AGV scheduling method, apparatus and device, medium and product

Abstract

The invention relates to the technical field of automatic control, and discloses an AGV scheduling method, device and equipment, a medium and a product, and the method comprises the steps: obtaining a to-be-executed task and AGV information, and the AGV information comprises the state information and position information of a plurality of AGVs; based on the space information of the to-be-executed task and the position information of the plurality of AGVs, a first AGV whose state information is schedulable is determined in the plurality of AGVs, and the space information comprises the starting point position of the to-be-executed task; generating a first driving path based on the space information of the to-be-executed task and the position information of the first AGV; and generating a motion instruction based on the first driving path, and sending the motion instruction to the first AGV to control the first AGV to execute the to-be-executed task. According to the invention, the task execution efficiency can be improved, and optimization of the logistics process is realized.

Description

Technical Field

[0001] The present invention relates to the field of automatic control technology, and in particular to an AGV scheduling method, device, equipment, medium and product. Background Art

[0002] AGV (Automated Guided Vehicle) is a transport vehicle that can travel along a specified guide path and has safety protection and various transport functions.

[0003] In the related technology, AGVs are usually used to dispatch AGVs by themselves for material transportation, but the overall optimization of the material transportation task cannot be achieved, and it is easy to cause the paths of multiple AGVs to overlap, affecting the execution of the task.

[0004] In view of this, a highly flexible and reliable AGV scheduling method is needed. Summary of the invention

[0005] In view of this, the present invention provides an AGV scheduling method, which can improve the efficiency of task execution and optimize the logistics process.

[0006] In a first aspect, the present invention provides an AGV scheduling method, the method comprising: obtaining tasks to be performed and AGV information, wherein the AGV information includes status information and position information of several AGVs; based on the spatial information of the task to be performed and the position information of the several AGVs, determining a first AGV with status information that is schedulable among the several AGVs, wherein the spatial information includes a starting position of the task to be performed; based on the spatial information of the task to be performed and the position information of the first AGV, generating a first driving path; based on the first driving path, generating motion instructions, and sending the motion instructions to the first AGV to control the first AGV to perform the task to be performed.

[0007] In this embodiment, the first AGV that can be dispatched is determined based on the spatial information of the task to be executed and the position information of the AGV, which can optimize resource allocation and improve operation efficiency. The driving path is planned for the first AGV to ensure that the AGV can operate safely, which can improve the accuracy of the work.

[0008] In an optional implementation, the acquisition of the status information includes: reading an andon signal on each AGV workstation, and determining the status information of the corresponding AGV based on the andon signal.

[0009] In this embodiment, by reading the andon signal on the AGV station and determining the status information, the status information of each AGV can be monitored in real time, thereby improving the efficiency of AGV task execution.

[0010] In an optional embodiment, based on the spatial information of the task to be performed and the position information of several AGVs, determining the first AGV whose status information is schedulable among several AGVs includes: determining a first distance sequence based on the spatial information and the position information of several AGVs, wherein the first distance sequence includes the relative distances between the starting position and the positions of each schedulable AGV; determining the first AGV based on the first distance sequence.

[0011] In this embodiment, the starting position of the task to be executed and the relative distance of each schedulable AGV are obtained through spatial information and the position information of each schedulable AGV to determine the first AGV, which can reduce the waiting time for task execution, improve response speed, and improve method efficiency.

[0012] In an optional embodiment, generating a first driving path includes: obtaining shape sensor data, wherein the shape sensor data is used to indicate obstacle information in an area where a task to be performed is located; performing path planning based on the position information and obstacle information of the first AGV to determine a number of driving paths; obtaining a score for each driving path based on evaluation indicators, wherein the evaluation indicators include driving time and / or driving distance; and selecting the driving path with the highest score as the first driving path.

[0013] In this embodiment, by scoring each driving path and selecting the first path according to the scoring result, the efficiency of task completion can be improved and unnecessary travel can be reduced.

[0014] In an optional embodiment, when the area where the task to be performed includes dynamic obstacles and the dynamic obstacles include a second AGV, generating motion instructions based on the first driving path includes: determining the overlapping portion of the second driving path of the second AGV and the first driving path; setting a non-overlapping first time window and a second time window; generating motion instructions based on the first time window and the second time window to control the first AGV to drive through the overlapping portion of the path within the first time window when passing through the first driving path, and to control the second AGV to drive through the overlapping portion of the path within the second time window when passing through the second driving path.

[0015] In this embodiment, by setting a time window, it is possible to ensure that they travel within a specific time period, thereby avoiding collisions and congestion between AGVs.

[0016] In an optional embodiment, after generating the motion instruction based on the first driving path, it also includes: obtaining expected data and actual feedback data after the first AGV executes the motion instruction; determining a first deviation based on the expected data and the actual feedback data; generating a correction instruction based on the first deviation, and sending the correction instruction to the first AGV to correct the motion trajectory of the first AGV.

[0017] In this embodiment, by acquiring the first deviation of the first AGV and generating a correction instruction to correct the motion trajectory of the first AGV, the accuracy of the AGV can be improved, and the stability and reliability of the system can be improved.

[0018] In a second aspect, the present invention provides an AGV scheduling device, which includes: an acquisition module for acquiring tasks to be performed and AGV information, wherein the AGV information includes status information and position information of several AGVs; a determination module for determining a first AGV with status information that is schedulable among several AGVs based on spatial information of the task to be performed and position information of the several AGVs, wherein the spatial information includes a starting position of the task to be performed; a path generation module for generating a first driving path based on the spatial information of the task to be performed and the position information of the first AGV, and an instruction module for generating motion instructions based on the first driving path, and sending the motion instructions to the first AGV to control the first AGV to perform the task to be performed.

[0019] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor are communicatively connected to each other, computer instructions are stored in the memory, and the processor executes the AGV scheduling method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0020] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the AGV scheduling method of the first aspect or any corresponding embodiment thereof.

[0021] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions, wherein the computer instructions are used to enable a computer to execute the AGV scheduling method of the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0023] Figure 1 is a flow chart of an AGV scheduling method according to an embodiment of the present invention;

[0024] Figure 2 is a flow chart of another AGV scheduling method according to an embodiment of the present invention;

[0025] Figure 3 is a flow chart of another AGV scheduling method according to an embodiment of the present invention;

[0026] Figure 4 is a structural block diagram of an AGV scheduling device according to an embodiment of the present invention;

[0027] Figure 5 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0029] The embodiment of the present invention provides an AGV scheduling method, which can realize real-time monitoring of AGV by obtaining the task to be executed and AGV information, thereby optimizing resource allocation. According to the spatial information and position information of the task to be executed, the first AGV and the first driving path that can be scheduled are determined, which can improve the efficiency of task execution and optimize the logistics process. According to the first driving path, instructions are generated to control the operation of the AGV, which can reduce the risk of collision between the AGV and other obstacles and improve the safety of the method.

[0030] According to an embodiment of the present invention, an AGV scheduling method embodiment is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0031] In this embodiment, an AGV scheduling method is provided. Figure 1 : is a flow chart of an AGV scheduling method according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0032] Step S101, obtaining tasks to be executed and AGV information, wherein the AGV information includes status information and location information of several AGVs.

[0033] There is no restriction on the method of obtaining tasks to be executed. When a material handling task is generated, the host computer sends the information of the tasks to be executed to the AGV scheduling system. The choice of the AGV scheduling system is not restricted, and a PLC (Programmable Logic Controller) system can be used.

[0034] The tasks to be executed include spatial information, priority information, etc. of the tasks to be executed. In an actual application, the host computer sends several task information, sorts the tasks according to the priority information of the tasks, and determines the tasks to be executed according to the sorting results of the tasks.

[0035] There is no restriction on the way to obtain AGV information. Various sensors can be equipped on the AGV to monitor various parameters of the AGV in real time and send the parameters to the controller. The sensors can include position sensors, speed sensors, power meters, etc.

[0036] Step S102, based on the spatial information of the task to be executed and the position information of the plurality of AGVs, determining the first AGV whose status information is schedulable among the plurality of AGVs, wherein the spatial information includes the starting position of the task to be executed.

[0037] The method for determining the first AGV is not limited. A schedulable AGV may be selected based on the status information of the AGV, and then a schedulable AGV with a short distance may be selected as the first AGV based on the distance between the schedulable AGV and the task to be executed.

[0038] In an actual application, there are two AGVs in an idle state, AGV1 is close to the task to be executed but has low battery, while AGV2 is far from the task to be executed but has sufficient battery. At this time, AGV2 is selected to execute the task.

[0039] Step S103: Generate a first driving path based on the spatial information of the task to be executed and the position information of the first AGV.

[0040] A global map is constructed based on the environmental information of the AGV. The location of the task to be executed and the location of the first AGV are marked on the global map, and then a suitable path planning method is selected to generate a first driving path. The choice of path planning method is not limited and can be Dijkstra algorithm, A* algorithm, etc. According to the selected path planning method, path calculation is performed on the global map to find an appropriate path.

[0041] During the driving process of the first AGV, the surrounding environment data of the first AGV can be collected in real time to ensure the safety of the path. If there are obstacles on the path, detours and other methods can be used to avoid collisions.

[0042] Step S104: Generate a motion instruction based on the first driving path, and send the motion instruction to the first AGV to control the first AGV to execute the task to be executed.

[0043] After determining the first driving path, a corresponding motion instruction needs to be generated to control the first AGV to perform the task. The motion instruction is an instruction that the AGV can recognize and execute, and may include a speed instruction, a steering angle instruction, etc. After receiving the motion instruction, the first AGV needs to quickly adjust the driving speed or direction.

[0044] The AGV scheduling method provided in this embodiment determines the first AGV that can be scheduled according to the spatial information of the task to be executed and the position information of the AGV, which can optimize resource allocation and improve operation efficiency. A driving path is planned for the first AGV to ensure that the AGV can operate safely, which can improve the accuracy of the work.

[0045] In this embodiment, an AGV scheduling method is provided. Figure 2 : is a flow chart of an AGV scheduling method according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0046] Step S201, obtaining tasks to be executed and AGV information, wherein the AGV information includes status information and location information of several AGVs.

[0047] For details, please see Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0048] In some optional implementations, obtaining the status information includes:

[0049] Step a1, read the andon signal on each AGV workstation, and determine the status information of the corresponding AGV based on the andon signal.

[0050] After reading the andon signal on each AGV station, the signal may be firstly processed, wherein the data processing method may include filtering, denoising, etc. After the data processing, the status information of the corresponding AGV may be determined.

[0051] Step S202: determining a first AGV with status information indicating that it is schedulable among the plurality of AGVs based on the spatial information of the task to be performed and the position information of the plurality of AGVs, wherein the spatial information includes the starting position of the task to be performed.

[0052] Specifically, the above step S202 includes:

[0053] Step S2021, based on the spatial information and the position information of several AGVs, determine a first distance sequence, wherein the first distance sequence includes the relative distances between the starting position and the position of each dispatchable AGV.

[0054] The relative distance between the starting point of the task to be executed and the location of the dispatchable AGV can be directly calculated. The relative distance can be the straight-line distance between the two points or the actual driving distance between the two points.

[0055] Step S2022: Determine the first AGV based on the first distance sequence.

[0056] According to the relative distance between the starting position of the task to be executed and the position of each dispatchable AGV in the first distance sequence, a selection rule is formulated to determine the first AGV. The selection rule can be specified by the staff. In a practical application, the dispatchable AGV with the closest relative distance is selected as the first AGV.

[0057] Step S203: Generate a first driving path based on the spatial information of the task to be executed and the position information of the first AGV.

[0058] For details, please see Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0059] Step S204: Generate a motion instruction based on the first driving path, and send the motion instruction to the first AGV to control the first AGV to execute the task to be executed.

[0060] For details, please see Figure 1 Step S104 of the illustrated embodiment will not be described in detail here.

[0061] In some optional implementations, step S204 further includes:

[0062] Step S205, obtaining expected data and actual feedback data after the first AGV executes the motion instruction.

[0063] The expected data after the first AGV executes the motion instruction can be obtained by analyzing the first driving path. The actual feedback data can be collected and obtained by the sensor carried by the first AGV.

[0064] Step S206: determining a first deviation based on the expected data and the actual feedback data.

[0065] The first deviation includes a driving distance deviation, a driving direction deviation, and the like.

[0066] Step S207: Generate a correction instruction according to the first deviation, and send the correction instruction to the first AGV to correct the motion trajectory of the first AGV.

[0067] According to the first deviation, a corresponding correction instruction is generated, wherein the correction instruction is an instruction that the AGV can recognize and execute, and may include a speed correction instruction, a steering angle correction instruction, and the like.

[0068] By acquiring the first deviation of the first AGV and generating a correction instruction to correct the motion trajectory of the first AGV, the accuracy of the AGV can be improved and the stability and reliability of the system can be improved.

[0069] The AGV scheduling method provided in this embodiment obtains the starting position of the task to be executed and the relative distance of each schedulable AGV through spatial information and the position information of each schedulable AGV to determine the first AGV, which can reduce the waiting time for task execution, improve response speed, and improve method efficiency.

[0070] In this embodiment, an AGV scheduling method is provided. Figure 3 : is a flow chart of an AGV scheduling method according to an embodiment of the present invention. Figure 3 As shown, the process includes the following steps:

[0071] Step S301, obtaining tasks to be executed and AGV information, wherein the AGV information includes status information and location information of several AGVs.

[0072] For details, please see Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0073] Step S302 : determining a first AGV with status information indicating that it is schedulable among the plurality of AGVs based on the spatial information of the task to be performed and the position information of the plurality of AGVs, wherein the spatial information includes the starting position of the task to be performed.

[0074] For details, please see Figure 1 Step S102 of the illustrated embodiment will not be described in detail here.

[0075] Step S303: Generate a first driving path based on the spatial information of the task to be executed and the position information of the first AGV.

[0076] Specifically, the above step S303 includes:

[0077] Step S3031, obtaining shape sensor data, wherein the shape sensor data is used to indicate obstacle information in the area where the task to be performed is located.

[0078] Among them, the road conditions can be checked and obstacle information can be determined based on the data from the walking sensor.

[0079] Step S3032: Perform path planning based on the location information and obstacle information of the first AGV to determine a plurality of driving paths.

[0080] The path planning method is not limited, and a suitable path planning algorithm can be selected to calculate several driving paths that meet the requirements. The driving path needs to avoid obstacles.

[0081] Step S3033, obtaining a score for each driving route based on evaluation indicators, wherein the evaluation indicators include driving time and / or driving distance.

[0082] Among them, the scores of the driving path under different evaluation indicators can be obtained according to the evaluation indicators. Then, the scores of the driving path under different evaluation indicators are fused to obtain the score of the driving path. The fusion can be weighted fusion.

[0083] Step S3034: Select the driving path with the highest score as the first driving path.

[0084] Step S304: Generate a motion instruction based on the first driving path, and send the motion instruction to the first AGV to control the first AGV to execute the task to be executed.

[0085] For details, please see Figure 1 Step S104 of the illustrated embodiment will not be described in detail here.

[0086] In some optional implementations, when the area where the task to be performed is located includes a dynamic obstacle, and the dynamic obstacle includes the second AGV, the above step S304 includes:

[0087] Step S3041, determining the overlapped path of the second driving path of the second AGV and the first driving path.

[0088] The overlapping paths can be determined by coordinate comparison, that is, whether the coordinates of the two paths are the same, and if they are the same, the parts with the same coordinates can be determined as the overlapping paths.

[0089] Step S3042: setting a first time window and a second time window that do not overlap.

[0090] There is no limit on the lengths of the first time window and the second time window.

[0091] Step S3043, generating motion instructions based on the first time window and the second time window to control the first AGV to travel through the overlapping path within the first time window when passing through the first driving path, and to control the second AGV to travel through the overlapping path within the second time window when passing through the second driving path.

[0092] By setting time windows, they can be ensured to travel within a specific time period, avoiding collisions and congestion between AGVs.

[0093] The AGV scheduling method provided in this embodiment can improve the efficiency of task completion and reduce unnecessary travel by scoring each driving path and selecting the first path according to the scoring result.

[0094] In this embodiment, an AGV scheduling device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0095] This embodiment provides an AGV scheduling device, such as Figure 4 As shown, including:

[0096] An acquisition module 401 is used to acquire tasks to be executed and AGV information, wherein the AGV information includes status information and location information of several AGVs;

[0097] A determination module 402 is used to determine a first AGV with status information that is schedulable among the plurality of AGVs based on the spatial information of the task to be performed and the position information of the plurality of AGVs, wherein the spatial information includes the starting position of the task to be performed;

[0098] The path generation module 403 is used to generate a first driving path based on the spatial information of the task to be executed and the position information of the first AGV.

[0099] The instruction module 404 is used to generate a motion instruction based on the first driving path, and send the motion instruction to the first AGV to control the first AGV to execute the task to be executed.

[0100] In some optional implementations, the acquisition module 401 includes:

[0101] The status information acquisition unit is used to read the andon signal on each AGV station and determine the status information of the corresponding AGV based on the andon signal.

[0102] In some optional implementations, the determination module 402 includes:

[0103] The first distance sequence determining unit is used to determine a first distance sequence based on the spatial information and the position information of a plurality of AGVs, wherein the first distance sequence includes the relative distances between the starting position and the positions of each dispatchable AGV.

[0104] The first AGV determining unit is used to determine the first AGV based on the first distance sequence.

[0105] In some optional implementations, the path generation module 403 includes:

[0106] The data acquisition unit is used to acquire the movement sensor data, wherein the movement sensor data is used to indicate the obstacle information of the area where the task to be performed is located.

[0107] The path planning unit is used to perform path planning based on the position information and obstacle information of the first AGV and determine a plurality of driving paths.

[0108] The score acquisition unit is used to acquire the score of each driving path based on the evaluation index, wherein the evaluation index includes the driving time and / or the driving distance.

[0109] The path selection unit is used to select the driving path with the highest score as the first driving path.

[0110] In some optional implementations, the instruction module 404 includes:

[0111] The overlapping path determining unit is used to determine the overlapping path of the second driving path of the second AGV and the first driving path.

[0112] The time window setting unit is used to set a first time window and a second time window that do not overlap.

[0113] A control unit is used to generate motion instructions based on a first time window and a second time window to control the first AGV to travel through the overlapping path within the first time window when passing through the first driving path, and to control the second AGV to travel through the overlapping path within the second time window when passing through the second driving path.

[0114] In some optional embodiments, the AGV scheduling device further includes:

[0115] The correction module 405 is used to correct the motion trajectory of the first AGV.

[0116] The correction module 405 includes:

[0117] The execution data acquisition unit is used to acquire expected data and actual feedback data after the first AGV executes the motion instruction.

[0118] The deviation determining unit is used to determine a first deviation based on the expected data and the actual feedback data.

[0119] The correction unit is used to generate a correction instruction according to the first deviation, and send the correction instruction to the first AGV to correct the motion trajectory of the first AGV.

[0120] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0121] The AGV scheduling device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0122] The embodiment of the present invention also provides a computer device having the above Figure 4 The AGV dispatching device shown.

[0123] See also Figure 5 , Figure 5 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 5 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 5 A processor 10 is taken as an example.

[0124] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0125] The memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.

[0126] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0127] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0128] The computer device also includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 5 The example of connecting through bus is taken in the following.

[0129] The input device 30 can receive input digital or character information, and generate key signal input related to the user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0130] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0131] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0132] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. An AGV scheduling method, characterized in that: The method comprises: Acquire tasks to be performed and AGV information, wherein the AGV information includes status information and location information of several AGVs; Based on the spatial information of the task to be performed and the position information of the plurality of AGVs, determining a first AGV whose status information is schedulable among the plurality of AGVs, wherein the spatial information includes the starting position of the task to be performed; Generate a first driving path based on the spatial information of the task to be performed and the position information of the first AGV; Based on the first driving path, a motion instruction is generated, and the motion instruction is sent to the first AGV to control the first AGV to perform the task to be performed.

2. The method according to claim 1, characterized in that The acquisition of the status information includes: Read the andon signal on each AGV station, and determine the status information of the corresponding AGV based on the andon signal.

3. The method according to claim 1, characterized in that The determining, based on the spatial information of the task to be performed and the position information of the plurality of AGVs, of the first AGV whose status information is schedulable among the plurality of AGVs comprises: Based on the spatial information and the position information of the plurality of AGVs, a first distance sequence is determined, wherein the first distance sequence includes the relative distances between the starting position and the positions of the respective dispatchable AGVs; Based on the first distance sequence, a first AGV is determined.

4. The method according to claim 1, characterized in that: Generating the first driving path includes: Acquire shape sensor data, wherein the shape sensor data is used to indicate obstacle information in an area where a task to be performed is located; Based on the position information of the first AGV and the obstacle information, path planning is performed to determine a plurality of driving paths; Obtaining a score for each driving path based on an evaluation index, wherein the evaluation index includes driving time and / or driving distance; The driving route with the highest score is selected as the first driving route.

5. The method according to claim 1, characterized in that When the area where the task to be performed is located includes a dynamic obstacle, and the dynamic obstacle includes the second AGV, generating a motion instruction based on the first driving path includes: Determine a portion of a second driving path of the second AGV that overlaps with the first driving path; Setting a non-overlapping first time window and a second time window; The motion instruction is generated based on the first time window and the second time window to control the first AGV to travel through the overlapping path within the first time window when passing through the first driving path, and to control the second AGV to travel through the overlapping path within the second time window when passing through the second driving path.

6. The method according to claim 1, characterized in that After generating the motion instruction based on the first driving path, the method further includes: Obtaining expected data and actual feedback data after the first AGV executes the motion instruction; Determining a first deviation based on the expected data and the actual feedback data; A correction instruction is generated according to the first deviation, and the correction instruction is sent to the first AGV to correct the motion trajectory of the first AGV.

7. An AGV dispatching device, characterized in that: The device comprises: An acquisition module, used to acquire tasks to be executed and AGV information, wherein the AGV information includes status information and location information of several AGVs; A determination module, configured to determine, based on the spatial information of the task to be performed and the position information of the plurality of AGVs, a first AGV whose status information is schedulable among the plurality of AGVs, wherein the spatial information includes the starting position of the task to be performed; A path generation module, used for generating a first driving path based on the spatial information of the task to be performed and the position information of the first AGV; The instruction module is used to generate a motion instruction based on the first driving path, and send the motion instruction to the first AGV to control the first AGV to perform the task to be performed.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the AGV scheduling method according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute an AGV scheduling method according to any one of claims 1 to 6.

10. A computer program product, characterized in that It includes computer instructions, and the computer instructions are used to enable a computer to execute an AGV scheduling method according to any one of claims 1 to 6.

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