An automatic guided vehicle avoidance scheduling method, device and medium

Abstract

The application discloses an automatic guided vehicle avoidance scheduling method and device and a medium, wherein the method comprises the following steps: in response to a single scheduling system generating a new distribution task, obtaining current scheduling information from multiple scheduling systems; in response to the current scheduling information causing a path conflict of a pre-constructed logistics simulation model, determining an avoidance path of each automatic guided vehicle and target optimization data corresponding to the avoidance path; based on a genetic algorithm, optimizing the target optimization data to obtain target scheduling information; and based on the target scheduling information, performing avoidance scheduling on each automatic guided vehicle. By integrating the automatic guided vehicles of multiple scheduling systems into the same virtual simulation model, potential conflicts in the movement process of the automatic guided vehicles of the multiple scheduling systems are predicted in advance, the path planning is dynamically adjusted according to the task urgency, and the paths are respectively sent to different scheduling systems, so that global optimization scheduling is realized.

Description

Technical Field

[0001] This application relates to the field of scheduling optimization, specifically to an automated guided vehicle (AGV) obstacle avoidance scheduling method, equipment, and medium. Background Technology

[0002] In recent years, Automated Guided Vehicles (AGVs) have been widely used in modern logistics and intelligent manufacturing systems due to their advantages such as automation and flexibility. However, due to changes in workshop processes or production line upgrades, overlapping or intersecting logistics areas may occur in parts of the logistics areas handled by multiple AGV scheduling systems. Since AGV scheduling systems often rely on their own obstacle avoidance algorithms for local obstacle avoidance and lack global predictive capabilities for logistics intersections, path conflicts, congestion, and deadlocks frequently occur, leading to decreased transportation efficiency, task delays, and even system paralysis. Therefore, for complex obstacle avoidance scenarios involving multiple scheduling systems, there is an urgent need for an AGV obstacle avoidance method that coordinates the various systems independently of the AGV scheduling system.

[0003] Common scheduling systems have long development cycles, high development difficulty, and high costs. Furthermore, the obstacle avoidance capability of automated guided vehicles (AGVs) depends on algorithm calculations, and the quality of the algorithm is closely related to the obstacle avoidance capability of the upper-level system. Summary of the Invention

[0004] To address the aforementioned problems, this application proposes an automated guided vehicle (AGV) obstacle avoidance scheduling method, device, and medium, wherein the method includes:

[0005] In response to a new delivery task generated by a single scheduling system, current scheduling information from multiple scheduling systems is acquired. In response to the current scheduling information causing path conflicts in a pre-built logistics simulation model, avoidance paths for each automated guided vehicle (AGV) are determined, along with target optimization data corresponding to those avoidance paths. The target optimization data includes at least one of the following: total task time, maximum time deviation of a single AGV compared to the expected time, and minimum consumption time for remaining materials. Based on a genetic algorithm, the target optimization data is optimized to obtain target scheduling information. The target scheduling information includes the optimized movement paths and optimized task priorities for each AGV. Based on the target scheduling information, avoidance scheduling is performed on each AGV.

[0006] In one example, the current scheduling information includes: the guide vehicle information of each automated guided vehicle (AGV), the current task information, and the material-related information; the guide vehicle information includes the real-time location of each AGV and the corresponding scheduling system; the current task information includes the task type, current movement path, task priority, and task deadline; the material-related information includes the order number, material name, quantity, and material distribution.

[0007] In one example, before determining the avoidance path for each automated guided vehicle (AGV) in response to the current scheduling information causing a path conflict in the pre-built logistics simulation model, the method further includes: dividing the AGV operating areas of multiple scheduling systems into several navigation points and creating them in the same simulation layer to construct a logistics simulation model; the passable direction of each navigation point is consistent with the actual direction, used to simulate the actual logistics direction, and each navigation point can be associated with one or more scheduling systems; the types of navigation points include at least one of: one-way passage points, multi-way passage points, rest points, avoidance points, charging points, and loading / unloading points; the avoidance point is a navigation point next to the main road that can be temporarily parked, associated with the navigation point corresponding to the main road.

[0008] In one example, the response to the current scheduling information causing a path conflict in the pre-built logistics simulation model, determining the avoidance path for each automated guided vehicle (AGV), specifically includes: determining the current task priority and current movement path of each AGV; based on the current task priority and current movement path, determining the farthest avoidance point for a low-priority AGV compared to a high-priority AGV; determining the target associated point corresponding to the farthest avoidance point; determining the first arrival time of the high-priority AGV to the target associated point and the second arrival time of the low-priority AGV to the target associated point; in response to the first arrival time being less than the second arrival time, removing the farthest avoidance point and re-determining the farthest avoidance point and its corresponding target associated point, until the first arrival time is greater than the second arrival time, and determining the avoidance path at this point.

[0009] In one example, determining the first arrival time of the high-priority automated guided vehicle (AGV) to the target associated point and the second arrival time of the low-priority AGV to the target associated point specifically includes: determining the target travel distance between the AGV and the target associated point based on the current position and current travel path of the AGV; and determining the arrival time of each AGV to the target associated point based on the target travel distance, travel speed, angle value during each rotation, rotational angular velocity, number of rotations, and acceleration / deceleration compensation time for a single rotation.

[0010] In one example, determining the target optimization data corresponding to the avoidance path specifically includes: determining the simulation time for each automated guided vehicle (AGV) to complete the delivery task during the simulation process; summing the simulation times corresponding to all AGVs to determine the total simulation time for all AGVs; determining the time deviation time corresponding to each AGV based on the simulation time and expected time of each AGV; and determining the minimum consumption time for the remaining materials based on the quantity of materials at the associated workstations and temporary storage locations of each AGV's movement endpoint, as well as the average consumption time of a single material of different types.

[0011] In one example, the step of performing obstacle avoidance scheduling for each automated guided vehicle (AGV) based on the target scheduling information specifically includes: determining the scheduling system corresponding to each AGV; returning the optimized movement path, optimized task priority, and waiting time of each AGV at the obstacle avoidance point to the corresponding scheduling system; and updating the AGV status and path information in the logistics simulation model.

[0012] In one example, the method further includes: in response to detecting a faulty automated guided vehicle (AGV) in the direction of travel of the target AGV, setting the navigation point where the faulty AGV is located as a temporary impassable point and replanning the candidate path corresponding to the target AGV; determining the completion time of the candidate path and the fault repair time of the faulty AGV within the original path; and determining the shortest path of the target AGV in the logistics simulation model based on the fault repair time and the candidate path completion time.

[0013] This application also provides an automated guided vehicle (AGV) obstacle avoidance scheduling device, including:

[0014] At least one processor; and a memory communicatively connected to said at least one processor; wherein,

[0015] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the steps of the method as described in any of the above examples.

[0016] This application also provides a non-volatile computer storage medium storing computer-executable instructions configured to perform the steps of the method as described in any of the above examples.

[0017] The method proposed in this application can bring the following benefits: by integrating the AGV logistics of multiple scheduling systems into the same virtual simulation model, potential conflicts (such as blockages and deadlocks) in the movement process of AGVs from multiple scheduling systems can be predicted in advance, and the path planning can be dynamically adjusted according to the urgency of the task, and the path can be sent to different AGV scheduling systems respectively, thereby achieving global optimized scheduling. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0019] Figure 1 This is a flowchart illustrating an automated guided vehicle (AGV) obstacle avoidance scheduling method according to an embodiment of this application.

[0020] Figure 2 This is a flowchart illustrating another automated guided vehicle (AGV) obstacle avoidance scheduling method in the embodiments of this application.

[0021] Figure 3 This is a schematic representation of a shortest path matrix for navigation points in an embodiment of this application;

[0022] Figure 4 This is a schematic representation of the path priority sorting for an automated guided vehicle in an embodiment of this application;

[0023] Figure 5 This is a schematic diagram of a conflict zone scenario for an automated guided vehicle (AGV) in an embodiment of this application. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0025] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0026] Figure 1 This diagram illustrates a process flow for an automated guided vehicle (AGV) obstacle avoidance scheduling method provided in one or more embodiments of this specification. This method can be applied to modern logistics and intelligent manufacturing systems, especially in scenarios where multiple scheduling systems coexist. The process can be executed by computing devices in the relevant field (e.g., servers located in the workshop).

[0027] The analysis method involved in the embodiments of this application can be implemented by a terminal device or a server, and this application does not impose any special limitations on it. For ease of understanding and description, the following embodiments are all described in detail using a server as an example.

[0028] It should be noted that the server can be a single device or a system composed of multiple devices, i.e., a distributed server. This application does not make any specific limitations in this regard.

[0029] like Figure 1 As shown in the figure, this application provides an automated guided vehicle (AGV) obstacle avoidance scheduling method, including:

[0030] S101: In response to a new delivery task generated by a single scheduling system, obtain current scheduling information from multiple scheduling systems.

[0031] First, if the server detects that a new delivery task has been generated by a single AGV scheduling system, the server will simultaneously obtain the current scheduling information from all AGV scheduling systems because the new delivery task will cause the corresponding AGV's forward route to change.

[0032] In one embodiment, the current scheduling information includes the guided vehicle information for each AGV, current task information, and material-related information. The guided vehicle information includes the real-time location of each AGV and its corresponding scheduling system. The current task information includes task type, current movement path, task urgency (e.g., priority level, deadline, etc., if the AGV scheduling system supports this type of information output), and task deadline. The material-related information includes order number, material name, quantity, and material distribution. If the AGV system does not support recording material information, it can be obtained from systems such as Enterprise Resource Planning (ERP).

[0033] S102: In response to the current scheduling information causing a path conflict in the pre-built logistics simulation model, determine the avoidance path for each automated guided vehicle and the target optimization data corresponding to the avoidance path.

[0034] After obtaining the current scheduling information, the server will conduct a simulation in a pre-built logistics simulation model based on the current scheduling information to determine whether a new delivery task will cause a path conflict in the pre-built logistics simulation model.

[0035] If the simulation predicts no path conflicts during AGV movement, a "no change to movement path" message is sent to each AGV scheduling system, maintaining the original plan. If the simulation predicts congestion or deadlock during AGV movement, an avoidance strategy is executed, and an avoidance path is confirmed. Simultaneously, based on pre-determined target optimization data types, the target optimization data corresponding to the avoidance path is obtained. This target optimization data includes at least one of the following: total task time, maximum time deviation of a single AGV compared to the expected time, and minimum consumption time for remaining materials.

[0036] In one embodiment, when constructing a logistics simulation model, the operating areas of automated guided vehicles (AGVs) from multiple scheduling systems are divided into several navigation points and created in the same simulation layer to construct the logistics simulation model. The passable direction of each navigation point is consistent with the actual direction, used to simulate the actual logistics direction, and each navigation point can be associated with one or more scheduling systems. The types of navigation points include at least one of the following: one-way passage points, multi-way passage points, rest points, yielding points, charging points, and loading / unloading points. The yielding point is a navigation point on the side of the main road that can be temporarily parked and associated with the navigation point corresponding to the main road.

[0037] Specifically, the first step is to create an AGV map, dividing the AGV operating areas of multiple scheduling systems into several navigation points on the same simulation layer. Navigation point types include one-way points, multi-way points, rest points, avoidance points, charging points, and loading / unloading points. Avoidance points are navigation points that allow temporary stopping beside the main AGV road and must be associated with the corresponding navigation point on the main road. The traversable direction of each navigation point is consistent with the actual direction of logistics, simulating the actual logistics flow. Each navigation point can be associated with one or more AGV scheduling systems. Then, material consumption buffers and workstations are created to simulate the actual consumption progress of materials on the production line.

[0038] Then you can create, such as Figure 3 The navigation point shortest path matrix table shown has row and column indices for a single navigation point ID, and the data storage format for a single cell is as follows:<double,string,double,integer> The first element represents the shortest distance between row index navigation point 1 and column index navigation point 2, and is of type double. The second element represents the shortest path string between the two navigation points, in the format "navigation point 1 + separator + intermediate navigation point 3... + navigation point 2", and is of type string. The third element represents the sum of the rotation angles required for the AGV to travel from navigation point 1 to navigation point 2, and is of type double. The fourth element represents the number of rotations required for the AGV to travel from navigation point 1 to navigation point 2, and is of type integer. The first and second elements can be calculated using existing algorithms (such as A*, Dijkstra, etc.). The third and fourth elements are calculated after the shortest path is found. The calculation method for the third element is: the sum of the rotation angles of the AGV at each intermediate navigation point, where the rotation angle of the AGV at the nth intermediate navigation point is the angle between the (n-1)th intermediate navigation point and the nth intermediate navigation point and the (n+1)th intermediate navigation point. The fourth element is the number of times the rotation angle of the AGV at each intermediate navigation point is not 0. The above data can also be split into multiple tables to store the data.

[0039] In one embodiment, when determining whether a path conflict will occur, the simulation model simulates the process of the AGV moving along the initial path. If the AGV reaches the destination without waiting, it is determined that there is no conflict, indicating that the delivery task can be completed normally. When the current task ends, the simulation runs and the simulation results are fed back.

[0040] If the AGV waits in place for more than a preset time (e.g., 30 seconds), it is considered a path conflict. The simulation ends at the time the conflict is determined to have occurred, and records the AGV involved, the time of occurrence, the conflict scheduling system, the conflict area, and the associated tasks. The conflict area is the region where the movement paths of two or more AGVs overlap, and the conflict system is the AGV scheduling system associated with the navigation points in the overlapping area.

[0041] If the simulation predicts that the AGV movement process is blocked or deadlocked, the simulation is restarted. First, task priorities are initialized, and an AGV path priority sorting table is created. Specifically, tasks from multiple systems are first integrated, and conflicting AGVs are sorted according to task type (delivery tasks, emergency delivery tasks, charging tasks, rest tasks, etc.) and task urgency (such as priority level and remaining time before deadline) to determine the AGV path occupancy priority.

[0042] One type of AGV path priority sorting table is as follows: Figure 4 As shown, the table is divided into 5 columns.<AGV_ID,route,Ori,Opt,Tasktype> The data includes AGVID, AGV's forward path in the conflict area, and AGV task type. The AGV_ID column records the AGVs that occurred in step 3; the route column records the entire movement path of the corresponding AGV from its current position to leaving the conflict area. When an AGV passes a navigation point, that navigation point is deleted from the movement path to prevent interference with lower-priority AGVs; the Tasktype column records the AGV task type; and the Ori and Opt columns contain genetic algorithm population information, initially assigned values ​​of 1, 2, 3, 4…

[0043] In one embodiment, when determining the avoidance paths of each automated guided vehicle, it is necessary to determine the current task priorities of each automated guided vehicle and the current movement paths of each guided vehicle; based on the current task priorities and the current movement paths, determine the farthest avoidance points of the low-priority automated guided vehicles compared to the high-priority automated guided vehicles; determine the target associated points corresponding to the farthest avoidance points; determine the first arrival time of the high-priority automated guided vehicle at the target associated point and the second arrival time of the low-priority automated guided vehicle at the target associated point; in response to the first arrival time being less than the second arrival time, eliminate the farthest avoidance point and re-determine the target associated point until the first arrival time is greater than the second arrival time, and then determine the avoidance path at this time.

[0044] Specifically, in a schematic diagram of an AGV conflict area scenario as shown in Figure 5 , if there is a path conflict, the strategy of waiting in place or finding the nearest avoidance point nearby is preferred. First, calculate all the running paths of the AGVs with higher priorities than this AGV. If the forward direction of the AGV is covered by the paths of the high-priority AGVs, find the point with the minimum waiting time and move after the high-priority AGV passes. Taking Figure 5 as an example to calculate the avoidance path of AGV_2. In the figure, the priority of AGV_1 is higher than that of AGV_2, the end point of AGV_1 is P_3, and the end point of AGV_2 is P_9. The specific calculation steps of the avoidance path of AGV_2 are as follows:

[0045] First, calculate the time T2_1 when AGV_2 leaves the conflict area (i.e., reaches point P_9) and the time T1_1 when AGV_1 enters the conflict area (reaches point P_8).

[0046] If T1_1 < T2_1, it means that when the high-priority AGV enters the conflict area, the low-priority AGV has not yet left the conflict area. At this time, it is necessary to calculate the time T2_2 when AGV_2 reaches the associated point (P_6) of the farthest avoidance point (R_2) within the conflict area of this system and the time T1_2 when AGV_1 reaches the associated point (P_6). If T1_1 > T2_1, it means that the low-priority AGV reaches the target associated point earlier than the high-priority AGV. At this time, the low-priority AGV can avoid through the avoidance point corresponding to the target associated point without delaying the passage of the high-priority AGV, and the calculation can be ended.

[0047] If T1_2 < T2_2, then the R_2 point is removed, and the time T2_i of the farthest avoidance point associated point for AGV_2 to reach the conflict area is recalculated and compared with the time T1_i for AGV_1 to reach the associated point until T1_i > T2_i, at which point the calculation ends, and the avoidance path of AGV_2 is recorded and overwrites the original path. If no avoidance point is found, AGV_2 waits outside the conflict area until AGV_1 leaves the conflict area (i.e., reaches P_3), and the avoidance path of AGV_2 is recorded and overwrites the original path.

[0048] In the above process, when calculating the path completion time for an AGV to reach a specified navigation point, based on the current position and current movement path of the automated guided vehicle, the target movement distance between the automated guided vehicle and the target associated point can be determined; based on the target movement distances, movement speeds, angle values at each rotation, rotational angular velocities, number of rotations, and acceleration / deceleration compensation times for a single rotation of each automated guided vehicle, the arrival times for each automated guided vehicle to reach the target associated point can be determined.

[0049] Specifically, the calculation can be performed through the following formula: ; where represents the path completion time for an AGV to reach a specified navigation point; d i is the movement distance of the i-th path segment (i = 1, 2,..., n - 1); represents the movement speed of the AGV, and this value is different when the AGV is in the unloaded and fully loaded states; θ j is the angle value of the j-th rotation (j = 1, 2,..., m); represents the rotational angular velocity of the AGV; is the number of rotations of the AGV (which can be obtained by querying the shortest path matrix table as shown in Figure 3 ); represents the acceleration / deceleration compensation time for a single rotation of the AGV.

[0050] The simulation ends when the AGV completes the task or is blocked again. If a blockage occurs, the total time used for the AGV task is output as null, the maximum deviation of a single AGV compared to the expected time (the time for the AGV to complete the task without conflicts) is null, and the minimum remaining material consumption time is null; if the AGV completes the task normally, the total time used for all AGVs' tasks, the maximum deviation of a single AGV compared to the expected time, and the minimum remaining material consumption time are recorded.

[0051] In one embodiment, determining the target optimization data corresponding to the avoidance path specifically includes: determining the simulation time for each automated guided vehicle (AGV) to complete the delivery task during the simulation process; summing the simulation times corresponding to all AGVs to determine the total simulation time for all AGVs; determining the time deviation time corresponding to each AGV based on the simulation time and expected time of each AGV; and determining the minimum consumption time for the remaining materials based on the quantity of materials at the associated workstations and temporary storage locations of each AGV's movement endpoint, as well as the average consumption time of a single material of different types.

[0052] Specifically, the formula for calculating the total task time for all AGVs is as follows: The formula for calculating the maximum deviation of a single AGV from the expected time is as follows: Among them, T sim Indicates the total time taken for the AGV task; Δ max T represents the maximum deviation of a single AGV from its expected time; simi T represents the total simulation time for the i-th AGV from the current time to the end time of the task; expi This represents the calculated theoretical path completion time for the i-th AGV from the current point to the task start point, and then from the task start point to the task end point (if the task start point has already been passed, the theoretical path completion time from the current point to the task end point is calculated). The formula for calculating the minimum remaining material consumption time is as follows: ;in, Indicates the minimum remaining material consumption time; This represents the quantity of materials at the associated workstation and temporary storage location of the i-th AGV (the quantity of materials in the model is counted when the simulation ends). This represents the average consumption time for a single material of type j.

[0053] S103: Based on the genetic algorithm, the target optimization data is optimized to obtain target scheduling information.

[0054] A genetic algorithm can be used to optimize the target data in each iteration, thereby obtaining target scheduling information. This target scheduling information includes the optimized movement paths and optimized task priorities for each automated guided vehicle (AGV).

[0055] After obtaining the above target optimization data, it can be used as follows: Figure 4 The AGV path priority sorting table shown is used as input for genetic algorithm optimization. One objective value formula is as follows:

[0056]

[0057] in, , , The weights of each indicator are defined. A genetic algorithm is used to rank the AGV tasks with the shortest total time, and the movement path of each AGV and the optimized AGV task priority are recorded.

[0058] S104: Based on the target scheduling information, perform obstacle avoidance scheduling for each automated guided vehicle.

[0059] After obtaining the target scheduling information, the autonomous guided vehicles can be scheduled to avoid obstacles.

[0060] Specifically, the server will return the replanned AGV movement path, the AGV's waiting time at the avoidance point, and the AGV task priority to each AGV scheduling system according to their respective systems, and update the AGV status and path information in the simulation model. If the AGV scheduling system does not support the strategy of waiting n seconds at the avoidance point, a similar waiting effect can be achieved by having the AGV reach the avoidance point and end the task, and then having other system AGVs reassign tasks after passing through the conflict area.

[0061] The server also continuously receives real-time data from the AGV scheduling system (such as AGVs arriving at avoidance points, new tasks being generated, and AGV malfunctions), dynamically adjusting the simulation scenario and path planning to form a closed-loop optimization. Through multiple iterations, the frequency of conflicts is gradually reduced, improving the overall transportation efficiency of the system.

[0062] In one embodiment, in response to detecting a faulty automated guided vehicle (AGV) in the direction of travel of the target AGV, the navigation point where the faulty AGV is located is set as a temporary impassable point, and a candidate path corresponding to the target AGV is replanned; the completion time of the candidate path and the fault repair time of the faulty AGV within the original path are determined; based on the fault repair time and the candidate path completion time, the shortest path of the target AGV in the logistics simulation model is determined.

[0063] Specifically, when a faulty AGV is present in the AGV's movement path, the server transmits the faulty AGV's ID number through the AGV scheduling system. If a faulty AGV exists in the AGV's forward direction, the area containing the faulty AGV must be marked as an impassable point, and the shortest path for the AGV must be replanned as a candidate path. The completion time of the candidate path is calculated (based on the aforementioned formula). The completion time of the candidate path is calculated and compared with the remaining predicted fault repair time of the AGV (mean fault repair time minus the fault time) to determine whether the AGV should change to a new path or move along the original path and wait, thereby determining the shortest path of the target automated guided vehicle in the logistics simulation model.

[0064] This application embodiment also provides an automated guided vehicle (AGV) obstacle avoidance scheduling device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to: in response to a new delivery task generated by a single scheduling system, acquire current scheduling information from multiple scheduling systems; in response to the current scheduling information causing a path conflict in a pre-built logistics simulation model, determine the obstacle avoidance path for each AGV, and target optimization data corresponding to the obstacle avoidance path; the target optimization data includes at least one of total task time, maximum time deviation of a single AGV compared to the expected time, and minimum consumption time of remaining materials; optimize the target optimization data based on a genetic algorithm to obtain target scheduling information; the target scheduling information includes the optimized movement path and optimized task priority of each AGV; and perform obstacle avoidance scheduling for each AGV based on the target scheduling information.

[0065] This application embodiment also provides a non-volatile computer storage medium storing computer-executable instructions, the computer-executable instructions being configured to: in response to a new delivery task generated by a single scheduling system, obtain current scheduling information from multiple scheduling systems; in response to the current scheduling information causing a path conflict in a pre-built logistics simulation model, determine the avoidance path for each automated guided vehicle (AGV) and the target optimization data corresponding to the avoidance path; the target optimization data includes at least one of the following: total task time, maximum time deviation of a single AGV compared to the expected time, and minimum consumption time of remaining materials; optimize the target optimization data based on a genetic algorithm to obtain target scheduling information; the target scheduling information includes the optimized movement path of each AGV and the optimized task priority; and perform avoidance scheduling for each AGV based on the target scheduling information.

[0066] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device and medium embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the description of the method embodiments.

[0067] The devices and media provided in this application are one-to-one with the methods. Therefore, the devices and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0068] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0069] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0070] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0071] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0072] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0073] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0074] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0075] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0076] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for scheduling obstacle avoidance by automated guided vehicles, characterized in that, include: In response to a new delivery task generated by a single scheduling system, current scheduling information is obtained from multiple scheduling systems; In response to the current scheduling information causing a path conflict in the pre-built logistics simulation model, the avoidance path of each automated guided vehicle (AGV) is determined, along with the target optimization data corresponding to the avoidance path. The target optimization data includes at least one of the following: total task time, maximum time deviation of a single AGV compared to the expected time, and minimum consumption time of remaining materials. Based on a genetic algorithm, the target optimization data is optimized to obtain target scheduling information; the target scheduling information includes the optimized movement path of each automated guided vehicle and the optimized task priority. Based on the target scheduling information, avoidance scheduling is performed on each automated guided vehicle; In this process, the operating areas of automated guided vehicles from multiple scheduling systems are divided into several navigation points and created in the same simulation layer to build a logistics simulation model. The response to the current scheduling information causes path conflicts in the pre-built logistics simulation model, and determines the avoidance paths for each automated guided vehicle, specifically including: Determine the current task priority of each automated guided vehicle and the current movement path of each vehicle; Based on the current task priority and the current movement path, determine the farthest avoidance point of the low-priority automated guided vehicle relative to the high-priority automated guided vehicle; Determine the target associated point corresponding to the farthest avoidance point; Determine the first arrival time of the high-priority automated guided vehicle to the target associated point, and the second arrival time of the low-priority automated guided vehicle to the target associated point; In response to the first arrival time being less than the second arrival time, the farthest avoidance point is removed, and the farthest avoidance point and its corresponding target associated point are re-determined until the first arrival time is greater than the second arrival time, and the avoidance path is determined at this time. The avoidance point is a navigation point next to the main road where temporary parking is allowed, which is associated with the navigation point corresponding to the main road.

2. The method according to claim 1, characterized in that, The current scheduling information includes: Information on each automated guided vehicle (AGV), including the vehicle's guidance information, current task information, and material-related information; The guided vehicle information includes the real-time location of each automated guided vehicle and its corresponding dispatch system; The current task information includes task type, current movement path, task priority, and task deadline; The material-related information includes order number, material name, quantity, and material distribution.

3. The method according to claim 2, characterized in that, Before determining the avoidance paths for each automated guided vehicle (AGV) in response to path conflicts arising in the pre-built logistics simulation model due to the current scheduling information, the method further includes: Each navigation point has the same passable direction as the actual one, which is used to simulate the actual logistics direction and each navigation point can be associated with one or more scheduling systems. The types of navigation points include at least one of the following: one-way traffic points, multi-way traffic points, rest points, avoidance points, charging points, and loading / unloading points.

4. The method according to claim 1, characterized in that, Determining the first arrival time of the high-priority automated guided vehicle (AGV) to the target associated point and the second arrival time of the low-priority AGV to the target associated point specifically includes: Based on the current position and current movement path of the automated guided vehicle, the target movement distance between the automated guided vehicle and the target associated point is determined; Based on the target moving distance, moving speed, angle value, rotational angular velocity, number of rotations, and acceleration / deceleration compensation time of each automated guided vehicle (AGV), the arrival time of each AGV to the target associated point is determined.

5. The method according to claim 1, characterized in that, Determining the target optimization data corresponding to the avoidance path specifically includes: Determine the simulation time for each automated guided vehicle to complete the delivery task during the simulation process; Add up the simulation times for all automated guided vehicles to determine the total simulation time for all automated guided vehicles; Based on the simulation time and expected time of each automated guided vehicle, the time deviation time corresponding to each automated guided vehicle is determined; Based on the quantity of materials at the associated workstations and temporary storage locations of each automated guided vehicle (AGV), and the average consumption time of a single material of different types, the minimum consumption time for the remaining materials is determined.

6. The method according to claim 1, characterized in that, The step of performing obstacle avoidance scheduling for each automated guided vehicle based on the target scheduling information specifically includes: Determine the corresponding dispatching system for each automated guided vehicle; The optimized movement paths, optimized task priorities, and waiting times of each automated guided vehicle at the avoidance points are returned to the corresponding dispatching systems. Update the status and path information of the automated guided vehicles in the logistics simulation model.

7. The method according to claim 1, characterized in that, The method further includes: In response to the detection of a faulty automated guided vehicle in the direction of travel of the target automated guided vehicle, the navigation point where the faulty automated guided vehicle is located is set as a temporary impassable point, and the candidate path corresponding to the target automated guided vehicle is replanned. Determine the completion time of the candidate path corresponding to the candidate path, and the fault repair time of the automatic guided vehicle within the original path; Based on the fault repair time and the candidate path completion time, the shortest path of the target automated guided vehicle in the logistics simulation model is determined.

8. An automated guided vehicle (AGV) obstacle avoidance scheduling device, characterized in that, include: At least one processor; And, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the steps of the method as claimed in any one of claims 1-7.

9. A non-volatile computer storage medium storing computer-executable instructions, characterized in that, The computer-executable instructions are configured to perform the steps of the method as claimed in any one of claims 1-7.

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