Hierarchical positioning method and device of mobile equipment and mobile equipment

Abstract

The application relates to the technical field of mobile robot positioning and navigation, and provides a hierarchical positioning method and device of a mobile device and the mobile device, the method comprising the following steps: determining global coarse positioning data of the mobile device in a global coordinate system based on first sensor data; in response to the mobile device approaching a target area, triggering a local fine positioning module based on an interaction timing signal of an external cooperative device to obtain local fine positioning data relative to a preset marker; compensating the global coarse positioning data based on the local fine positioning data to update the global coarse positioning data; and adaptively adjusting the triggering frequency of the local fine positioning module based on the real-time confidence of the global coarse positioning data. The application effectively suppresses the global positioning cumulative drift, improves the positioning robustness and docking accuracy in a complex environment without sacrificing the operation rhythm.

Description

Technical Field

[0001] This invention relates to the field of mobile robot positioning and navigation technology, specifically to a hierarchical positioning method, device, and mobile device for mobile devices. Background Technology

[0002] Currently, in the fields of industrial automation and mobile robotics, mobile devices typically employ global positioning technologies such as laser SLAM for autonomous navigation to meet the path planning requirements of long-distance travel. However, in actual production operations, mobile devices not only need to maintain stable position estimation globally, but also need to achieve extremely high positioning accuracy when exchanging materials or precisely docking with external collaborative equipment. Existing global positioning technologies are prone to cumulative drift errors due to environmental changes, sensor noise, and other factors during long-term operation, leading to a gradual decrease in positioning accuracy. While relying solely on high-precision local positioning methods can meet docking accuracy requirements, they are time-consuming. If only passively triggered upon reaching the target point, it significantly increases the overall work cycle time and affects production efficiency. Furthermore, when the working environment undergoes sudden changes or the mobile device stops abruptly due to safety strategies, the confidence level of the global positioning system often fails to recover quickly, further exacerbating the risk of positioning failure. Therefore, there is an urgent need for a hierarchical positioning scheme that can effectively suppress long-term global positioning drift and improve positioning robustness in complex environments without sacrificing the overall work cycle time. Summary of the Invention

[0003] To address the problem in existing technologies that it is difficult to balance global positioning cumulative drift of mobile devices with high-precision docking operation cycle time, this invention provides a hierarchical positioning method, device, and mobile device for mobile devices, achieving closed-loop coordination and adaptive adjustment of global coarse positioning and local fine positioning.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: A hierarchical positioning method for a mobile device, comprising global coarse positioning and local fine positioning, including: determining global coarse positioning data of the mobile device in a global coordinate system based on data from a first sensor; triggering a local fine positioning module based on an interaction timing signal from an external cooperating device in response to the mobile device approaching a target area, to obtain local fine positioning data of the mobile device relative to a preset marker; performing online compensation on the global coarse positioning data based on the local fine positioning data to update the global coarse positioning data; and adaptively adjusting the triggering frequency of the local fine positioning module based on the real-time confidence level of the updated global coarse positioning data to ensure the hierarchical positioning accuracy of the mobile device.

[0005] By constructing a hierarchical architecture of global coarse positioning and local fine positioning, the fine positioning is predicted and triggered by the interaction timing signals of external collaborative devices, and the fine positioning results are fed back to compensate for the coarse positioning error. At the same time, the triggering strategy is dynamically adjusted according to the confidence level. This effectively suppresses the long-term drift of global positioning and improves the positioning robustness and docking accuracy of the system in complex environments and abnormal working conditions without increasing additional hardware costs or sacrificing the overall operation cycle time.

[0006] Optionally, the local fine positioning module triggered by the interaction timing signal of the external collaborative device includes: acquiring a preparation status signal sent by the external collaborative device; and triggering the local fine positioning module in response to the preparation status signal at a preset distance before the mobile device reaches the target area, so that acquiring the local fine positioning data and executing the preparation action triggered by the external collaborative device based on the preparation status signal are carried out in parallel.

[0007] By placing the triggering time of local fine positioning before the mobile device arrives at the target area, and by using the preparation action time window of external collaborative equipment to execute the fine positioning reading process in parallel, the fine positioning time is cleverly hidden in the timing gap of equipment interaction. This ensures sub-millimeter docking accuracy while avoiding the loss of overall operation cycle time caused by waiting for fine positioning to complete, and significantly improves the turnover efficiency of the production line.

[0008] Optionally, the preparation status signal includes at least one of an advance material call signal or a safety door opening signal; the preset distance is dynamically calculated based on the current moving speed of the mobile device and the estimated reading time of the local precision positioning module, and the preset distance satisfies that the time for the mobile device to travel to the target area at the current moving speed is greater than or equal to the estimated reading time.

[0009] Optionally, the step of online compensation of the global coarse positioning data based on the local fine positioning data to update the global coarse positioning data includes: calculating the positional deviation between the absolute position indicated by the local fine positioning data and the estimated position indicated by the global coarse positioning data; compensating for the cumulative drift error of the global coarse positioning data based on the positional deviation; and updating the feature matching weight of the corresponding area in the global coarse positioning map based on the positional deviation.

[0010] By injecting high-precision absolute position data as observations back into the global coarse positioning system, not only is the accumulated drift error eliminated in real time, but the positioning stability of key areas in the map is also enhanced by updating feature matching weights. This constructs a closed-loop correction mechanism of "fine positioning feeding back to coarse positioning," ensuring the continuous reliability of global positioning quality of mobile devices during long-term continuous operation.

[0011] Optionally, the step of compensating for the cumulative drift error of the global coarse localization data based on the position deviation includes: inputting the position deviation as an observation into an extended Kalman filter or a graph optimization model, and updating the pose state of the mobile device through state estimation; wherein, the state vector of the extended Kalman filter includes the position coordinates, attitude angle, and velocity components of the mobile device in the global coordinate system, and the position deviation is used as an observation to correct the pose state by minimizing the residual between the observation equation and the state prediction value; the step of updating the feature matching weight of the corresponding area in the global coarse localization map based on the position deviation includes: adjusting the confidence weight of the environmental features in the corresponding area in the subsequent pose matching process in the global coarse localization map, wherein the adjustment amount of the confidence weight is negatively correlated with the absolute value of the position deviation.

[0012] Optionally, the step of adaptively adjusting the triggering frequency of the local fine positioning module based on the real-time confidence level of the updated global coarse positioning data to ensure the hierarchical positioning accuracy of the mobile device includes: real-time evaluation of the real-time confidence level of the global coarse positioning data to obtain a confidence index; increasing the triggering frequency of the local fine positioning module when the confidence index is lower than a preset confidence threshold; and decreasing or maintaining the triggering frequency of the local fine positioning module when the confidence index is greater than or equal to the preset confidence threshold.

[0013] Optionally, the method further includes: when a security policy trigger causes the mobile device to stop operating, or when manual intervention is detected, after the security state is lifted, forcibly resetting the confidence index of the global coarse positioning data to zero; when the mobile device moves again, increasing the trigger frequency of the local fine positioning module until the local fine positioning data that meets the preset accuracy requirement is reacquired; wherein, the preset accuracy requirement is that the position accuracy of the local fine positioning data reaches a preset accuracy threshold, and the confidence index of the global coarse positioning data recovers to be greater than or equal to the preset confidence threshold.

[0014] Optionally, the method further includes: after completing the online compensation based on the local fine positioning data, triggering the mechanical fine-tuning mechanism inside the mobile device to perform secondary pose fine-tuning; linking the vision system set at the end of the mobile device to perform visual compensation on the target object to form a multi-level precision transmission closed loop; wherein, the visual compensation includes: taking a picture of the target object through the vision system to obtain the visual deviation between the actual position of the target object and the current pose of the mobile device, and performing end pose compensation based on the visual deviation.

[0015] Furthermore, the present invention also provides a hierarchical positioning device for a mobile device, comprising: a global coarse positioning module, used to determine global coarse positioning data of the mobile device in a global coordinate system based on first sensor data; a predictive triggering module, used to trigger a local fine positioning module based on an interaction timing signal from an external cooperating device in response to the mobile device approaching a target area, to obtain local fine positioning data of the mobile device relative to a preset marker; a feedback correction module, used to perform online compensation on the global coarse positioning data based on the local fine positioning data to update the global coarse positioning data; and an adaptive adjustment module, used to adaptively adjust the triggering frequency of the local fine positioning module based on the real-time confidence level of the updated global coarse positioning data to ensure the hierarchical positioning accuracy of the mobile device.

[0016] In addition, the present invention also provides a mobile device, comprising: a vehicle body; a first sensor assembly disposed on the vehicle body; a local fine positioning sensor assembly disposed on the vehicle body; and a processor communicatively connected to the first sensor assembly and the local fine positioning sensor assembly, wherein the processor is configured to execute the hierarchical positioning method as described above.

[0017] Beneficial effects: This invention determines global coarse positioning data based on data from a first sensor, and predictively triggers a local fine positioning module to acquire absolute position data based on the interaction timing signal of an external collaborative device when approaching the target area. This local fine positioning data is then fed back to the global coarse positioning system for online compensation to update the global coarse positioning data. Simultaneously, the triggering frequency of the local fine positioning module is adaptively adjusted based on the real-time confidence level of the updated global coarse positioning data to ensure hierarchical positioning accuracy. This technical solution utilizes a hierarchical positioning architecture and a closed-loop feedback mechanism, using high-precision local absolute position information as a correction source to eliminate the accumulated drift error of global coarse positioning in real time. Furthermore, through a predictive triggering strategy, the time required for fine positioning is hidden in parallel within the preparation timing of the external device. This solves the problem of mutual constraints between long-term global positioning drift and high-precision docking operation cycle time. Without increasing hardware costs or sacrificing overall operational efficiency, it significantly improves the positioning robustness and docking accuracy of mobile devices in complex dynamic environments and abnormal operating conditions. Attached Figure Description

[0018] Figure 1 This is a schematic flowchart of a hierarchical positioning method for mobile devices according to an embodiment of this application; Figure 2 This is a schematic diagram of the constituent modules of the hierarchical positioning device for a mobile device according to an embodiment of this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0021] This embodiment provides a hierarchical positioning method, apparatus, and mobile device for mobile devices. For example... Figure 1 As shown, the implementation method of this hierarchical positioning method will first be described in detail.

[0022] In the implementation section, the hierarchical localization method includes steps S100 to S400, which construct a complete closed-loop process from global coarse localization to adaptive adjustment.

[0023] Step S100: Based on the first sensor data, determine the global coarse positioning data of the mobile device in the global coordinate system.

[0024] Specifically, the first sensor data may include one or more combinations of environmental point cloud data collected by LiDAR, acceleration and angular velocity information provided by the inertial measurement unit (IMU), wheel speed and mileage data provided by the encoder, and scene image data collected by the visual sensor. Global coarse positioning data refers to the real-time pose estimation of the mobile device in the global map coordinate system, and its accuracy usually meets the requirements of channel driving and path planning, such as ±10mm to ±15mm.

[0025] In this embodiment, global coarse localization can be achieved using either a laser SLAM (Simultaneous Localization and Mapping) algorithm or a visual SLAM algorithm. By fusing multi-source sensor data for joint state estimation, a continuous and stable global navigation reference is provided for the mobile device. It should be understood that although this embodiment uses laser SLAM as an example, other implementations can also employ other technologies that can provide global position reference, such as UWB ultra-wideband positioning or Wi-Fi fingerprint positioning, as long as global position estimation can be achieved.

[0026] In step S200, in response to the mobile device approaching the target area, the local fine positioning module is triggered based on the interaction timing signal of the external collaborative device to obtain the local fine positioning data of the mobile device relative to the preset marker.

[0027] Specifically, the system acquires a preparation status signal sent by an external collaborative device, and at a preset distance before the mobile device reaches the target area, triggers a local fine positioning module in response to the preparation status signal, so that the acquisition of local fine positioning data and the execution of preparation actions triggered by the external collaborative device based on the preparation status signal can be carried out in parallel.

[0028] The preparation status signal can include at least one of the following: an advance material call signal or a safety door opening signal. It can also be other timing signals indicating that the external collaborative equipment is about to enter an interactive state. The preset distance is not a fixed value, but is dynamically calculated based on the current moving speed of the mobile device and the estimated reading time of the local precision positioning module. It satisfies the requirement that the time for the mobile device to travel to the target area at its current speed is greater than or equal to the estimated reading time. For example, under the production cycle constraints of a composite robot docking with a CNC material handling system requiring CT ≤ 30s and docking with a buffer machine requiring CT ≤ 60s, if the current speed of the mobile device is 1m / s and the estimated time for the local precision positioning module (such as the PGV reader) to complete one full reading and calculation is approximately 10s, then the preset distance is at least 10m. Through this dynamic calculation and parallel execution mechanism, the originally sequential 10s precision positioning time is hidden within the preparation action time window of the external collaborative equipment. This achieves sub-millimeter-level high-precision positioning data acquisition without increasing the overall work cycle, significantly improving the production line's throughput efficiency.

[0029] The local fine positioning module can be a PGV (PositionGuidedVision) reader, a QR code recognition camera, a UWB tag reader, or a magnetic navigation sensor, etc. The preset markers can be DataMatrix tags, QR codes, UWB base stations, or magnetic strips, etc. This embodiment does not limit this to a single one.

[0030] Step S300: Online compensation is performed on the global coarse positioning data based on the local fine positioning data to update the global coarse positioning data.

[0031] Specifically, by calculating the positional deviation between the absolute position indicated by local fine positioning data and the estimated position indicated by global coarse positioning data, the cumulative drift error of the global coarse positioning data is compensated based on the positional deviation, and the feature matching weights of the corresponding area in the global coarse positioning map are updated based on the positional deviation. When compensating for cumulative drift error, the positional deviation is input as an observation into an Extended Kalman Filter (EKF) or a graph optimization model, and the pose state of the mobile device is updated through state estimation. Taking EKF as an example, its state vector includes the position coordinates, attitude angles, and velocity components of the mobile device in the global coordinate system. The positional deviation, as an observation, corrects the pose state by minimizing the residual between the observation equation and the state prediction value, thereby eliminating the cumulative error generated by long-term operation in real time. After correction, the docking accuracy of the mobile device can reach the ±5mm level. When updating the feature matching weights, the confidence weights of environmental features in the corresponding area in the global coarse positioning map are adjusted in the subsequent pose matching process, and the adjustment amount of the confidence weights is negatively correlated with the absolute value of the positional deviation. In other words, when a fine-positioning correction shows a small deviation, it indicates that the coarse-positioning map features of that area are relatively reliable, thus increasing the matching weight of those features; conversely, if the deviation is large, the weight is reduced to avoid incorrect matching. This mechanism enables the global coarse-positioning system to continuously learn from local fine-positioning and optimize its map quality, ensuring the continuous reliability of global positioning quality for mobile devices during long-term continuous operation.

[0032] Step S400: Based on the real-time confidence level of the updated global coarse positioning data, adaptively adjust the triggering frequency of the local fine positioning module to ensure the hierarchical positioning accuracy of the mobile device.

[0033] Specifically, a confidence index is obtained by evaluating the real-time confidence level of the global coarse localization data. When the confidence index is lower than a preset confidence threshold, the triggering frequency of the local fine localization module is increased; when the confidence index is greater than or equal to the preset confidence threshold, the triggering frequency of the local fine localization module is decreased or maintained. The confidence index is calculated based on a combination of factors, including sensor data quality, feature matching score, and pose estimation covariance.

[0034] As a specific calculation method, the first score is obtained by normalizing the inverse of the trace of the pose estimation covariance matrix. The second score is obtained by normalizing the feature matching score between the current frame and the map. The first and second scores are then weighted and summed according to preset weights to obtain the confidence index. When sensor data is missing or abnormal, the score of the corresponding factor drops to zero, thus causing the overall confidence index to decrease. Furthermore, when a safety policy is triggered (e.g., a deceleration policy is triggered within 1m of an obstacle, an emergency stop policy is triggered within 0.5m, or a 50% speed reduction policy is triggered within 0.5m of the collaborative arm) causing the mobile device to stop operating, or when manual intervention is detected (e.g., manually dragging the mobile device or manually taking over control), after the safety state is lifted, the confidence index of the global coarse positioning data is forcibly reset to zero, and the triggering frequency of the local fine positioning module is increased when the mobile device moves again, until local fine positioning data that meets the preset accuracy requirements is reacquired. The preset accuracy requirements are that the positional accuracy of the local fine positioning data reaches a preset accuracy threshold, and the confidence index of the global coarse positioning data recovers to a level greater than or equal to a preset confidence threshold. This recovery mechanism, which uses confidence zeroing and forced high-frequency triggering, effectively solves the problem of irreversible cumulative errors in odometer and point cloud matching caused by abnormal conditions such as sudden stops and manual trolley pushing, ensuring a rapid and reliable recovery of high-precision positioning after an anomaly.

[0035] Furthermore, in real-world industrial scenarios, environmental changes can significantly reduce the accuracy of global coarse positioning. For example, in metalworking workshops, dripping cutting fluid or oil can cause drastic changes in ground reflectivity, and metal debris and temporary material cart obstructions can lead to missing LiDAR point clouds. These factors can reduce the accuracy of global coarse positioning to ±15mm or even lower. In such extreme conditions that cause global coarse positioning failure, the physical properties of local fine positioning modules and pre-set markers (such as DataMatrix tags)—resistance to dirt, scratches, and ambient light interference—are used as a last resort to ensure stable sub-millimeter absolute position output even when global coarse positioning is limited, guaranteeing uninterrupted docking operations.

[0036] Furthermore, to form a multi-level precision transfer closed loop, after completing online compensation based on local fine positioning data, a secondary pose fine adjustment can be triggered by the mechanical fine-tuning mechanism inside the mobile device, and the vision system set at the end of the mobile device can be linked to perform visual compensation for the target object. Specifically, the mechanical fine-tuning mechanism can be a ball screw lifting platform (ball screw transmission accuracy can reach ±0.02mm) or an XYθ fine-tuning platform, used to further eliminate millimeter-level residual errors on the basis of coarse positioning correction; the end vision system can be a 3D industrial camera, which takes pictures of the target object (such as the material rack inside a CNC machine tool) to obtain the visual deviation between the actual position of the target object and the current pose of the mobile device, and performs end pose compensation based on the visual deviation. This constructs a multi-level precision transfer chain of "global coarse positioning → local fine positioning correction → mechanical fine-tuning → end vision compensation", which can ultimately meet the complex docking requirements of ±0.5mm or even higher precision. It should be understood that the above-mentioned multi-level precision transfer closed loop is an optional preferred implementation method. In some scenarios where the end precision requirements are not high, executing only up to step S400 can also achieve the core inventive objective of this invention.

[0037] refer to Figure 2 This embodiment also provides a hierarchical positioning device for mobile devices, which includes a global coarse positioning module 21, a predictive triggering module 22, a feedback correction module 23, and an adaptive adjustment module 24. The specific implementation methods of each module are described below.

[0038] The global coarse positioning module 21 is used to determine the global coarse positioning data of the mobile device in the global coordinate system based on the data from the first sensor.

[0039] Specifically, the global coarse localization module 21 integrates a SLAM front-end odometry and a back-end optimization engine. The front-end odometry is responsible for receiving and processing environmental point cloud data collected by the LiDAR, acceleration and angular velocity information provided by the IMU, wheel speed and mileage data provided by the encoder, and scene image data collected by the visual sensor in real time. The back-end optimization engine performs joint state estimation on the multi-source sensor data and outputs the real-time pose of the mobile device in the global map coordinate system. The global coarse localization module 21 can be implemented using a LiDAR SLAM algorithm or a visual SLAM algorithm, or it can use other technologies that can provide global position reference, such as UWB ultra-wideband positioning or Wi-Fi fingerprint positioning, as long as it can achieve continuous position estimation within the global range.

[0040] Predictive triggering module 22. In response to the mobile device approaching the target area, it triggers the local fine positioning module based on the interaction timing signal of the external cooperating device to obtain local fine positioning data of the mobile device relative to a preset marker.

[0041] Specifically, the predictive triggering module 22 obtains preparation status signals sent by external cooperating equipment, such as advance material call signals or safety door opening signals, through the communication interface with the external scheduling system or equipment PLC. It then dynamically calculates a preset distance based on the current moving speed of the mobile device and the estimated reading time of the local precision positioning module. At the preset distance before the mobile device reaches the target area, the local precision positioning module is triggered, allowing the acquisition of precision positioning data to be executed in parallel with the preparation actions of the external cooperating equipment. Through this predictive triggering mechanism, the system hides the precision positioning time within the preparation time window of the external equipment, completing the acquisition of high-precision positioning data without increasing the overall work cycle time.

[0042] The feedback correction module 23 is used to perform online compensation on the global coarse positioning data based on the local fine positioning data, so as to update the global coarse positioning data.

[0043] Specifically, the feedback correction module 23 internally runs an extended Kalman filter or a graph optimization solver. First, it calculates the positional deviation between the absolute position indicated by the local fine positioning data and the estimated position indicated by the global coarse positioning data. Then, it inputs the positional deviation as an observation into the filter or optimization model. The pose state of the mobile device is updated through state estimation to compensate for accumulated drift errors. Simultaneously, the confidence weights of environmental features within the corresponding area are adjusted in the global coarse positioning map, with the adjustment amount negatively correlated with the absolute value of the positional deviation. The feedback correction module 23 not only corrects the current pose but also updates the feature matching weights of the global map in reverse, enabling continuous learning and optimization of coarse positioning by fine positioning, ensuring a data loop between the two levels of positioning.

[0044] The adaptive adjustment module 24 is used to adaptively adjust the triggering frequency of the local fine positioning module based on the real-time confidence level of the updated global coarse positioning data, thereby ensuring the hierarchical positioning accuracy of the mobile device. Specifically, the adaptive adjustment module 24 evaluates the confidence index of the global coarse positioning data in real time. This index can be calculated based on a combination of factors such as sensor data quality, feature matching score, and pose estimation covariance. When the confidence index is lower than a preset confidence threshold, the triggering frequency of the local fine positioning module is increased to enhance positioning accuracy. When the confidence index is greater than or equal to the preset confidence threshold, the triggering frequency is reduced or maintained to reduce system overhead. In addition, the adaptive adjustment module 24 also includes abnormal state monitoring and confidence reset logic. When a safety policy trigger causes the mobile device to stop operating or a manual intervention is detected, the confidence index is forcibly reset to zero after the safety state is lifted. The triggering frequency is increased the next time the mobile device moves until local fine positioning data that meets the preset accuracy requirements is reacquired, thereby ensuring the positioning reliability of the system after environmental changes or abnormal recovery.

[0045] Furthermore, this embodiment also provides a mobile device, including a vehicle body, a first sensor assembly, a local precision positioning sensor assembly, and a processor.

[0046] The first sensor assembly is mounted on the vehicle body and is used to collect first sensor data. It may include a lidar, IMU, encoder, and vision camera. A local fine-positioning sensor assembly is mounted on the vehicle body and is used to acquire local fine-positioning data; for example, it may be a PGV reader installed on the bottom of the vehicle body. The processor is communicatively connected to the first sensor assembly and the local fine-positioning sensor assembly and is configured to execute the hierarchical positioning method described in any of the above embodiments. In a preferred installation layout, the lidar in the first sensor assembly can be arranged in a diagonal dual-radar configuration at diagonal positions on the vehicle body to achieve 360° environmental perception without blind spots. The local fine-positioning sensor assembly can be positioned at the center of the bottom of the vehicle chassis or near the drive wheels to minimize optical reading errors caused by vehicle pitch or roll. Simultaneously, to ensure the optical reading quality of local fine positioning, the ground flatness of the key docking area can be controlled within 3mm / m².

[0047] It should be understood that the above hardware layout is only a preferred example. In other embodiments, the installation location, quantity, and type of sensor components can be adjusted according to the actual application scenario, as long as the execution requirements of the hierarchical positioning method can be met. The processor can be an embedded industrial control computer, FPGA, ASIC, or general-purpose CPU, which implements the above method steps by executing the program instructions stored inside it, thereby tightly integrating the software algorithm with the hardware carrier to form a complete mobile device product form.

[0048] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A hierarchical positioning method for a mobile device, wherein the hierarchical positioning includes global coarse positioning and local fine positioning, characterized in that, include: Based on the data from the first sensor, the global coarse positioning data of the mobile device in the global coordinate system is determined; In response to the mobile device approaching the target area, a local fine positioning module is triggered based on the interaction timing signal of the external collaborative device to obtain local fine positioning data of the mobile device relative to a preset marker. The global coarse positioning data is compensated online based on the local fine positioning data in order to update the global coarse positioning data. Based on the real-time confidence level of the updated global coarse positioning data, the triggering frequency of the local fine positioning module is adaptively adjusted to ensure the hierarchical positioning accuracy of the mobile device.

2. The hierarchical positioning method according to claim 1, characterized in that, The local fine positioning module triggered by the interactive timing signal of the external collaborative device includes: Obtain the preparation status signal sent by the external collaborative device; At a preset distance before the mobile device reaches the target area, the local fine positioning module is triggered in response to the preparation status signal, so that the acquisition of the local fine positioning data and the execution of the preparation action triggered by the external cooperative device based on the preparation status signal are carried out in parallel.

3. The hierarchical positioning method according to claim 2, characterized in that, The preparation status signal includes at least one of an advance material call signal or a safety door opening signal; The preset distance is dynamically calculated based on the current moving speed of the mobile device and the estimated reading time of the local precision positioning module. The preset distance satisfies the requirement that the time it takes for the mobile device to travel to the target area at the current moving speed is greater than or equal to the estimated reading time.

4. The hierarchical positioning method according to claim 1, characterized in that, The step of performing online compensation on the global coarse positioning data based on the local fine positioning data to update the global coarse positioning data includes: Calculate the positional deviation between the absolute position indicated by the local fine positioning data and the estimated position indicated by the global coarse positioning data; The cumulative drift error of the global coarse positioning data is compensated based on the position deviation. The feature matching weights of the corresponding areas in the global coarse positioning map are updated based on the location deviation.

5. The hierarchical positioning method according to claim 4, characterized in that, The compensation for the cumulative drift error of the global coarse positioning data based on the position deviation includes: The position deviation is input as an observation into an extended Kalman filter or a graph optimization model, and the pose state of the mobile device is updated through state estimation. The state vector of the extended Kalman filter includes the position coordinates, attitude angles and velocity components of the mobile device in the global coordinate system. The position deviation is used as an observation to correct the pose state by minimizing the residual between the observation equation and the state prediction value. The step of updating the feature matching weights of the corresponding region in the global coarse localization map based on the position deviation includes: adjusting the confidence weights of environmental features within the corresponding region in the subsequent pose matching process in the global coarse localization map, wherein the adjustment amount of the confidence weights is negatively correlated with the absolute value of the position deviation.

6. The hierarchical positioning method according to claim 1, characterized in that, The method of adaptively adjusting the trigger frequency of the local fine positioning module based on the real-time confidence level of the updated global coarse positioning data to ensure the hierarchical positioning accuracy of the mobile device includes: The real-time confidence level of the global coarse localization data is evaluated to obtain a confidence index; When the confidence index is lower than the preset confidence threshold, the triggering frequency of the local fine positioning module is increased; When the confidence index is greater than or equal to the preset confidence threshold, the triggering frequency of the local precision positioning module is reduced or maintained.

7. The hierarchical positioning method according to claim 6, characterized in that, The method further includes: When a security policy is triggered, causing the mobile device to stop operating, or when manual intervention is detected, the confidence index of the global coarse positioning data is forcibly reset to zero after the security status is lifted. When the mobile device moves again, the triggering frequency of the local fine positioning module is increased until the local fine positioning data that meets the preset accuracy requirements is acquired again; wherein, the preset accuracy requirements are that the position accuracy of the local fine positioning data reaches a preset accuracy threshold, and the confidence index of the global coarse positioning data is restored to be greater than or equal to the preset confidence threshold.

8. The hierarchical positioning method according to claim 1, characterized in that, The method further includes: After completing the online compensation based on the local fine positioning data, the mechanical fine-tuning mechanism inside the mobile device is triggered to perform secondary pose fine-tuning. The vision system, which is linked to the end of the mobile device, performs visual compensation on the target object to form a multi-level accuracy transmission closed loop. The visual compensation includes: taking a picture of the target object through the vision system to obtain the visual deviation between the actual position of the target object and the current pose of the mobile device, and performing end pose compensation based on the visual deviation.

9. A hierarchical positioning device for a mobile device, characterized in that, include: A global coarse positioning module is used to determine the global coarse positioning data of the mobile device in the global coordinate system based on the data from the first sensor. A predictive triggering module is used to trigger a local fine positioning module based on the interaction timing signal of an external cooperating device in response to the mobile device approaching the target area, so as to obtain local fine positioning data of the mobile device relative to a preset marker. The feedback correction module is used to perform online compensation on the global coarse positioning data based on the local fine positioning data, so as to update the global coarse positioning data; An adaptive adjustment module is used to adaptively adjust the triggering frequency of the local fine positioning module based on the real-time confidence level of the updated global coarse positioning data, so as to ensure the hierarchical positioning accuracy of the mobile device.

10. A mobile device, characterized in that, include: Vehicle body; A first sensor assembly is disposed on the vehicle body; A local precision positioning sensor assembly is disposed on the vehicle body; A processor, communicatively connected to the first sensor assembly and the local fine positioning sensor assembly, is configured to perform the hierarchical positioning method as described in any one of claims 1 to 8.

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