Automatic guided vehicle control method, device, storage medium and automatic guided vehicle

Abstract

The present disclosure relates to mobile robot technology, and more particularly to a control method, device, storage medium, and automated guided vehicle (AGV). The AGV control method includes: establishing a local grid map and mapping a desired operating path of the AGV into the local grid map; determining the position of the AGV in the local grid map at the beginning of each control cycle; determining an error integral term of the AGV based on the position of the AGV in the local grid map at the beginning of each control cycle and the desired operating path mapped in the local grid map; calculating an offset position of the AGV in the local grid map based on the error integral term; determining an optimal path for the AGV based on the offset position; and controlling the AGV to travel according to the optimal path during the current control cycle.

Description

Technical Field

[0001] The present disclosure relates to mobile robot technology, and in particular to a control method, device, storage medium, and an automated guided vehicle. Background Art

[0002] Intelligent mobile robots are robots that operate in complex environments and have the ability to autonomously plan, self-organize, and adapt. AGVs (automated guided vehicles) based on laser SLAM (simultaneous localization and mapping) navigation are a type of intelligent mobile robot. AGVs based on laser SLAM navigation are increasingly being used in the logistics automation of industrial production. Laser SLAM navigation offers advantages such as flexibility, high agility, and easy deployment, making it adaptable to the changing and complex scenarios of electronic manufacturing. Typically, laser SLAM navigation methods require users to first create an environmental map by remotely controlling the AGV, then specify a virtual path on the environmental map, and then automatically enable the AGV to run along the given path, i.e., path tracking, to complete the material delivery task. The virtual path is usually given in the form of a straight line, arc, or high-order Bezier curve.

[0003] Currently, sampling algorithms such as DWA (Dynamic Window Approch) are widely used in AGV path tracking control. Specifically, each grid in the local grid map is scored, with the grid where the desired path is located set to 0 points. The grid farther away from the grid where the desired path is located is set to a higher score. By scoring the simulated path, for example, the score of the grid where the path end point is located is used as the path score, the optimal path is determined. However, due to the resolution of the grid map, when the given path is close to the simulated path, Figure 1 As shown, static errors can occur. This results in suboptimal overall path tracking, and large angle adjustments near the endpoint can cause the AGV to stop unsmoothly. The existing conventional solution is to increase grid resolution accuracy. However, increasing grid map resolution leads to a sharp increase in overall computational complexity, affecting real-time control and causing time lag or instability. Summary of the Invention

[0004] The embodiments of the present disclosure provide a control method, device, storage medium and automatic guided vehicle, which can solve the problems caused by static errors.

[0005] In a first aspect, an embodiment of the present application provides a control method for an automated guided vehicle, comprising: establishing a local grid map and mapping an expected operating path of the automated guided vehicle into the local grid map, determining a position of the automated guided vehicle in the local grid map at the beginning of each control cycle, determining an error integral term of the automated guided vehicle based on the position of the automated guided vehicle in the local grid map at the beginning of each control cycle and the expected operating path mapped in the local grid map, calculating an offset position of the automated guided vehicle in the local grid map based on the error integral term, determining an optimal path of the automated guided vehicle based on the offset position, and controlling the automated guided vehicle to travel according to the optimal path in the current control cycle.

[0006] Optionally, determining the error integral term of the automatic guided vehicle based on the position of the automatic guided vehicle in the local grid map at the beginning of each control cycle and the expected operating path mapped in the local grid map includes: calculating the error integral term of each control cycle based on the position of the automatic guided vehicle in the local grid map at the beginning of each control cycle and the expected operating path mapped in the local grid map, accumulating the error integral term of each control cycle, and determining the error integral term of the automatic guided vehicle.

[0007] Optionally, the desired operation path is mapped in the local grid map in the form of a position point sequence, and the error integral term of each control cycle is calculated based on the position of the automatic guided vehicle in the local grid map at the beginning of each control cycle and the desired operation path mapped in the local grid map, including: determining the positions of two position points in the position point sequence that are closest to the first position based on a first position of each control cycle, the first position being the position of the automatic guided vehicle in the local grid map at the beginning of each control cycle, and determining the second position of each control cycle based on the first position and the positions of the two position points that are closest to the first position. The second position is the position on the desired operating path that is closest to the first position. Based on the first position of each control cycle and the second position of each control cycle, the distance between the automatic guided vehicle and the desired operating path in each control cycle is determined, and the error integral term of each control cycle is calculated respectively. For any control cycle in each control cycle, the error integral term of the control cycle is determined based on the distance between the automatic guided vehicle and the desired operating path in the control cycle, the distance between the automatic guided vehicle and the desired operating path in the previous control cycle of the control cycle, the second position of the control cycle, and the second position of the previous control cycle of the control cycle.

[0008] Optionally, calculating the offset position of the automatic guided vehicle in the local grid map based on the error integral term includes: calculating the inclination angle of the line connecting the first position and the second position in the current control cycle, and calculating the offset position of the automatic guided vehicle in the local grid map based on the first position of the automatic guided vehicle in the current control cycle, the distance between the automatic guided vehicle and the expected operating path in the current control cycle, the error integral term of the automatic guided vehicle and the inclination angle.

[0009] Optionally, based on the offset position, the optimal path of the automatic guided vehicle is determined, and the automatic guided vehicle is controlled to travel according to the optimal path in the current control cycle, including: based on a grid path algorithm, simulating multiple running paths of the automatic guided vehicle in the local grid map, the starting points of the multiple running paths are the offset positions, determining the optimal path among the multiple running paths, the optimal path being the path among the multiple running paths that is closest to the expected running path, obtaining the linear speed and angular speed of the automatic guided vehicle corresponding to the optimal path, and controlling the automatic guided vehicle to travel according to the linear speed and angular speed in the current control cycle.

[0010] Optionally, before determining the error integral term of the automatic guided vehicle based on the position of the automatic guided vehicle in the local grid map at the beginning of each control cycle and the expected operating path mapped in the local grid map, the method includes: determining a first distance, the first distance being the distance between the position of the automatic guided vehicle in the local grid map in the current control cycle and the expected operating path mapped in the local grid map, and when the first distance is greater than or equal to a preset dead zone error and less than or equal to a preset integral allowable error, executing the step of determining the error integral term of the automatic guided vehicle based on the position of the automatic guided vehicle in the local grid map in each control cycle and the expected operating path.

[0011] Optionally, when the first distance is greater than or equal to the dead zone error and less than or equal to the integral allowable error, the method includes: simulating multiple operating paths of the automatic guided vehicle in the local grid map according to the grid path algorithm, the starting points of the multiple operating paths being the positions of the automatic guided vehicle in the local grid map at the beginning of the current control cycle, determining the optimal path among the multiple operating paths, the optimal path being the path among the multiple operating paths that is closest to the expected operating path, obtaining the linear velocity and angular velocity of the automatic guided vehicle corresponding to the optimal path, and controlling the automatic guided vehicle to travel according to the linear velocity and angular velocity in the current control cycle.

[0012] In a second aspect, an embodiment of the present application provides a control device for an automatic guided vehicle, comprising a processor and a memory, wherein the memory stores computer instructions, and when the computer instructions are executed by the processor, the steps of the method described in any one of the first aspects are implemented.

[0013] In a third aspect, an embodiment of the present application provides a storage medium having computer instructions stored thereon, which, when executed by a processor, implement the steps of the method described in any one of the first aspects above.

[0014] In a fourth aspect, an embodiment of the present application provides an automated guided vehicle having a processor and a memory, wherein the memory stores computer instructions, and when the computer instructions are executed by the processor, the steps of the method described in any one of the first aspects are implemented.

[0015] One beneficial effect of the disclosed embodiment is that, by establishing a local grid map and mapping the desired path of the automated guided vehicle to the local grid map, the offset position of the automated guided vehicle in the local grid map is determined based on the position of the automated guided vehicle in the local grid map at the beginning of each control cycle and the desired path of the automated guided vehicle. Based on the offset position, the optimal path of the automated guided vehicle is determined, and the automated guided vehicle is controlled to travel according to the optimal path in the current control cycle. In this way, when a static error occurs in the automated guided vehicle, the offset position of the automated guided vehicle can be calculated based on the error integral, and the optimal path of the automated guided vehicle can be re-determined based on the offset position. This can improve the tracking accuracy of the path without increasing the resolution of the grid map, and enhance the flexibility and balance of the AGV in reaching the target point.

[0016] Other features and advantages of the embodiments of the present disclosure will become apparent from the following detailed description of exemplary embodiments of the present disclosure with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the present disclosure and, together with the description, serve to explain the principles of the embodiments of the present disclosure.

[0018] Figure 1 A schematic diagram illustrating an example of background art is shown.

[0019] Figure 2 A flow chart of a method for controlling an automatic guided vehicle according to an embodiment of the present disclosure is shown.

[0020] Figure 3-5 A schematic diagram illustrating an example of a method for controlling an automatic guided vehicle according to an embodiment of the present disclosure.

[0021] Figure 6 A block diagram of a control device for an automated guided vehicle according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0022] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that unless otherwise specifically stated, the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention.

[0023] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0024] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0025] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0026] It should be noted that like reference numerals and letters refer to like items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0027] The present application discloses a control method for an automatic guided vehicle. Figure 2 As shown, the method includes steps S11-S15.

[0028] Step S11: establishing a local grid map and mapping the expected running path of the automatic guided vehicle into the local grid map.

[0029] In one example of this embodiment, a local grid map with a specified side length and a specified resolution can be constructed. For example, a local grid map with a side length of 3 meters and a grid resolution of 0.05 meters can be specified. Based on the SLAM algorithm, the position, i.e., coordinates, of the AGV in the local coordinate system of the local grid map can be obtained.

[0030] In one example of this embodiment, the desired path of the automated guided vehicle is the target path of the AGV specified by the user. In one example, the desired path can be a target path specified as a sequence of position points. The global desired path can be truncated in the local grid map, and the coordinates of each position point in the local grid map are calculated and mapped to the local grid map as a sequence of position points.

[0031] Step S12: determining the position of the automated guided vehicle in the local grid map at the beginning of each control cycle.

[0032] In one example of this embodiment, the AGV may periodically receive control instructions and move according to the instructions within the period. The control period of the AGV may be flexibly set based on actual conditions, for example, the control period may be set to 10ms. At the beginning of each control period, the SLAM algorithm may be used to obtain the AGV's position, i.e., coordinates, in the local grid map at the beginning of the current control period and all previous control periods.

[0033] Step S13 , determining an error integral term of the automatic guided vehicle based on the position of the automatic guided vehicle in the local grid map at the beginning of each control cycle and the expected running path mapped in the local grid map.

[0034] In an example of this embodiment, the error integral term of the automatic guided vehicle is determined based on the position of the automatic guided vehicle in the local grid map at the beginning of each control cycle and the expected operating path mapped in the local grid map, including: calculating the error integral term of each control cycle based on the position of the automatic guided vehicle in the local grid map at the beginning of each control cycle and the expected operating path mapped in the local grid map, accumulating the error integral term of each control cycle, and determining the error integral term of the automatic guided vehicle.

[0035] In one example of this embodiment, a desired path is mapped in a local grid map as a sequence of position points. An error integral term is calculated for each control cycle based on the position of the automated guided vehicle in the local grid map at the beginning of each control cycle and the desired path mapped in the local grid map. The calculation includes: determining the positions of the two position points in the position point sequence closest to the first position based on a first position in each control cycle, where the first position is the position of the automated guided vehicle in the local grid map at the beginning of each control cycle. Determining a second position in each control cycle based on the first position and the positions of the two position points closest to the first position, where the second position is the position on the desired path closest to the first position. Determining the distance between the automated guided vehicle and the desired path in each control cycle based on the first position and the second position in each control cycle. Calculating an error integral term for each control cycle, wherein for any control cycle within each control cycle, the error integral term for that control cycle is determined based on the distance between the automated guided vehicle and the desired path in that control cycle, the distance between the automated guided vehicle and the desired path in the previous control cycle, the second position in that control cycle, and the second position in the previous control cycle.

[0036] Specifically, such as Figure 3 As shown, Figure 3 The coordinate system is that of the local grid map. For clear display, Figure 3 The grids in the local grid map are not drawn. Figure 3 Middle, click P m 、P n is the position point in the expected running path, The positions of the automatic guided vehicle in the kth control cycle and the k-1th control cycle respectively. When the control cycle is the kth control cycle, the position of the automatic guided vehicle in the grid map at the beginning of the kth control cycle is Determine the distance between the position points in the desired running path The two nearest position points P m 、P n , determine the two nearest position points P m 、P n Afterwards, you can P m 、P n The coordinates of in the local grid map coordinate system P m =(x m ,y m ) and P n =(x n ,y n ), determine the position closest to the automatic guided vehicle on the expected running path of the kth control cycle Specifically, according to formula (1):

[0037]

[0038] The solved (x, y) is the coordinate of the second position After determining the second position of the kth control cycle, the distance between the automatic guided vehicle and the desired running path in the kth control cycle can be determined according to formula (2), that is, the distance between the first position and the second position

[0039] in is a symbolic function, The symbol is as follows Similarly, the distance between the automatic guided vehicle and the desired operating path can be determined in each control cycle.

[0040] After determining the distance between the AGV and the desired path for each control cycle, the error integral term for each control cycle can be calculated. For the error integral term of the kth control cycle, the distance between the AGV and the desired path for the kth control cycle can be calculated based on the distance between the AGV and the desired path. The distance between the automatic guided vehicle and the expected operating path in the k-1th control cycle and the second position of the kth control cycle and the second position of the k-1th control cycle Determine the error integral term for the kth control cycle.

[0041] Specifically, the error integral term of each control cycle is the area between the running path of the automatic guided vehicle and the expected running path in each control cycle. Figure 3 As shown, the error integral term of the kth control cycle can be solved by Figure 3 Specifically, from formula (3), we can see that the height h of the trapezoid is k , the second position of the kth control cycle can be The coordinates of the second position of the k-1th control cycle to determine the coordinates of .

[0042]

[0043] According to formula (4), the error integral term I can be determined in the kth control cycle. k , Similarly, the error integral term of each control cycle can be calculated, and the error integral term of each cycle can be accumulated to determine the error integral term I of the automatic guided vehicle.

[0044]

[0045] Step S14: Calculate the offset position of the automatic guided vehicle in the local grid map based on the error integral term.

[0046] In one example of this embodiment, the offset position of the AGV in the local grid map is the simulated position after the current position of the AGV is offset, rather than the actual position of the AGV. Figure 4 shown.

[0047] In an example of this embodiment, the offset position of the automatic guided vehicle in the local grid map is calculated based on the error integral term, including: calculating the inclination angle of the line connecting the first position and the second position of the current control cycle, and calculating the offset position of the automatic guided vehicle in the local grid map based on the first position of the automatic guided vehicle in the current control cycle, the distance between the automatic guided vehicle and the expected operating path in the current control cycle, the error integral term and the inclination angle of the automatic guided vehicle.

[0048] Specifically, such as Figure 4 As shown, the inclination angle of the line connecting the first position and the second position is the angle between the line connecting the first position and the second position and the x-axis. and second position The relative relationship in the local coordinate system is determined by formula (5):

[0049]

[0050] After calculating the inclination angle θ of the line connecting the first position and the second position, the offset position of the AGV in the local grid map can be calculated based on the first position of the AGV in the current control cycle, the distance between the AGV and the desired running path in the current control cycle, the error integral term of the AGV, and the inclination angle. Specifically, the horizontal coordinate of the offset position can be The product of the error integral term I of the automatic guided vehicle and the cosine value of the inclination angle cosθ is the product of the first position abscissa The sum of the product, where the sign of the product is related to the distance between the automatic guided vehicle and the desired operating path in the current control cycle The signs are consistent, that is, formula (6):

[0051]

[0052] The vertical coordinate of the offset position The product of the error integral term I of the automatic guided vehicle and the sine value of the tilt angle cosθ and the sum of the vertical coordinate of the first position The sign of the product is related to the distance between the AGV and the desired path in the current control cycle. The signs are consistent, that is, formula (7):

[0053]

[0054] Step S15: determining the optimal path of the automated guided vehicle according to the offset position, and controlling the automated guided vehicle to travel according to the optimal path in the current control cycle.

[0055] In this example, a local grid map is established and the desired path of the AGV is mapped onto it. Based on the AGV's position in the local grid map and the desired path at the start of each control cycle, the AGV's offset position in the local grid map is determined. Based on this offset position, the AGV's optimal path is determined, and the AGV is controlled to follow this optimal path during the current control cycle. This approach allows the AGV's offset position to be calculated based on the error integral when a static error occurs, and the AGV's optimal path to be re-determined based on the offset position. This improves path tracking accuracy without increasing the grid map's resolution, enhancing the AGV's compliance and balance as it reaches its destination.

[0056] In an example of this embodiment, an optimal path for an automated guided vehicle is determined based on an offset position, and the automated guided vehicle is controlled to travel according to the optimal path in a current control cycle, including: simulating multiple operating paths of the automated guided vehicle in a local grid map based on a grid path algorithm, where the starting points of the multiple operating paths are offset positions, determining an optimal path among the multiple operating paths, where the optimal path is the path among the multiple operating paths that is closest to the desired operating path, obtaining the linear speed and angular speed of the automated guided vehicle corresponding to the optimal path, and controlling the automated guided vehicle to travel according to the linear speed and angular speed in the current control cycle.

[0057] In one example of this embodiment, the grid path algorithm can be a DWA (Dynamic Window Approch) algorithm. After determining the offset position of the AGV, the linear velocity and angular velocity are sampled in a uniform sampling manner, taking the offset position as the starting point and the current speed and acceleration of the AGV as the reference. For example, the current linear velocity is 0.5m / s and the acceleration is 1m / s. Taking a control cycle of 10ms as an example, in the current control cycle, the maximum value that the linear velocity can reach is 0.51m / s and the minimum value is 0.49m / s. Within the range of the maximum and minimum values, uniform sampling is performed, for example, 20 linear velocities are sampled, and similarly, 20 angular velocities are sampled. At this point, 400 sets of linear velocity and angular velocity samples are obtained, and simulation can be performed for each set of samples. Multiple cycle operation paths can be simulated according to demand, such as 400 3-cycle operation paths.

[0058] The optimal path among multiple simulated paths is determined based on the degree of closeness between the simulated path and the expected running path. Specifically, each grid in the local grid map can be scored, and the point occupied by the expected running path is used as a reference point with a score of 0. The farther the grid is from the expected running path, the higher the score. Figure 5 As shown in FIG, each simulated path can be scored according to the grid score of the path end point, and the optimal path can be determined.

[0059] It should be noted that although the example shows that the simulated path can be scored according to the grid score of the path end point, those skilled in the art will understand that the present disclosure is not limited to this. The specific method of scoring the simulated path can be flexibly set by those skilled in the art according to actual conditions, such as scoring through evaluation functions of target point proximity, path proximity, and path alignment.

[0060] After determining the optimal path, the linear speed and angular speed of the AGV corresponding to the optimal path can be determined, and the AGV can be controlled to travel according to the linear speed and angular speed in the current control cycle, that is, to run from the actual position of the AGV using the same actual running path as the optimal path. Figure 4 shown.

[0061] In an example of this embodiment, before determining the error integral term of the automatic guided vehicle based on the position of the automatic guided vehicle in the local grid map at the beginning of each control cycle and the expected operating path mapped in the local grid map, the method includes: determining a first distance, the first distance being the distance between the position of the automatic guided vehicle in the local grid map in the current control cycle and the expected operating path mapped in the local grid map, and when the first distance is greater than or equal to a preset dead zone error and less than or equal to a preset integral allowable error, executing the step of determining the error integral term of the automatic guided vehicle based on the position of the automatic guided vehicle in the local grid map in each control cycle and the expected operating path.

[0062] In one example of this embodiment, the preset dead zone error E B It can be determined according to the resolution of the grid map, and the dead zone error E B It is usually smaller than the resolution of the grid map and can be set to one-fifth of the resolution. For example, when the grid map resolution is 0.05m, the dead zone error E B It can be set to 0.01m. The preset integral tolerance E I It can be determined according to the grid map resolution, the integral tolerance E I It can be set to twice the grid map resolution. For example, when the grid map resolution is 0.05m, the integral tolerance E I It can be 0.1m.

[0063] Before calculating the error integral term, the distance between the position of the AGV in the local grid map and the desired running path mapped in the local grid map can also be determined by the above method. At the first distance Greater than or equal to dead zone error E B and is less than the integral tolerance E I , static errors may occur in AGV. Therefore, the influence of static errors can be eliminated by the above-mentioned method. Without increasing the resolution of the raster map, the path tracking accuracy can be improved, and the flexibility and balance of the AGV in reaching the target point can be improved.

[0064] In one example of this example, when the first distance is greater than or equal to the dead zone error and less than or equal to the integral allowable error, the method includes: simulating multiple operating paths of the automatic guided vehicle in a local grid map according to a grid path algorithm, the starting points of the multiple operating paths are the positions of the automatic guided vehicle in the local grid map at the beginning of the current control cycle, determining an optimal path among the multiple operating paths, the optimal path being the path among the multiple operating paths that is most similar to the expected operating path, obtaining the linear velocity and angular velocity of the automatic guided vehicle corresponding to the optimal path, and controlling the automatic guided vehicle to travel according to the linear velocity and angular velocity in the current control cycle.

[0065] In one example of this embodiment, the distance between the position of the AGV in the local grid map and the desired running path mapped in the local grid map in the current control cycle is Less than dead zone error E B , it proves that the actual running path of the AGV is very close to the expected running path; the distance between the position of the automatic guided vehicle in the local grid map in the current control cycle and the expected running path mapped in the local grid map is When E is greater than or equal to the integral tolerance I , the actual position of the AGV is far from the expected running path; when these two situations occur, the influence of static error can be basically ignored. The grid path algorithm, such as the DWA algorithm, can be used to simulate multiple running paths, and the optimal path can be determined, and the vehicle can drive according to the optimal path.

[0066] See also Figure 3 As shown, this embodiment provides a control device 100 for an automatic guided vehicle, including a processor 101 and a memory 102, wherein the memory 102 stores computer instructions, and when the computer instructions are executed by the processor 101, the various processes of the above-mentioned automatic guided vehicle control method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, they are not described here.

[0067] This embodiment provides a computer-readable storage medium, which stores executable commands. When the executable commands are executed by the processor, the various processes of the above-mentioned automatic guided vehicle control method embodiment are implemented and can achieve the same technical effect. To avoid repetition, they are not repeated here.

[0068] This embodiment provides an automatic guided vehicle, which has a processor and a memory. The memory stores computer instructions. When the computer instructions are executed by the processor, the various processes of the above-mentioned automatic guided vehicle control method embodiment are implemented and can achieve the same technical effect. To avoid repetition, they will not be repeated here.

[0069] It should be noted that all actions of obtaining signals, information or data in this application are carried out in compliance with the relevant data protection laws and policies of the country where they are located and with the authorization given by the owner of the corresponding device / account.

[0070] The various embodiments of this disclosure are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the device and apparatus embodiments are generally similar to the method embodiments, so their descriptions are relatively simple. For relevant portions, reference can be made to the descriptions of the method embodiments.

[0071] The foregoing description describes specific embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0072] The embodiments of the present disclosure may be systems, methods, and / or computer program products. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the embodiments of the present disclosure.

[0073] A computer-readable storage medium can be a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination thereof. As used herein, a computer-readable storage medium is not to be construed as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through an electrical wire.

[0074] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions to be stored in the computer-readable storage medium in each computing / processing device.

[0075] The computer program instructions for performing the operation of the embodiments of the present disclosure can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, and conventional procedural programming languages ​​such as "C" language or similar programming languages. Computer-readable program instructions can be executed entirely on a user's computer, partially on a user's computer, executed as an independent software package, partially on a user's computer and partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer via any type of network including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, by utilizing the state information of computer-readable program instructions to personalize an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit can execute computer-readable program instructions, thereby realizing the various aspects of the embodiments of the present disclosure.

[0076] Various aspects of the embodiments of the present disclosure are described herein with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer-readable program instructions.

[0077] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device is generated that implements the functions / actions specified in one or more blocks in the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, where these instructions cause the computer, programmable data processing device, and / or other device to operate in a specific manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing various aspects of the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0078] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0079] The flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment or part of an instruction, and a part of the module, program segment or instruction contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or can be implemented using a combination of dedicated hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, implementation by software, and implementation by a combination of software and hardware are all equivalent.

[0080] While various embodiments of the present disclosure have been described above, the above descriptions are illustrative, non-exhaustive, and not intended to be limiting of the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope of the described embodiments. The terminology used herein is selected to best explain the principles of the embodiments, their practical applications, or improvements to existing technologies, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A control method for an automatic guided vehicle, characterized in that: include: Establishing a local grid map, and mapping the expected running path of the automatic guided vehicle into the local grid map, wherein the expected running path is mapped into the local grid map in the form of a sequence of position points; determining a position of the automated guided vehicle in the local grid map at the beginning of each control cycle; determining an error integral term of the automatic guided vehicle based on a position of the automatic guided vehicle in the local grid map at the beginning of each control cycle and the desired operation path mapped in the local grid map; calculating an offset position of the automatic guided vehicle in a local grid map based on the error integral term; determining an optimal path for the automated guided vehicle according to the offset position, and controlling the automated guided vehicle to travel according to the optimal path in a current control cycle; Determining an error integral term of the automatic guided vehicle based on a position of the automatic guided vehicle in the local grid map at the beginning of each control cycle and the desired operating path mapped in the local grid map comprises: determining, based on a first position of each control cycle, positions of two position points in the position point sequence that are closest to the first position, the first position being the position of the automatic guided vehicle in the local grid map at the beginning of each control cycle; determining a second position in each control cycle according to the first position and the positions of the two points closest to the first position, wherein the second position is the position closest to the first position on the desired running path; determining a distance between the automatic guided vehicle and the desired operating path in each control cycle according to the first position in each control cycle and the second position in each control cycle; calculating an error integral term for each control period respectively, wherein, for any one of each control period, the error integral term for the control period is determined based on a distance between the automated guided vehicle and the desired operating path in the control period, a distance between the automated guided vehicle and the desired operating path in a control period immediately preceding the control period, a second position in the control period, and a second position in a control period immediately preceding the control period; The error integral term of each control cycle is accumulated to determine the error integral term of the automatic guided vehicle.

2. The control method according to claim 1, characterized in that: Calculating the offset position of the automatic guided vehicle in the local grid map according to the error integral term includes: calculating an inclination angle of a line connecting the first position and the second position in a current control cycle; The offset position of the automatic guided vehicle in the local grid map is calculated according to the first position of the automatic guided vehicle in the current control cycle, the distance between the automatic guided vehicle and the expected operation path in the current control cycle, the error integral term of the automatic guided vehicle and the tilt angle.

3. The control method according to claim 1, wherein: Determining an optimal path for the automatic guided vehicle according to the offset position, and controlling the automatic guided vehicle to travel according to the optimal path in a current control cycle, including: Simulating multiple running paths of the automatic guided vehicle in the local grid map according to a grid path algorithm, where the starting points of the multiple running paths are the offset positions; Determining an optimal path among the multiple running paths, where the optimal path is a path among the multiple running paths that is most close to the expected running path; The linear velocity and angular velocity of the automatic guided vehicle corresponding to the optimal path are obtained, and the automatic guided vehicle is controlled to travel according to the linear velocity and angular velocity in a current control cycle.

4. The control method according to claim 1, wherein: Before determining the error integral term of the automated guided vehicle based on the position of the automated guided vehicle in the local grid map at the beginning of each control cycle and the expected operation path mapped in the local grid map, the method includes: Determining a first distance, where the first distance is a distance between a position of the automatic guided vehicle in the local grid map and the desired operating path mapped in the local grid map in a current control period; When the first distance is greater than or equal to a preset dead zone error and less than or equal to a preset integral allowable error, the step of determining the error integral term of the automatic guided vehicle based on the position of the automatic guided vehicle in the local grid map and the expected operating path in each control cycle is performed.

5. The control method according to claim 4, characterized in that: When the first distance is greater than or equal to the dead zone error and less than or equal to the integral allowable error, the method includes: Simulating multiple running paths of the automatic guided vehicle in the local grid map according to a grid path algorithm, where the starting points of the multiple running paths are positions of the automatic guided vehicle in the local grid map at the beginning of a current control cycle; Determining an optimal path among the multiple running paths, where the optimal path is a path among the multiple running paths that is most close to the expected running path; The linear velocity and angular velocity of the automatic guided vehicle corresponding to the optimal path are obtained, and the automatic guided vehicle is controlled to travel according to the linear velocity and angular velocity in a current control cycle.

6. A control device for an automatic guided vehicle, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer instructions, and when the computer instructions are executed by the processor, the steps of the method according to any one of claims 1 to 5 are implemented.

7. A storage medium, characterized in that: Computer instructions are stored thereon, and when the computer instructions are executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

8. An automatic guided vehicle, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer instructions, and when the computer instructions are executed by the processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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