Path planning method, device and system and storage medium

Abstract

The invention provides a path planning method, device and system and a storage medium, and relates to the field of intelligent warehouse management. The path planning method comprises the following steps: constructing a road network model of a warehouse; according to the real-time environment information of the warehouse, the weight of each channel in the road network model of the warehouse is dynamically set to obtain a dynamic weight road network model, and according to the demand information of the task of the automated guided vehicle in the road network model, a path is planned for the automated guided vehicle in the dynamic weight road network model. Therefore, under the real-time changing warehouse environment and task requirements, a good path can be found for the automatic guided vehicle, and the operation efficiency of the automatic guided vehicle is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of intelligent warehouse management, and in particular to a path planning method, device, system and storage medium for an automatic guided vehicle. Background Art

[0002] Automated Guided Vehicle (AGV) is an automated transport device that can be used in warehouses, production lines and other scenarios to handle and transport goods. Automated guided vehicles play an important role in the operation of smart warehouses. They have significant advantages in handling goods in warehouses and assisting warehouse management.

[0003] AGVs need to run and perform tasks according to the planned paths. However, in some related technologies, the path planning algorithms of AGVs usually only consider static warehouse environments and task requirements. They can find better paths under static warehouse environments and fixed task requirements, but it is difficult to cope with real-time changing warehouse environments and task requirements. Summary of the invention

[0004] In the disclosed embodiment, the weights of each channel in the warehouse road network model are dynamically set according to the real-time environment information of the warehouse to obtain a dynamic weighted road network model, and a path is planned for the automatic guided vehicle in the dynamic weighted road network model according to the demand information of the automatic guided vehicle's task in the road network model. Thus, a better path can be found for the automatic guided vehicle under the real-time changing warehouse environment and task requirements, thereby improving the operation efficiency of the automatic guided vehicle.

[0005] Some embodiments of the present disclosure provide a path planning method, including:

[0006] Get the road network model of the warehouse;

[0007] Determine the demand information of the task of the automated guided vehicle in the road network model;

[0008] According to the real-time environment information of the warehouse, the weight of each channel in the road network model is dynamically set to obtain a dynamic weight road network model;

[0009] According to the demand information of the task of the automatic guided vehicle in the road network model, a path is planned for the automatic guided vehicle in the dynamic weighted road network model.

[0010] In some embodiments, obtaining a road network model of a warehouse includes: representing channels in the warehouse as edges according to the geographical layout and channel distribution of the warehouse, representing intersections between different channels as nodes, and connecting the edges and nodes of the warehouse to obtain the road network model of the warehouse.

[0011] In some embodiments, determining the requirement information of the task of the automated guided vehicle in the road network model includes: determining the starting point, the end point, and the waypoint of the task of the automated guided vehicle in the road network model.

[0012] In some embodiments, according to the real-time environmental information of the warehouse, the weights of each channel in the road network model are dynamically set to obtain a dynamic weight road network model, including: according to at least one of the real-time congestion information of any channel in the warehouse and the shelf utilization rate information, the weight of any channel in the road network model is dynamically set to obtain a dynamic weight road network model.

[0013] In some embodiments, the congestion information and shelf usage rate information of any channel are weighted and summed to obtain the weight of any channel.

[0014] In some embodiments, planning a path for the automatic guided vehicle in the dynamic weighted road network model includes: planning a path for the automatic guided vehicle in the dynamic weighted road network model using a heuristic algorithm, a reinforcement learning algorithm, or a genetic algorithm based on the demand information of the automatic guided vehicle's task in the road network model.

[0015] In some embodiments, the heuristic algorithm comprises an A* algorithm.

[0016] In some embodiments, the reinforcement learning algorithm comprises a Q-learning algorithm or a deep Q-network algorithm.

[0017] In some embodiments, the genetic algorithm comprises a non-dominated sorting genetic algorithm.

[0018] In some embodiments, planning a path for the automatic guided vehicle in the dynamic weighted road network model further includes: using a local search algorithm to adjust the path.

[0019] In some embodiments, using a local search algorithm to adjust the path includes: using a local search algorithm to adjust adjacent nodes in the path, comparing the paths before and after the adjustment, and if the adjusted path has a smaller cost than the path before the adjustment, selecting the adjusted path.

[0020] In some embodiments, the local search algorithm comprises a 2-Opt algorithm.

[0021] Some embodiments of the present disclosure provide a path planning device, comprising: a memory; and a processor coupled to the memory, wherein the processor is configured to execute a path planning method based on instructions stored in the memory.

[0022] Some embodiments of the present disclosure provide a path planning device, including: a module for executing a path planning method.

[0023] Some embodiments of the present disclosure provide a path planning system, including:

[0024] A path planning device, configured to execute a path planning method; and

[0025] The automatic guided vehicle is configured to run and perform tasks according to the path planned by the path planning device.

[0026] In some embodiments, it also includes: a monitoring device configured to obtain real-time environmental information of the warehouse and send it to the path planning device.

[0027] Some embodiments of the present disclosure provide a non-transitory computer-readable storage medium having a computer program stored thereon, which implements the steps of a path planning method when executed by a processor. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The following is a brief introduction to the drawings required for use in the embodiments or related technical descriptions. The present disclosure can be more clearly understood according to the following detailed description with reference to the drawings.

[0029] Obviously, the drawings described below are only some embodiments of the present disclosure, and a person skilled in the art can obtain other drawings based on these drawings without creative work.

[0030] Figure 1 A schematic flowchart of a path planning method according to some embodiments of the present disclosure is shown.

[0031] Figure 2 A schematic diagram showing a path planning system according to some embodiments of the present disclosure.

[0032] Figure 3 A schematic diagram showing a path planning device according to some embodiments of the present disclosure.

[0033] Figure 4 A schematic diagram showing a path planning device according to some embodiments of the present disclosure. DETAILED DESCRIPTION

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

[0035] Figure 1 A schematic flowchart of a path planning method according to some embodiments of the present disclosure is shown.

[0036] like Figure 1 As shown, the path planning method of this embodiment can be executed by a path planning device, for example, and the method includes the following steps.

[0037] In step 110, a road network model of the warehouse is obtained.

[0038] In some embodiments, a pre-built warehouse road network model can be obtained, or a warehouse road network model can be constructed to obtain a warehouse road network model. Among them, constructing a warehouse road network model includes: according to the geographical layout and channel distribution of the warehouse, the channels in the warehouse are represented as edges, the intersections between different channels are represented as nodes, and the key locations in the warehouse (such as shelves, workstations, etc.) can also be represented as nodes, and the edges and nodes of the warehouse are connected to construct a warehouse road network model. In the road network model, a node represents a specific location in the warehouse, for example, including but not limited to intersections, shelf locations, turning points, starting points or end points. Each node has a navigation mark or ground code associated with it, which is used for navigation and positioning of automatic guided vehicles.

[0039] In step 120, requirement information of the task of the automated guided vehicle in the road network model is determined.

[0040] In some embodiments, determining the requirement information of the task of the automated guided vehicle in the road network model includes: determining the starting point, end point and waypoint of the task of the automated guided vehicle in the road network model to facilitate subsequent path planning.

[0041] In some embodiments, the task of the automated guided vehicle may be an initial task or a task dynamically adjusted according to real-time information. For the initial task and each dynamically adjusted task, steps 120-140 may be re-executed to dynamically plan a path for the automated guided vehicle.

[0042] In step 130, the weights of the various channels in the road network model are dynamically set according to the real-time environmental information of the warehouse to obtain a dynamic weight road network model.

[0043] The real-time environment information of the warehouse is obtained through monitoring equipment such as cameras and sensors, and sent to the path planning device. The real-time environment information of the warehouse includes, but is not limited to, channel congestion information, shelf utilization information, etc.

[0044] The congestion information is described below. The passages in the warehouse may be congested for various reasons (such as employees walking, other vehicles, etc.). If the automated guided vehicle still uses the statically planned path, it may lead to low work efficiency or even safety accidents such as collisions. By monitoring the congestion information of the passages in real time and dynamically adjusting the weights, the automated guided vehicle can choose a smoother and safer path. When the congestion information of a certain passage shows that the passage is very busy, the weight of this passage should be increased, so that the automated guided vehicle is more likely to choose other passages in path planning. The situation of each passage can be monitored in real time by cameras installed in the warehouse. For example, computer vision technology can be used to detect and count moving objects (such as people, vehicles, etc.) in the passage to obtain the degree of congestion in the passage.

[0045] The shelf utilization rate is described below. The shelf utilization rate may affect the tasks of the automatic guided vehicle. For example, a shelf area with a high utilization rate means that there are more warehousing or outbound tasks in this area, and the automatic guided vehicle needs to go to this area frequently. Therefore, the shelf utilization rate can be used to predict the future task volume of certain areas and adjust the path planning accordingly. When the utilization rate of a certain shelf is very high, the channel weight near the shelf may be reduced to make it easier for the automatic guided vehicle to go to this area. The shelf utilization rate can be obtained in a variety of ways. One method is to use RFID (Radio Frequency Identification) technology. Each product is attached with an RFID tag. The RFID reader on the shelf can read the number of products on the shelf in real time, and determine the shelf utilization rate accordingly. In addition, cameras can also be used to determine or assist in determining the shelf utilization rate. For example, the number of products on the shelf is analyzed by computer vision technology to determine the shelf utilization rate.

[0046] In some embodiments, the weight of any channel in the road network model is dynamically set according to at least one of the real-time congestion information of any channel in the warehouse and the shelf utilization rate information to obtain a dynamic weight road network model. That is, a road network model in which channels (or "edges") have weights and the weights change dynamically is called a dynamic weight road network model.

[0047] The congestion information and shelf utilization rate information of any channel are weighted and summed to obtain the weight of any channel. The calculation formula of the channel weight can be expressed as:

[0048] W_ij=α×C_ij+β×S_ij

[0049] Among them, Wij represents the weight of the channel from node i to j; Cij represents the congestion level of the channel from node i to j; Sij represents the shelf utilization rate of the channel from node i to j; α and β are weight coefficients, which can be adjusted according to actual conditions.

[0050] In step 140, a path is planned for the automatic guided vehicle in the dynamic weighted road network model according to the demand information of the task of the automatic guided vehicle in the road network model.

[0051] Wherein, step 140 includes step 140-1, and according to the planning requirements, may further include step 140-2. The path planned in step 140-1 is also called the initial path, and the path adjusted in step 140-2 is the final path to be output.

[0052] In step 140-1, according to the demand information of the task of the automatic guided vehicle in the road network model, a heuristic algorithm, a reinforcement learning algorithm, or a genetic algorithm is used to plan a path for the automatic guided vehicle in the dynamic weighted road network model. The heuristic algorithm includes, but is not limited to, the A* algorithm. The reinforcement learning algorithm includes, but is not limited to, the Q-learning algorithm or the Deep Q-Network (DQN) algorithm. The genetic algorithm includes, but is not limited to, the non-dominated sorting genetic (NSGA-II) algorithm. The non-dominated sorting genetic algorithm, a multi-objective optimization algorithm, uses methods such as non-dominated sorting and congestion distance to find the Pareto optimal solution of the multi-objective problem. Q-learning algorithm: a reinforcement learning algorithm that learns to take different actions in different states to obtain the maximum cumulative reward by interacting with the environment. Deep Q-Network algorithm: a reinforcement learning algorithm that combines deep learning and Q-learning, uses a neural network as a function approximator to represent the Q value, and is suitable for processing high-dimensional, continuous state space problems.

[0053] The following describes a method for planning a path using a heuristic algorithm. The heuristic algorithm uses heuristic information (such as distance, time, etc.) to guide the search direction during the search process to reduce the search space and computational complexity.

[0054] (a) Define a heuristic function: Select or design a heuristic function, such as Euclidean distance, to estimate the cost from the current node to the target node. This function will take into account the impact of dynamic weights.

[0055] (b) Initialization: Select a starting node, put it into the open list, and set the initial cost to 0.

[0056] (c) Iteration: Find the node with the lowest cost in the open list, expand this node and calculate the cost to its neighbor nodes, including the impact of dynamic weights. Add the neighbor node to the open list and update the cost and parent node information. Repeat this step until the target node is found or the open list is empty.

[0057] (d) Path reconstruction: trace back from the target node to the start node and reconstruct the path along the parent node pointer.

[0058] By combining weights with the A* algorithm, it is possible to respond to dynamic environments, such as dynamically adjusting paths as the congestion level of aisles and the utilization rate of shelves change.

[0059] The following describes a method for planning a path using a reinforcement learning algorithm.

[0060] (a) Define the environment and state space: In the scenario of AGV path planning, the state space may include the location of the AGV, the destination, the layout of the warehouse, and other dynamic factors (e.g., obstacles). Weights may be part of the state to indicate the priority or cost of various paths or directions.

[0061] (b) Select a suitable reinforcement learning algorithm, such as Q-learning or Deep Q-Network, which can be used to train the automated guided vehicle to choose the optimal path in a given environment.

[0062] (c) Training and real-time updating: By interacting with the environment, the AGV can learn which path is the best to choose in different situations. Weights can be used to adjust the learning process to reflect the importance or priority of different paths, and real-time updating strategies allow the AGV to adapt to changes in the environment.

[0063] (d) Apply the trained reinforcement learning method to the path planning system.

[0064] Reinforcement learning methods can adaptively learn the optimal path under different environments and task requirements through interaction with the environment. In real-time changing warehouse environments and task scheduling scenarios, reinforcement learning methods can update path planning strategies in real time to improve the accuracy and practicality of path planning.

[0065] The following describes a method for planning a path using a genetic algorithm.

[0066] (a) Define optimization objectives and constraints: Identify and quantify multiple optimization objectives, such as minimizing aisle congestion and shelf utilization, and introduce weights to balance different objectives. Set constraints based on the warehouse’s geographic layout and aisle distribution, as well as the deployment information of the AGVs.

[0067] (b) Select a multi-objective optimization algorithm: For example, use the non-dominated sorting genetic algorithm (NSGA-II) to solve this multi-objective problem.

[0068] (c) Set and adjust algorithm parameters: Set and adjust algorithm parameters such as population size, crossover and mutation rate according to the specific problem.

[0069] (d) Perform optimization and select solutions: Run the multi-objective optimization algorithm and select a set of approximately optimal solutions. The optimal solution can be selected and presented through various techniques, such as Pareto optimal frontier. The approximately optimal solution is also the planned path.

[0070] The automatic guided vehicle path planning algorithm based on multi-objective optimization incorporates real-time changing warehouse environment information (such as channel congestion, shelf utilization rate, etc.) into the optimization objectives to find a trade-off solution between multiple objectives and find a set of approximate optimal solutions under limited computing resources, thereby improving the efficiency and adaptability of path planning.

[0071] In step 140 - 2 , a local search algorithm is used to adjust the planned path.

[0072] Local search optimization: Based on the initial path, the local search method is used to optimize the initial path. The local search method attempts to find a better path by making local adjustments to the adjacent nodes in the initial path. The local search algorithm includes, for example, but is not limited to, the 2-Opt algorithm. The 2-Opt algorithm is a local search algorithm that optimizes the path by exchanging two adjacent nodes on the path. The 2-Opt algorithm can find an approximate optimal solution with limited computing resources.

[0073] In a path, any two consecutive nodes are considered adjacent nodes. In other words, adjacent nodes are consecutive in the path sequence. For example, if the path consists of nodes 1, 2, 3, 4, 5 in that order, then nodes 1 / 2, nodes 2 / 3, nodes 3 / 4, nodes 4 / 5, etc. are all adjacent nodes.

[0074] In some embodiments, a local search algorithm is used to adjust the adjacent nodes in the path (i.e., the planned initial path), and the paths before and after the adjustment are compared. If the adjusted path has a lower cost than the path before the adjustment, the adjusted path is selected. The adjustment and selection process is repeated until a preset number of iterations is reached or other iteration stop conditions are met. The adjustment of adjacent nodes includes, for example, changing the connection method between these nodes or replacing some of the nodes. For example, the initial path is composed of nodes 1, 2, 3, 4, and 5 in sequence. By adjusting the order of nodes 3 and 4, a path with a lower overall cost is obtained, which is composed of nodes 1, 2, 4, 3, and 5 in sequence.

[0075] Local adjustments can be made by changing the connection between these nodes or replacing some of the nodes to find a better path, thereby fine-tuning the existing path.

[0076] In the disclosed embodiment, the weights of each channel in the warehouse road network model are dynamically set according to the real-time environment information of the warehouse to obtain a dynamic weighted road network model, and a path is planned for the automatic guided vehicle in the dynamic weighted road network model according to the demand information of the automatic guided vehicle's task in the road network model. Thus, a better path can be found for the automatic guided vehicle under the real-time changing warehouse environment and task requirements, thereby improving the operation efficiency of the automatic guided vehicle.

[0077] Figure 2 A schematic diagram showing a path planning system according to some embodiments of the present disclosure.

[0078] like Figure 2 As shown, the path planning system of this embodiment includes the following devices.

[0079] The path planning device 210 is configured to execute the path planning method in each embodiment.

[0080] The automatic guided vehicle 220 is configured to run and perform tasks according to the path planned by the path planning device 210.

[0081] The monitoring device 230 is configured to obtain real-time environment information of the warehouse and send it to the path planning device 210. So that the path planning device 210 plans an immediate route for the automatic guided vehicle 220 according to the real-time environment information of the warehouse.

[0082] Figure 3 A schematic diagram showing a path planning device according to some embodiments of the present disclosure.

[0083] like Figure 3 As shown, the path planning device 300 of this embodiment includes: a memory 310 and a processor 320 coupled to the memory 310 , and the processor 320 is configured to execute the path planning method in any of the aforementioned embodiments based on instructions stored in the memory 310 .

[0084] The path planning device 300 may further include an input / output interface 330 , a network interface 340 , a storage interface 350 , etc. These interfaces 330 , 340 , 350 , the memory 310 , and the processor 320 may be connected, for example, via a bus 360 .

[0085] The memory 310 may include, for example, a system memory, a fixed non-volatile storage medium, etc. The system memory may store, for example, an operating system, an application program, a boot loader, and other programs.

[0086] The processor 320 may be implemented by a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistors and other discrete hardware components.

[0087] Among them, the input and output interface 330 provides a connection interface for input and output devices such as a display, a mouse, a keyboard, and a touch screen. The network interface 340 provides a connection interface for various networked devices. The storage interface 350 provides a connection interface for external storage devices such as SD cards and USB flash drives. The bus 360 can use any bus structure among a variety of bus structures. For example, the bus structure includes but is not limited to the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MCA) bus, and the Peripheral Component Interconnect (PCI) bus.

[0088] Figure 4 A schematic diagram showing a path planning device according to some embodiments of the present disclosure.

[0089] like Figure 4 As shown, the path planning device 400 of this embodiment includes: a module for executing the path planning method.

[0090] An acquisition module 410 is configured to acquire a road network model of a warehouse;

[0091] A determination module 420 is configured to determine demand information of the task of the automated guided vehicle in the road network model;

[0092] A setting module 430 is configured to dynamically set the weight of each channel in the road network model according to the real-time environment information of the warehouse to obtain a dynamic weight road network model;

[0093] The planning module 440 is configured to plan a path for the automatic guided vehicle in the dynamic weighted road network model according to the demand information of the task of the automatic guided vehicle in the road network model.

[0094] The acquisition module 410 is configured to represent the channels in the warehouse as edges, represent the intersections between different channels as nodes, and connect the edges and nodes of the warehouse to obtain the road network model of the warehouse according to the geographical layout and channel distribution of the warehouse.

[0095] The determination module 420 is configured to determine the starting point, the end point and the waypoint of the task of the automated guided vehicle in the road network model.

[0096] The setting module 430 is configured to dynamically set the weight of any channel in the road network model according to at least one of the real-time congestion information of any channel in the warehouse and the shelf utilization rate information, so as to obtain a dynamic weight road network model.

[0097] The setting module 430 is configured to perform weighted summation on the congestion information and shelf usage rate information of any channel to obtain the weight of any channel.

[0098] The planning module 440 is configured to plan a path for the automatic guided vehicle in the dynamic weighted road network model by using a heuristic algorithm, a reinforcement learning algorithm, or a genetic algorithm according to the demand information of the automatic guided vehicle's task in the road network model.

[0099] Wherein: the heuristic algorithm includes an A* algorithm; or, the reinforcement learning algorithm includes a Q learning algorithm or a deep Q network algorithm; or, the genetic algorithm includes a non-dominated sorting genetic algorithm.

[0100] The planning module 440 is configured to use a local search algorithm to adjust the path.

[0101] The planning module 440 is configured to use a local search algorithm to adjust adjacent nodes in the path, compare the paths before and after the adjustment, and select the adjusted path if the adjusted path has a smaller cost than the path before the adjustment.

[0102] Wherein: the local search algorithm includes the 2-Opt algorithm.

[0103] (1) A path planning method, comprising:

[0104] Get the road network model of the warehouse;

[0105] Determine the demand information of the task of the automated guided vehicle in the road network model;

[0106] According to the real-time environment information of the warehouse, the weight of each channel in the road network model is dynamically set to obtain a dynamic weight road network model;

[0107] According to the demand information of the task of the automatic guided vehicle in the road network model, a path is planned for the automatic guided vehicle in the dynamic weighted road network model.

[0108] (2) According to (1), obtaining the road network model of the warehouse includes: according to the geographical layout and channel distribution of the warehouse, representing the channels in the warehouse as edges, representing the intersection points between different channels as nodes, and connecting the edges and nodes of the warehouse to obtain the road network model of the warehouse.

[0109] (3) According to any one of (1-2), determining the demand information of the task of the automated guided vehicle in the road network model includes: determining the starting point, end point and waypoint of the task of the automated guided vehicle in the road network model.

[0110] (4) According to any one of (1-3), based on the real-time environmental information of the warehouse, the weight of each channel in the road network model is dynamically set to obtain a dynamic weight road network model, including: based on at least one of the real-time congestion information of any channel in the warehouse and the shelf utilization rate information, the weight of any channel in the road network model is dynamically set to obtain a dynamic weight road network model.

[0111] (5) According to (4), the congestion information and shelf utilization rate information of any channel are weighted and summed to obtain the weight of any channel.

[0112] (6) According to any one of (1-5), planning a path for the automatic guided vehicle in the dynamic weighted road network model includes: using a heuristic algorithm, a reinforcement learning algorithm, or a genetic algorithm to plan a path for the automatic guided vehicle in the dynamic weighted road network model based on the demand information of the task of the automatic guided vehicle in the road network model.

[0113] (7) According to (6), wherein: the heuristic algorithm includes an A* algorithm; or, the reinforcement learning algorithm includes a Q learning algorithm or a deep Q network algorithm; or, the genetic algorithm includes a non-dominated sorting genetic algorithm.

[0114] (8) According to any one of (1-7), a local search algorithm is used to adjust the path.

[0115] (9) According to any one of items (1-8), using a local search algorithm to adjust the path includes: using a local search algorithm to adjust adjacent nodes in the path, comparing the paths before and after the adjustment, and if the adjusted path has a smaller cost than the path before the adjustment, selecting the adjusted path.

[0116] (10) According to any one of (8-9), the local search algorithm includes a 2-Opt algorithm.

[0117] Unless specifically stated otherwise, the relative arrangement of components and steps, the numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present disclosure.

[0118] It should be understood that in the embodiments of the present disclosure, “plurality” may refer to two or more than two, and “at least one / item” may refer to one, two or more than two.

[0119] It should also be understood that any component, data or structure mentioned in the embodiments of the present disclosure can generally be understood as one or more, unless explicitly limited or otherwise indicated in the context.

[0120] In addition, the term "and / or" in the present disclosure is only a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present disclosure generally indicates that the associated objects before and after are in an "or" relationship.

[0121] It should also be understood that the description of the various embodiments in the present disclosure focuses on the differences between the various embodiments, and the same or similar aspects thereof can be referenced to each other, and for the sake of brevity, they will not be described one by one.

[0122] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0123] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present disclosure, its application, or uses.

[0124] 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 as part of the specification.

[0125] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0126] In addition, in order to avoid obscuring the present disclosure due to unnecessary details, only the processing steps and / or device structures closely related to at least the scheme according to the present disclosure are shown in the drawings, and other details that are not closely related to the present disclosure are omitted. It should also be noted that similar reference numerals and letters in the drawings indicate similar items, and therefore once an item is defined in one drawing, it does not need to be discussed again for subsequent drawings.

[0127] The embodiments of the present disclosure can be applied to electronic devices such as terminal devices, computer systems, servers, etc., which can operate with many other general or special computing system environments or configurations. Examples of well-known terminal devices, computing systems, environments and / or configurations suitable for use with electronic devices such as terminal devices, computer systems, servers, etc. include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, small computer systems, large computer systems, and distributed cloud computing technology environments including any of the above systems, etc.

[0128] Electronic devices such as terminal devices, computer systems, servers, etc. can be described in the general context of computer system executable instructions (such as program modules) executed by computer systems. Generally, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in a distributed cloud computing environment, where tasks are performed by remote processing devices linked through a communication network. In a distributed cloud computing environment, program modules can be located on local or remote computing system storage media including storage devices.

[0129] Those skilled in the art will appreciate that the embodiments of the present disclosure may be provided as methods, systems, or computer program products. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present disclosure may take the form of a computer program product implemented on one or more non-transient computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer program code.

[0130] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0131] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0132] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0133] The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present disclosure should be included in the protection scope of the present disclosure.

Claims

1. A path planning method, comprising: Get the road network model of the warehouse; Determine the demand information of the task of the automated guided vehicle in the road network model; According to the real-time environment information of the warehouse, the weight of each channel in the road network model is dynamically set to obtain a dynamic weight road network model; According to the demand information of the task of the automatic guided vehicle in the road network model, a path is planned for the automatic guided vehicle in the dynamic weighted road network model.

2. According to the method of claim 1, obtaining the road network model of the warehouse comprises: According to the geographical layout and channel distribution of the warehouse, the channels in the warehouse are represented as edges, the intersections between different channels are represented as nodes, and the edges and nodes of the warehouse are connected to obtain the road network model of the warehouse.

3. The method according to claim 1, determining the demand information of the task of the automated guided vehicle in the road network model comprises: The starting point, the end point and the waypoint of the task of the automated guided vehicle in the road network model are determined.

4. The method according to claim 1, dynamically setting the weight of each channel in the road network model according to the real-time environment information of the warehouse to obtain a dynamic weight road network model comprises: According to at least one of the real-time congestion information of any channel in the warehouse and the shelf utilization rate information, the weight of any channel in the road network model is dynamically set to obtain a dynamic weight road network model.

5. The method according to claim 4, wherein: The congestion information and shelf utilization rate information of any channel are weighted and summed to obtain the weight of any channel.

6. According to the method according to any one of claims 1 to 5, planning a path for the automatic guided vehicle in the dynamic weighted road network model comprises: According to the demand information of the task of the automatic guided vehicle in the road network model, a heuristic algorithm, a reinforcement learning algorithm, or a genetic algorithm is used to plan a path for the automatic guided vehicle in the dynamic weighted road network model.

7. The method according to claim 6, wherein: The heuristic algorithm includes an A* algorithm; or, The reinforcement learning algorithm includes a Q learning algorithm or a deep Q network algorithm; or, The genetic algorithm comprises a non-dominated sorting genetic algorithm.

8. The method according to claim 6, wherein planning a path for the automatic guided vehicle in the dynamic weighted road network model further comprises: The path is adjusted using a local search algorithm.

9. The method according to claim 8, wherein the path is adjusted by using a local search algorithm, comprising: Adopting a local search algorithm, the adjacent nodes in the path are adjusted, and the paths before and after the adjustment are compared. If the adjusted path has a smaller cost than the path before the adjustment, the adjusted path is selected.

10. The method of claim 9, wherein the local search algorithm comprises a 2-Opt algorithm.

11. A path planning device, comprising: Memory; as well as A processor coupled to the memory, wherein the processor is configured to execute the path planning method according to any one of claims 1 to 10 based on instructions stored in the memory.

12. A path planning device, comprising: A module for executing the path planning method according to any one of claims 1 to 10.

13. A path planning system, comprising: A path planning device, configured to execute the path planning method according to any one of claims 1 to 10; as well as The automatic guided vehicle is configured to run and perform tasks according to the path planned by the path planning device.

14. The system of claim 13, further comprising: The monitoring device is configured to obtain real-time environmental information of the warehouse and send it to the path planning device.

15. A non-transitory computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the steps of the path planning method according to any one of claims 1 to 10 are implemented.

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