AMR robot secondary positioning method based on laser ranging and two-dimensional code fusion

Abstract

This invention provides a secondary localization method for AMR robots based on laser ranging and QR code fusion, comprising: a system calibration phase to clarify the laser module installation parameters and ideal reference; after the AMR enters the localization area, measuring the distance to a planar reference object using front and rear laser ranging modules, and obtaining the real-time angle based on the arctangent of the laser distance and the distance difference; first, performing coarse calibration of the y-axis and angle to ensure that the QR code is displayed in the center area of ​​the camera during fine calibration, and then performing fine calibration in the order of x-axis, angle, and y-axis through QR code recognition; and quantifying the error into motion speed through segmented PID control throughout the process. This method, by fusing laser ranging and QR code recognition data, provides a high-precision, low-cost, and highly adaptable AMR localization solution, suitable for efficient navigation in complex environments. Compared with traditional single-sensor solutions, this invention significantly improves the localization accuracy and robustness of AMRs under varying lighting conditions and complex environments.

Description

Technical Field

[0001] This invention relates to the field of mobile robot technology, and in particular to a secondary localization method for AMR robots based on laser ranging and QR code fusion. Background Technology

[0002] Autonomous mobile robots, or automated guided vehicles (AGVs or AMRs), are mobile robots equipped with various sensors, such as LiDAR, cameras, and QR code recognition devices, capable of automatically transporting goods along a trajectory. To ensure accurate delivery in different environments, these mobile robots require higher parking precision.

[0003] For example, patent publication number CN 115014338 A, entitled "A Mobile Robot Localization System and Method Based on QR Code Vision and Laser SLAM," describes a localization system comprising a lidar localization module, a MEMS inertial measurement unit module, a QR code module, a vision module, a cleaning module, a lighting module, a mobile robot module, and a scheduling system. The lidar localization module and the MEMS inertial measurement unit module acquire environmental point cloud information to achieve mobile robot localization and map construction. The QR code module includes a positioning QR code and a loop closure detection QR code, used for secondary precise localization and loop closure detection, respectively, improving localization and mapping accuracy. The vision module identifies the positioning QR code and the loop closure detection QR code. The cleaning and lighting module cleans up chips and dust generated in the machine tool environment, improving image acquisition quality. While its secondary localization accuracy is high, it requires the use of a MEMS inertial measurement unit module, making the entire system and localization method complex and costly. Summary of the Invention

[0004] In view of this, the purpose of this invention is to propose a secondary localization method for AMR robots based on laser ranging and QR code fusion. The method first achieves coarse calibration by laser ranging and then performs fine calibration by QR code recognition. The error is quantified into motion speed through segmented PID control throughout the process. The secondary localization has high accuracy and low system cost, and is suitable for efficient navigation in complex environments.

[0005] To achieve the above-mentioned technical objectives, the technical solution adopted by this invention is as follows:

[0006] A secondary localization method for AMR robots based on laser ranging and QR code fusion includes the following steps:

[0007] Establish a vehicle coordinate system with the AMR geometric center as the vehicle body reference point O;

[0008] Laser ranging modules are symmetrically installed on the front and rear sides of the AMR vehicle body. The center distance between the two modules is set to a fixed value L. The vertical distance is adjusted according to the environment so that the laser illuminates the same surface. A camera with QR code recognition function is mounted in the middle of the front of the vehicle body.

[0009] Move the AMR to the ideal stopping position, identify a planar reference object in the environment, and read the distance between the laser ranging module and the planar reference object before and after. , The ideal stopping point of the vehicle body reference point O relative to the plane reference object is calculated based on geometric relationships. A QR code is fixed in front of the ideal stopping position, with the center of the QR code aligned with the center of the camera. The camera then identifies the ideal stopping point of the vehicle body reference point O relative to the QR code. ;

[0010] During calibration, first determine the current position. Coordinate data and ideal stopping point If the difference in coordinate data is within the error range, then the AMR is determined to have stopped precisely within the coarse calibration station, and fine calibration is initiated; otherwise, the AMR is determined not to have stopped at the designated position, and coarse calibration begins. After calibration, the coordinate data of the current position P is compared with the ideal stopping point. If the difference in the coordinate data is within the error range, it is determined that the AMR has stopped precisely within the station; otherwise, it is determined that the AMR has not stopped at the specified position.

[0011] Furthermore, the ideal stopping point of the vehicle body reference point O relative to the planar reference is calculated based on geometric relationships. The calculation method is as follows:

[0012] ,

[0013] .

[0014] Furthermore, the ideal stopping point of the vehicle body reference point O relative to the QR code is obtained through camera recognition. The calculation method includes establishing the coordinate relationship between the camera coordinate system and the vehicle coordinate system, transforming the camera coordinate system to the vehicle coordinate system through a rotation matrix, and ensuring that the QR code coordinates acquired by the camera are based on the vehicle coordinate system.

[0015] Furthermore, the coordinate data of the current position P and the ideal stopping point , The methods for calculating the difference in coordinate data include:

[0016] ,

[0017]

[0018] ,

[0019] ,

[0020] .

[0021] Further steps to determine whether the positioning deviation is within the error range include:

[0022] (1) Judgment of coarse calibration deviation:

[0023] ① Calculate the current position P and the ideal stopping point y-axis deviation Then, first judge If the deviation is less than or equal to 10mm, then it is determined that the AMR has not stopped at the specified position for coarse calibration, and the coarse calibration adjustment process is triggered; if it is less than 10mm, then the angle deviation judgment step is entered.

[0024] ② Calculate the current position P and the ideal stopping point angular deviation ,judge If the angle is less than 3°, it is determined that the AMR has not stopped at the specified position for coarse calibration, and the coarse calibration adjustment process is triggered; if it is less than 3°, it is determined that the AMR has completed coarse calibration and stopped precisely within the coarse calibration station, and can enter the fine calibration stage.

[0025] (2) Judgment of fine calibration deviation:

[0026] ① Calculate the current position P and the ideal stopping point x-axis deviation Then, first judge If the error is less than or equal to 5mm, then it is determined that the AMR has not stopped at the specified position for fine calibration, and the fine calibration adjustment process is triggered; if it is less than 5mm, then the angle deviation judgment step is entered.

[0027] ② Calculate the current position P and the ideal stopping point angular deviation ,judge If the deviation is less than 0.3°, it is determined that the AMR has not stopped at the specified position for fine calibration, and the fine calibration adjustment process is triggered; if it is less than 0.3°, the process proceeds to the y-axis deviation judgment stage.

[0028] ③ Calculate the current position P and the ideal stopping point y-axis deviation ,judge If the value is less than or equal to 5mm, it is determined that the AMR has not stopped at the specified position for fine calibration, and the fine calibration adjustment process is triggered; if it is less than 5mm, it is determined that the AMR has completed fine calibration and stopped precisely within the site.

[0029] Furthermore, if the coarse and fine calibrations determine that the AMR has not stopped at the specified position, the following steps must be performed for calibration and error / speed conversion:

[0030] Based on the direction of the difference between the current position P's x and y coordinates and the ideal stop point's x and y coordinates, determine the AMR's x and y axis adjustment direction. If the error value is positive, it is determined that the AMR needs to be translated along the negative x and y axes; if the error value is negative, it is determined that the AMR needs to be translated along the positive x and y axes. With the ideal stopping point The direction of the error value determines the rotation adjustment direction of the AMR. If the error value is positive, it is determined that the rotation should be counterclockwise; if the error value is negative, it is determined that the rotation should be clockwise.

[0031] The speed output corresponding to the error is calculated using a PID algorithm to ensure fast response and smooth control during the calibration process. The calculation formula includes:

[0032] ,

[0033] ,

[0034] ,

[0035] AMR is calculated based on , , Perform translation and rotation actions. After the actions are completed, obtain the error required for each calibration stage and repeat the difference calculation and calibration deviation judgment steps in the previous steps until the error is within the range, and the calibration is completed.

[0036] Furthermore, when establishing the vehicle coordinate system, the x-axis is defined as the direction of the vehicle's movement, and the y-axis is defined as the horizontal axis perpendicular to the x-axis and pointing to the right.

[0037] Furthermore, when the AMR moves to the ideal stopping position, the wall or the side of the machine is used as a planar reference in the environment.

[0038] Beneficial effects

[0039] This invention first performs coarse calibration of the y-axis and angle using laser ranging to ensure the QR code enters the camera's field of view. Then, fine calibration is performed using the QR code, solving the problem of QR code recognition failure due to limited camera field of view and large initial AMR deviation. Simultaneously, the coarse and fine calibrations have clearly defined error thresholds for switching. During fine calibration, the calibration attitude process follows the sequence x-axis → angle → y-axis, ensuring that calibration accuracy is not affected by camera distortion or distance from the QR code. Throughout the calibration process, segmented PID directly quantifies the deviation into the AMR's translation and rotation speeds. By fusing laser ranging and QR code recognition data, this invention provides a high-precision, low-cost, and highly adaptable AMR positioning solution suitable for efficient navigation in complex environments. Compared to traditional single-sensor solutions, this invention significantly improves the positioning accuracy and robustness of AMRs under varying lighting conditions and complex environments. Attached Figure Description

[0040] Figure 1 This is a flowchart of the present invention;

[0041] Figure 2 This is a schematic diagram of the system composition and coordinate system of the present invention;

[0042] Figure 3 This is a schematic diagram of the ideal coordinate position of the present invention;

[0043] Figure 4 This is a schematic diagram of error calculation during the calibration stage of this invention. Detailed Implementation

[0044] The following embodiments provide a further detailed description of the present invention. It should be noted that the following embodiments are for illustrative purposes only and do not limit the scope of the invention. Similarly, the following embodiments are only some, not all, embodiments of the present invention, and all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of the present invention.

[0045] See Figures 1 to 4 As shown, a secondary localization method for AMR robots based on laser ranging and QR code fusion includes the following steps:

[0046] Establish a vehicle coordinate system with the geometric center of the AMR as the vehicle body reference point O; when establishing the vehicle coordinate system, take the direction of vehicle movement as the x-axis and the y-axis as the horizontal rightward perpendicular to the x-axis;

[0047] Laser ranging modules are symmetrically installed on the front and rear sides of the AMR vehicle body. The center distance between the two modules is set to a fixed value L. The vertical distance is adjusted according to the environment so that the laser illuminates the same surface. A camera with QR code recognition function is mounted in the middle of the front of the vehicle body.

[0048] Move the AMR to the ideal stopping position, using a wall or the side of the machine as a planar reference in the environment, and read the distance between the laser ranging module and the planar reference. , The ideal stopping point of the vehicle body reference point O relative to the plane reference object is calculated based on geometric relationships. A QR code is fixed in front of the ideal stopping position, with the center of the QR code aligned with the center of the camera. The camera then identifies the ideal stopping point of the vehicle body reference point O relative to the QR code. ;

[0049] During calibration, first determine the current position. Coordinate data and ideal stopping point If the difference in coordinate data is within the error range, then the AMR is determined to have stopped precisely within the coarse calibration station, and fine calibration is initiated; otherwise, the AMR is determined not to have stopped at the designated position, and coarse calibration begins. After calibration, the coordinate data of the current position P is compared with the ideal stopping point. If the difference in the coordinate data is within the error range, it is determined that the AMR has stopped precisely within the station; otherwise, it is determined that the AMR has not stopped at the specified position.

[0050] Furthermore, the ideal stopping point of the vehicle body reference point O relative to the planar reference is calculated based on geometric relationships. The calculation method is as follows:

[0051] ,

[0052] .

[0053] Furthermore, the ideal stopping point of the vehicle body reference point O relative to the QR code is obtained through camera recognition. The calculation method includes establishing the coordinate relationship between the camera coordinate system and the vehicle coordinate system, transforming the camera coordinate system to the vehicle coordinate system through a rotation matrix, and ensuring that the QR code coordinates acquired by the camera are based on the vehicle coordinate system.

[0054] Furthermore, the coordinate data of the current position P and the ideal stopping point , The methods for calculating the difference in coordinate data include:

[0055] ,

[0056]

[0057] ,

[0058] ,

[0059] .

[0060] Further steps to determine whether the positioning deviation is within the error range include:

[0061] (1) Judgment of coarse calibration deviation:

[0062] ① Calculate the current position P and the ideal stopping point y-axis deviation Then, first judge If the deviation is less than or equal to 10mm, then it is determined that the AMR has not stopped at the specified position for coarse calibration, and the coarse calibration adjustment process is triggered; if it is less than 10mm, then the angle deviation judgment step is entered.

[0063] ② Calculate the current position P and the ideal stopping point angular deviation ,judge If the angle is less than 3°, it is determined that the AMR has not stopped at the specified position for coarse calibration, and the coarse calibration adjustment process is triggered; if it is less than 3°, it is determined that the AMR has completed coarse calibration and stopped precisely within the coarse calibration station, and can enter the fine calibration stage.

[0064] (2) Judgment of fine calibration deviation:

[0065] ① Calculate the current position P and the ideal stopping point x-axis deviation Then, first judge If the error is less than or equal to 5mm, then it is determined that the AMR has not stopped at the specified position for fine calibration, and the fine calibration adjustment process is triggered; if it is less than 5mm, then the angle deviation judgment step is entered.

[0066] ② Calculate the current position P and the ideal stopping point angular deviation ,judge If the deviation is less than 0.3°, it is determined that the AMR has not stopped at the specified position for fine calibration, and the fine calibration adjustment process is triggered; if it is less than 0.3°, the process proceeds to the y-axis deviation judgment stage.

[0067] ③ Calculate the current position P and the ideal stopping point y-axis deviation ,judge If the value is less than or equal to 5mm, it is determined that the AMR has not stopped at the specified position for fine calibration, and the fine calibration adjustment process is triggered; if it is less than 5mm, it is determined that the AMR has completed fine calibration and stopped precisely within the site.

[0068] Furthermore, if the coarse and fine calibrations determine that the AMR has not stopped at the specified position, the following steps must be performed for calibration and error / speed conversion:

[0069] Based on the direction of the difference between the current position P's x and y coordinates and the ideal stop point's x and y coordinates, determine the AMR's x and y axis adjustment direction. If the error value is positive, it is determined that the AMR needs to be translated along the negative x and y axes; if the error value is negative, it is determined that the AMR needs to be translated along the positive x and y axes. With the ideal stopping point The direction of the error value determines the rotation adjustment direction of the AMR. If the error value is positive, it is determined that the rotation should be counterclockwise; if the error value is negative, it is determined that the rotation should be clockwise.

[0070] The speed output corresponding to the error is calculated using a PID algorithm to ensure fast response and smooth control during the calibration process. The calculation formula includes:

[0071] ,

[0072] ,

[0073] ,

[0074] AMR is calculated based on , , Perform translation and rotation actions. After the actions are completed, obtain the error required for each calibration stage and repeat the difference calculation and calibration deviation judgment steps in the previous steps until the error is within the range, and the calibration is completed.

Claims

1. A secondary localization method for AMR robots based on laser ranging and QR code fusion, characterized in that, Includes the following steps: Establish a vehicle coordinate system with the AMR geometric center as the vehicle body reference point O; Laser ranging modules are symmetrically installed on the front and rear sides of the AMR vehicle body. The center distance between the two modules is set to a fixed value L. The vertical distance is adjusted according to the environment so that the laser illuminates the same surface. A camera with QR code recognition function is mounted in the middle of the front of the vehicle body. Move the AMR to the ideal stopping position, identify a planar reference object in the environment, and read the distance between the laser ranging module and the planar reference object before and after. , The ideal stopping point of the vehicle body reference point O relative to the plane reference object is calculated based on geometric relationships. A QR code is fixed in front of the ideal stopping position, with the center of the QR code aligned with the center of the camera. The camera then identifies the ideal stopping point of the vehicle body reference point O relative to the QR code. ; During calibration, first determine the current position. Coordinate data and ideal stopping point If the difference in coordinate data is within the error range, then the AMR is determined to have stopped precisely within the coarse calibration station, and fine calibration is initiated; otherwise, the AMR is determined not to have stopped at the designated position, and coarse calibration begins. After calibration, the coordinate data of the current position P is compared with the ideal stopping point. If the difference in the coordinate data is within the error range, it is determined that the AMR has stopped precisely within the station; otherwise, it is determined that the AMR has not stopped at the specified position.

2. The secondary localization method for AMR robots based on laser ranging and QR code fusion according to claim 1, characterized in that: Calculate the ideal stopping point of the vehicle body reference point O relative to the planar reference object based on geometric relationships. The calculation method is as follows: ， 。 3. The secondary localization method for AMR robots based on laser ranging and QR code fusion according to claim 1, characterized in that: The ideal stopping point is obtained by recognizing the vehicle body reference point O relative to the QR code through camera recognition. The calculation method includes establishing the coordinate relationship between the camera coordinate system and the vehicle coordinate system, transforming the camera coordinate system to the vehicle coordinate system through a rotation matrix, and ensuring that the QR code coordinates acquired by the camera are based on the vehicle coordinate system.

4. The secondary localization method for AMR robots based on laser ranging and QR code fusion according to claim 1, characterized in that: The coordinates of the current position P and the ideal stopping point , The methods for calculating the difference in coordinate data include: ， ， ， 。 5. The secondary localization method for AMR robots based on laser ranging and QR code fusion according to claim 4, characterized in that: The steps to determine whether the positioning deviation is within the error range include: (1) Judgment of coarse calibration deviation: ① Calculate the current position P and the ideal stopping point y-axis deviation Then, first judge If the deviation is less than or equal to 10mm, then it is determined that the AMR has not stopped at the specified position for coarse calibration, and the coarse calibration adjustment process is triggered; if it is less than 10mm, then the angle deviation judgment step is entered. ② Calculate the current position P and the ideal stopping point angular deviation ,judge If the angle is less than 3°, it is determined that the AMR has not stopped at the specified position for coarse calibration, and the coarse calibration adjustment process is triggered; if it is less than 3°, it is determined that the AMR has completed coarse calibration and stopped precisely within the coarse calibration station, and can enter the fine calibration stage. (2) Judgment of fine calibration deviation: ① Calculate the current position P and the ideal stopping point x-axis deviation Then, first judge If the error is less than or equal to 5mm, then it is determined that the AMR has not stopped at the specified position for fine calibration, and the fine calibration adjustment process is triggered; if it is less than 5mm, then the angle deviation judgment step is entered. ② Calculate the current position P and the ideal stopping point angular deviation ,judge If the deviation is less than 0.3°, it is determined that the AMR has not stopped at the specified position for fine calibration, and the fine calibration adjustment process is triggered; if it is less than 0.3°, the process proceeds to the y-axis deviation judgment stage. ③ Calculate the current position P and the ideal stopping point y-axis deviation ,judge If the value is less than or equal to 5mm, it is determined that the AMR has not stopped at the specified position for fine calibration, and the fine calibration adjustment process is triggered; if it is less than 5mm, it is determined that the AMR has completed fine calibration and stopped precisely within the site.

6. The secondary localization method for AMR robots based on laser ranging and QR code fusion according to claim 5, characterized in that: If the AMR is determined not to have stopped at the specified position during coarse and fine calibration, the following steps must be performed for calibration and error / speed conversion: Based on the direction of the difference between the current position P's x and y coordinates and the ideal stop point's x and y coordinates, determine the AMR's x and y axis adjustment direction. If the error value is positive, it is determined that the AMR needs to be translated along the negative x and y axes; if the error value is negative, it is determined that the AMR needs to be translated along the positive x and y axes. With the ideal stopping point The direction of the error value determines the rotation adjustment direction of the AMR. If the error value is positive, it is determined that the rotation should be counterclockwise; if the error value is negative, it is determined that the rotation should be clockwise. The speed output corresponding to the error is calculated using a PID algorithm to ensure fast response and smooth control during the calibration process. The calculation formula includes: ， ， ， AMR is calculated based on , , Perform translation and rotation actions. After the actions are completed, repeat the difference calculation in claim 4 and the calibration deviation judgment steps in claim 5 by obtaining the error required for each calibration stage, until the error is within the range, and the calibration is completed.

7. The secondary localization method for AMR robots based on laser ranging and QR code fusion according to claim 1, characterized in that: When establishing the vehicle coordinate system, the x-axis is the direction of the vehicle's movement, and the y-axis is the horizontal axis perpendicular to the x-axis and pointing to the right.

8. The secondary localization method for AMR robots based on laser ranging and QR code fusion according to claim 1, characterized in that: When the AMR moves to the ideal stopping position, the wall or the side of the machine is used as a planar reference in the environment.

Images