Delivery methods, delivery systems, delivery equipment, and computer-readable storage media using multi-purpose automated guided vehicles.

Abstract

This invention provides a multi-AGV delivery method, system, equipment, and medium that not only ensures balance and efficiency in task assignment during execution and improves the computational efficiency of route planning, but also enhances the flexibility of collaborative task assignment and solves the uneven load on delivery resources caused by the lack of dynamic adjustment capability of AGVs in collaborative tasks, thereby offering significant application value. [Solution] The method includes the steps of: obtaining a global map of delivery tasks and generating a route queue for delivery destinations (101); determining a target starting point where an automated guided vehicle (AGV) is located within the route queue (102); dividing the route queue into target routes based on the target starting point (103) and setting a target delivery task for the AGV based on the target routes (103); and re-dividing the target routes based on status feedback for the target delivery tasks and dynamically updating the target delivery tasks based on the re-divided routes (104).

Description

[Technical Field]

[0001] The present invention relates to the technology of smart delivery, and more specifically to a delivery method using multiple automated guided vehicles, a delivery system, delivery equipment, and a computer-readable storage medium. [Background technology]

[0002] In recent years, in the field of autonomous delivery using multiple automated guided vehicles (AGVs), AGVs are gradually replacing traditional human-powered delivery methods as smart delivery tools, demonstrating particular advantages in complex, dynamic, and large-scale urban environments. To ensure efficient, accurate, and safe transportation of goods from one delivery point to the next, AGV autonomous delivery systems typically rely on core technologies such as high-precision maps, sensor-based data fusion, route planning, and task scheduling.

[0003] However, conventional route planning and task assignment algorithms lack the dynamic adjustment capabilities necessary for the coordinated tasks of multiple automated guided vehicles, making it difficult to cope with complex urban delivery environments. Furthermore, the coordination technology is limited by communication and computing capabilities, preventing reliable, highly efficient coordination and optimal resource allocation.

[0004] Therefore, there is an urgent need for a delivery method using multiple automated guided vehicles (AGVs) to solve the problem of uneven loading of delivery resources due to the lack of dynamic adjustment capabilities of AGVs in collaborative tasks. [Overview of the Initiative] [Problems that the invention aims to solve]

[0005] In view of the shortcomings of prior art, the present invention aims to provide a delivery method using multiple automated guided vehicles, a delivery system, delivery equipment, and a computer-readable storage medium. [Means for solving the problem]

[0006] A first aspect of the present invention discloses a delivery method using a multi-purpose automated guided vehicle. Obtain a global map of delivery tasks and generate a route queue for delivery destinations, The automated guided vehicle determines the target starting point located within the aforementioned route queue, Based on the aforementioned target starting point, the route queue is divided into target routes, and the target delivery task for the automated guided vehicle is set based on the target routes. This includes subdividing the target route based on status feedback for the target delivery task, and dynamically updating the target delivery task based on the subdivided route.

[0007] As one alternative embodiment, obtaining a global map of the delivery task is: Based on the delivery task, a set of N automated guided vehicles within the distribution center is determined, and a set of M target points for delivery destinations outside the distribution center is determined, where the set of vehicles is It is represented as JPEG2026105841000002.jpg12165, and the target point set is Represented as JPEG2026105841000003.jpg12166, the location of the distribution center, delivery destination, and automated guided vehicle is obtained based on the delivery task.

[0008] As one alternative embodiment, generating the destination route queue described above is: The first delivery destination S in the aforementioned set of target points j1 and the second delivery address S j2 The distance cost and commuting cost are established, and the said distance cost is It is represented as JPEG2026105841000004.jpg13166, where, JPEG2026105841000005.jpg10164 is the first delivery destination S j1 and the second delivery address S j2 This represents the cost of generating a non-shortest route using global map navigation, and the commuting cost is is represented as JPEG2026105841000006.jpg11166, where JPEG2026105841000007.jpg12164 represents the cost affected by commuting for the first destination S j1 and the second destination S j2 which are located on the global map navigation route, allocate the weight α1 of the distance cost and the weight α2 of the commuting cost, and define the TSP cost for the first destination S j1 and the second destination S j2 where the TSP cost is represented as JPEG2026105841000008.jpg12166, establish a cost matrix based on the TSP cost to solve the routing problem of the destinations, determine the order in which the destinations are located on the route and the shortest distance between the destinations, and output a route queue that forms a closed loop surrounding the distribution center, where the route queue is represented as JPEG2026105841000009.jpg11165, including that JPEG2026105841000010.jpg14166 represents the routing order of the i-th destination.

[0009] In one alternative embodiment, the determination by the AGV of the target starting point located within the route queue is based on the initial position of the target AGV in the vehicle set at the distribution center, determine the proximity points of the distances of each AGV to N destinations, select the proximity point farthest from the target AGV among the proximity points as the target starting point of the target AGV, and include that the number of the target starting points is at least N?2.

[0010] In one alternative embodiment, the splitting of the route queue into a target route based on the target starting point and the setting of the target delivery task of the AGV based on the target route is The route queue is uniformly divided between two adjacent target starting points, and a target sub-route is generated from which the target automated guided vehicle departs from the target starting point. Two adjacent target automated guided vehicles (AGVs) at the target starting point are set as a single task group, and the target AGVs in the task group move from the target starting point along the target sub-path to the division point to perform a sub-delivery task. This includes generating a task list for the automated guided vehicle (AGV) based on the sub-delivery tasks and task groups, and setting target delivery tasks for the AGV based on the target sub-routes and task list.

[0011] As one alternative embodiment, the aforementioned re-dividing of the target route based on the state feedback of the target delivery task is: In response to the status information returned from the automated guided vehicle, the task status and motion status of the automated guided vehicle in each task group are traversed, Based on uniform division points and state information, the first progress value of the first automated guided vehicle and the second progress value of the second automated guided vehicle in the task group are determined, Based on road condition environmental information and progress value journey information obtained from status information, a dynamic threshold is updated in real time, and the dynamic threshold is set as an adjustment condition for re-dividing the target route. This includes comparing the dynamic threshold based on the progress difference between a first progress value and a second progress value acquired in real time, and determining whether the first and second automated guided vehicles (AGVs) will perform subdivision in the current task group.

[0012] As one alternative embodiment, dynamically updating the target delivery task based on the subdivided routes is: If the current task group needs to be re-divided, the incomplete values ​​of the target subpaths in the task group are obtained, and a weighted average is performed on these incomplete values ​​to determine the re-dividing point of the task group. Based on the aforementioned subdivision point, the target subpaths for the first and second automated guided vehicles in the task group are determined again, and the target delivery task is updated based on the aforementioned target subpaths. This includes continuously updating the target delivery task until the automated guided vehicle has completed all tasks.

[0013] A second aspect of the present invention discloses a delivery system using multiple automated guided vehicles, the system comprising: A route resolution module for obtaining a global map of delivery tasks and generating a route queue for delivery destinations, A starting point inquiry module for determining the target starting point located within the route queue for an automated guided vehicle, A task assignment module for dividing the route queue into target routes based on the target starting point and setting up target delivery tasks for automated guided vehicles based on the target routes, It includes a dynamic adjustment module for re-dividing the target route based on status feedback of the target delivery task and dynamically updating the target delivery task based on the re-divided route.

[0014] As one alternative embodiment, obtaining the global map of the delivery task in the route resolution module is: Based on the delivery task, a set of N automated guided vehicles within the distribution center is determined, and a set of M target points for delivery destinations outside the distribution center is determined, where the set of vehicles is It is represented as JPEG2026105841000011.jpg12165, and the target point set is Represented as JPEG2026105841000012.jpg12166, the location of the distribution center, delivery destination, and automated guided vehicle is obtained based on the delivery task.

[0015] As one alternative embodiment, generating the destination route queue in the route resolution module is: The first delivery destination S in the target point set j1 and the second delivery destination S j2 Establish the distance cost and commuting cost, where the distance cost is represented as JPEG2026105841000013.jpg13166, where JPEG2026105841000014.jpg10164 represents the cost for the first delivery destination S j1 and the second delivery destination S j2 to generate a non - shortest path by global map navigation, and the commuting cost is represented as JPEG2026105841000015.jpg11166, where JPEG2026105841000016.jpg12164 represents the cost for the first delivery destination S j1 and the second delivery destination S j2 to be located on the global map navigation route and be affected by commuting, and allocate the weight α1 of the distance cost and the weight α2 of the commuting cost, and define the TSP cost for the first delivery destination S j1 and the second delivery destination S j2 , where the TSP cost is represented as JPEG2026105841000017.jpg12166, and Establish a cost matrix based on the TSP cost to solve the routing problem of the delivery destinations, determine the order in which the delivery destinations are located on the route and the shortest distance between the delivery destinations, and output a route queue that forms a closed loop surrounding the distribution center, where the route queue is represented as JPEG2026105841000018.jpg11165, and JPEG2026105841000019.jpg14166 represents the routing order of the i - th delivery destination.

[0016] As an alternative embodiment, in the starting point query module, the automated guided vehicle determines the target starting point located within the route queue Based on the initial position of the target automated guided vehicles (AGVs) in the aforementioned vehicle collection at the distribution center, the nearest neighbors of each AGV to N delivery destinations are determined, the nearest neighbor from which the target AGV is furthest is selected as the target starting point for the AGV, and the number of target starting points is at least N-2.

[0017] As one alternative embodiment, the task assignment module divides the route queue into target routes based on the target starting point and sets up target delivery tasks for automated guided vehicles based on the target routes. The route queue is uniformly divided between two adjacent target starting points, and a target sub-route is generated from which the target automated guided vehicle departs from the target starting point. Two adjacent target automated guided vehicles (AGVs) at the target starting point are set as a single task group, and the target AGVs in the task group move from the target starting point along the target sub-path to the division point to perform a sub-delivery task. This includes generating a task list for the automated guided vehicle (AGV) based on the sub-delivery tasks and task groups, and setting target delivery tasks for the AGV based on the target sub-routes and task list.

[0018] As one alternative embodiment, the dynamic adjustment module may re-divide the target route based on state feedback of the target delivery task. In response to the status information returned from the automated guided vehicle, the task status and motion status of the automated guided vehicle in each task group are traversed, Based on uniform division points and state information, the first progress value of the first automated guided vehicle and the second progress value of the second automated guided vehicle in the task group are determined, Based on road condition environmental information and progress value journey information obtained from status information, a dynamic threshold is updated in real time, and the dynamic threshold is set as an adjustment condition for re-dividing the target route. This includes comparing the dynamic threshold based on the progress difference between a first progress value and a second progress value acquired in real time, and determining whether the first and second automated guided vehicles (AGVs) will perform subdivision in the current task group.

[0019] As one alternative embodiment, the dynamic adjustment module dynamically updates the target delivery task based on the subdivided paths, If the current task group needs to be re-divided, the incomplete values ​​of the target subpaths in the task group are obtained, and a weighted average is performed on these incomplete values ​​to determine the re-dividing point of the task group. Based on the aforementioned subdivision point, the target subpaths for the first and second automated guided vehicles in the task group are determined again, and the target delivery task is updated based on the aforementioned target subpaths. This includes continuously updating the target delivery task until the automated guided vehicle has completed all tasks.

[0020] A third aspect of the present invention discloses a delivery system using a multi-autonomous transport vehicle, At least one processor, The memory is connected to the at least one processor, where, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a delivery method using a multi-unmanned transport vehicle as described in any one of the first aspects of the present invention.

[0021] A fourth aspect of the present invention discloses a computer-readable storage medium in which computer-executable instructions are stored, and the computer-executable instructions are used to cause a computer to execute a delivery method using a multi-unmanned transport vehicle as described in any one of the first aspects of the present invention. [Effects of the Invention]

[0022] Compared to the prior art, the present invention has the following advantages.

[0023] The present invention provides a delivery method, system, equipment, and medium using multiple automated guided vehicles (AGVs), the method comprising: obtaining a global map of delivery tasks; generating a route queue of delivery destinations; determining target starting points where the AGVs are located within the route queue; dividing the route queue into target routes based on the target starting points; setting target delivery tasks for the AGVs based on the target routes; and subdividing the target routes based on status feedback for the target delivery tasks; and dynamically updating the target delivery tasks based on the subdivided routes. This invention solves the traveling salesman problem during global map route generation by obtaining a closed-loop route point queue, querying the route point closest to the center of the route using a search algorithm, and initially grouping all route points based on the number of automated guided vehicles (AGVs). At the same time, it introduces a subdivision scheme to dynamically adjust the workload of AGVs, thereby ensuring balance and efficiency in task allocation during execution, improving the computational efficiency of route planning, enhancing the flexibility of collaborative task allocation, solving the problem of uneven load on delivery resources due to the lack of dynamic adjustment capability of AGVs in collaborative tasks, and having great application value in the field of AGV delivery. [Brief explanation of the drawing]

[0024] To more clearly illustrate the embodiments of this application or the technical solutions in the prior art, the following is a brief introduction of the drawings that may be used in the descriptions of the embodiments or the prior art. Obviously, the drawings in the following descriptions are only a few embodiments of this application, and those skilled in the art can obtain other drawings based on the structures shown in these drawings without expending any creative effort.

[0025] [Figure 1]This is a flowchart of the delivery method using the multi-unmanned transport vehicle of the present invention. [Figure 2] This is a schematic diagram of the delivery system using the multi-unmanned transport vehicle of the present invention. [Modes for carrying out the invention]

[0026] To clarify the purpose, technical solutions, and advantages of this application, the application is described and explained below in combination with drawings and embodiments. It should be understood that the specific embodiments described herein are used solely for interpreting this application and not to limit it. All other embodiments obtained by a person skilled in the art without any creative effort based on the embodiments provided herein are all included within the scope of protection of this application.

[0027] It is obvious that the drawings in the following description are merely examples or embodiments of the present application, and a person skilled in the art can apply the present application to other similar scenarios based on these drawings, without requiring any creative effort. Also, as can be understood, although the efforts undertaken in such a development process can be complex and lengthy, a person skilled in the art who relates to the content disclosed in the present application should not consider that the content disclosed in the present application is insufficient, as some design, manufacturing, or production modifications made based on the technical content disclosed in the present application are merely ordinary technical means.

[0028] The “Examples” as used in this Application mean that any particular feature, structure, or characteristic described in combination with the Examples may be included in at least one Example of this Application. The fact that this phrase appears in various places in the Specification does not necessarily mean that each instance refers to the same Example, nor does it mean that each is an independent, exclusive, or substitutable Example. Those skilled in the art will understand, both explicitly and implicitly, that the Examples described in this Application may be combined with other Examples in a non-contradictory manner.

[0029] Unless otherwise defined, technical or scientific terms relating to this application shall have the ordinary meaning that can be understood by a person with general skill in the art to which this application pertains. Similar terms relating to this application, such as “1,” “one,” “one kind,” and “the,” are not limited to a number and may refer to singular or plural. Terms relating to this application, such as “include,” “incorporate,” “have,” and any variations thereof, are intended to cover, but not be limited to, the following: for example, a process, method, system, product, or apparatus comprising a series of steps or modules (units) may include, but not be limited to, steps or units not listed, or may include other steps or units specific to those processes, methods, products, or apparatus. Similar terms relating to this application, such as “connect,” “linked,” and “joined,” are not limited to physical or mechanical connections and may also include electrical connections, whether direct or indirect. “Multiple” relating to this application refers to two or more. "And / or" describes the related relationship between related objects, meaning that three relationships may exist. For example, A and / or B can represent three situations: A simply exists, A and B exist simultaneously, and B simply exists. The symbol " / " generally indicates that the preceding and succeeding related objects are in an "OR" relationship. Terms such as "first," "second," and "third" in this application are used solely to distinguish similar objects and do not represent a specific order of objects. [Examples]

[0030] Example 1 Referring to Figure 1, an embodiment of the present invention discloses a delivery method using a multi-unmanned transport vehicle, and includes the following:

[0031] 101. Obtain a global map of delivery tasks and generate a route queue for delivery destinations.

[0032] A delivery task refers to the process in which an automated guided vehicle (AGV) transports goods (e.g., mail, quick dispatch items, fresh produce, drugs, daily consumer goods, etc.) along a planned route to all destinations (e.g., housing complexes, campuses, residential areas, warehouses) within an area where a distribution center is the center of a circle. Autonomous delivery by AGVs typically requires the collaborative implementation of methods such as high-precision maps, data fusion using multi-sensors, route planning by AGVs, and task scheduling by AGVs. Specifically, when a distribution center receives various delivery needs regarding destinations, it generates a delivery task corresponding to the delivery. Here, high-precision maps can provide accurate road information and location references for the location of the distribution center (which is the area center) and the locations of the destinations. Next, it is necessary to generate selectable routes for the AGVs to execute the delivery task.

[0033] Specifically, when generating task routes for automated guided vehicles (AGVs), various influencing factors must be considered. For example, if the AGVs depart from a distribution center, the total distance each AGV needs to travel must be minimized. The shortest travel route improves the efficiency of the distribution center's operations. Similarly, how to rationally assign delivery tasks to each independent AGV must also be considered. A rational assignment scheme allows the AGVs to efficiently complete all delivery tasks. Thus, AGV route selection and task assignment are currently problems that need to be addressed. Before determining route selection and task assignment, corresponding parameters must be obtained as data support. Subsequent embodiments will further describe how to complete AGV route selection and task assignment.

[0034] As one alternative embodiment, obtaining the global map of the delivery task described above includes the following:

[0035] Based on the delivery task, a set of N automated guided vehicles (AGVs) within the distribution center is determined, and a set of M target points for delivery destinations outside the distribution center is determined, where the set of vehicles is It is represented as JPEG2026105841000020.jpg12165, and the target point set is Represented as JPEG2026105841000021.jpg12166, the locations of the distribution center, delivery destination, and automated guided vehicle are obtained based on the delivery task.

[0036] Specifically, when communication is possible, the distribution center receives delivery tasks issued from delivery destinations. Here, the number of unit delivery tasks and delivery destinations are set to M in one-to-one correspondence, and the M delivery destinations are set as the target points (endpoints of the tasks) for the delivery tasks. The number of selectable automated guided vehicles (AGVs) to perform the delivery tasks is set to N. Here, the location information of the delivery destinations can be confirmed based on the received address information, and the location of the AGVs can be confirmed by multi-sensors, vehicle internet, and high-precision maps. The road conditions and environment of the delivery destination area can be queried on the high-precision map using the address information, and influencing factors that affect the driving efficiency of AGVs (e.g., traffic congestion, poor road conditions, weather effects, etc.) can be identified by acquiring environmental information from the AGV's sensors and then fusing it with high-precision map information. This has been shown to allow for the identification of the degree of impact on delivery efficiency by AGVs in actual scenarios.

[0037] As one alternative embodiment, generating the destination route queue as described above includes the following:

[0038] The first delivery destination S in the aforementioned set of target points j1 and the second delivery address S j2 The distance cost and commuting cost are established, and the said distance cost is It is represented as JPEG2026105841000022.jpg13166, where, JPEG2026105841000023.jpg10164 is the first delivery destination S j1 and the second delivery address S j2 This represents the cost of generating a non-shortest route using global map navigation, and the commuting cost is It is represented as JPEG2026105841000024.jpg11166, where, JPEG2026105841000025.jpg12164 is the first delivery destination S j1 and the second delivery address S j2 This represents the cost affected by commuting, located along the global map navigation route.

[0039] Specifically, in order to obtain the shortest possible route, the automated guided vehicle (AGV) can start from the furthest destination from the distribution center to achieve the best delivery efficiency, and then travel sequentially to the next destination until the destinations form a closed loop surrounding the distribution center. Therefore, in this embodiment, the TSP model can be introduced and the route planning for each destination can be transformed into a traveling salesman problem. In this embodiment, the resolution of the route queue can be understood as the problem of finding the shortest route for the AGV to start from the first destination, visit each destination once, and finally return to the first destination. However, a difference from the conventional traveling salesman problem is that there is not just one AGV acting as a traveling salesman, so the shortest route and task assignment problems for each AGV must also be considered. Therefore, here, we first generate the route queue for the first closed loop.

[0040] Furthermore, to improve the accuracy of route queue generation, two factors affecting delivery efficiency, namely route distance and commute time, are considered as issues to be taken into account when generating routes. These two factors are established as TSP costs in the TSP model. Here, the route distance cost can be queried by querying the distance between the delivery start and end points using a high-precision map before executing the delivery task, or by querying the navigation route. The commute time issue requires considering not only the route distance but also influencing factors that affect the actual commute time, such as weather, road conditions, and traffic congestion during the actual driving process. The established commute costs further increase the probability of generating the shortest route queue, avoiding situations where the actual route takes too long due to some influencing factors, and minimizing the route between two adjacent delivery destinations in the generated closed-loop route queue.

[0041] The weights α1 for distance cost and α2 for commuting cost are assigned to the first delivery destination S. j1 and the second delivery address S j2 Define the TSP cost, where the TSP cost is It is represented as JPEG2026105841000026.jpg12166.

[0042] Based on TSP costs, a cost matrix is ​​established to solve the destination route problem, the order in which destinations are located along the route and the shortest distance between destinations are determined, and a route queue is output that forms a closed loop surrounding the distribution center. It is represented as JPEG2026105841000027.jpg11165, JPEG2026105841000028.jpg14166 represents the route sequence for the i-th delivery destination.

[0043] Specifically, after assigning weights to different TSP costs, a cost matrix is ​​established to resolve the route queue, and in order to ensure that the distance of each route is the shortest, the cost matrix includes all possible situations in which a destination generates a route, and therefore, when establishing the TSP costs, any two destinations S j1and S j2 Based on this, it is established that when generating a route queue, it is possible to traverse any possible shortest route, and within the route queue, JPEG2026105841000029.jpg14166 is the shipping address s i The sequence of routes starting from a certain point can be recorded.

[0044] 102. The automated guided vehicle determines the target starting point located within the route queue.

[0045] Specifically, after generating a route queue, it is necessary to further confirm the starting point of each automated guided vehicle (AGV) in a delivery task in order to plan the route each AGV needs to travel and the corresponding delivery task. Since the route queue has a route order and is a closed-loop route, setting two AGVs to the same starting point and having them perform delivery tasks in opposite directions can reduce the resource consumption of subsequent computational task allocation and route allocation, which is especially important in collaborative tasks that require real-time performance, and allows for the rapid deployment of AGVs to delivery tasks.

[0046] As one alternative embodiment, the above-described determination of a target starting point in which the automated guided vehicle is located within the route queue includes the following:

[0047] Based on the initial position of the target automated guided vehicles (AGVs) in the aforementioned vehicle collection at the distribution center, the nearest neighbors of each AGV to N delivery destinations are determined, and the nearest neighbor from which the target AGV is furthest is selected as the target starting point for the AGV, with the number of target starting points being at least N-2.

[0048] Specifically, using four automated guided vehicles (AGVs) as an example, by continuously querying delivery destinations in the route queue and selecting the furthest delivery destination as the starting point based on the actual distance, and by using the first delivery task as the starting point so that the AGV moves from the distribution center to the first delivery destination to complete the first delivery task, and then returning from the final delivery destination to the distribution center and selecting the furthest delivery destination as the starting point, the commuting time of the AGVs when going back and forth to the distribution center can be reduced. If four AGVs are performing delivery tasks, by using two delivery destinations as starting points to average the actual commuting time of each AGV as much as possible, the AGVs only need to complete the journey of each quarter arc of the closed-loop route queue sequentially from the starting point, thereby increasing the efficiency with which the distribution center can complete batch delivery tasks per unit time.

[0049] 103. The route queue is divided into target routes based on the target starting point, and target delivery tasks for automated guided vehicles are set based on the target routes.

[0050] As one alternative embodiment, the above-mentioned method of dividing the route queue into target routes based on the target starting point and setting up target delivery tasks for automated guided vehicles based on the target routes is: The route queue is uniformly divided between two adjacent target starting points, and a target sub-route is generated from which the target automated guided vehicle departs from the target starting point. Two adjacent target automated guided vehicles (AGVs) at the target starting point are set as a single task group, and the target AGVs in the task group move from the target starting point along the target sub-path to the division point to perform a sub-delivery task. This includes generating a task list for the automated guided vehicle (AGV) based on the sub-delivery tasks and task groups, and setting target delivery tasks for the AGV based on the target sub-routes and task list.

[0051] Specifically, in order to uniformly divide the routes between adjacent starting points, the routes can be uniformly divided based on route elements, road condition elements, traffic elements, and weather elements, so as to ensure that two autonomous vehicles traveling in the opposite direction to adjacent delivery destinations can reach the task's endpoint simultaneously and complete the delivery task at the same time. Here, the target delivery task is a sub-delivery task that the corresponding N delivery destinations must complete, and the target sub-route is a delivery task that each autonomous vehicle must independently complete.

[0052] 104. The target route is re-divided based on status feedback for the target delivery task, and the target delivery task is dynamically updated based on the re-divided route.

[0053] As one alternative embodiment, the aforementioned re-dividing of the target route based on the state feedback of the target delivery task is: In response to the status information returned from the automated guided vehicle, the task status and motion status of the automated guided vehicle in each task group are traversed, Based on uniform division points and state information, the first progress value of the first automated guided vehicle and the second progress value of the second automated guided vehicle in the task group are determined, Based on road condition environmental information and progress value journey information obtained from status information, a dynamic threshold is updated in real time, and the dynamic threshold is set as an adjustment condition for re-dividing the target route. This includes comparing the dynamic threshold based on the progress difference between a first progress value and a second progress value acquired in real time, and determining whether the first and second automated guided vehicles (AGVs) will perform subdivision in the current task group.

[0054] Specifically, for two automated guided vehicles (AGVs) traveling in the opposite direction between adjacent delivery destinations, there is a tendency for the delivery task to be unreasonable. For example, in the actual delivery process, if the road conditions for AGV c1 are good and the delivery task is completed quickly, while the progress of the delivery task for AGV c2 is slowed due to traffic congestion, it is necessary to adjust for this situation in real time. Assuming a dynamic threshold of 2, the first progress value of the i-th AGV (first AGV) is set to length(l ci Let ) and the second progress value of the jth automated guided vehicle (second automated guided vehicle) be length(l cj ) If so, length(l ci )-length(l cj The decision result of )>2 allows for the selection of whether or not to perform re-division, and since the process of obtaining the progress value is confirmed based on the status information already returned from the automated guided vehicle, the data that the delivery task is unreasonable, which is analyzed from this status information, can be reused in the subsequent process of adjusting the re-division points, thereby reducing the computational loss at the distribution center.

[0055] As one alternative embodiment, dynamically updating the target delivery task based on the subdivided routes is: If the current task group needs to be re-divided, the incomplete values ​​of the target subpaths in the task group are obtained, and a weighted average is performed on these incomplete values ​​to determine the re-dividing point of the task group. Based on the aforementioned subdivision point, the target subpaths for the first and second automated guided vehicles in the task group are determined again, and the target delivery task is updated based on the aforementioned target subpaths. This includes continuously updating the target delivery task until the automated guided vehicle has completed all tasks.

[0056] Furthermore, for ongoing delivery tasks, continuous detection is repeatedly performed for automated guided vehicles (AGVs) that have not completed sub-delivery tasks. Based on the actual operating status of each AGV, the workload of each AGV can be adjusted in real time, thereby improving the efficiency of the entire AGV system in completing the target delivery task.

[0057] The multi-AGV delivery method provided in the present invention includes acquiring a global map of delivery tasks, generating a route queue for delivery destinations, determining target starting points where AGVs are located within the route queue, dividing the route queue into target routes based on the target starting points, setting target delivery tasks for AGVs based on the target routes, and subdividing the target routes based on status feedback for the target delivery tasks, and dynamically updating the target delivery tasks based on the subdivided routes. The present invention acquires a closed-loop route point queue by solving the traveling salesman problem during route generation of the global map, queries the route queue for the route point closest to the center of the route using a search algorithm, and initially groups all route points based on the number of AGVs. At the same time, it introduces a subdivision scheme to dynamically adjust the workload of AGVs, thereby ensuring balance and efficiency in task allocation during execution, improving the computational efficiency of route planning, enhancing the flexibility of collaborative task allocation, solving the problem of uneven load on delivery resources due to the lack of dynamic adjustment capability of AGVs in collaborative tasks, and having great application value in the field of AGV delivery.

[0058] As shown in Figure 2, a second aspect of the present invention discloses a delivery system using multiple automated guided vehicles, the system being: A route resolution module for obtaining a global map of delivery tasks and generating a route queue for delivery destinations, A starting point inquiry module for determining the target starting point located within the route queue for an automated guided vehicle, A task assignment module for dividing the route queue into target routes based on the target starting point and setting up target delivery tasks for automated guided vehicles based on the target routes, It includes a dynamic adjustment module for re-dividing the target route based on status feedback of the target delivery task and dynamically updating the target delivery task based on the re-divided route.

[0059] As one alternative embodiment, obtaining the global map of the delivery task in the route resolution module is: Based on the delivery task, a set of N automated guided vehicles within the distribution center is determined, and a set of M target points for delivery destinations outside the distribution center is determined, where the set of vehicles is It is represented as JPEG2026105841000030.jpg12165, and the target point set is Represented as JPEG2026105841000031.jpg12166, the location of the distribution center, delivery destination, and automated guided vehicle is obtained based on the delivery task.

[0060] As one alternative embodiment, generating the destination route queue in the route resolution module is: The first delivery destination S in the aforementioned set of target points j1 and the second delivery address S j2 The distance cost and commuting cost are established, and the said distance cost is It is represented as JPEG2026105841000032.jpg13166, where, JPEG2026105841000033.jpg10164 is the first delivery destination S j1 and the second delivery address S j2 This represents the cost of generating a non-shortest route using global map navigation, and the commuting cost is It is represented as JPEG2026105841000034.jpg11166, where, JPEG2026105841000035.jpg12164 is the first delivery destination Sj1 and the second delivery address S j2 This represents the cost affected by commuting, located on a global map navigation route, The weights α1 for distance cost and α2 for commuting cost are assigned to the first delivery destination S. j1 and the second delivery address S j2 Define the TSP cost, where the TSP cost is It is represented as JPEG2026105841000036.jpg12166, Based on TSP costs, a cost matrix is ​​established to solve the destination route problem, the order in which destinations are located along the route and the shortest distance between destinations are determined, and a route queue is output that forms a closed loop surrounding the distribution center. It is represented as JPEG2026105841000037.jpg11165, This includes the fact that JPEG2026105841000038.jpg14166 represents the route sequence of the i-th delivery destination.

[0061] As one alternative embodiment, the starting point inquiry module determines the target starting point in which the automated guided vehicle is located within the route queue, Based on the initial position of the target automated guided vehicles (AGVs) in the aforementioned vehicle collection at the distribution center, the nearest neighbors of each AGV to N delivery destinations are determined, the nearest neighbor from which the target AGV is furthest is selected as the target starting point for the AGV, and the number of target starting points is at least N-2.

[0062] As one alternative embodiment, the task assignment module divides the route queue into target routes based on the target starting point and sets up target delivery tasks for automated guided vehicles based on the target routes. The route queue is uniformly divided between two adjacent target starting points, and a target sub-route is generated from which the target automated guided vehicle departs from the target starting point. Two adjacent target automated guided vehicles (AGVs) at the target starting point are set as a single task group, and the target AGVs in the task group move from the target starting point along the target sub-path to the division point to perform a sub-delivery task. This includes generating a task list for the automated guided vehicle (AGV) based on the sub-delivery tasks and task groups, and setting target delivery tasks for the AGV based on the target sub-routes and task list.

[0063] As one alternative embodiment, the dynamic adjustment module may re-divide the target route based on the status feedback of the target delivery task. In response to the status information returned from the automated guided vehicle, the task status and motion status of the automated guided vehicle in each task group are traversed, Based on uniform division points and state information, the first progress value of the first automated guided vehicle and the second progress value of the second automated guided vehicle in the task group are determined, Based on road condition environmental information and progress value journey information obtained from status information, a dynamic threshold is updated in real time, and the dynamic threshold is set as an adjustment condition for re-dividing the target route. This includes comparing the dynamic threshold based on the progress difference between a first progress value and a second progress value acquired in real time, and determining whether the first and second automated guided vehicles (AGVs) will perform subdivision in the current task group.

[0064] As one alternative embodiment, the dynamic adjustment module dynamically updates the target delivery task based on the subdivided paths, If the current task group needs to be re-divided, the incomplete values ​​of the target subpaths in the task group are obtained, and a weighted average is performed on these incomplete values ​​to determine the re-dividing point of the task group. Based on the aforementioned subdivision point, the target subpaths for the first and second automated guided vehicles in the task group are determined again, and the target delivery task is updated based on the aforementioned target subpaths. This includes continuously updating the target delivery task until the automated guided vehicle has completed all tasks.

[0065] A third aspect of the present invention discloses a delivery system using a multi-autonomous transport vehicle, At least one processor, The memory is connected to the at least one processor, where, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a delivery method using a multi-unmanned transport vehicle as described in any one of the first aspects of the present invention.

[0066] The computer device may be a terminal and includes a processor, memory, network interface, display, and input device connected via a system bus. Here, the processor of the computer device is used to provide computation and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the execution of the operating system and computer programs on the non-volatile storage medium. The network interface of the computer device is used to connect to and communicate with an external terminal via a network. The computer program, when executed by the processor, realizes a delivery method using multiple automated guided vehicles. The display of the computer device may be a liquid crystal display or an electronic ink display, and the input device of the computer device may be a touch layer covered by the display, a key, trackball, or touch panel installed in the housing of the computer device, or an externally connected keyboard, touch panel, or mouse.

[0067] A fourth aspect of the present invention discloses a computer-readable storage medium in which computer-executable instructions are stored, and the computer-executable instructions are used to cause a computer to execute a delivery method using a multi-unmanned transport vehicle as described in any one of the first aspects of the present invention.

[0068] As those skilled in the art will understand, all or part of the processes in the above embodiments can be implemented by a computer program instructing the relevant hardware, the computer program can be stored in a non-volatile computer-readable storage medium, and when the computer program is executed, it can include the processes of the embodiments of the multi-AGV delivery method described above. Here, any use of memory, storage, database or other media in each embodiment provided by this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. Rather than being an limitation, RAM can be obtained in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), extended SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0069] Alternatively, if the above-mentioned module of the present invention is implemented as a software functional module and sold or used as an independent product, it may be stored on a single computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present invention can be embodied essentially, or in part, in the form of a software product, the computer software product being stored on a single storage medium and containing several instructions for executing all or part of the methods of each embodiment of the present invention on a single computer device (which may be a personal computer, terminal, or network device, etc.). The aforementioned storage mediums include a variety of media capable of storing program code, such as mobile storage devices, RAM, ROM, magnetic disks, or optical disks.

[0070] The foregoing describes only preferred embodiments of the present invention and does not limit it. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention should also be included within the scope of protection of the present invention.

Claims

1. A delivery method using a multi-purpose automated guided vehicle, wherein the method is Obtain a global map of delivery tasks and generate a route queue for delivery destinations, The automated guided vehicle determines the target starting point located within the aforementioned route queue, Based on the aforementioned target starting point, the route queue is divided into target routes, and target delivery tasks for automated guided vehicles are set based on the target routes. This includes subdividing the target route based on status feedback for the target delivery task, and dynamically updating the target delivery task based on the subdivided route. The above-mentioned method of dividing the route queue into target routes based on the target starting point and setting up target delivery tasks for automated guided vehicles based on the target routes is: The route queue is uniformly divided between two adjacent target starting points, and a target sub-route is generated from which the target automated guided vehicle departs from the target starting point. Two adjacent target automated guided vehicles (AGVs) at the target starting point are set as a single task group, and the target AGVs in the task group move from the target starting point along the target sub-path to the division point to perform a sub-delivery task. This includes generating a task list for automated guided vehicles based on the sub-delivery tasks and task groups, and setting target delivery tasks for automated guided vehicles based on the target sub-routes and task list. Redividing the target route based on the status feedback of the target delivery task is, In response to the status information returned from the automated guided vehicle, the task status and motion status of the automated guided vehicle in each task group are traversed, Based on uniform division points and state information, the first progress value of the first automated guided vehicle and the second progress value of the second automated guided vehicle in the task group are determined. Based on road condition environmental information and progress value journey information obtained from status information, a dynamic threshold is updated in real time, and the dynamic threshold is set as an adjustment condition for re-dividing the target route. This includes comparing the dynamic threshold based on the progress difference between a first progress value and a second progress value acquired in real time, and determining whether the first and second automated guided vehicles should perform re-division in the current task group. Dynamically updating the target delivery task based on the aforementioned re-divided routes is: If the current task group needs to be re-divided, the incomplete values ​​of the target subpaths in the task group are obtained, and a weighted average is performed on these incomplete values ​​to determine the re-dividing point of the task group. Based on the aforementioned subdivision point, the target subpaths for the first and second automated guided vehicles in the task group are determined again, and the target delivery task is updated based on the aforementioned target subpaths. A delivery method using multiple automated guided vehicles, characterized by continuing to update the target delivery task until all tasks are completed by the automated guided vehicles.

2. Obtaining the global map of the aforementioned delivery tasks is Based on the delivery task, a set of N automated guided vehicles within the distribution center is determined, and a set of M target points for delivery destinations outside the distribution center is determined, where the set of vehicles is It is expressed as and the target point set is A delivery method using a multi-autonomous vehicle according to claim 1, characterized in that it is represented as such, and the locations of the distribution center, delivery destination, and automated guided vehicle are obtained based on the delivery task.

3. The above-mentioned process of generating a route queue for the delivery destination is: The first delivery destination S in the aforementioned set of target points j1 and the second delivery destination S j2 The distance cost and commuting cost are established, and the said distance cost is It is expressed as, and here, The first delivery destination S j1 and the second delivery destination S j2 This represents the cost of generating a non-shortest route using global map navigation, and the commuting cost is It is expressed as, and here, The first delivery destination S j1 and the second delivery destination S j2 This represents the cost affected by commuting, located on a global map navigation route, The weight α of the distance cost 1 and the weight α of the commuting cost 2 are assigned, and the TSP costs of the first delivery destination S j1 and the second delivery destination S j2 are defined, where the TSP cost is C (s_j1, s_j2) = α_1 C_dis (s_j1, s_j2) + α_2 C_tra (s_j1, s_j2) It is expressed as, Based on TSP costs, a cost matrix is ​​established to solve the route problem for the delivery destination, the order in which the delivery destinations are located along the route and the shortest distance between delivery destinations are determined, and a route queue is output that forms a closed loop surrounding the distribution center. It is expressed as, The delivery method using a multi-unmanned transport vehicle according to claim 2, characterized in that the i-th delivery destination is represented by the route sequence.

4. The above-mentioned determination of the target starting point located within the route queue by the automated guided vehicle is: The multi-autonomous vehicle delivery method according to claim 3, characterized in that, based on the initial position of the target automated guided vehicles in the vehicle assembly located at the delivery center, the nearest point in distance to any delivery destination for each automated guided vehicle is determined, the nearest point furthest from the target automated guided vehicle is selected as the target starting point for the target automated guided vehicle, and the number of target starting points is at least N-2.

5. A delivery system using multiple automated guided vehicles, wherein the system is A route resolution module for obtaining a global map of delivery tasks and generating a route queue for delivery destinations, A starting point inquiry module for determining the target starting point located within the route queue for an automated guided vehicle, A task assignment module for dividing the route queue into target routes based on the target starting point and setting up target delivery tasks for automated guided vehicles based on the target routes, Includes a dynamic adjustment module for re-dividing the target route based on status feedback of the target delivery task and dynamically updating the target delivery task based on the re-divided route, The above-mentioned method of dividing the route queue into target routes based on the target starting point and setting up target delivery tasks for automated guided vehicles based on the target routes is: The route queue is uniformly divided between two adjacent target starting points, and a target sub-route is generated from which the target automated guided vehicle departs from the target starting point. Two adjacent target automated guided vehicles (AGVs) at the target starting point are set as a single task group, and the target AGVs in the task group move from the target starting point along the target sub-path to the division point to perform a sub-delivery task. This includes generating a task list for automated guided vehicles based on the sub-delivery tasks and task groups, and setting target delivery tasks for automated guided vehicles based on the target sub-routes and task list. Redividing the target route based on the status feedback of the target delivery task is, In response to the status information returned from the automated guided vehicle, the task status and motion status of the automated guided vehicle in each task group are traversed, Based on uniform division points and state information, the first progress value of the first automated guided vehicle and the second progress value of the second automated guided vehicle in the task group are determined. Based on road condition environmental information and progress value journey information obtained from status information, a dynamic threshold is updated in real time, and the dynamic threshold is set as an adjustment condition for re-dividing the target route. This includes comparing the dynamic threshold based on the progress difference between a first progress value and a second progress value acquired in real time, and determining whether the first and second automated guided vehicles will perform subdivision in the current task group. Dynamically updating the target delivery task based on the aforementioned subdivided routes is: If the current task group needs to be re-divided, the incomplete values ​​of the target subpaths in the task group are obtained, and a weighted average is performed on these incomplete values ​​to determine the re-dividing point of the task group. Based on the aforementioned subdivision point, the target subpaths for the first and second automated guided vehicles in the task group are determined again, and the target delivery task is updated based on the aforementioned target subpaths. A delivery system using multiple automated guided vehicles, characterized by including the continuous updating of the target delivery tasks until all automated guided vehicles have completed all tasks.

6. At least one processor, The memory is connected to the aforementioned at least one processor, where, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the delivery method using the multi-automated guided vehicle described in any one of claims 1 to 4, characterized in that the multi-automated guided vehicle delivery device is provided for.

7. A computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to cause a computer to execute the delivery method using a multi-unmanned transport vehicle as described in any one of claims 1 to 4.

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