switching to QR code navigation, the convolutional neural network model is used to predict the visual integrity of the QR code label. If the oil coverage area is >15% or physical damage is detected, the detour command is triggered 200ms in advance; if the oil coverage area is not >15% or physical damage is not detected, the autonomous mobile vehicle continues to travel along the predetermined path of the QR code navigation.

[0010] Preferably, the following steps are also included: the autonomous mobile vehicle performs QR code navigation switching through several QR codes regularly placed on the navigation switching point; the regular placement is specifically to arrange the main QR code label at the center position of the navigation switching point of the QR code navigation, and expand it according to a diamond topology with it as the geometric center.

[0011] Preferably, the step S6 specifically includes the following steps: S6-1, obtaining a QR code label, and denoising, graying, and contrast enhancing the QR code label, and locating the QR code area by contour extraction to obtain a preprocessed QR code image; S6-2, extracting the processed QR code image features layer by layer from the feature extraction part of the convolutional neural network model, and outputting a global feature vector; S6-3, inputting the global feature vector into the classification decision part of the convolutional neural network model, performing classification judgment, and outputting the recognition results of the oil coverage area and physical damage; S6-4, comparing the recognition result with the preset threshold, if the oil coverage area is greater than 15% or the physical damage exceeds the preset threshold, triggering a detour instruction and outputting a detour path suggestion; S6-5, if the threshold is not exceeded, the autonomous mobile vehicle continues to travel along the predetermined path of the QR code navigation.

[0012] Preferably, the step of establishing the detour path specifically includes the following steps: loading detailed map information of the current area from a pre-stored environmental map, and determining the current position and posture of the autonomous mobile vehicle in real time by fusing data from the QR code sensor, IMU and wheel encoder; converting the obtained map information into a weighted directed graph G(V,E), where V is a node set, E is an edge set, and taking the current vehicle position as the starting point C of the detour path, selecting the next available QR code marker point or path node as the detour target point G; The algorithm starts from the target node G, traces back to the starting node C through the parent node, records all the nodes on the path, and obtains the optimal path from the current position to the detour target point.

[0013] Preferably, the detailed map information is stored in the form of a grid graph or a node graph, wherein each node represents a reachable location, each edge represents a path between nodes, and the detailed map information at least includes feasible areas, obstacle locations, and alternate paths.

[0014] Preferably, each node v∈V in the weighted directed graph G(V,E) represents a location, each edge (u,v)∈E represents a path between two locations, and the edge weight w(u,v) represents the cost of the path, and the cost of the path is specifically any one of distance, time and real-time traffic status.

[0015] Preferably, during the process of driving according to the QR code navigation, the autonomous mobile vehicle will continuously monitor the deviation between itself and the predetermined path, and use the data provided by the inertial measurement unit to make real-time corrections to ensure that the autonomous mobile vehicle can accurately move along the predetermined path.

[0016] The beneficial effects of the technical solution of the present invention are: By monitoring the change in ground reflectivity in real time, it is possible to accurately determine whether the current environment is suitable for switching navigation modes, thereby avoiding switching when the environment is unstable, which may lead to navigation failure or path deviation. Multi-line laser radar is used to periodically detect dynamic obstacle density and ground reflectivity, and combined with the calculation of reflectivity mutation rate, it can effectively identify mutations in the environment.

[0017] After confirming that the environment is stable, the autonomous mobile vehicle continues to move toward the navigation switching point and begins to scan the ground QR code mark when approaching the navigation switching point, ensuring that the navigation mode is switched at the best time, improving the accuracy and efficiency of navigation. The convolutional neural network model predicts the visual integrity of the QR code label, and can detect and deal with potential problems in advance, such as excessive oil coverage or physical damage, thereby triggering detour instructions in time to avoid possible navigation interruptions or accidents.

[0018] By regularly placing several QR codes at the navigation switching point, the detection area of ​​the QR code is significantly expanded, so that when the autonomous mobile vehicle approaches the navigation switching point, the QR code sensor can scan the QR code mark more easily and quickly, thereby reducing the probability of navigation switching delay or failure due to QR code detection failure. Especially in complex environments (such as scenes with large changes in light or uneven ground reflectivity), the arrangement of several QR codes can effectively improve the robustness of the system.

[0019] At the same time, after the autonomous mobile vehicle reaches the navigation switching point under the guidance of the laser navigation system, the QR code navigation system accurately determines the starting point of the QR code navigation through the scanned QR code ID, ensuring the accuracy and smoothness of the navigation switching and avoiding the path deviation or vehicle stagnation caused by the wrong starting point judgment. At the same time, the system can re-plan the QR code navigation path according to the starting point to ensure that the vehicle can seamlessly connect and continue to move along the predetermined path after switching. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a flow chart of an embodiment of the present application; Figure 2 This is a diagram of the placement of a QR code in one embodiment of the present application.

[0021] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0022] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0023] In addition, if the descriptions of "first", "second", etc. are involved in this application, they are only used for descriptive purposes (such as for distinguishing the same or similar elements), and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in this field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0024] See also Figure 1 The present invention proposes a method for switching from laser navigation to two-dimensional code navigation, comprising the following steps: S1. In the laser navigation stage, the ground reflectivity within a radius of 5m around the navigation switching point is periodically detected by a multi-line laser radar. The navigation switching point is a key position point pre-set on the laser navigation path and connected to the QR code navigation area; S2. According to the ground reflectivity obtained in step S1, it is recorded as According to formula (1), the reflectivity change of adjacent sampling points within a radius of 5m around the navigation switching point during the laser navigation phase is calculated: ; Formula (1): .

[0025] in, It is expressed as the reflectivity value of the i-th sampling point. Therefore, the reflectivity change can be obtained as the absolute value of the reflectivity difference between each sampling point and the next point.

[0026] S3, preset a mutation threshold M, and compare the mutation threshold M with the reflectivity change within a detection cycle calculated in step S2 In contrast, when the mutation threshold M is greater than the reflectivity change , then it is considered that at the sampling point and A reflectivity mutation occurred between , and the reflectivity mutation rate was calculated according to formula (2); Formula (2): .

[0027] in, represents the reflectivity mutation rate based on the mutation threshold M; is the total number of reflectivity data points collected by the LiDAR in one detection cycle; is the mutation indicator function, which is defined as: ;when hour, ; In other cases ; S4. According to the reflectivity mutation rate calculated in step S3, determine whether the current environment is suitable for navigation switching; specifically, if the reflectivity mutation rate is If the reflectivity mutation rate is lower than the preset stability threshold, the environment is considered stable and suitable for navigation switching. If it is higher than the stability threshold, the environment is considered unstable, navigation switching is delayed, and laser navigation is continued until the reflectivity mutation rate drops below the stability threshold; S5. After confirming that the environment is stable, the autonomous mobile vehicle continues to move toward the navigation switching point, and when the autonomous mobile vehicle approaches the navigation switching point, the QR code sensor starts to scan the QR code mark on the ground. If the QR code sensor successfully recognizes the QR code mark, the navigation mode is immediately switched from laser navigation to QR code navigation; S6. After switching to QR code navigation, a convolutional neural network model is used to predict the visual integrity of the QR code label. If the oil coverage area is greater than 15% or physical damage is detected, a detour command is triggered 200ms in advance. If the oil coverage area is not greater than 15% or physical damage is not detected, the autonomous mobile vehicle continues to travel along the predetermined path of the QR code navigation.

[0028] In this embodiment, by real-time monitoring of the change in ground reflectivity, it is possible to accurately determine whether the current environment is suitable for switching navigation modes, thereby avoiding switching when the environment is unstable, resulting in navigation failure or path deviation. Multi-line laser radar is used to periodically detect dynamic obstacle density and ground reflectivity, combined with the calculation of reflectivity mutation rate, to effectively identify mutations in the environment.

[0029] Furthermore, after confirming that the environment is stable, the autonomous mobile vehicle continues to move toward the navigation switching point and begins to scan the ground QR code mark when approaching the navigation switching point, ensuring that the navigation mode is switched at the best time, improving the accuracy and efficiency of navigation. The convolutional neural network model predicts the visual integrity of the QR code label, and can detect and deal with potential problems in advance, such as excessive oil coverage or physical damage, thereby triggering detour instructions in time to avoid possible navigation interruptions or accidents.

[0030] In one embodiment, the following steps are also included: The autonomous mobile vehicle performs QR code navigation switching through a number of QR codes regularly placed at the navigation switching point; the regular placement specifically arranges the main QR code label at the center position of the navigation switching point of the QR code navigation, and expands it in a diamond topology with it as the geometric center.

[0031] In this embodiment, by regularly placing several QR codes at the navigation switching point, the detection area of ​​the QR code is significantly expanded, so that when the autonomous mobile vehicle approaches the navigation switching point, the QR code sensor can scan the QR code mark more easily and quickly, thereby reducing the probability of navigation switching delay or failure due to QR code detection failure. Especially in complex environments (such as scenes with large changes in light or uneven ground reflectivity), the arrangement of several QR codes can effectively improve the robustness of the system.

[0032] At the same time, after the autonomous mobile vehicle reaches the navigation switching point under the guidance of the laser navigation system, the QR code navigation system accurately determines the starting point of the QR code navigation through the scanned QR code ID, ensuring the accuracy and smoothness of the navigation switching and avoiding the path deviation or vehicle stagnation caused by the wrong starting point judgment. At the same time, the system can re-plan the QR code navigation path according to the starting point to ensure that the vehicle can seamlessly connect and continue to move along the predetermined path after switching.

[0033] Specifically, see Figure 2 Among them, point 374 is the navigation switching point and also the target point of laser navigation. Under normal circumstances, the autonomous mobile vehicle reaches point 374 under the guidance of the laser navigation system. After the laser navigation system completes the navigation, the QR code navigation system scans the QR code at point 374 and continues to guide the vehicle.

[0034] When the laser navigation system deviates due to the influence of the external environment and fails to guide the car to the QR code at point 374, it will deviate to any direction in front, back, left or right of point 374, and will scan a QR code point placed in the four directions of point 373.

[0035] Since the autonomous mobile vehicle always scans a QR code point around 374, the QR code navigation system avoids the risk of downtime due to code loss; the QR code navigation system re-determines the position of the autonomous mobile vehicle and the starting point of the QR code navigation based on the scanned QR code ID, and plans a new QR code navigation path based on the starting point and guides the vehicle to continue moving forward.

[0036] In one embodiment, the step S6 specifically includes the following steps: S6-1, obtaining a QR code label, and denoising, graying, and contrast enhancing the QR code label, and locating the QR code area by contour extraction to obtain a preprocessed QR code image; S6-2, extracting the processed QR code image features layer by layer from the feature extraction part of the convolutional neural network model, and outputting a global feature vector; S6-3, inputting the global feature vector into the classification decision part of the convolutional neural network model, performing classification judgment, and outputting the recognition results of the oil coverage area and physical damage; S6-4, comparing the recognition result with the preset threshold, if the oil coverage area is greater than 15% or the physical damage exceeds the preset threshold, triggering a detour instruction and outputting a detour path suggestion; S6-5, if the threshold is not exceeded, the autonomous mobile vehicle continues to travel along the predetermined path of the QR code navigation.

[0037] In this embodiment, by denoising, graying and contrast enhancement of the collected images, the clarity and quality of the images are significantly improved, thereby ensuring that the subsequent feature extraction is more accurate and reliable, effectively reducing the impact of ambient light changes, camera noise, etc. on image quality, and providing a stable foundation for the entire navigation switching process. At the same time, the convolutional neural network model is used to automatically extract the feature vector of the image, eliminating the need for manual intervention, improving processing efficiency and consistency, and quickly and accurately capturing the key information in the QR code image, providing strong support for subsequent classification judgment.

[0038] On the other hand, the extracted feature vector is input into the classification decision part of the full convolutional neural network model for classification judgment, which can accurately output the identification results of the oil coverage area and physical damage, so that it can timely discover and deal with potential navigation risks and ensure the safe operation of the autonomous mobile vehicle. Furthermore, when the identification results show that the oil coverage area is greater than 15% or the physical damage exceeds the preset threshold, the system not only triggers the detour instruction, but also outputs the detour route suggestion, which effectively avoids navigation failure or path deviation caused by environmental problems, and provides clear detour guidance for the autonomous mobile vehicle to ensure that it can quickly restore the correct navigation state. When the identification results show that the environmental conditions meet the requirements, the autonomous mobile vehicle will continue to travel along the predetermined path of the QR code navigation to ensure the continuity and stability of the navigation system in most cases.

[0039] In one embodiment, the step of establishing the detour path specifically includes the following steps: The system loads detailed map information of the current area from a pre-stored environmental map, and determines the current position and posture of the autonomous mobile vehicle in real time by integrating data from a QR code sensor, an IMU (inertial measurement unit), and a wheel encoder; wherein the detailed map information includes at least feasible areas, obstacle locations, and backup paths; and the detailed map information is stored in the form of a grid graph or a node graph, wherein each node represents a reachable location and each edge represents a path between nodes.

[0040] The map information obtained above is converted into a weighted directed graph G(V,E), where V is a node set, E is an edge set, and the current vehicle position is used as the starting point C of the detour path, and the next available QR code mark point or path node is selected as the detour target point G; each node v∈V in the weighted directed graph G(V,E) represents a position, each edge (u,v)∈E represents a path between two positions, and the edge weight w(u,v) represents the cost of the path, and the cost of the path is specifically any one of distance, time and real-time traffic status.

[0041] pass The algorithm starts from the target node G, traces back to the starting node C through the parent node, records all the nodes on the path, and obtains the optimal path from the current position to the detour target point.

[0042] In this embodiment, the map information is converted into a weighted directed graph and combined with The algorithm performs path planning, significantly improving the navigation capability and adaptability of autonomous mobile vehicles in complex environments. This technology is efficient, accurate and robust, can dynamically respond to environmental changes, reduce computational complexity, and support multi-objective optimization, providing strong technical support for high-precision navigation needs in industrial automation.

[0043] In one embodiment, while driving according to the QR code navigation, the autonomous mobile vehicle continuously monitors the deviation between it and the predetermined path, and uses the data provided by the inertial measurement unit (IMU) to make real-time corrections to ensure that the vehicle can move accurately along the predetermined path.

[0044] It should be noted that, in this article, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method comprising a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the existence of other identical elements in the process, device, article or method comprising the element.

[0045] The above description is only a preferred embodiment of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly used in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for switching from laser navigation to QR code navigation, characterized in that: Applied to an autonomous mobile vehicle, the method comprises the following steps: S1, in the laser navigation stage, periodically detecting the ground reflectivity within a radius of 5m around a navigation switching point by means of a multi-line laser radar, wherein the navigation switching point is a key position point pre-set on the laser navigation path and connected to the QR code navigation area; S2. According to the ground reflectivity obtained in step S1, it is recorded as ;according to The calculated reflectivity change of adjacent sampling points within a radius of 5m around the navigation switching point during the laser navigation phase is ; in, It is expressed as the reflectivity value of the i-th sampling point, and the reflectivity change It is represented by the absolute value of the reflectivity difference between each sampling point and the next point; S3, preset the mutation threshold M, and compare the mutation threshold M with the reflectivity change within a detection cycle calculated in step S2 In contrast, when the mutation threshold M is greater than the reflectivity change , then it is considered that at the sampling point and There was a sudden change in reflectivity between Calculate the reflectivity mutation rate ;in, represents the reflectivity mutation rate based on the mutation threshold M; is the total number of reflectivity data points collected by the LiDAR in one detection cycle; is the mutation indicator function, which is defined as: ;when hour, ; In other cases ; S4, according to the reflectivity mutation rate calculated in step S3, determine whether the current environment is suitable for navigation switching; if the reflectivity mutation rate If the reflectivity mutation rate is lower than the preset stability threshold, the environment is considered stable and suitable for navigation switching. If the value is higher than the stability threshold, the environment is considered unstable, the navigation switch is delayed and laser navigation is continued until the reflectivity mutation rate drops below the stability threshold; S5. After confirming that the environment is stable, the autonomous mobile vehicle continues to move towards the navigation switching point, and when the autonomous mobile vehicle approaches the navigation switching point, the QR code sensor starts to scan the QR code mark on the ground; if the QR code sensor successfully recognizes the QR code mark, the navigation mode is immediately switched from laser navigation to QR code navigation; S6. After switching to QR code navigation, the convolutional neural network model is used to predict the visual integrity of the QR code label. If the oil coverage area is >15% or physical damage is detected, the detour command is triggered 200ms in advance; if the oil coverage area is not >15% or physical damage is not detected, the autonomous mobile vehicle continues to travel along the predetermined path of the QR code navigation.

2. The method of switching from laser navigation to two-dimensional code navigation according to claim 1, characterized in that: The following steps are also included: The autonomous mobile vehicle performs QR code navigation switching through a number of QR codes regularly placed at the navigation switching point; the regular placement specifically arranges the main QR code label at the center position of the navigation switching point of the QR code navigation, and expands it in a diamond topology with it as the geometric center.

3. The method of switching from laser navigation to two-dimensional code navigation according to claim 1, characterized in that: The step S6 specifically includes the following steps: S6-1, obtaining a QR code label, and denoising, graying, and contrast enhancing the QR code label, and locating the QR code area by contour extraction to obtain a preprocessed QR code image; S6-2, extracting the processed QR code image features layer by layer from the feature extraction part of the convolutional neural network model, and outputting a global feature vector; S6-3, inputting the global feature vector into the classification decision part of the convolutional neural network model, performing classification judgment, and outputting the recognition results of the oil coverage area and physical damage; S6-4, comparing the recognition result with the preset threshold, if the oil coverage area is greater than 15% or the physical damage exceeds the preset threshold, triggering a detour instruction and outputting a detour path suggestion; S6-5, if the threshold is not exceeded, the autonomous mobile vehicle continues to travel along the predetermined path of the QR code navigation.

4. The method of switching from laser navigation to two-dimensional code navigation according to claim 3, characterized in that: The step of establishing the detour path specifically includes the following steps: loading detailed map information of the current area from a pre-stored environmental map, and determining the current position and posture of the autonomous mobile vehicle in real time by fusing data from a QR code sensor, an IMU, and a wheel encoder; converting the obtained map information into a weighted directed graph G(V,E), where V is a node set, E is an edge set, and taking the current vehicle position as the starting point C of the detour path, selecting the next available QR code marker point or path node as the detour target point G; The algorithm starts from the target node G, traces back to the starting node C through the parent node, records all the nodes on the path, and obtains the optimal path from the current position to the detour target point.

5. The method of switching from laser navigation to two-dimensional code navigation according to claim 4, characterized in that: The detailed map information is stored in the form of a grid graph or a node graph, wherein each node represents a reachable location, each edge represents a path between nodes, and the detailed map information at least includes a feasible area, obstacle locations, and alternate paths.

6. The method of switching from laser navigation to two-dimensional code navigation according to claim 5, characterized in that: In the weighted directed graph G(V,E), each node v∈V represents a location, each edge (u,v)∈E represents a path between two locations, and the edge weight w(u,v) represents the cost of the path, which is specifically any one of distance, time and real-time traffic status.

7. The method of switching from laser navigation to QR code navigation according to claim 1, characterized in that: While driving according to the QR code navigation, the deviation between the autonomous mobile vehicle and the predetermined path is continuously monitored, and real-time corrections are made using data provided by the inertial measurement unit to ensure that the autonomous mobile vehicle can move accurately along the predetermined path.

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