Vehicle task allocation method based on path generation

Abstract

The invention discloses a vehicle task allocation method based on path generation. The method comprises the following steps: S1, obtaining a task; s2, selecting all AGV vehicles which do not execute tasks; s3, calculating paths of all vehicles from the current point to the task starting point; s4, judging whether the current path is higher than a safe path length threshold value or not, if so, entering the step S5, and if not, entering the step S5; if not, selecting the vehicle with the current path smaller than or equal to the safe path length threshold to execute the task; and S5, path segmentation and competition are carried out on all vehicles. According to the method, it is guaranteed that the vehicles are preferentially selected to the maximum extent when the tasks are issued, data of multiple dimensions are added to serve as reference for overall design of a user, and idle AGVs are fully utilized for exploration. The method can be applied to the scene that multiple AGVs are in an unsaturated task, AGV resources are fully utilized, and meanwhile the situation that the terminal point cannot be reached due to the uncontrollable situation existing in actual movement can be avoided.

Description

Technical Field

[0001] This invention belongs to the technical field of intelligent warehouse scheduling systems, specifically relating to a vehicle task allocation method based on path generation. Background Technology

[0002] In recent years, with the increasing demand for AGVs in small warehouses, the high-frequency use of all AGVs simultaneously does not account for a large portion of the overall production cycle, resulting in AGVs being idle most of the time. When a station needs to call an AGV to perform a task, the traditional approach is to first use Dijkstra's algorithm to calculate the vehicle with the shortest distance between the actual starting point and the destination of the actual travel route, and then call that vehicle to move to the task starting point to execute the task. After selecting the vehicle, various situations can occur during the actual movement to the task station (i.e., the task starting point), such as: not arriving within the expected time, or being unable to arrive due to special circumstances. The limitations of the traditional task allocation vehicle method mean that this selection method is a one-time calculation, and it is impossible to change or handle any uncontrollable situations that may arise later. Other idle AGVs cannot be used to make timely corrections, ultimately resulting in the task allocation failing and the task being unable to be executed.

[0003] Therefore, a vehicle task allocation method based on path generation is urgently needed. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a vehicle task allocation method based on path generation.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] This invention provides a vehicle task allocation method based on path generation, comprising the following steps:

[0007] S1. Obtain the task;

[0008] S2. Select all AGV vehicles that have not yet performed a task;

[0009] S3. Calculate the path for all vehicles from the current point to the task start point, and move all vehicles to the task start point simultaneously according to the path.

[0010] S4. Determine whether the current path of all vehicles is higher than the safe path length threshold. If yes, all vehicles continue to move towards the task starting point and proceed to step S5. If no, select vehicles whose current path is less than or equal to the safe path length threshold to perform the task, and stop the other vehicles from moving forward. The waypoints are scored and divided into the candidate vehicle queue.

[0011] S5. All vehicles will engage in route segmentation and competition, specifically including the following steps:

[0012] S51. Divide the current path of all vehicles into segments;

[0013] S52, Dynamically calculate the time window;

[0014] S53. Control each vehicle's route through the corresponding time window.

[0015] Preferably, step S53 controls each vehicle's path segment through a corresponding time window, specifically including:

[0016] S531. Determine whether the travel time of each vehicle within this route exceeds the corresponding time window. If yes, proceed to step S532; otherwise, proceed to step S4.

[0017] S532. Determine if there are other competing vehicles for the vehicle. If so, stop moving forward, score the waypoints, and classify the vehicle into the candidate vehicle queue. If not, continue moving forward and release all vehicles in the candidate vehicle queue to proceed to step S3.

[0018] Preferably, in step S4, selecting vehicles whose current path is less than or equal to the safe path length threshold to perform tasks specifically includes: for vehicles that continue to move, requesting the coordinates of points within the safe path length threshold; if successful, selecting the vehicle to perform the task and scoring the vehicle's execution timeliness; if unsuccessful, stopping the vehicle from moving forward, scoring the points along the way, and classifying the vehicle into the candidate vehicle queue.

[0019] Preferably, if multiple moving vehicles simultaneously request the coordinates of points within the safe path length threshold, the vehicle that arrives at the safe path length threshold first will be prioritized to perform the task.

[0020] Preferably, the dynamic calculation time window in step S522 is set based on fault alarms, number of dynamic route planning attempts, emergency stop duration, vehicle execution timeliness score, and route point score.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] This invention ensures that vehicles are selected optimally to the greatest extent possible when tasks are assigned. It incorporates data from multiple dimensions as a reference for the user's overall design and makes full use of idle AGV vehicles for exploration. This invention is applicable to scenarios with multiple AGV vehicles and underutilized tasks, making full use of AGV vehicle resources while avoiding situations where the destination cannot be reached due to uncontrollable circumstances during actual movement. Attached Figure Description

[0023] Figure 1This is a flowchart of a vehicle task allocation method based on path generation according to the present invention. Detailed Implementation

[0024] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0025] like Figure 1 As shown, this embodiment provides a vehicle task allocation method based on path generation, including the following steps:

[0026] S1. Obtain the task;

[0027] S2. Select all AGV vehicles that have not yet performed a task;

[0028] S3. Calculate the path for all vehicles from the current point to the task start point, and move all vehicles to the task start point simultaneously according to the path.

[0029] S4. Determine if the current path of all vehicles is higher than the safe path length threshold. If yes, all vehicles continue to move towards the task starting point and proceed to step S5. If no, select vehicles whose current path is less than or equal to the safe path length threshold to perform the task, and stop the remaining vehicles. The vehicles will then be scored for their waypoints and added to the candidate vehicle queue. Specifically, selecting a vehicle to perform the task involves: requesting coordinates of points within the safe path length threshold for vehicles that continue moving. If successful, the vehicle will be selected to perform the task, and its execution time will be scored. If unsuccessful, the vehicle will stop moving, its waypoints will be scored, and it will be added to the candidate vehicle queue.

[0030] S5. All vehicles will engage in route segmentation and competition, specifically including the following steps:

[0031] S51. Divide the current path of all vehicles into segments;

[0032] S52, Dynamically calculate the time window;

[0033] S53. Control each vehicle's route within a corresponding time window, specifically including:

[0034] S531. Determine whether the travel time of each vehicle within this route exceeds the corresponding time window. If yes, proceed to step S532; otherwise, proceed to step S4.

[0035] S532. Determine if there are other competing vehicles for the vehicle. If so, stop moving forward, score the waypoints, and classify the vehicle into the candidate vehicle queue. If not, continue moving forward and release all vehicles in the candidate vehicle queue to proceed to step S3.

[0036] Specifically, if multiple moving vehicles simultaneously request coordinates of points within the safe path length threshold, the vehicle that arrives at the safe path length threshold first will be prioritized to perform the task.

[0037] Specifically, the dynamic calculation time window in step S522 is set based on fault alarms, the number of times dynamic path planning is performed, emergency stop duration, vehicle execution efficiency score, and waypoint score. The segmented dynamic calculation time window effectively eliminates slow-moving and unreasonable AGV vehicles, avoiding various anomalies during actual movement. For AGV vehicles unable to reach the starting point, multiple attempts can be made, greatly reducing the number of tasks that cannot be completed. The final score of the overall task execution also provides users with more data for optimization.

[0038] In this embodiment, as multiple vehicles move towards the task starting point, they are continuously eliminated through various pre-defined mechanisms. When all vehicles fail to reach the task starting point, all vehicles in the candidate vehicle queue are "revived" and competed again. This cycle repeats continuously, greatly improving the robustness of the vehicle selection function module.

[0039] In this embodiment, when multiple AGVs remain after being eliminated, the AGVs are selected based on the first one to reach the safe path length threshold. This ensures that only one AGV can successfully apply for the safe path length threshold coordinates when multiple AGVs apply at the same time, thus determining the final vehicle to perform the task.

[0040] This embodiment also scores the execution time and route points of all AGVs after they successfully compete to become the vehicles performing the task, using this score as a reference for the next dynamic calculation of the time window threshold. Simultaneously, the system will provide prompts to the user for vehicles or locations with extremely low scores, serving as a basis for adjustment or maintenance.

[0041] This embodiment's overall design ensures that vehicles are selected optimally to the greatest extent possible when tasks are assigned. Multiple dimensions of data are added as a reference for the user's overall design, making full use of idle AGV vehicles for exploration. This invention is applicable to scenarios with multiple AGV vehicles and unsaturated tasks, fully utilizing AGV vehicle resources while avoiding situations where the destination cannot be reached due to uncontrollable circumstances during actual movement.

[0042] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A vehicle task allocation method based on path generation, characterized in that, Includes the following steps: S1. Obtain the task; S2. Select all AGV vehicles that have not yet performed a task; S3. Calculate the path for all vehicles from the current point to the task start point, and move all vehicles to the task start point simultaneously according to the path. S4. Determine whether the current path of all vehicles is higher than the safe path length threshold. If yes, all vehicles continue to move towards the task starting point and proceed to step S5. If no, select vehicles whose current path is less than or equal to the safe path length threshold to perform the task, and stop the other vehicles from moving forward. The waypoints are scored and divided into the candidate vehicle queue. S5. All vehicles will engage in route segmentation and competition, specifically including the following steps: S51. Divide the current path of all vehicles into segments; S52, Dynamically calculate the time window; S53. Control each vehicle's route through the corresponding time window.

2. The vehicle task allocation method based on path generation according to claim 1, characterized in that, Step S53 controls each vehicle's route within a corresponding time window, specifically including: S531. Determine whether the travel time of each vehicle within this route exceeds the corresponding time window. If yes, proceed to step S532; otherwise, proceed to step S4. S532. Determine if there are other competing vehicles for the vehicle. If so, stop moving forward, score the waypoints, and classify the vehicle into the candidate vehicle queue. If not, continue moving forward and release all vehicles in the candidate vehicle queue to proceed to step S3.

3. The vehicle task allocation method based on path generation according to claim 2, characterized in that, In step S4, vehicles whose current path length is less than or equal to the safe path length threshold are selected to perform tasks. Specifically, this includes: for vehicles that continue to move, requesting the coordinates of points within the safe path length threshold; if successful, selecting the vehicle to perform the task and scoring the vehicle's execution timeliness; if unsuccessful, stopping the vehicle from moving forward, scoring the points along the way, and classifying the vehicle into the candidate vehicle queue.

4. The vehicle task allocation method based on path generation according to claim 3, characterized in that, If multiple moving vehicles simultaneously request coordinates of points within the safe path length threshold, the vehicle that arrives at the safe path length threshold first will be prioritized to perform the task.

5. The vehicle task allocation method based on path generation according to claim 1, characterized in that, In step S522, the dynamic calculation time window is set based on fault alarms, the number of times dynamic path planning is performed, emergency stop duration, vehicle execution timeliness score, and route point score.

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