Production workshop multi-AGV scheduling method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention discloses a production workshop multi-AGV scheduling method and device, electronic equipment and a storage medium, and the method comprises the steps: S101, obtaining a grid map of a workshop environment based on the sizes of AGVs and obstacles, and distributing a transportation task to each AGV based on the priority of each AGV and a set cost function to obtain a transportation path of each AGV; s102, conflict detection is carried out based on the transportation path of each AGV, if path conflict exists between any two AGVs, S103 is executed, and otherwise, the transportation path of each AGV is output; and S103, generating a constraint tree based on the path conflict between the AGVs, pruning the constraint tree to obtain a re-planned transportation path of each AGV, and then executing the step S102. According to the method, whether the AGVs collide in the transportation task execution process or not is detected through conflict detection, and then the re-planned transportation path of each AGV is constructed according to the path conflict, so that the safety of the AGVs in the transportation task execution process is improved, materials are ensured to be supplied in time, and the production efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of workshop scheduling, and in particular to a method, device, electronic equipment and storage medium for scheduling multiple AGVs in a production workshop. Background Art

[0002] In modern logistics and warehousing scenarios, AGVs (Automated Guided Vehicles) are widely used in cargo transportation and sorting tasks to improve overall operational efficiency and reduce labor costs. The introduction of AGVs enables automated transportation of goods between different areas, reducing the need for traditional forklifts and manual operations.

[0003] In a multi-AGV system, dozens or even hundreds of AGVs are typically dispatched within the same space, each following a designated path according to instructions. While this system significantly improves the automation level of logistics and warehousing, as the number of AGVs increases and the complexity of tasks increases, path conflicts often arise. This makes it impossible to meet the requirements of production scenarios with high real-time requirements, ultimately leading to material supply delays and affecting production efficiency. Summary of the Invention

[0004] To solve the problems existing in the prior art, one or more embodiments of this specification describe a method, device, electronic device, and storage medium for scheduling multiple AGVs in a production workshop.

[0005] According to a first aspect, a method for scheduling multiple AGVs in a production workshop is provided, the method comprising:

[0006] S101, obtaining a grid map of the workshop environment based on the sizes of the AGVs and obstacles, and assigning a transportation task to each AGV based on the priority of each AGV and a set cost function to obtain a transportation path for each AGV;

[0007] S102, performing conflict detection based on the transport path of each AGV, if there is a path conflict between any two AGVs, executing S103, otherwise outputting the transport path of each AGV;

[0008] S103 , generating a constraint tree based on the path conflicts between the AGVs, pruning the constraint tree to obtain the replanned transport path for each AGV, and then executing S102 .

[0009] Preferably, the priority of the AGV is obtained based on the remaining time and the remaining path length of the task currently being executed by the AGV.

[0010] Preferably, the conflict detection includes position overlap conflict detection, path intersection conflict detection and direction conflict detection performed in order from high to low priority, and the generation of a constraint tree based on path conflicts between AGVs includes: generating each node of the constraint tree in order based on the priority of the conflict detection.

[0011] Preferably, pruning the constraint tree includes: pruning the constraint tree branches whose path costs of the constraint tree nodes are greater than a set pruning threshold, and the constraint tree nodes represent the restriction conditions of the AGV when planning the path.

[0012] Preferably, the path cost of the constraint tree node is obtained based on the path length, the number of turns and the time delay.

[0013] Preferably, the cost function is set based on the path length of the AGV, the number of turns when the AGV moves, and the smoothness of the AGV's moving direction. The method also includes: if there is a path conflict between any two AGVs, replacing the constraint tree with the path length of the AGV in the cost function.

[0014] Preferably, obtaining the grid map of the workshop environment based on the sizes of the AGV and the obstacles includes: performing expansion processing on the AGV and the obstacles, and obtaining the grid map of the workshop environment based on the expanded AGV and the obstacles.

[0015] According to a second aspect, a multi-AGV scheduling device for a production workshop is provided, the device comprising:

[0016] The map rasterization and path initialization module is used to obtain a grid map of the workshop environment based on the size of the AGV and obstacles, and to assign a transportation task to each AGV based on its priority to obtain the transportation path of each AGV;

[0017] a conflict detection module, configured to perform conflict detection based on the transport path of each AGV, and execute an AGV path adjustment module if there is a path conflict between any two AGVs; otherwise, output the transport path of each AGV;

[0018] The AGV path adjustment module is used to generate a constraint tree based on the path conflicts between AGVs, prune the constraint tree to obtain the re-planned transportation path for each AGV, and then execute the conflict detection module.

[0019] According to a third aspect, there is provided an electronic device comprising a processor and a memory;

[0020] The processor is connected to the memory;

[0021] The memory is used to store executable program code;

[0022] The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the steps of the method provided in the first aspect or any possible implementation manner of the first aspect.

[0023] According to a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and the computer-readable storage medium stores instructions, which, when the instructions are executed on a computer or a processor, enable the computer or the processor to execute the method provided in the first aspect or any possible implementation of the first aspect.

[0024] The beneficial effects of the present invention are:

[0025] 1. The methods and devices provided in the embodiments of this specification detect whether collisions will occur between AGVs during the execution of transportation tasks by performing conflict detection, and then construct a constraint tree based on path conflicts. Then, a replanned transportation path for each AGV is constructed based on the path conflicts, thereby reducing the probability of path conflicts between AGVs, thereby improving the safety of the AGVs when executing transportation tasks, ensuring the timely supply of materials, and improving production efficiency.

[0026] 2. The method and apparatus provided in the embodiments of this specification determine the priority of an AGV based on the remaining time of the AGV's current task and the remaining path length. The shorter the remaining time of the AGV's current task and the shorter the remaining path length, the higher the AGV's priority. This makes it easier for AGVs that complete their tasks sooner to be assigned transport tasks, thereby shortening the waiting time for transport tasks and improving the production efficiency of the production workshop.

[0027] 3. The methods and devices provided in the embodiments of this specification prune the constraint tree, reducing the amount of computation and thereby improving the efficiency of AGV transport path replanning;

[0028] 4. The method and device provided in the embodiments of this specification establish a grid map of the workshop environment after performing expansion processing on the AGV and obstacles to ensure that there is a certain gap between the AGV and the obstacles, thereby reducing the mechanical wear of the AGV, increasing the service life of the AGV, and reducing the risk of the AGV colliding with obstacles. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0030] Figure 1 This is a flowchart of a multi-AGV scheduling method for a production workshop in the specific implementation of this specification;

[0031] Figure 2 This is a structural diagram of a multi-AGV scheduling device for a production workshop in the specific implementation of this specification;

[0032] Figure 3 It is a schematic diagram of the structure of an electronic device in the specific implementation of this specification;

[0033] Figure 4 It is a specific flow chart of a multi-AGV scheduling method for a production workshop in the specific implementation of this specification. DETAILED DESCRIPTION

[0034] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application.

[0035] In the following introduction, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance. The following introduction provides multiple embodiments of the present application. Different embodiments can be replaced or combined, so the present application can also be considered to include all possible combinations of the same and / or different embodiments described. Therefore, if one embodiment includes features A, B, and C, and another embodiment includes features B and D, then the present application should also be considered to include embodiments containing one or more of all other possible combinations of A, B, C, and D, even though the embodiment may not be clearly described in the following text.

[0036] The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made to the function and arrangement of the elements described without departing from the scope of the present application. Various examples may appropriately omit, replace, or add various processes or components. For example, the described method may be performed in an order different from the order described, and various steps may be added, omitted, or combined. In addition, features described in some examples may be combined in other examples.

[0037] See Figure 1 , Figure 1 : is a flow chart of a multi-AGV scheduling method for a production workshop provided in an embodiment of the present application. In the embodiment of the present application, the method includes:

[0038] S101 , obtaining a grid map of the workshop environment based on the sizes of the AGVs and obstacles, and assigning a transportation task to each AGV based on the priority of each AGV and a set cost function to obtain a transportation path for each AGV.

[0039] The execution entity of this application can be a cloud server.

[0040] In the embodiment of this specification, the workshop environment is grid-modeled, and the size of the grid is set to L×L, where L=L AGV +σ,L AGV is the side length of AGV, σ is the safety redundancy (the range is 0.1L≤σ≤0.3L), and the obstacles in the workshop are expanded. The expansion radius is To ensure the minimum distance between AGV and obstacles is This reduces mechanical wear on AGVs, increases their service life, and reduces the risk of collisions. This results in a rasterized map of the workshop environment with expanded obstacles. Inaccessible areas within the raster map are marked as 1, and accessible areas are marked as 0. Each AGV is assigned a transport task based on a priority calculation formula, and a transport path is generated for each AGV according to a predefined cost function.

[0041] In one possible implementation, the priority of the AGV is obtained based on the remaining time and the remaining path length of the task currently being executed by the AGV.

[0042] In the embodiment of this specification, the calculation formula of AGV priority is: Among them, T deadline T is the deadline of the task currently executed by AGV. current is the current time of the task currently executed by AGV, T deadline -T current is the remaining time of the task currently executed by AGV, C i The path conflict degree can be expressed by the number of path conflicts during the execution of the task by the AGV. remaining is the remaining path length, α, β, and γ are weight coefficients, and the weight coefficients satisfy α+β+γ=1. Preferably, α=0.5, β=0.3, and γ=0.2. In this application, the priority of the AGV is obtained by the remaining time of the AGV's current task and the remaining path length. The smaller the remaining time of the AGV's current task and the smaller the remaining path length, the higher the priority of the AGV. This makes it easier for the AGV whose task ends sooner to be assigned to the transportation task, thereby shortening the waiting time of the transportation task and improving the production efficiency of the production workshop.

[0043] S102 , performing conflict detection based on the transport path of each AGV. If there is a path conflict between any two AGVs, executing S103 ; otherwise, outputting the transport path of each AGV.

[0044] In the embodiment of this specification, conflict detection is performed based on the transport path of each AGV. If there is a path conflict between any two AGVs, S103 is executed. Otherwise, the transport path of each AGV is output and each AGV is driven to perform tasks according to the transport path of each AGV.

[0045] S103 , generating a constraint tree based on the path conflicts between the AGVs, pruning the constraint tree to obtain the replanned transport path for each AGV, and then executing S102 .

[0046] In the embodiment of this specification, all path conflicts are obtained, a constraint tree is generated according to the path conflicts between the AGVs, the constraint tree is pruned, and a replanned transport path for each AGV is generated according to the pruned constraint tree and the cost function, and then S102 is executed.

[0047] In one embodiment, the conflict detection includes position overlap conflict detection, path intersection conflict detection, and direction conflict detection performed in order of priority from high to low, and the generation of a constraint tree based on path conflicts between AGVs includes: generating each node of the constraint tree in order based on the priority of the conflict detection.

[0048] In the embodiment of this specification, a four-dimensional space-time conflict model (x, y, θ, t) is constructed, where the time resolution Δt is determined by the maximum speed of the AGV V max Calculated from the minimum safety distance Δd, according to the formula Δt=Δd / V max , when V max =2m / s, Δd=0.2m, preferably, Δt=0.1s to ensure the timeliness of conflict detection. Conflict detection includes position overlap conflict detection, path intersection conflict detection and direction conflict detection, wherein the priority of each conflict detection is from high to low: position overlap conflict detection, path intersection conflict detection and direction conflict detection. In this application, conflict detection is performed to detect whether a collision will occur between the AGVs in the process of performing the transportation task, and a constraint tree is constructed according to the path conflict, and then the re-planned transportation path of each AGV is constructed according to the path conflict, thereby reducing the probability of path conflicts between AGVs, thereby improving the safety of the AGVs in performing the transportation task, ensuring the timely supply of materials, and improving production efficiency.

[0049]

[0050] Where R is the radius of AGV, x i (t) represents the position of AGV i in the x-axis direction at time t, θ i(t) represents the transport direction angle of the AGV labeled i at time t. Position overlap conflict detection: If there are any two AGVs whose coordinates in the x-axis and y-axis directions are the same at time t, it means that there is a position overlap conflict between the two AGVs. Path intersection conflict detection: If there are any two AGVs whose distance between the two AGVs is less than 2R at time t, it means that physical friction will occur between the two AGVs, that is, there is a path intersection conflict between the two AGVs. Direction conflict detection: If there are any two AGVs whose transport direction angles differ by 180° at time t, and the distance between the two AGVs is less than R, it means that the two AGVs are traveling in opposite directions and may collide, that is, there is a direction conflict between the two AGVs.

[0051] When constructing the constraint tree, two types of constraint branches will be generated for each conflict according to the priority of each conflict type, respectively constraining the AGV in conflict, and the constraint conditions will be extended to child nodes. The path set of the child node adds new constraints based on the parent node path. If the conflict type is consistent, the node relationship of the constraint tree is determined according to the size relationship of the AGV labels. As an example, if it is detected that AGV1 and AGV2 have a position overlap conflict, AGV1 and AGV5 have a position overlap conflict, AGV1 and AGV3 have a path intersection conflict, and AGV3 and AGV4 have a direction conflict, then the constraints on AGV1 and AGV2 are generated first, and two branches are generated. Sub-node 1: AGV1 must not be at the position where the conflict occurs (x1, y1) at the time t1 when the conflict occurs, and sub-node 2: AGV2 must not be at the position where the conflict occurs (x1, y1) at the time t1 when the conflict occurs. Based on the first position overlap conflict, the constraints of AGV1 and AGV5 are generated, and two branch nodes are generated under each upper node, such as: Sub-node 1-1: AGV1 must not be at the position where the conflict occurs (x2, y1) at the time t2 when the conflict occurs. 2) Subnode 1-2: AGV5 must not be at the location (x2, y2) where the conflict occurred at time t2. Based on the second overlapping conflict, constraints are generated for AGV1 and AGV3, with two branch nodes generated under each upper-level node. For example, subnode 1-1-1: AGV1 must not enter the area with a radius of R centered at (x3, y3) at time t3. Subnode 1-1-2: AGV3 must not enter the area with a radius of R centered at (x3, y3) at time t3. Based on the path intersection conflict, constraints are generated for AGV3 and AGV4, with two branch nodes generated under each upper-level node. For example, subnode 1-1-1-1: AGV3 adjusts its direction at time t4. Subnode 1-1-1-2: AGV4 adjusts its direction at time t4. The cloud server then replans the AGV's transportation path based on the set cost function and constraint tree.

[0052] In one possible implementation, pruning the constraint tree includes: pruning the constraint tree branches whose path costs of the constraint tree nodes are greater than a set pruning threshold, and the constraint tree nodes represent the restriction conditions of the AGV when planning the path.

[0053] In the embodiments of the present specification, in order to improve the efficiency of re-planning the AGV transport path, it is necessary to prune the constraint tree. The constraint tree pruning process includes: setting a pruning threshold, calculating the total path cost of each child node generated, and pruning the branch if the calculated total path cost exceeds the pruning threshold.

[0054] In one embodiment, the path cost of the constraint tree node is obtained based on the path length, the number of turns, and the time delay.

[0055] In the embodiment of this specification, the calculation formula of the path cost of the constraint tree node is: Q = path length + α·number of turns + β·time delay, where α and β are weight coefficients. Preferably, α is 0.6 and β is 0.4.

[0056] In one embodiment, the cost function is set based on the path length of the AGV, the number of turns when the AGV moves, and the smoothness of the AGV's moving direction. The method also includes: if there is a path conflict between any two AGVs, replacing the constraint tree with the path length of the AGV in the cost function.

[0057] In the embodiment of this specification, the cost function is expressed as follows: f(n)=g(n)+a(x)×h ， (n)+e(n)+s(n), where n represents the current node, g(n) represents the actual distance from the AGV starting point to node n, and h ， (n)=h(n)+h(f)+∈, h(n) is the Euclidean distance from the current node to the target node, h(f) is the Euclidean distance from the parent node of the current node to the target node, and ∈ is a very small positive number (∈≤10 -5 ), a(x) is a dynamic weight function, preferably, x is the number of searches. Initially, global search is emphasized, while later local search is emphasized to avoid over-reliance on heuristics and falling into local optimality. e(n) is the turning penalty factor. Preferably, when the AGV path direction changes, the turning penalty factor is 0.5, otherwise it is 0. s(n) is the smoothness of the AGV's moving direction. s(n) ensures that the AGV is more inclined to choose a smooth direction during the path search process, reducing unnecessary twists and turns. s(n) = S·δ(|X n -X f |,1)·δ(|Y n -Yf |, 1), where S represents the smoothing factor constant, preferably, S is 0.5, X n is the coordinate of the current node in the X-axis direction, X f is the coordinate of the parent node of the current node in the X-axis direction, Y n is the coordinate of the current node in the Y-axis direction, f is the coordinate of the parent node of the current node in the Y-axis direction, δ(|X n -X f |, 1) is the Kronecker function.

[0058] If there is a path conflict between any two AGVs, when replanning the AGV transportation path, h'(n) is replaced with the AGV constraints represented by the constraint tree to form a new cost function.

[0059] The following will be combined with the Figure 2 , the multi-AGV scheduling device for the production workshop provided by the embodiment of the present application is introduced in detail. Figure 2 The multi-AGV scheduling device in the production workshop shown is used to execute this application Figure 1 For the convenience of explanation, only the part related to the embodiment of the present application is shown. For the specific technical details not disclosed, please refer to the present application. Figure 1 The embodiment shown.

[0060] See Figure 2 , Figure 2 This is a schematic diagram of the structure of the multi-AGV scheduling device for the production workshop provided by the embodiment of the present application. Figure 2 As shown, the device includes:

[0061] A map rasterization and path initialization module 201 is used to obtain a grid map of the workshop environment based on the sizes of the AGVs and obstacles, and to assign a transportation task to each AGV based on its priority to obtain a transportation path for each AGV;

[0062] A conflict detection module 202 is configured to perform conflict detection based on the transport path of each AGV. If there is a path conflict between any two AGVs, the AGV path adjustment module is executed; otherwise, the transport path of each AGV is output;

[0063] The AGV path adjustment module 203 is used to generate a constraint tree based on the path conflicts between AGVs, prune the constraint tree to obtain the re-planned transportation path for each AGV, and then execute the conflict detection module.

[0064] In one embodiment, the map rasterization and path initialization module 201 is specifically configured to:

[0065] The AGV and the obstacles are expanded, and a grid map of the workshop environment is obtained based on the expanded AGV and the obstacles.

[0066] In one embodiment, the map rasterization and path initialization module 201 is specifically configured to:

[0067] The priority of an AGV is determined based on the remaining time of the task currently being executed by the AGV and the remaining path length.

[0068] In one embodiment, the conflict detection module 202 is specifically configured to:

[0069] Conflict detection includes position overlap conflict detection, path intersection conflict detection and direction conflict detection performed in descending order of priority. Generating a constraint tree based on path conflicts between AGVs includes generating each node of the constraint tree in sequence based on the priority of the conflict detection.

[0070] In one embodiment, the conflict detection module 202 is specifically configured to:

[0071] The constraint tree branches whose path costs of the constraint tree nodes are greater than the set pruning threshold are pruned, and the constraint tree nodes represent the restriction conditions of the AGV when planning the path.

[0072] In one embodiment, the conflict detection module 202 is specifically configured to:

[0073] The path cost of the constraint tree node is obtained based on the path length, number of turns and time delay.

[0074] In one embodiment, the AGV path adjustment module 203 is specifically configured to:

[0075] The cost function is set based on the path length of the AGV, the number of turns when the AGV moves, and the smoothness of the AGV's moving direction. The method also includes: if there is a path conflict between any two AGVs, replacing the constraint tree with the path length of the AGV in the cost function.

[0076] Those skilled in the art will clearly understand that the technical solutions of the embodiments of the present application can be implemented with the help of software and / or hardware. "Unit" and "module" in this specification refer to software and / or hardware that can independently perform or cooperate with other components to perform specific functions, where the hardware can be, for example, a field programmable gate array (FPGA), an integrated circuit (IC), etc.

[0077] Each processing unit and / or module in the embodiments of the present application may be implemented by an analog circuit that implements the functions described in the embodiments of the present application, or may be implemented by software that executes the functions described in the embodiments of the present application.

[0078] See also Figure 3 , which shows a schematic diagram of the structure of an electronic device involved in an embodiment of the present application, the electronic device can be used to implement Figure 1 The method in the embodiment shown. Figure 3 As shown, the electronic device 300 may include: at least one central processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .

[0079] The communication bus 302 is used to implement the connection and communication between these components.

[0080] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0081] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0082] The central processing unit 301 may include one or more processing cores. The central processing unit 301 utilizes various interfaces and circuits to connect various components within the entire electronic device 300. It executes instructions, programs, code sets, or instruction sets stored in the memory 305, and accesses data stored in the memory 305 to perform various functions and process data for the terminal 300. Optionally, the central processing unit 301 may be implemented using at least one hardware form selected from the group consisting of a digital signal processing (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA). The central processing unit 301 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing the content displayed on the display screen; and the modem handles wireless communications. It is understood that the modem may not be integrated into the central processing unit 301 and may be implemented as a separate chip.

[0083] Among them, the memory 305 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may also be optionally at least one storage device located away from the aforementioned central processing unit 301. As Figure 3 As shown, the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module, and program instructions.

[0084] exist Figure 3 In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain data input by the user; and the central processing unit 301 can be used to call the application stored in the memory 305 and perform the following operations:

[0085] S101, obtaining a grid map of the workshop environment based on the sizes of the AGVs and obstacles, and assigning a transportation task to each AGV based on the priority of each AGV and a set cost function to obtain a transportation path for each AGV;

[0086] S102, performing conflict detection based on the transport path of each AGV, if there is a path conflict between any two AGVs, executing S103, otherwise outputting the transport path of each AGV;

[0087] S103 , generating a constraint tree based on the path conflicts between the AGVs, pruning the constraint tree to obtain the replanned transport path for each AGV, and then executing S102 .

[0088] The present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above method. The computer-readable storage medium may include, but is not limited to, any type of disk, including a floppy disk, an optical disk, a DVD, a CD-ROM, a microdrive, a magneto-optical disk, a ROM, a RAM, an EPROM, an EEPROM, a DRAM, a VRAM, a flash memory device, a magnetic card or an optical card, a nanosystem (including a molecular memory IC), or any type of medium or device suitable for storing instructions and / or data.

[0089] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0090] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0091] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interface, and the indirect coupling or communication connection of the device or unit can be electrical or other forms.

[0092] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0093] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0094] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, including a number of instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0095] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0096] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure herein, those skilled in the art will easily think of the implementation scheme of the present disclosure. This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art that are not recorded in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A multi-AGV scheduling method for a production workshop, characterized in that: The method comprises: S101, obtaining a grid map of the workshop environment based on the sizes of the AGVs and obstacles, and assigning a transportation task to each AGV based on the priority of each AGV and a set cost function to obtain a transportation path for each AGV; S102, performing conflict detection based on the transport path of each AGV, if there is a path conflict between any two AGVs, executing S103, otherwise outputting the transport path of each AGV; S103 , generating a constraint tree based on the path conflicts between the AGVs, pruning the constraint tree to obtain the replanned transport path for each AGV, and then executing S102 .

2. A multi-AGV scheduling method for a production workshop according to claim 1, characterized in that: The priority of the AGV is obtained based on the remaining time of the task currently being executed by the AGV and the remaining path length.

3. A method for scheduling multiple AGVs in a production workshop according to claim 1, characterized in that: The conflict detection includes position overlap conflict detection, path intersection conflict detection and direction conflict detection performed in order from high to low priority. The generation of the constraint tree based on the path conflict between AGVs includes: generating each node of the constraint tree in order based on the priority of the conflict detection.

4. A method for scheduling multiple AGVs in a production workshop according to claim 3, characterized in that: The pruning of the constraint tree includes: pruning the constraint tree branches whose path costs of the constraint tree nodes are greater than a set pruning threshold, and the constraint tree nodes represent the restriction conditions of the AGV when planning the path.

5. A method for scheduling multiple AGVs in a production workshop according to claim 4, characterized in that: The path cost of the constraint tree node is obtained based on the path length, the number of turns and the time delay.

6. A method for scheduling multiple AGVs in a production workshop according to claim 1, characterized in that: The cost function is set based on the path length of the AGV, the number of turns when the AGV moves, and the smoothness of the AGV's moving direction. The method also includes: if there is a path conflict between any two AGVs, replacing the constraint tree with the path length of the AGV in the cost function.

7. The method for scheduling multiple AGVs in a production workshop according to claim 1, characterized in that: The obtaining of the grid map of the workshop environment based on the sizes of the AGV and the obstacles includes: performing expansion processing on the AGV and the obstacles, and obtaining the grid map of the workshop environment based on the expanded AGV and the obstacles.

8. A multi-AGV scheduling device for a production workshop, characterized in that: The device comprises: The map rasterization and path initialization module is used to obtain a grid map of the workshop environment based on the size of the AGV and obstacles, and to assign a transportation task to each AGV based on its priority to obtain the transportation path of each AGV; a conflict detection module, configured to perform conflict detection based on the transport path of each AGV, and execute an AGV path adjustment module if there is a path conflict between any two AGVs; otherwise, output the transport path of each AGV; The AGV path adjustment module is used to generate a constraint tree based on the path conflicts between AGVs, prune the constraint tree to obtain the re-planned transportation path for each AGV, and then execute the conflict detection module.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, wherein the computer-readable storage medium stores instructions, which, when the instructions are executed on a computer or a processor, cause the computer or processor to execute the steps of the method according to any one of claims 1 to 7.

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