Intelligent trolley navigation method, device, medium and product integrating multiple navigation modes

Abstract

This application relates to the technical field of vehicle navigation, and in particular to a method, device, medium, and product for intelligent vehicle navigation integrating multiple navigation modes. The method includes: analyzing the transportation task route area based on automated transportation tasks and factory area division information to determine multiple work areas. Then, based on each work area and factory area division information, analyzing the navigation mode to determine the corresponding navigation mode for each work area, controlling the intelligent vehicle to select the appropriate navigation mode in different work area scenarios, fully utilizing the advantages of different navigation modes for complementarity, and greatly improving the accuracy of intelligent vehicle navigation. Furthermore, based on the target work area and the corresponding navigation mode, navigation planning is performed to determine target navigation information. Finally, by integrating the target navigation information corresponding to each target work area, transportation navigation information is determined and sent to the intelligent vehicle terminal.

Description

Technical Field

[0001] This application relates to the technical field of car navigation, and in particular to intelligent car navigation methods, devices, media and products that integrate multiple navigation modes. Background Technology

[0002] Factory production environments are becoming increasingly complex, with ever-higher demands for production efficiency and precision. Traditional material handling methods are no longer sufficient to meet the needs of modern production. Therefore, with the rapid development of industrial automation and intelligent manufacturing technologies, intelligent mobile carts have demonstrated significant advantages in improving production efficiency and reducing labor costs. With their high efficiency, flexibility, and reliability, intelligent carts have become a key tool for solving these problems.

[0003] In modern factories, intelligent mobile carts are a key component of automated production processes, and the performance of their navigation systems directly affects the efficiency and safety of the production line. However, with the increasing complexity of factory environments, intelligent cart navigation methods with only a single mode suffer from low navigation accuracy and cannot meet the navigation needs of carts in complex environments.

[0004] Therefore, how to improve the navigation accuracy of intelligent vehicles is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] The purpose of this application is to provide a navigation method, device, medium, and product for intelligent vehicles that integrates multiple navigation modes, in order to solve at least one of the above-mentioned technical problems.

[0006] The above-mentioned inventive objective of this application is achieved through the following technical solutions:

[0007] Firstly, this application provides a navigation method for intelligent vehicles that integrates multiple navigation modes, employing the following technical solution:

[0008] A navigation method for an intelligent vehicle integrating multiple navigation modes, comprising:

[0009] Obtain automated transportation tasks and factory area division information, and perform transportation task route area analysis based on the automated transportation tasks and factory area division information to determine multiple work areas;

[0010] Navigation mode analysis is performed based on the division information of each work area and the factory area to determine the navigation mode corresponding to each work area.

[0011] Navigation planning is performed based on the target working area and the corresponding navigation mode to determine the target navigation information, wherein the target working area is any one of multiple working areas;

[0012] By combining the target navigation information corresponding to each target work area, the final transportation navigation information is determined and sent to the intelligent vehicle terminal to control the intelligent vehicle terminal to perform transportation tasks according to the navigation mode and the transportation navigation information.

[0013] By employing the aforementioned technical solution, automated transportation tasks and factory area division information are acquired. Based on this information, the transportation task route area is analyzed to identify multiple work areas. By pre-determining the work areas traversed by the intelligent vehicle, the appropriate navigation mode can be selected more flexibly for each work area. Then, based on each work area and factory area division information, navigation mode analysis is performed to determine the corresponding navigation mode for each work area. This allows the intelligent vehicle to select the appropriate navigation mode in different work area scenarios, fully utilizing the advantages of different navigation modes for mutual complementarity, greatly improving the accuracy of the intelligent vehicle's navigation. Furthermore, navigation planning is performed based on the target work area and the corresponding navigation mode to determine target navigation information. This target navigation information provides precise road guidance for the intelligent vehicle to work within the target work area, improving its work efficiency. Finally, by combining the target navigation information corresponding to each target work area, the final transportation navigation information is determined and sent to the intelligent vehicle terminal to control the terminal to execute the transportation task according to the navigation mode and transportation navigation information.

[0014] In a preferred embodiment, this application can be further configured such that the navigation mode includes: magnetic navigation mode, SLAM navigation mode, and QR code navigation mode.

[0015] The step of analyzing navigation patterns based on the division information of each work area and the factory area to determine the navigation pattern corresponding to each work area includes:

[0016] Based on the target work area and the factory area division information, the environmental condition characteristics, work task characteristics, and facility deployment characteristics are determined by matching.

[0017] When the facility deployment features include the deployment of QR code labels, the navigation mode corresponding to the target work area is determined to be the QR code navigation mode;

[0018] When the environmental conditions include complex dynamic changes, the navigation mode corresponding to the target working area is determined to be the SLAM navigation mode;

[0019] When the work task features include fixed-path transportation, the navigation mode corresponding to the target work area is determined to be the magnetic navigation mode;

[0020] Otherwise, obtain the navigation mode priority, and determine the navigation mode corresponding to the target working area based on the navigation mode priority.

[0021] In a preferred embodiment, this application can be further configured as follows: the navigation planning based on the target working area and the corresponding navigation mode, and the determination of target navigation information, includes:

[0022] When the navigation mode is the SLAM navigation mode, a dynamic area map, work start point and work end point corresponding to the target work area are obtained, and feasible path planning is performed based on the dynamic area map, the work start point and the work end point to determine the target navigation information;

[0023] When the navigation mode is the QR code navigation mode, QR code analysis is performed based on the automated transportation task and the target work area to determine the QR code combination, and navigation planning is performed based on the QR code combination to determine the target navigation information. The QR code combination includes: a deceleration QR code and a positioning and stopping QR code.

[0024] When the navigation mode is the magnetic navigation mode, the magnetic strip laying information corresponding to the target working area is obtained, and magnetic strip path planning is performed based on the magnetic strip laying information and the automated transportation task to determine the target navigation information.

[0025] In a preferred embodiment, this application can be further configured such that: the step of integrating the target navigation information corresponding to each target working area to finally determine the transportation navigation information includes:

[0026] Based on the automated transportation task and each of the target work areas, task operation analysis is performed to determine the work task corresponding to each target work area;

[0027] Based on the navigation mode corresponding to each work area, a mode switching analysis is performed to determine the navigation mode switching information.

[0028] Based on the navigation mode switching information, the work tasks corresponding to each target work area, and the target navigation information, the transportation navigation information is finally determined.

[0029] In a preferred embodiment, this application can be further configured such that, after sending the transportation navigation information to the smart car terminal, it also includes:

[0030] The system acquires radar detection data sent by the intelligent vehicle terminal in real time, performs obstacle analysis based on the radar detection data, determines obstacle distribution information, and evaluates the positional relationship based on the obstacle distribution information and the transportation navigation information to determine the positional relationship evaluation result.

[0031] When the positional relationship assessment result is obstruction to driving, obstacle avoidance analysis is performed based on the obstacle position and obstacle size in the transportation navigation information and obstacle distribution information to determine the obstacle avoidance control command, and the obstacle avoidance control command is sent to the intelligent vehicle terminal.

[0032] In a preferred embodiment, this application can be further configured such that, after sending the transportation navigation information to the smart car terminal, it also includes:

[0033] The system acquires periodic working data sent by the intelligent vehicle terminal, performs a navigation mode feasibility analysis based on the periodic working data, and determines the navigation mode feasibility analysis result.

[0034] When the feasibility analysis result of the navigation mode is that it does not meet the requirements, a backup navigation mode is determined, and the intelligent vehicle terminal is controlled to switch to the backup navigation mode and continue to work.

[0035] Secondly, this application provides an electronic device that adopts the following technical solution:

[0036] At least one processor;

[0037] Memory;

[0038] At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, the at least one application being configured to: execute the above-described intelligent vehicle navigation method with integrated multi-navigation modes.

[0039] Thirdly, this application provides a computer-readable storage medium, which adopts the following technical solution:

[0040] A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the aforementioned intelligent vehicle navigation method integrating multiple navigation modes.

[0041] Fourthly, this application provides a computer program product, which adopts the following technical solution:

[0042] A computer program product includes a computer program that, when executed by a processor, implements the aforementioned intelligent vehicle navigation method integrating multiple navigation modes.

[0043] In summary, this application includes at least one of the following beneficial technical effects:

[0044] The system acquires automated transportation tasks and factory area division information. Based on this information, it analyzes the transportation route areas to identify multiple work zones. By pre-determining the work zones the intelligent vehicle will traverse, it can more flexibly select the appropriate navigation mode for each work zone. Then, based on each work zone and factory area division information, it analyzes the navigation mode to determine the corresponding navigation mode for each work zone. This allows the intelligent vehicle to select the appropriate navigation mode for different work zone scenarios, fully utilizing the advantages of different navigation modes to complement each other and greatly improve the accuracy of the intelligent vehicle's navigation. Next, based on the target work zone and its corresponding navigation mode, it performs navigation planning to determine target navigation information. This target navigation information provides precise road guidance for the intelligent vehicle to work within the target work zone, improving its work efficiency. Finally, by combining the target navigation information for each target work zone, it determines the final transportation navigation information and sends it to the intelligent vehicle terminal to control the terminal to execute the transportation task according to the navigation mode and transportation navigation information.

[0045] Multiple navigation modes are provided, enabling the intelligent vehicle to automatically select the most suitable navigation method for its work area under different environments and task requirements, greatly enhancing its adaptability and flexibility. Therefore, based on the target work area and factory area division information, environmental condition characteristics, work task characteristics, and facility deployment characteristics are determined. When the facility deployment characteristics include the deployment of QR code tags, the navigation mode corresponding to the target work area is determined to be the QR code navigation mode; when the environmental condition characteristics include complex dynamic changes, the navigation mode corresponding to the target work area is determined to be the SLAM navigation mode; when the work task characteristics include fixed-path transportation, the navigation mode corresponding to the target work area is determined to be the magnetic navigation mode; otherwise, the navigation mode priority is obtained, and the navigation mode corresponding to the target work area is determined based on the navigation mode priority. Controlling the intelligent vehicle to select the appropriate navigation mode in different work area scenarios, and fully utilizing the advantages of different navigation modes for complementarity, greatly improves the accuracy and robustness of the intelligent vehicle's navigation. Attached Figure Description

[0046] Figure 1 This is a flowchart illustrating a smart car navigation method integrating multiple navigation modes according to one embodiment of this application.

[0047] Figure 2 This is a schematic diagram of the structure of an intelligent car navigation device integrating multiple navigation modes according to one embodiment of this application;

[0048] Figure 3 This is a schematic diagram of the structure of an electronic device according to one embodiment of this application. Detailed Implementation

[0049] The following combination Figures 1 to 3 This application will be described in further detail.

[0050] This specific embodiment is merely an explanation of this application and is not intended to limit it. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they are within the scope of this application.

[0051] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. It should be noted that in the optional embodiments of this application, the object information and other related data involved require the permission or consent of the object when the embodiments of this application are applied to specific products or technologies, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. That is to say, if the embodiments of this application involve data related to the object, it needs to be obtained with the authorization and consent of the object, the authorization and consent of the relevant departments, and in compliance with the relevant laws, regulations, and standards of the country and region. If personal information is involved in the embodiments, the acquisition of all personal information requires the consent of the individual. If sensitive information is involved, the separate consent of the information subject is required, and the embodiments also need to be implemented with the authorization and consent of the object.

[0052] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0053] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0054] This application provides a navigation method for an intelligent vehicle integrating multiple navigation modes, executed by an electronic device. This electronic device can be a server or a terminal device. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, tablet, laptop, desktop computer, etc., but is not limited to these. The terminal device and the server can be directly or indirectly connected via wired or wireless communication. This application does not impose any limitations on this connection. Figure 1 As shown, the method includes steps S101, S102, S103, and S104, wherein:

[0055] Step S101: Obtain automated transportation task and factory area division information, and perform transportation task route area analysis based on the automated transportation task and factory area division information to determine multiple work areas.

[0056] In this embodiment, the automated transportation task clearly and in detail records the specific work tasks that the intelligent vehicle needs to complete. These tasks include, but are not limited to, cargo transportation from the starting point to the destination, material distribution, and inspection operations. Each specific task clearly records key information such as the intelligent vehicle's starting position, target position, type and quantity of transported items, and time requirements. The factory area division information details the specific layout, functional division, obstacle locations, and routes of each area within the factory. This factory area division information is typically presented in the form of maps, CAD drawings, or real-time sensor data, providing the environmental foundation required for the intelligent vehicle's navigation. Furthermore, based on the automated transportation task and the factory area division information, a transportation task path area analysis is performed to determine multiple work areas. These multiple work areas represent all areas that the intelligent vehicle must pass through and may stop in order to complete the transportation task. Regarding the transportation task path area analysis, a preliminary transportation path analysis is performed based on the automated transportation task and the factory area division information to determine the initial transportation path. The factory areas traversed along the initial transportation path are recorded as work areas. Since different work areas in a factory have different characteristics, such as environmental conditions, work tasks, facility deployment, spatial layout, road conditions, and lighting conditions, the work areas that the intelligent vehicle will pass through can be determined in advance, so as to more flexibly select the navigation mode suitable for the work area and ensure that the intelligent vehicle can operate stably and accurately in different environments.

[0057] Step S102: Analyze the navigation mode based on the division information of each work area and factory area to determine the navigation mode corresponding to each work area.

[0058] In this embodiment of the application, different work areas within the factory have different characteristics. If the intelligent vehicle uses only a single navigation mode, it cannot adapt to the diverse work environments, resulting in low navigation accuracy. To improve the navigation accuracy of the intelligent vehicle in complex and changing factory environments, the intelligent vehicle is controlled to select appropriate navigation modes in different work area scenarios, fully utilizing the advantages of different navigation modes for complementarity, thereby greatly improving the accuracy and robustness of the intelligent vehicle's navigation. Therefore, navigation mode analysis is performed based on the division information of each work area and factory area to determine the corresponding navigation mode for each work area. The navigation modes include, but are not limited to: magnetic navigation mode, SLAM navigation mode, and QR code navigation mode. There are various methods for navigation mode analysis, and this application embodiment does not limit them. In one feasible method, matching is performed based on the target work area and factory area division information to determine environmental condition characteristics, work task characteristics, and facility deployment characteristics. When the facility deployment characteristics include the deployment of QR code tags, the navigation mode corresponding to the target work area is determined to be the QR code navigation mode. Preferably, the navigation modes in the intelligent vehicle terminal within the target work area are prioritized from high to low as follows: QR code navigation mode, magnetic navigation mode, and SLAM navigation mode. When the environmental condition characteristics include complex dynamic changes, the navigation mode corresponding to the target work area is determined to be the SLAM navigation mode. Preferably, the... The navigation modes within the intelligent vehicle terminal within the target work area are prioritized from highest to lowest as follows: SLAM navigation mode, magnetic navigation mode, and QR code navigation mode. When the work task features include fixed-path transportation, the navigation mode corresponding to the target work area is determined to be magnetic navigation mode. Preferably, the navigation modes within the intelligent vehicle terminal within the target work area are prioritized from highest to lowest as follows: magnetic navigation mode, SLAM navigation mode, and QR code navigation mode. Otherwise, the navigation mode priority is obtained, and based on the navigation mode priority, the navigation mode corresponding to the target work area is determined, wherein the navigation mode priority is ranked from highest to lowest as follows: magnetic navigation mode, SLAM navigation mode, and QR code navigation mode. This application embodiment does not limit the specific sorting method of the navigation mode priority corresponding to different target work areas; users can adjust it according to actual conditions.

[0059] Step S103: Perform navigation planning based on the target working area and the corresponding navigation mode, and determine the target navigation information, wherein the target working area is any one of multiple working areas;

[0060] Step S104: Integrate the target navigation information corresponding to each target work area, finally determine the transportation navigation information, and send the transportation navigation information to the intelligent vehicle terminal to control the intelligent vehicle terminal to execute the transportation task according to the navigation mode and transportation navigation information.

[0061] In the embodiments of this application, different types of navigation modes focus on different content when performing navigation planning, and the execution process is also different. In order to control the intelligent vehicle to autonomously complete automated transportation tasks in multiple work areas, navigation planning is performed based on the target work area and the corresponding navigation mode to determine the target navigation information. The target navigation information provides accurate road guidance for the intelligent vehicle to work in the target work area, ensuring that the intelligent vehicle can follow a feasible path, avoiding unnecessary detours and waiting, thereby improving the working efficiency of the intelligent vehicle. There are various ways to implement navigation rules, and this application embodiment does not limit them. In one possible implementation, when the navigation mode is SLAM navigation mode, a dynamic area map, work start point, and work end point corresponding to the target work area are obtained. Feasible path planning is performed based on the dynamic area map, work start point, and work end point to determine the target navigation information. When the navigation mode is QR code navigation mode, QR code analysis is performed based on the automated transportation task and the target work area to determine the QR code combination. Navigation planning is performed based on the QR code combination to determine the target navigation information. The QR code combination includes: deceleration QR code and positioning stop QR code. When the navigation mode is magnetic navigation mode, magnetic strip laying information corresponding to the target work area is obtained. Magnetic strip path planning is performed based on the magnetic strip laying information and the automated transportation task to determine the target navigation information.

[0062] Furthermore, by integrating the target navigation information corresponding to each target work area, the final transportation navigation information is determined. This transportation navigation information includes the work tasks that the intelligent vehicle needs to perform in each target work area, the road guidance situation in each target work area, and the navigation mode switching information corresponding to the switching between different navigation modes. The specific implementation process of determining the transportation navigation information based on the integrated target navigation information is as follows: Task operation analysis is performed based on the automated transportation task and each target work area to determine the work task corresponding to each target work area; mode switching analysis is performed based on the navigation mode corresponding to each work area to determine the navigation mode switching information; based on the navigation mode switching information, the work tasks corresponding to each target work area, and the target navigation information, the final transportation navigation information is determined. After this, the transportation navigation information is sent to the intelligent vehicle terminal to control the intelligent vehicle terminal to execute the transportation task according to the navigation mode and the transportation navigation information.

[0063] As can be seen, in this embodiment, automated transportation tasks and factory area division information are obtained. Based on these information, the transportation task route area is analyzed to determine multiple work areas. By pre-determining the work areas traversed by the intelligent vehicle, a suitable navigation mode can be selected more flexibly. Then, based on each work area and factory area division information, navigation mode analysis is performed to determine the corresponding navigation mode for each work area. This controls the intelligent vehicle to select the appropriate navigation mode in different work area scenarios, fully utilizing the advantages of different navigation modes for complementarity, greatly improving the accuracy of the intelligent vehicle's navigation. Furthermore, navigation planning is performed based on the target work area and the corresponding navigation mode to determine target navigation information. This target navigation information provides precise road guidance for the intelligent vehicle to work within the target work area, improving the vehicle's work efficiency. Finally, by combining the target navigation information corresponding to each target work area, transportation navigation information is determined and sent to the intelligent vehicle terminal to control the intelligent vehicle terminal to execute the transportation task according to the navigation mode and transportation navigation information.

[0064] Furthermore, to improve the accuracy and robustness of the intelligent vehicle navigation, in this embodiment, the navigation modes include: magnetic navigation mode, SLAM navigation mode, and QR code navigation mode.

[0065] Based on the division information of each work area and factory area, navigation mode analysis is performed to determine the corresponding navigation mode for each work area, including:

[0066] Matching is performed based on the target work area and factory area division information to determine environmental condition characteristics, work task characteristics, and facility deployment characteristics;

[0067] When the facility deployment features include the deployment of QR code labels, the navigation mode corresponding to the target work area is determined to be the QR code navigation mode;

[0068] When the environmental conditions include complex and dynamic changes, the navigation mode corresponding to the target working area is determined to be the SLAM navigation mode.

[0069] When the characteristics of the work task include fixed-path transportation, the navigation mode corresponding to the target work area is determined to be magnetic navigation mode.

[0070] Otherwise, obtain the navigation mode priority, and determine the navigation mode corresponding to the target work area based on the navigation mode priority.

[0071] This application provides multiple navigation modes to help the intelligent vehicle automatically select the most suitable navigation method for the work area under different environments and task requirements, greatly enhancing the adaptability and flexibility of the intelligent vehicle. The navigation modes include: magnetic strip navigation mode, SLAM navigation mode, and QR code navigation mode. For the magnetic strip navigation mode, the intelligent vehicle needs to travel along a fixed magnetic strip route, providing stable and reliable navigation and positioning within a fixed-path transportation and stable working area, ensuring that the intelligent vehicle maintains high-precision positioning even at high speeds. For the SLAM navigation mode, in work areas with numerous temporary obstacles or complex terrain, SLAM navigation can perceive and quickly respond to environmental changes in real time, effectively avoiding obstacles. For the QR code navigation mode, by scanning densely arranged QR code labels, rapid path planning and direction adjustment are achieved, improving the intelligent vehicle's operating efficiency in complex environments.

[0072] To improve the navigation accuracy of intelligent vehicles in complex and ever-changing factory environments, the system selects appropriate navigation modes for different work areas, fully leveraging the complementary advantages of various navigation modes to significantly enhance the accuracy and robustness of intelligent vehicle navigation. Therefore, for any target work area among multiple work areas, matching is performed based on the target work area and factory area division information to determine environmental condition characteristics, work task characteristics, and facility deployment characteristics. Environmental condition characteristics characterize whether the layout within the work area changes frequently, whether there are numerous temporary obstacles or complex terrain, etc., including complex dynamic changes and static stability. Work task characteristics characterize whether the work tasks within the work area require transportation along fixed paths, including fixed-path transportation and custom-path transmission. Facility deployment characteristics characterize whether there are dense QR code tags within the work area to control the intelligent vehicle to accurately park at specific locations, including deployed QR code tags and undeployed QR code tags.

[0073] Furthermore, when the facility deployment features include the deployment of QR code tags, the navigation mode corresponding to the target work area is determined to be the QR code navigation mode. The QR code tags provide clear coordinate information, enabling the intelligent vehicle to accurately identify and locate the position of each QR code, thus achieving high-precision navigation and docking. When the environmental conditions include complex dynamic changes, the navigation mode corresponding to the target work area is determined to be the SLAM navigation mode. SLAM navigation mode can perceive and adapt to environmental changes in real time, including the appearance and disappearance of dynamic obstacles and temporary changes in the path. Its strong adaptability allows the intelligent vehicle to maintain stable navigation performance in complex and dynamic work environments. When the work task features include fixed-path transportation, the navigation mode corresponding to the target work area is determined to be the magnetic navigation mode. Magnetic navigation mode utilizes physical magnetic field signals for positioning, is stable and not easily affected by environmental interference, and can provide stable and reliable navigation performance, ensuring that the intelligent vehicle travels accurately along the predetermined path. Of course, when none of the above conditions are met, that is, when the environmental condition is static and stable, the work task is a custom path transmission, and the facility deployment is no QR code label, the navigation mode priority is obtained. This navigation mode priority is the priority order of multiple navigation modes that are preset and stored in the electronic device. Preferably, they are arranged in order of priority from high to low as: magnetic navigation mode and SLAM navigation mode. Based on the navigation mode priority, the navigation mode corresponding to the target work area is determined.

[0074] As can be seen, this application embodiment provides multiple navigation modes, which helps the intelligent vehicle automatically select the most suitable navigation method for the work area under different environments and task requirements, greatly enhancing the adaptability and flexibility of the intelligent vehicle. Therefore, based on the target work area and factory area division information, environmental condition characteristics, work task characteristics, and facility deployment characteristics are determined. When the facility deployment characteristics include the deployment of QR code tags, the navigation mode corresponding to the target work area is determined to be the QR code navigation mode; when the environmental condition characteristics include complex dynamic changes, the navigation mode corresponding to the target work area is determined to be the SLAM navigation mode; when the work task characteristics include fixed-path transportation, the navigation mode corresponding to the target work area is determined to be the magnetic navigation mode; otherwise, the navigation mode priority is obtained, and the navigation mode corresponding to the target work area is determined based on the navigation mode priority. By controlling the intelligent vehicle to select the appropriate navigation mode in different work area scenarios, the advantages of different navigation modes are fully utilized for complementarity, greatly improving the accuracy and robustness of the intelligent vehicle navigation.

[0075] Furthermore, to improve the compatibility between target navigation information and navigation modes, and to help the intelligent vehicle operate safely in each navigation mode, in this embodiment of the application, navigation planning is performed based on the target working area and the corresponding navigation mode to determine the target navigation information, including:

[0076] When the navigation mode is SLAM navigation mode, the dynamic area map, work start point and work end point corresponding to the target work area are obtained, and feasible path planning is performed based on the dynamic area map, work start point and work end point to determine the target navigation information;

[0077] When the navigation mode is QR code navigation mode, QR code analysis is performed based on the automated transportation task and the target work area to determine the QR code combination, and navigation planning is performed based on the QR code combination to determine the target navigation information. The QR code combination includes: deceleration QR code and positioning stop QR code.

[0078] When the navigation mode is magnetic navigation mode, the magnetic strip laying information corresponding to the target working area is obtained, and magnetic strip path planning is performed based on the magnetic strip laying information and automated transportation tasks to determine the target navigation information.

[0079] In this embodiment of the application, since multiple navigation modes are integrated, navigation planning needs to be performed based on the working characteristics of each navigation mode during the navigation planning process to determine the target navigation information under that mode. Specifically, when the navigation mode is SLAM navigation mode, the layout of the target work area will change frequently, and there may be a large number of temporary obstacles or complex terrain. Therefore, a dynamic area map, a work start point, and a work end point corresponding to the target work area are obtained. The work start point is the starting point where the intelligent vehicle enters the target work area, and the work end point is the final location where the intelligent vehicle completes its work task within the target work area. The dynamic area map is a map representing the current environmental conditions of the target work area. It can be constructed using image data periodically collected by cameras within the target work area to obtain a dynamic area map. This dynamic area map can promptly display the layout changes, obstacle distribution, and other conditions within the target work area. Furthermore, based on the dynamic region map, the starting point, and the ending point, a path planning algorithm (e.g., A* algorithm, Dijkstra's algorithm, RRT algorithm, etc.) is used to search for feasible paths. This algorithm considers factors such as obstacles, road width, and turning restrictions in the dynamic region map to find an optimal path from the starting point to the ending point. Then, based on the planned optimal path, specific target navigation information is generated. This target navigation information includes, but is not limited to, the vehicle's speed, steering angle, acceleration, and deceleration control information. Simultaneously, when the vehicle is in SLAM navigation mode, SLAM navigation can perceive environmental changes in real time and flexibly adjust the path based on the optimal path.

[0080] When the navigation mode is QR code navigation, a task analysis is performed based on the automated transportation task and the target work area to determine the precise stopping position of the intelligent vehicle and the corresponding positioning stop QR code. This allows the intelligent vehicle to accurately and smoothly stop at the specific location after scanning the positioning stop QR code. However, since the intelligent vehicle operates at a relatively high speed when performing navigation operations according to the QR code mode, using only a positioning stop QR code is insufficient to immediately stop the intelligent vehicle from its high-speed operation. This causes the intelligent vehicle to continue sliding a distance near the positioning stop QR code, increasing the risk of collisions with surrounding obstacles, people, or other vehicles, thus failing to achieve precise stopping. To improve the stopping accuracy of the intelligent vehicle, a deceleration QR code is selected at a preset distance from the positioning stop QR code. The size of the preset distance is set by technicians according to actual conditions, and this embodiment does not limit it. This allows the intelligent vehicle to perform a deceleration operation upon scanning the deceleration QR code, ensuring that the intelligent vehicle can smoothly and accurately stop at the specific location corresponding to the positioning stop QR code.

[0081] When the navigation mode is magnetic navigation mode, the magnetic strip laying information corresponding to the target work area is obtained. This information characterizes the starting point, ending point, path shape, and connection relationships of the magnetic strips within the target work area, and can be represented graphically. Furthermore, the automated transportation task details the locations where the intelligent vehicle needs to stop or perform operations within the target work area; these locations are called waypoints. Then, based on the magnetic strip laying information, waypoints, work start point, and work end point, magnetic strip path planning is performed to determine the magnetic strip travel path. During the magnetic strip path planning process, the kinematic characteristics of the intelligent vehicle, such as maximum speed and steering capability, are comprehensively considered. Finally, based on the planned magnetic strip travel path, specific target navigation information is generated. This target navigation information includes, but is not limited to, the intelligent vehicle's travel speed, steering angle, acceleration, and deceleration control information.

[0082] As can be seen, in this embodiment, when the navigation mode is SLAM navigation mode, a dynamic area map, work start point, and work end point corresponding to the target work area are obtained. Feasible path planning is performed based on the dynamic area map, work start point, and work end point to determine the target navigation information. When the navigation mode is QR code navigation mode, QR code analysis is performed based on the automated transportation task and the target work area to determine QR code combinations. Navigation planning is then performed based on these QR code combinations to determine the target navigation information. The QR code combinations include a deceleration QR code and a positioning / stop QR code. Simultaneously, when the navigation mode is magnetic navigation mode, magnetic strip laying information corresponding to the target work area is obtained. Magnetic strip path planning is performed based on the magnetic strip laying information and the automated transportation task to determine the target navigation information. During the navigation planning process, navigation planning needs to be based on the working characteristics of each navigation mode, improving the adaptability between the target navigation information and the navigation mode, which helps the intelligent vehicle work safely in each navigation mode.

[0083] Furthermore, to improve the accuracy and reliability of automated transportation tasks, in this embodiment, the transportation navigation information is ultimately determined by integrating the target navigation information corresponding to each target work area, including:

[0084] Based on the automated transportation task and each target work area, task operation analysis is performed to determine the work task corresponding to each target work area.

[0085] Perform mode switching analysis based on the navigation mode corresponding to each work area to determine navigation mode switching information;

[0086] Based on navigation mode switching information, the work tasks corresponding to each target work area, and target navigation information, the final transportation navigation information is determined.

[0087] In this embodiment of the application, in order to ensure that the intelligent vehicle can successfully complete the automated transportation task, and to facilitate the intelligent vehicle to switch the appropriate navigation mode smoothly in different work areas, so as to improve the navigation accuracy of the intelligent vehicle in the factory, when the intelligent vehicle works according to the navigation information, it can greatly reduce errors and deviations in the task execution process and improve the accuracy and reliability of the automated transportation task.

[0088] Specifically, the automated transportation task details the specific tasks that the intelligent vehicle needs to complete. These tasks include, but are not limited to, cargo transportation from origin to destination, material delivery, and inspection operations. Since different tasks are distributed across different target areas, task operation analysis is performed based on the automated transportation task and each target work area to determine the corresponding tasks for each area. Simultaneously, to improve the navigation accuracy of the intelligent vehicle in complex and changing factory environments and to control the vehicle to select appropriate navigation modes in different work areas, mode switching analysis is performed based on the navigation mode for each work area to determine navigation mode switching information. This information represents the location information where a navigation mode switch is required, allowing the intelligent vehicle to complete the appropriate navigation mode switch as soon as it arrives at the work area. Finally, based on the navigation mode switching information, the tasks corresponding to each target work area, and the target navigation information, transportation navigation information is determined. This ensures that the intelligent vehicle can smoothly complete task execution and navigation mode switching within the target work area after receiving the transportation navigation information, improving the accuracy and reliability of the automated transportation task.

[0089] As can be seen, in this embodiment, task operation analysis is performed based on the automated transportation task and each target work area to determine the work task corresponding to each target work area. Then, mode switching analysis is performed based on the navigation mode corresponding to each work area to determine the navigation mode switching information. Finally, based on the navigation mode switching information, the work task corresponding to each target work area, and the target navigation information, the transportation navigation information is finally determined. In the process of determining the transportation navigation information, the work tasks of the intelligent vehicle in the target work area and the switching between different navigation modes are comprehensively considered, which improves the accuracy and reliability of the automated transportation task.

[0090] Furthermore, to reduce the risk of accidents caused by collisions and protect the safety of people and property, in this embodiment of the application, after sending the transportation navigation information to the intelligent vehicle terminal, it also includes:

[0091] The system acquires radar detection data sent by the intelligent vehicle terminal in real time, performs obstacle analysis based on the radar detection data, determines obstacle distribution information, and evaluates the positional relationship based on the obstacle distribution information and transportation navigation information to determine the positional relationship evaluation result.

[0092] When the positional relationship assessment result is that it hinders driving, obstacle avoidance analysis is performed based on the obstacle position and size in the transportation navigation information and obstacle distribution information to determine the obstacle avoidance control command, and the obstacle avoidance control command is sent to the intelligent vehicle terminal.

[0093] In this embodiment, the intelligent vehicle is equipped with a LiDAR. Therefore, the intelligent vehicle can promptly detect surrounding obstacles during its operation using the LiDAR, which is crucial for avoiding collisions and ensuring the safety of personnel and goods. Thus, the intelligent vehicle acquires radar detection data sent by its terminal in real time, performs obstacle analysis based on this data, and determines obstacle distribution information. The intelligent vehicle and electronic devices communicate wirelessly. The obstacle distribution information includes, but is not limited to, obstacle location and size. The specific implementation process for obstacle analysis is as follows: the radar detection data is filtered to remove noise and reduce data volume. The preprocessed point cloud data is then divided into different modules to distinguish between ground points and non-ground points. An unsupervised clustering algorithm (e.g., Euclidean clustering, density clustering, etc.) is used to divide the non-ground point cloud into multiple clusters, each cluster representing an obstacle. Then, attribute calculations are performed on each obstacle to determine its location and size. Preferably, a map is generated by combining the relevant information of each obstacle to obtain a distributed map of obstacle distribution information. Furthermore, a positional relationship assessment is performed based on obstacle distribution information and transportation navigation information to determine the positional relationship assessment result, which includes: obstructing driving and normal driving. The specific implementation process of the positional relationship assessment is as follows: using coordinate transformation and geometric calculation methods, the coordinate systems of obstacle distribution information and transportation navigation information are unified to calculate the spatial positional relationship between obstacles and the path. A positional relationship assessment standard is obtained. This standard is set by technicians based on factors such as the performance indicators, safety standards, and traffic rules of the intelligent vehicle. Users can adjust it according to actual conditions; this embodiment does not impose limitations. Finally, the calculated spatial positional relationship is compared with the positional relationship assessment standard to determine the positional relationship assessment result between obstacles and the transportation navigation path.

[0094] When the positional relationship assessment result indicates normal driving, no other operations are performed, and the intelligent vehicle is controlled to drive normally according to the transportation navigation path. When the positional relationship assessment result indicates obstruction, obstacle avoidance analysis is performed based on the obstacle position and size in the transportation navigation information and obstacle distribution information to determine obstacle avoidance control commands. These commands include, but are not limited to, steering commands, speed commands, and braking commands. For obstacle avoidance analysis, based on the intelligent vehicle size, obstacle size, and traffic safety standards, the minimum safe distance between the intelligent vehicle and the obstacle is calculated. An obstacle avoidance zone is then defined, centered on the obstacle position and based on the minimum safe distance and obstacle size. This zone should completely cover the obstacle and extend outwards with a certain safety margin. Furthermore, an obstacle avoidance starting point is selected based on the transportation navigation information and the obstacle avoidance zone. This starting point should be as close to the obstacle as possible but not immediately enter the obstacle avoidance zone. An obstacle avoidance path is then planned based on the starting point and the obstacle avoidance zone to determine the obstacle path curvature. Finally, based on the obstacle avoidance area, obstacle avoidance starting point, obstacle avoidance path curvature, and obstacle avoidance speed, instructions are generated to determine the obstacle avoidance control commands, which guide the intelligent vehicle to perform obstacle avoidance operations. By identifying and avoiding obstacles in a timely manner, the risk of accidents caused by collisions can be significantly reduced, protecting the safety of people and property.

[0095] As can be seen, in this embodiment, radar detection data sent by the intelligent vehicle terminal is acquired in real time. Obstacle analysis is performed based on the radar detection data to determine obstacle distribution information. Then, a positional relationship assessment is conducted based on the obstacle distribution information and transportation navigation information to determine the positional relationship assessment result. Furthermore, when the positional relationship assessment result indicates obstruction to movement, obstacle avoidance analysis is performed based on the obstacle position and size in the transportation navigation information and obstacle distribution information to determine obstacle avoidance control commands, which are then sent to the intelligent vehicle terminal. Timely identification and avoidance of obstacles can significantly reduce the risk of accidents caused by collisions, protecting the safety of people and property.

[0096] Furthermore, to improve the reliability and stability of the intelligent vehicle's operation, in this embodiment, after sending the transportation navigation information to the intelligent vehicle terminal, the following steps are also included:

[0097] Acquire periodic working data sent by the intelligent vehicle terminal, perform a navigation mode feasibility analysis based on the periodic working data, and determine the results of the navigation mode feasibility analysis.

[0098] When the feasibility analysis of the navigation mode shows that it does not meet the requirements, a backup navigation mode is determined, and the intelligent vehicle terminal is controlled to switch to the backup navigation mode and continue to work.

[0099] In the embodiments of this application, the intelligent vehicle may experience navigation mode failure during navigation mode operation, which may affect the normal operation and task completion of the intelligent vehicle. Therefore, in order to ensure the continuous and reliable operation of the intelligent vehicle navigation system, it is necessary to switch to the backup navigation mode in a timely manner when the navigation mode fails, so as to quickly restore the navigation function, ensure that the intelligent vehicle continues to perform tasks, and improve the reliability and stability of the intelligent vehicle's operation.

[0100] Specifically, the system acquires periodic working data sent by the intelligent vehicle terminal. This periodic working data includes, but is not limited to, the real-time status of the navigation module, measurement data during navigation, and communication status. The periodic working data is then cleaned, verified, and formatted to ensure accuracy and consistency. Next, key state parameters related to the navigation mode are extracted from the preprocessed data, such as GPS signal strength, LiDAR scan results, and visual sensor image clarity. Standard key state parameters for the navigation mode under normal conditions are obtained. A feasibility analysis is performed based on these standard key state parameters and the extracted key states to determine the navigation mode feasibility analysis result. If any one of the standard key state parameters is not met, the navigation mode feasibility analysis result is determined to be unsatisfactory; otherwise, it is determined to be satisfactory. When the navigation mode feasibility analysis result is unsatisfactory, it indicates a fault in the intelligent vehicle's current navigation mode. Therefore, a backup navigation mode is selected, and the intelligent vehicle terminal switches to the backup navigation mode and continues operation. The backup navigation mode is pre-stored in the electronic device, and users can set it according to their actual needs; this embodiment does not limit this setting.

[0101] As can be seen, in this embodiment, to ensure the continuous and reliable operation of the intelligent vehicle navigation system, periodic working data sent by the intelligent vehicle terminal is acquired, and a navigation mode feasibility analysis is performed based on the periodic working data to determine the navigation mode feasibility analysis result. When the navigation mode feasibility analysis result indicates that the requirements are not met, a backup navigation mode is determined, and the intelligent vehicle terminal is controlled to switch to the backup navigation mode and continue working. Timely switching to the backup navigation mode quickly restores the navigation function, ensuring the intelligent vehicle continues to perform its tasks and improving the reliability and stability of the intelligent vehicle's operation.

[0102] The above embodiments describe a smart car navigation method integrating multiple navigation modes from the perspective of method flow. The following embodiments describe a smart car navigation device integrating multiple navigation modes from the perspective of virtual modules or virtual units. For details, please refer to the following embodiments.

[0103] This application provides an intelligent car navigation device integrating multiple navigation modes, such as... Figure 2As shown, the intelligent car navigation device integrating multiple navigation modes may specifically include:

[0104] The area analysis module 210 is used to acquire automated transportation task and factory area division information, and to perform transportation task route area analysis based on the automated transportation task and factory area division information to determine multiple work areas.

[0105] The navigation pattern analysis module 220 is used to perform navigation pattern analysis based on the division information of each work area and factory area, and to determine the navigation pattern corresponding to each work area.

[0106] The navigation planning module 230 is used to perform navigation planning based on the target working area and the corresponding navigation mode, and to determine the target navigation information, wherein the target working area is any one of multiple working areas;

[0107] The navigation information confirmation module 240 is used to integrate the target navigation information corresponding to each target work area, finally determine the transportation navigation information, and send the transportation navigation information to the intelligent vehicle terminal to control the intelligent vehicle terminal to execute the transportation task according to the navigation mode and transportation navigation information.

[0108] In this embodiment, automated transportation tasks and factory area division information are obtained. Based on these information, the transportation task route area is analyzed to determine multiple work areas. By pre-determining the work areas traversed by the intelligent vehicle, a suitable navigation mode can be selected more flexibly. Then, based on each work area and factory area division information, navigation mode analysis is performed to determine the corresponding navigation mode for each work area. The intelligent vehicle is controlled to select the appropriate navigation mode in different work area scenarios, fully utilizing the advantages of different navigation modes for complementarity, greatly improving the accuracy of the intelligent vehicle's navigation. Furthermore, navigation planning is performed based on the target work area and the corresponding navigation mode to determine target navigation information. This target navigation information provides precise road guidance for the intelligent vehicle to work within the target work area, improving the vehicle's work efficiency. Finally, by combining the target navigation information corresponding to each target work area, transportation navigation information is determined and sent to the intelligent vehicle terminal to control the intelligent vehicle terminal to execute the transportation task according to the navigation mode and transportation navigation information.

[0109] One possible implementation of this application's embodiments includes navigation modes such as: magnetic navigation mode, SLAM navigation mode, and QR code navigation mode.

[0110] When performing navigation pattern analysis based on the division information of each work area and factory area to determine the navigation pattern corresponding to each work area, the navigation pattern analysis module 220 is used for:

[0111] Matching is performed based on the target work area and factory area division information to determine environmental condition characteristics, work task characteristics, and facility deployment characteristics;

[0112] When the facility deployment features include the deployment of QR code labels, the navigation mode corresponding to the target work area is determined to be the QR code navigation mode;

[0113] When the environmental conditions include complex and dynamic changes, the navigation mode corresponding to the target working area is determined to be the SLAM navigation mode.

[0114] When the characteristics of the work task include fixed-path transportation, the navigation mode corresponding to the target work area is determined to be magnetic navigation mode.

[0115] Otherwise, obtain the navigation mode priority, and determine the navigation mode corresponding to the target work area based on the navigation mode priority.

[0116] In one possible implementation of this application embodiment, when the navigation planning module 230 performs navigation planning based on the target working area and the corresponding navigation mode to determine target navigation information, it is used to:

[0117] When the navigation mode is SLAM navigation mode, the dynamic area map, work start point and work end point corresponding to the target work area are obtained, and feasible path planning is performed based on the dynamic area map, work start point and work end point to determine the target navigation information;

[0118] When the navigation mode is QR code navigation mode, QR code analysis is performed based on the automated transportation task and the target work area to determine the QR code combination, and navigation planning is performed based on the QR code combination to determine the target navigation information. The QR code combination includes: deceleration QR code and positioning stop QR code.

[0119] When the navigation mode is magnetic navigation mode, the magnetic strip laying information corresponding to the target working area is obtained, and magnetic strip path planning is performed based on the magnetic strip laying information and automated transportation tasks to determine the target navigation information.

[0120] In one possible implementation of this application embodiment, when the navigation information confirmation module 240 performs the comprehensive analysis of the target navigation information corresponding to each target working area and finally determines the transportation navigation information, it is used to:

[0121] Based on the automated transportation task and each target work area, task operation analysis is performed to determine the work task corresponding to each target work area.

[0122] Perform mode switching analysis based on the navigation mode corresponding to each work area to determine navigation mode switching information;

[0123] Based on navigation mode switching information, the work tasks corresponding to each target work area, and target navigation information, the final transportation navigation information is determined.

[0124] One possible implementation of this application embodiment, an intelligent car navigation device integrating multiple navigation modes, further includes:

[0125] The intelligent obstacle avoidance module is used to acquire radar detection data sent by the intelligent vehicle terminal in real time, perform obstacle analysis based on the radar detection data, determine obstacle distribution information, and evaluate the positional relationship based on the obstacle distribution information and transportation navigation information to determine the positional relationship evaluation result.

[0126] When the positional relationship assessment result is that it hinders driving, obstacle avoidance analysis is performed based on the obstacle position and size in the transportation navigation information and obstacle distribution information to determine the obstacle avoidance control command, and the obstacle avoidance control command is sent to the intelligent vehicle terminal.

[0127] One possible implementation of this application embodiment, an intelligent car navigation device integrating multiple navigation modes, further includes:

[0128] The navigation mode switching module is used to acquire periodic working data sent by the intelligent vehicle terminal, perform navigation mode feasibility analysis based on the periodic working data, and determine the navigation mode feasibility analysis results.

[0129] When the feasibility analysis of the navigation mode shows that it does not meet the requirements, a backup navigation mode is determined, and the intelligent vehicle terminal is controlled to switch to the backup navigation mode and continue to work.

[0130] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the intelligent car navigation device integrating multiple navigation modes described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0131] This application provides an electronic device, such as... Figure 3 As shown, Figure 3 The illustrated electronic device 300 includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, via a bus 302. Optionally, the electronic device 300 may also include a transceiver 304. It should be noted that in practical applications, the transceiver 304 is not limited to one type, and the structure of this electronic device 300 does not constitute a limitation on the embodiments of this application.

[0132] Processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 301 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0133] Bus 302 may include a pathway for transmitting information between the aforementioned components. Bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 302 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The symbol is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0134] The memory 303 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0135] The memory 303 is used to store application code that executes the solution of this application, and its execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the foregoing method embodiments.

[0136] Electronic devices include, but are not limited to: mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (such as in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Servers can also be included. Figure 3 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0137] This application provides a computer-readable storage medium storing a computer program that, when run on a computer, enables the computer to execute the corresponding content in the aforementioned method embodiments.

[0138] This application provides a computer program product, including a computer program that, when executed by a processor, implements the methods described in any of the above embodiments. Compared with related technologies, this application provides an embodiment that acquires automated transportation tasks and factory area division information, performs transportation task route area analysis based on the automated transportation tasks and factory area division information, determines multiple work areas, and pre-determines the work areas traversed by the intelligent vehicle to more flexibly select the appropriate navigation mode for each work area. Then, based on each work area and factory area division information, it performs navigation mode analysis to determine the navigation mode corresponding to each work area, controls the intelligent vehicle to select the appropriate navigation mode in different work area scenarios, and fully utilizes the advantages of different navigation modes to complement each other, greatly improving the accuracy of intelligent vehicle navigation. Furthermore, based on the target work area and the corresponding navigation mode, it performs navigation planning to determine target navigation information. The target navigation information provides accurate road guidance for the intelligent vehicle to work within the target work area, improving the working efficiency of the intelligent vehicle. Combining the target navigation information corresponding to each target work area, it finally determines transportation navigation information and sends the transportation navigation information to the intelligent vehicle terminal to control the intelligent vehicle terminal to execute the transportation task according to the navigation mode and transportation navigation information.

[0139] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0140] The above are only some embodiments of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A smart car navigation method integrating multiple navigation modes, characterized in that, The method comprises the following steps: acquiring an automated transportation task and factory area division information, performing transportation task path area analysis based on the automated transportation task and the factory area division information, and determining a plurality of work areas; performing navigation mode analysis based on each of the work areas and the factory area division information, and determining a navigation mode corresponding to each of the work areas; the navigation mode comprises a two-dimensional code navigation mode, and when facility deployment features comprise deploying a two-dimensional code label, it is determined that the navigation mode corresponding to a target work area is the two-dimensional code navigation mode; performing navigation planning based on a target work area and the corresponding navigation mode, and determining target navigation information, wherein the target work area is any one of the plurality of work areas; specifically, when the navigation mode is the two-dimensional code navigation mode, performing two-dimensional code analysis based on the automated transportation task and the target work area, determining a two-dimensional code combination, and performing navigation planning based on the two-dimensional code combination to determine target navigation information, wherein the two-dimensional code combination comprises a deceleration two-dimensional code and a positioning stop two-dimensional code, the deceleration two-dimensional code is arranged at a preset distance from the positioning stop two-dimensional code, and when the intelligent trolley scans the deceleration two-dimensional code, it performs a deceleration operation to ensure that the intelligent trolley can stop smoothly and accurately at a specific position corresponding to the positioning stop two-dimensional code; comprehensively determining transportation navigation information based on the target navigation information corresponding to each of the target work areas, comprising: performing task operation analysis based on the automated transportation task and each of the target work areas to determine a work task corresponding to each of the target work areas; performing mode switching analysis based on the navigation mode corresponding to each of the work areas to determine navigation mode switching information; and finally determining transportation navigation information based on the navigation mode switching information, the work task corresponding to each of the target work areas, and the target navigation information; sending the transportation navigation information to an intelligent trolley terminal to control the intelligent trolley terminal to perform a transportation task according to the navigation mode and the transportation navigation information.

2. The intelligent cart navigation method integrating multiple navigation modes according to claim 1, wherein, The navigation mode further comprises a magnetic navigation mode and a SLAM navigation mode, The navigation mode analysis based on each of the work areas and the factory area division information to determine a navigation mode corresponding to each of the work areas comprises: matching the target work area and the factory area division information to determine environmental condition features, work task features, and facility deployment features; when the environmental condition features comprise complex dynamic changes, it is determined that the navigation mode corresponding to the target work area is the SLAM navigation mode; when the work task features comprise fixed path transportation, it is determined that the navigation mode corresponding to the target work area is the magnetic navigation mode; otherwise, a navigation mode priority is acquired, and the navigation mode corresponding to the target work area is determined based on the navigation mode priority.

3. The intelligent cart navigation method integrating multiple navigation modes according to claim 2, wherein, The navigation planning based on a target work area and a corresponding navigation mode to determine target navigation information comprises: When the navigation mode is the SLAM navigation mode, a dynamic area map corresponding to the target work area, a work starting point and a work ending point are acquired, a feasible path is planned based on the dynamic area map, the work starting point and the work ending point, and target navigation information is determined; When the navigation mode is the magnetic navigation mode, magnetic strip laying information corresponding to the target work area is acquired, a magnetic strip path is planned based on the magnetic strip laying information and the automated transportation task, and target navigation information is determined.

4. The intelligent cart navigation method integrating multiple navigation modes according to claim 1, wherein, After the transportation navigation information is sent to the intelligent trolley terminal, the method further includes: Real-time radar detection data sent by the intelligent trolley terminal is acquired, obstacle analysis is performed based on the radar detection data, obstacle distribution information is determined, and position relationship evaluation is performed based on the obstacle distribution information and the transportation navigation information, and a position relationship evaluation result is determined; When the position relationship evaluation result is that the intelligent trolley is hindered from driving, obstacle avoidance analysis is performed based on the transportation navigation information, obstacle positions and obstacle sizes in the obstacle distribution information, an obstacle avoidance control instruction is determined, and the obstacle avoidance control instruction is sent to the intelligent trolley terminal.

5. The intelligent cart navigation method integrating multiple navigation modes according to claim 1, wherein, After the transportation navigation information is sent to the intelligent trolley terminal, the method further includes: Periodic work data sent by the intelligent trolley terminal is acquired, navigation mode feasibility analysis is performed based on the periodic work data, and a navigation mode feasibility analysis result is determined; When the navigation mode feasibility analysis result is that the requirements are not met, a backup navigation mode is determined, and the intelligent trolley terminal is controlled to change to the backup navigation mode and continue working.

6. An electronic device, comprising: It includes: At least one processor; Memory; At least one application program, wherein the at least one application program is stored in the memory and is configured to be executed by the at least one processor, and the at least one application program is configured to execute the intelligent trolley navigation method integrated with multiple navigation modes as claimed in any one of claims 1-5.

7. A computer-readable storage medium, characterized in that, A computer program is stored thereon, and when the computer program is executed in a computer, the computer program causes the computer to execute the intelligent trolley navigation method integrated with multiple navigation modes as claimed in any one of claims 1-5.

8. A computer program product, characterised in that, A computer program is stored thereon, and when the computer program is executed in a computer, the computer program causes the computer to execute the intelligent trolley navigation method integrated with multiple navigation modes as claimed in any one of claims 1-5.

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