AGV trolley driving optimization method, device, AGV trolley and readable storage medium

Abstract

This invention provides an AGV (Automated Guided Vehicle) driving optimization method, apparatus, AGV, and readable storage medium. The method includes: obtaining the total number of QR code points in the straight-line driving path of the AGV; dividing the total number of QR code points into several driving segments; obtaining the interval displacement generated by the AGV passing every two QR code points and the total displacement generated in the current driving segment at the current acquisition time; substituting the interval displacements and corresponding total displacements obtained by the AGV in the current driving segment into a preset position calculation model to calculate the precise position of each QR code point of the AGV in the current driving segment; and adjusting the control parameters of the AGV according to the precise position of each QR code point in the current driving segment to enter the next driving segment. This invention avoids the problem that the entire path will malfunction when a QR code appears on a path.

Description

Technical Field

[0001] This invention relates to the field of industrial automation, and more particularly to an AGV (Automated Guided Vehicle) driving optimization method, apparatus, AGV, and readable storage medium. Background Technology

[0002] QR code navigation AGVs are commonly used in scenarios such as unmanned automated warehouses, and are devices that improve the efficiency and automation of logistics transportation.

[0003] In practical use, QR code-guided AGVs may encounter some interference. If one of the QR codes on a path is partially damaged and cannot be recognized, the AGV will derail or stop when it reaches that QR code. In other words, if a problem occurs with one QR code on a path, the entire path will malfunction. Summary of the Invention

[0004] This invention proposes an AGV vehicle driving optimization method, device, AGV vehicle, and readable storage medium to solve the problem in the prior art that the entire path will become abnormal when a QR code appears on a path.

[0005] One aspect of the present invention provides an AGV (Automated Guided Vehicle) driving optimization method, the method comprising:

[0006] Obtain the total number of QR code points in the straight-line driving path of the AGV, divide the total number of QR code points into several driving segments, and each driving segment contains QR code points of a first number threshold.

[0007] During the AGV's journey through various travel segments, the interval displacement generated by the AGV passing through every two adjacent QR code points and the total displacement generated in the current travel segment at the current acquisition time are obtained. Specifically, when the AGV travels to a preset QR code point, if the QR code information of the current QR code point is obtained, the interval displacement and total displacement are calculated based on the QR code information. If the QR code information of the current QR code point is not obtained, the interval displacement and total displacement are calculated based on the information obtained by the AGV's inertial sensor.

[0008] Substitute the displacements of each interval and the corresponding total displacement of the AGV in the current driving segment into the preset position calculation model to calculate the precise position of each QR code point of the AGV in the current driving segment.

[0009] The control parameters of the AGV are adjusted according to the precise position of each QR code point in the current driving segment to enter the next driving segment, until the current straight driving path is completely completed.

[0010] Furthermore, the preset position calculation model is as follows:

[0011]

[0012] Where n is the first quantity threshold, x i Let c0 be the position of the (i+1)th QR code point in the current driving segment. n-2 c represents the displacement between two adjacent QR code points. i Let c be the displacement between the i-th QR code point and the (i+1)-th QR code point in the current driving segment. n The total displacement generated by the current driving segment, k1~k n The weight of each term in the product is given.

[0013] Furthermore, the step of substituting the displacements of each interval and the corresponding total displacement of the AGV in the current driving segment into a preset position calculation model to calculate the precise position of each QR code point of the AGV in the current driving segment includes:

[0014] Calculate the partial derivative of the preset position calculation model. Obtain the x values ​​of each segment in the current driving sequence. i The exact value of x, where the calculated x i The precise value is the exact position of the i-th QR code point in the current driving segment of the AGV.

[0015] Furthermore, the k1~k n The value of is related to the displacement value in the product term:

[0016] When with k i The displacement value c in the product term of the multiplication i-1 When the displacement of the AGV between the (i-1)th and ith QR code points is obtained based on the QR code information,

[0017]

[0018] When with k i The displacement value c in the product term of the multiplication i-1 When calculating the displacement of the AGV between the (i-1)th and ith QR code points based on the data acquired by the inertial sensor,

[0019]

[0020] in, The variance of the displacement information calculated from the data acquired by the inertial sensor and pre-stored in the system. The variance of the displacement information obtained through QR code information is pre-stored in the system.

[0021] Furthermore, before acquiring the interval displacement generated by the AGV passing through every two adjacent QR code points and the total displacement generated in the current travel segment at the current acquisition time during the AGV's travel in each travel segment, the method further includes:

[0022] When the AGV reaches a first preset distance relative to the previous QR code point, it is determined that the AGV has reached the preset QR code point. The first preset distance is the distance between two adjacent QR code points pre-stored by the system.

[0023] Furthermore, before obtaining the total number of QR code points along the straight-line travel path of the AGV and dividing the total number of QR code points into several travel segments, the method further includes:

[0024] Receive the driving instructions from the AGV and obtain the driving path of the AGV based on the driving instructions;

[0025] Using the rotation points in the driving path as dividing points, the driving path of the AGV is divided into several straight driving paths.

[0026] Furthermore, the method also includes:

[0027] After completing the current straight-line driving path, the AGV is controlled to rotate at the QR code point at the end of the current driving path in a preset rotation direction to enter the next straight-line driving path, until the AGV's driving path ends.

[0028] In another aspect of the present invention, an AGV (Automated Guided Vehicle) driving optimization device is provided, the device comprising:

[0029] The first acquisition module is used to acquire the total number of QR code points in the straight driving path of the AGV, and divide the total number of QR code points into several driving segments, each driving segment containing QR code points of a first number threshold.

[0030] The second acquisition module is used to acquire the interval displacement generated by the AGV passing through every two adjacent QR code points and the total displacement generated in the current driving segment at the current acquisition time during the AGV's travel in each driving segment. Specifically, when the AGV travels to a preset QR code point, if the QR code information of the current QR code point is acquired, the interval displacement and total displacement are calculated based on the QR code information. If the QR code information of the current QR code point is not acquired, the interval displacement and total displacement are calculated based on the information acquired by the AGV's inertial sensor.

[0031] The calculation module is used to substitute the displacement of each interval and the corresponding total displacement of the AGV in the current driving segment into a preset position calculation model to calculate the precise position of each QR code point of the AGV in the current driving segment.

[0032] The control module is used to adjust the control parameters of the AGV according to the precise position of each QR code point in the current driving segment so as to enter the next driving segment, until the current straight driving path is completely completed.

[0033] In addition, another aspect of the present invention provides an AGV (Automated Guided Vehicle) including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the above-described AGV driving optimization method.

[0034] Furthermore, another aspect of the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program is configured to execute the steps of the above-described AGV vehicle driving optimization method at runtime.

[0035] The present invention provides an AGV (Automated Guided Vehicle) driving optimization method, apparatus, AGV, and readable storage medium. The AGV driving optimization method divides the straight-line driving path of the AGV into several driving segments. In each driving segment, it acquires the interval displacement generated by the AGV passing every two adjacent QR code points and the total displacement generated in the current driving segment at the current acquisition time. Specifically, when the AGV reaches a preset QR code point, if the QR code information of the current QR code point is acquired, the interval displacement and total displacement are calculated based on the QR code information. If the QR code information of the current QR code point is not acquired, the interval displacement and total displacement are calculated based on the information acquired by the AGV's inertial sensor. The interval displacements and corresponding total displacements acquired by the AGV in the current driving segment are substituted into a preset position calculation model to calculate the precise position of the AGV at each QR code point in the current driving segment. Based on the precise position of the AGV at each QR code point in the current driving segment, the control parameters of the AGV are adjusted to enter the next driving segment, until the current straight-line driving path is completely completed. This invention, based on a preset position calculation model, can calculate the precise position of the AGV at a preset QR code location even when the QR code location is missing. This allows the AGV to adjust its control parameters according to the precise position of the AGV at the preset QR code location, thus avoiding the problem that the entire path will malfunction when a QR code appears on a path.

[0036] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0037] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0038] Figure 1 This is a flowchart illustrating an AGV (Automated Guided Vehicle) driving optimization method provided in an embodiment of the present invention.

[0039] Figure 2 This is a schematic diagram of the structure of an AGV (Automated Guided Vehicle) driving optimization device provided in an embodiment of the present invention. Detailed Implementation

[0040] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0041] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the meaning consistent with their meaning in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined.

[0042] Figure 1 A flowchart illustrating an embodiment of the AGV (Automated Guided Vehicle) driving optimization method of the present invention is shown. (Refer to...) Figure 1 The AGV vehicle driving optimization method proposed in this embodiment of the invention specifically includes steps S11 to S14, as shown below:

[0043] S11. Obtain the total number of QR code points in the straight-line driving path of the AGV vehicle, divide the total number of QR code points into several driving segments, and each driving segment contains QR code points of a first number threshold.

[0044] In this embodiment of the invention, before obtaining the total number of QR code points in the straight-line driving path of the AGV vehicle and dividing the total number of QR code points into several driving segments, the method further includes: receiving a driving instruction from the AGV vehicle, obtaining the driving path of the AGV vehicle according to the driving instruction; and dividing the driving path of the AGV vehicle into several straight-line driving paths by using rotation points in the driving path as dividing points.

[0045] It should be noted that, in the embodiments of the present invention, the number of QR code points contained in each driving segment, i.e., the value of the first quantity threshold, is related to the specific control requirements, and the present invention does not impose specific limitations on this.

[0046] S12. During the AGV's journey through each travel segment, the interval displacement generated by the AGV passing through every two adjacent QR code points and the total displacement generated in the current travel segment at the current acquisition time are obtained. When the AGV travels to a preset QR code point, if the QR code information of the current QR code point is obtained, the interval displacement and total displacement are calculated based on the QR code information. If the QR code information of the current QR code point is not obtained, the interval displacement and total displacement are calculated based on the information obtained by the AGV's inertial sensor.

[0047] In this embodiment of the invention, before acquiring the interval displacement generated by the AGV passing through every two adjacent QR code points and the total displacement generated in the current driving segment at the current acquisition time during the AGV's travel in each driving segment, the method further includes: when the AGV's travel distance relative to the previous QR code point reaches a first preset distance, it is determined that the AGV has traveled to a preset QR code point, wherein the first preset distance is the distance between two adjacent QR code points pre-stored by the system.

[0048] It should be noted that since the QR code points are evenly distributed, the distance between each QR code point can be considered a fixed value, which is set as the first preset distance. However, because the QR code points are manually affixed to the ground, there is a certain error in the distance and position between two adjacent QR code points. Therefore, in actual control, it is necessary to calculate the precise position of the AGV. The first preset distance pre-stored in the system is the average distance between adjacent QR code points obtained through statistics, or the theoretical distance between adjacent QR code points when manually affixing the QR codes.

[0049] S13. Substitute the displacement of each interval and the corresponding total displacement of the AGV in the current driving segment into the preset position calculation model to calculate and obtain the precise position of each QR code point of the AGV in the current driving segment.

[0050] In this embodiment of the invention, the preset position calculation model is:

[0051]

[0052] Where n is the first quantity threshold, x i Let c0 be the position of the (i+1)th QR code point in the current driving segment. n-2 c represents the displacement between two adjacent QR code points. i Let c be the displacement between the i-th QR code point and the (i+1)-th QR code point in the current driving segment. nThe total displacement generated by the current driving segment, k1~k n The weight of each term in the product is given.

[0053] Furthermore, the step of substituting the displacements of each interval and the corresponding total displacement of the AGV in the current driving segment into a preset position calculation model to calculate the precise position of each QR code point of the AGV in the current driving segment includes:

[0054] Calculate the partial derivative of the preset position calculation model. Obtain the x values ​​of each segment in the current driving sequence. i The exact value of x, where the calculated x i The precise value is the exact position of the i-th QR code point in the current driving segment of the AGV.

[0055] Furthermore, the k1~k n The value of is related to the displacement value in the product term:

[0056] When with k i The displacement value c in the product term of the multiplication i-1 When the displacement of the AGV between the (i-1)th and ith QR code points is obtained based on the QR code information,

[0057]

[0058] When with k i The displacement value c in the product term of the multiplication i-1 When calculating the displacement of the AGV between the (i-1)th and ith QR code points based on the data acquired by the inertial sensor,

[0059]

[0060] in, The variance of the displacement information calculated from the data acquired by the inertial sensor and pre-stored in the system. The variance of the displacement information obtained through QR code information is pre-stored in the system.

[0061] To provide a detailed explanation of the precise positions of each QR code point in the current driving segment of the AGV in this embodiment of the invention, a specific embodiment is described below. The driving segment in this specific embodiment includes three QR code points, whose position information is represented as x0, x1, and x2, respectively. Here, x0 = 0, meaning the initial point within the segment is zero. ② x1 = x0 + c0, indicating that the displacement c0 is calculated from the inertial sensor data to reach the second point. ③ x2 = x1 + c1, indicating that the displacement c1 is obtained from the QR code data to reach the third point. ④ x2 = x0 + c3, indicating that the displacement c3 is obtained from the initial point to the end point of the segment using the QR code data.

[0062] At this point, the preset position calculation model is:

[0063]

[0064] in:

[0065]

[0066]

[0067] S14. Adjust the control parameters of the AGV according to the precise position of each QR code point in the current driving segment to enter the next driving segment, until the current straight driving path is completely completed.

[0068] In this embodiment of the invention, the method further includes: after the current straight-line driving path is completed, controlling the AGV to rotate at the end of the QR code point of the current driving path in a preset rotation direction to enter the next straight-line driving path, until the driving path of the AGV ends.

[0069] For the sake of simplicity, the method embodiments are described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0070] Figure 2 The diagram illustrates the structure of an AGV (Automated Guided Vehicle) driving optimization device according to an embodiment of the present invention. (Refer to...) Figure 2 The AGV vehicle driving optimization device of this embodiment specifically includes a first acquisition module 201, a second acquisition module 202, a calculation module 203, and a control module 204. Wherein:

[0071] The first acquisition module 201 is used to acquire the total number of QR code points in the straight driving path of the AGV, and divide the total number of QR code points into several driving segments, each driving segment containing QR code points of a first number threshold.

[0072] The second acquisition module 202 is used to acquire the interval displacement generated by the AGV passing through every two adjacent QR code points and the total displacement generated in the current driving segment at the current acquisition time during the AGV's travel in each driving segment. When the AGV travels to a preset QR code point, if the QR code information of the current QR code point is acquired, the interval displacement and total displacement are calculated based on the QR code information. If the QR code information of the current QR code point is not acquired, the interval displacement and total displacement are calculated based on the information acquired by the AGV's inertial sensor.

[0073] The calculation module 203 is used to substitute the displacement of each interval and the corresponding total displacement of the AGV in the current driving segment into a preset position calculation model to calculate and obtain the precise position of each QR code point of the AGV in the current driving segment.

[0074] The control module 204 is used to adjust the control parameters of the AGV according to the precise position of each QR code point in the current driving segment so as to enter the next driving segment, until the current straight driving path is completely completed.

[0075] Furthermore, the calculation module 203 is specifically used to calculate the partial derivative of the preset position calculation model. Obtain the x values ​​of each segment in the current driving sequence. i The exact value of x, where the calculated x i The precise value is the exact position of the i-th QR code point in the current driving segment of the AGV.

[0076] Furthermore, the AGV vehicle driving optimization in this embodiment of the invention also includes:

[0077] The path division module is used to receive the driving instructions of the AGV, obtain the driving path of the AGV according to the driving instructions, and divide the driving path of the AGV into several straight driving paths by using the rotation points in the driving path as dividing points.

[0078] Furthermore, the control module 204 is also used to control the AGV to rotate at the QR code point at the end of the current driving path in a preset rotation direction after the current straight driving path has been completed, and enter the next straight driving path until the driving path of the AGV ends.

[0079] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0080] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0081] The AGV (Automated Guided Vehicle) driving optimization method and apparatus provided in this invention divides the straight-line driving path of the AGV into several driving segments. In each driving segment, the interval displacement generated by the AGV passing every two adjacent QR code points and the total displacement generated in the current driving segment at the current acquisition time are obtained. Specifically, when the AGV reaches a preset QR code point, if the QR code information of the current QR code point is obtained, the interval displacement and total displacement are calculated based on the QR code information. If the QR code information of the current QR code point is not obtained, the interval displacement and total displacement are calculated based on the information obtained by the AGV's inertial sensor. The interval displacements and corresponding total displacements obtained by the AGV in the current driving segment are substituted into a preset position calculation model to calculate the precise position of the AGV at each QR code point in the current driving segment. The control parameters of the AGV are adjusted according to the precise position of each QR code point in the current driving segment to enter the next driving segment, until the current straight-line driving path is completely completed. This invention, based on a preset position calculation model, can calculate the precise position of the AGV at a preset QR code location even when the QR code location is missing. This allows the AGV to adjust its control parameters according to the precise position of the AGV at the preset QR code location, thus avoiding the problem that the entire path will malfunction when a QR code appears on a path.

[0082] Furthermore, the AGV provided in this embodiment of the invention also includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the various AGV driving optimization method embodiments described above, for example... Figure 1 The steps of the AGV vehicle driving optimization method shown in S11-S14. Alternatively, the functions of each module / unit in the AGV vehicle driving optimization device embodiment, for example... Figure 2The first acquisition module 201, the second acquisition module 202, the calculation module 203, and the control module 204 are shown.

[0083] Furthermore, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program is configured to execute the steps of the AGV vehicle driving optimization method in the above embodiments when running.

[0084] In this embodiment, if the modules / units integrated in the assembly and testing device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0085] In the embodiments of the present invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical or other forms.

[0086] Those skilled in the art will understand that although some embodiments herein include certain features included in other embodiments but not others, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.

[0087] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the driving of an AGV (Automated Guided Vehicle), characterized in that, The method includes: Obtain the total number of QR code points in the straight-line driving path of the AGV, divide the total number of QR code points into several driving segments, and each driving segment contains QR code points of a first number threshold. During the AGV's journey through various travel segments, the interval displacement generated by the AGV passing through every two adjacent QR code points and the total displacement generated in the current travel segment at the current acquisition time are obtained. Specifically, when the AGV travels to a preset QR code point, if the QR code information of the current QR code point is obtained, the interval displacement and total displacement are calculated based on the QR code information. If the QR code information of the current QR code point is not obtained, the interval displacement and total displacement are calculated based on the information obtained by the AGV's inertial sensor. Substitute the displacements of each interval and the corresponding total displacement of the AGV in the current driving segment into the preset position calculation model to calculate the precise position of each QR code point of the AGV in the current driving segment. The control parameters of the AGV are adjusted according to the precise position of each QR code point in the current driving segment to enter the next driving segment, until the current straight driving path is completely completed.

2. The method according to claim 1, characterized in that, The preset position calculation model is as follows: Where n is the first quantity threshold, x i Let c0 be the position of the (i+1)th QR code point in the current driving segment. n-2 c represents the displacement between two adjacent QR code points. i Let c be the displacement between the i-th QR code point and the (i+1)-th QR code point in the current driving segment. n The total displacement generated by the current driving segment, k1~k n The weight of each term in the product is given.

3. The method according to claim 2, characterized in that, The step of substituting the displacements of each interval and the corresponding total displacement of the AGV in the current driving segment into a preset position calculation model to calculate the precise position of each QR code point of the AGV in the current driving segment includes: Calculate the partial derivative of the preset position calculation model. Obtain the x values ​​of each segment in the current driving sequence. i The exact value of x, where the calculated x i The precise value is the exact position of the i-th QR code point in the current driving segment of the AGV.

4. The method according to claim 2, characterized in that, The k1~k n The value of is related to the displacement value in the product term: When with k i The displacement value c in the product term of the multiplication i-1 When the displacement of the AGV between the (i-1)th and ith QR code points is obtained based on the QR code information, When with k i The displacement value c in the product term of the multiplication i-1 When calculating the displacement of the AGV between the (i-1)th and ith QR code points based on the data acquired by the inertial sensor, in, The variance of the displacement information calculated from the data acquired by the inertial sensor and pre-stored in the system. The variance of the displacement information obtained through QR code information is pre-stored in the system.

5. The method according to claim 1, characterized in that, Before acquiring the interval displacement generated by the AGV passing through every two adjacent QR code points and the total displacement generated in the current travel segment at the current acquisition time during the AGV's travel in various travel segments, the method further includes: When the AGV reaches a first preset distance relative to the previous QR code point, it is determined that the AGV has reached the preset QR code point. The first preset distance is the distance between two adjacent QR code points pre-stored by the system.

6. The method according to claim 1, characterized in that, Before obtaining the total number of QR code points along the straight-line travel path of the AGV and dividing the total number of QR code points into several travel segments, the method further includes: Receive the driving instructions from the AGV and obtain the driving path of the AGV based on the driving instructions; Using the rotation points in the driving path as dividing points, the driving path of the AGV is divided into several straight driving paths.

7. The method according to claim 6, characterized in that, The method further includes: After completing the current straight-line driving path, the AGV is controlled to rotate at the QR code point at the end of the current driving path in a preset rotation direction to enter the next straight-line driving path, until the AGV's driving path ends.

8. An AGV (Automated Guided Vehicle) driving optimization device, characterized in that, The device includes: The first acquisition module is used to acquire the total number of QR code points in the straight driving path of the AGV, and divide the total number of QR code points into several driving segments, each driving segment containing QR code points of a first number threshold. The second acquisition module is used to acquire the interval displacement generated by the AGV passing through every two adjacent QR code points and the total displacement generated in the current driving segment at the current acquisition time during the AGV's travel in each driving segment. Specifically, when the AGV travels to a preset QR code point, if the QR code information of the current QR code point is acquired, the interval displacement and total displacement are calculated based on the QR code information. If the QR code information of the current QR code point is not acquired, the interval displacement and total displacement are calculated based on the information acquired by the AGV's inertial sensor. The calculation module is used to substitute the displacement of each interval and the corresponding total displacement of the AGV in the current driving segment into a preset position calculation model to calculate the precise position of each QR code point of the AGV in the current driving segment. The control module is used to adjust the control parameters of the AGV according to the precise position of each QR code point in the current driving segment so as to enter the next driving segment, until the current straight driving path is completely completed.

9. An AGV (Automated Guided Vehicle) comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program is configured to perform the steps of the method according to any one of claims 1-7 when executed.

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