An agv scheduling system path dynamic optimization method and system

Abstract

This application relates to the field of AGV intelligent scheduling technology, and discloses a method and system for dynamic path optimization in an AGV scheduling system, including: Step 1, identifying and obtaining the material model and batch number transported by the AGV, and obtaining the production data of the material based on the production management system, product model, and batch number; Step 2, performing priority analysis on the material based on the production data updated at preset time intervals, and obtaining the priority value of all AGV transport tasks based on the priority analysis results; Step 3, performing a dynamic path optimization process based on an improved spatiotemporal A* algorithm and the priority value of each AGV transport task. This invention establishes an information channel with the production system, and uses the production information corresponding to the transported materials to determine the priority of each AGV transport task. It calculates the time priority based on the predicted transport time, waiting time, and demand time, thereby improving the accuracy of AGV transport task priority determination.

Description

Technical Field

[0001] This application relates to the technical field of AGV intelligent scheduling, and in particular to a method and system for dynamic path optimization in an AGV scheduling system. Background Technology

[0002] AGV (Automated Guided Vehicle) is an intelligent logistics device based on automatic navigation technology. It achieves autonomous movement through technologies such as magnetic strips, lasers, RFID, and SLAM. It is widely used in manufacturing, for example, to realize material transfer on production lines and the management of automated warehouses.

[0003] In a factory's AGV scheduling system, different AGVs have different transport tasks, and each AGV has a different transport priority. Therefore, it is necessary to assign corresponding priorities to different transport tasks. In the existing technology, the priority of AGV transport tasks is mainly determined based on the type of transport task. For example, for some urgent tasks, the priority of AGV transport tasks will be set higher, while for some routine tasks, the priority of AGV transport tasks will be set lower. Although this method can achieve the division of priorities for different AGV transport tasks, the accuracy of the division is low. At the same time, the priority of AGV transport tasks is static and cannot be adaptively adjusted according to the actual transport status. Therefore, how to assign corresponding priorities to different transport tasks in real time and accurately is the fundamental problem that this invention aims to solve. Summary of the Invention

[0004] The fundamental problem this invention aims to solve is to determine the corresponding priorities for different transportation tasks in real time and accurately. This application provides a method and system for dynamic path optimization in an AGV scheduling system.

[0005] Firstly, this application provides a method for dynamic path optimization in an AGV scheduling system, employing the following technical solution: A method for dynamic path optimization in an AGV scheduling system includes: Step 1: Identify and obtain the material model and batch number transported by AGV, and obtain the material production data based on the production management system, product model and batch number; Step 2: Perform priority analysis on the materials based on the production data updated at preset time intervals, and obtain the priority values ​​of all AGV transport tasks based on the priority analysis results; Step 3: Dynamic path optimization based on the improved spatiotemporal A* algorithm and the priority value of each AGV transport task.

[0006] By adopting the above technical solution, the AGV scheduling system's path dynamic optimization method establishes an information channel with the factory's production system, thereby determining the priority of each AGV's transport task through the production information corresponding to the transported materials.

[0007] Optionally, the production data includes material type, material transportation task type, waiting time, demand time, order type, transportation information, and transportation congestion coefficient; The material types include critical materials, important materials, and ordinary materials; The material transport task types include emergency material replenishment tasks, non-emergency material replenishment tasks, and regular material delivery tasks. The order types involved include trial production orders, VIP customer orders, replenishment orders, and regular orders.

[0008] By adopting the above technical solution, different priority scores are set for key materials, important materials and ordinary materials respectively. Priority scores are obtained according to material type, material transportation task type and order type, which improves the accuracy of priority judgment for AGV transportation tasks.

[0009] Optionally, the process of prioritization analysis includes: Different priority scores are pre-set according to different material types, material transportation task types, and related order types, and priority scores are obtained according to material type, material transportation task type, and related order type respectively; Based on transportation information and transportation congestion coefficient, the delivery time is predicted, and the time priority is calculated and divided according to the predicted delivery time, the waiting time, and the demand time. The priority score of the AGV transportation task is obtained by summing the priority scores of the material type, material transportation task type, and related order type with the time priority score.

[0010] By adopting the above technical solution, the delivery time is predicted based on transportation information and transportation congestion coefficient. The time priority is calculated and divided according to the predicted delivery time, waiting time, and demand time. The priority scores corresponding to material type, material transportation task type, and related order type are summed with the time priority scores to obtain the priority value of the AGV transportation task. Through the process of obtaining the time priority, the priority of each AGV transportation task is adjusted in real time according to the actual execution status of each AGV transportation task and the congestion status of the AGV transportation path at the current time, which improves the accuracy and real-time performance of AGV transportation task priority judgment.

[0011] Optionally, the process of predicting delivery time includes: The origin and destination of material transportation are obtained from the transportation information. The transportation route area is obtained based on the location of the origin and destination. The transportation route area is divided into n regions. Obtain the reference transport distance for each segmented region. and route congestion coefficient , i∈[1,n]; through mathematical model Calculate and obtain the predicted delivery time .

[0012] By adopting the above technical solution, the process of obtaining the predicted delivery time can be realized.

[0013] Optionally, the process of calculating the time priority equally includes: Through mathematical models Calculate the time priority score at the current time point. ;in, To preset the base score, The predicted delivery time is given by t, where t is the current time. For the required time point, For the waiting time, and All are defined functions, when When <1, , ;when When ≥1, , , For preset fixed coefficients, <1.

[0014] By adopting the above technical solution, the real-time priority judgment of AGV transportation tasks is guaranteed. Through the calculation logic in the mathematical model, the time priority value can be dynamically adjusted according to the remaining time and the value of the waiting time. When the remaining time is low, the waiting time factor is ignored, and the priority is adjusted according to the length of the remaining time. The less the remaining time, the higher the priority value. When the remaining time is sufficient, the time priority value is adaptively adjusted according to the waiting time of the AGV transportation task. The longer the waiting time, the higher the priority value, thus ensuring the accuracy and real-time performance of the time priority score.

[0015] Optionally, the process for determining the transportation congestion coefficient includes: Obtain the critical value of the number of AGVs in each division area and the actual number of AGVs in the current division area. Obtain the location points of all AGVs in the current division area. Calculate the AGV distribution concentration based on the location points of all AGVs. Obtain the transportation congestion coefficient based on the ratio of the actual number of AGVs to the critical value of the number of AGVs and the AGV distribution concentration.

[0016] By adopting the above technical solution, the transportation congestion coefficient is obtained by comprehensively judging the number and distribution of AGVs in different areas. The higher the number of AGVs and the greater the concentration of AGV distribution, the higher the path congestion coefficient. The larger.

[0017] Optionally, the process of performing dynamic path optimization includes: In the improved spatiotemporal A* algorithm, the weight of the conflict cost c(n) in the algorithm is dynamically adjusted according to the priority value of the AGV transport task. The higher the priority value, the lower the weight of the conflict cost c(n).

[0018] By adopting the above technical solution, the weight of the conflict cost c(n) in the algorithm is dynamically adjusted according to the priority range of the AGV transport task. The weight of the conflict cost (c(n)) of high priority tasks should be reduced so that they tend to follow the "shortest time path" rather than the "least conflict path" during planning. Therefore, the higher the priority value, the lower the weight of the conflict cost c(n), which ensures the rationality of the order of AGV transport tasks of different priorities during AGV scheduling.

[0019] Secondly, this application provides a dynamic path optimization system for an AGV scheduling system, which adopts the following technical solution: An AGV scheduling system path dynamic optimization system, wherein the system adopts any one of the above-mentioned AGV scheduling system path dynamic optimization methods, including a production data docking module, an AGV transport task priority rating module, and a path dynamic optimization module; The production data docking module is used to identify and obtain the material model and batch number transported by AGV, and obtain the production data of the material based on the production management system, product model and batch number; The AGV transport task priority rating module is used to perform priority analysis on materials based on production data updated at preset time intervals, and obtain the priority value of all AGV transport tasks based on the priority analysis results. The path dynamic optimization module is used to perform a path dynamic optimization process based on the improved spatiotemporal A* algorithm and the priority value of each AGV transport task.

[0020] In summary, this application includes at least one of the following beneficial technical effects: The AGV scheduling system path dynamic optimization method in this invention establishes an information channel with the factory's production system. By using the production information corresponding to the transported materials, it can determine the priority of each AGV transport task. The method predicts transport time based on transport information and congestion coefficients, and calculates time priority scores based on the predicted transport time, waiting time, and demand time. The priority scores corresponding to material type, material transport task type, and related order type are summed with the time priority scores to obtain the AGV transport task's priority value. Through the process of obtaining time priority scores, the priority of each AGV transport task is adjusted in real time based on its actual execution status and the congestion status of the AGV transport path at the current time, improving the accuracy and real-time performance of AGV transport task priority determination. Attached Figure Description

[0021] Figure 1 This is a flowchart of the steps in the AGV scheduling system path dynamic optimization method of the present invention.

[0022] Figure 2 This is a logical schematic diagram of the AGV scheduling system path dynamic optimization system in this invention. Detailed Implementation

[0023] The embodiments of this application are described in detail below, and examples of the embodiments are shown in the accompanying drawings.

[0024] In the description of this specification, the references to "certain embodiments," "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples" refer to specific features, structures, materials, or characteristics described in connection with the described embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0025] This application discloses a method and system for dynamic path optimization in an AGV scheduling system, referring to... Figure 1 , Figure 2The system includes a production data integration module, an AGV transport task priority rating module, and a path dynamic optimization module. It completes steps one through three of the path dynamic optimization method. Step one involves identifying and obtaining the material model and batch number transported by the AGV. Based on the production management system, product model, and batch number, the system obtains the material's production data. This production data includes material type, material transport task type, waiting time, demand time, relevant order type, transportation information, and transportation congestion coefficient. Material types include critical materials, important materials, and ordinary materials. Material transport task types include emergency replenishment tasks, non-emergency replenishment tasks, and regular delivery tasks. Relevant order types include trial production orders, VIP customer orders, replenishment orders, and regular orders. In this embodiment, the AGV scheduling system's path dynamic optimization method establishes an information channel with the factory's production system, using the production information corresponding to the transported materials to determine the priority of each AGV transport task.

[0026] In step two, production data updated at preset time intervals is used to perform priority analysis on materials, and the priority values ​​of all AGV transport tasks are obtained based on the priority analysis results. This periodic priority analysis ensures the real-time nature of priority determination for all AGV transport tasks. The priority analysis process includes: pre-setting different priority scores for different material types, material transport task types, and related order types; specifically, setting different priority scores for critical materials, important materials, and ordinary materials, with critical materials having a higher priority score than important materials, which in turn have a higher priority score than ordinary materials; setting different priority scores for emergency replenishment tasks, non-emergency replenishment tasks, and regular delivery tasks, with emergency replenishment tasks having a higher priority score than non-emergency replenishment tasks, which in turn have a higher priority score than regular delivery tasks; and setting different priority scores for trial production orders, VIP customer orders, replenishment orders, and regular orders. The priority score is determined by the order type. Replenishment orders have a higher priority score than VIP customer orders, which in turn have a higher priority score than trial production orders, which in turn have a higher priority score than regular orders. Therefore, priority scores are obtained based on material type, material transport task type, and the type of order involved. More importantly, this embodiment also predicts delivery time based on transportation information and congestion coefficients. Time priority is calculated by dividing the time into equal parts based on the predicted delivery time, waiting time, and demand time. The priority scores corresponding to material type, material transport task type, and the type of order involved are summed with the time priority scores to obtain the priority value of the AGV transport task. Through the process of obtaining the time priority, the priority of each AGV transport task is adjusted in real time based on the actual execution status of each AGV transport task and the congestion status of the AGV transport path at the current time, improving the accuracy and real-time performance of AGV transport task priority judgment.

[0027] In the above technical solution, the process of predicting transportation time includes: obtaining the starting and ending points of material transportation from the transportation information; obtaining the transportation route area based on the locations of the starting and ending points; dividing the transportation route area into n sub-regions; it is important to explain the sub-region division process, as managers will set different management areas based on the layout of the factory workshop to facilitate the management of the AGV scheduling process. After obtaining the transportation route area based on the locations of the starting and ending points, it is determined how many management areas appear within the transportation route area, thus realizing the sub-region division process. The sub-regions are the management areas appearing within the transportation route area; then, the reference transportation distance for each sub-region is obtained. and route congestion coefficient , i∈[1,n]; where, reference transport distance This is determined based on the pre-set distance to the management area corresponding to each divided zone and the area ratio of the divided zone. When the management area falls entirely within the transportation route area, the transportation distance is used as a reference. This refers to the pre-set distance of the corresponding management area. When the entire management area falls within the transportation route area, the transportation distance is used as a reference. It is the product of the pre-set distance to the corresponding management area and the area ratio of the divided area.

[0028] In addition, the path congestion coefficient The judgment process includes: first, obtaining the critical value of the number of AGVs in each divided area and the actual number of AGVs in the current divided area; second, obtaining the location points of all AGVs in the current divided area; third, calculating the AGV distribution concentration based on the location points of all AGVs; and fourth, obtaining the transportation congestion coefficient based on the ratio of the actual number of AGVs to the critical value of the number of AGVs and the AGV distribution concentration. The ratio of the actual number of AGVs to the critical value of the number of AGVs can be directly obtained through the AGV management system. The calculation method for the AGV distribution concentration can be implemented using existing algorithms for discrete point distribution. Therefore, by comprehensively judging the number and distribution degree of AGVs in different areas, the transportation congestion coefficient is obtained. The higher the number of AGVs and the greater the AGV distribution concentration, the higher the path congestion coefficient. The larger.

[0029] After obtaining the path congestion coefficient, a mathematical model is used. Calculate and obtain the predicted delivery time By predicting delivery time The process of calculating time priority scores includes: using a mathematical model. Calculate the time priority score at the current time point. ;in, The preset base score is adaptively set based on the priority score range corresponding to the material type, material transportation task type, and order type involved. The predicted delivery time is given by t, where t is the current time. For the required time point, For the waiting time, and All are defined functions, when When <1, , ;when When ≥1, , , For preset fixed coefficients, <1, which is set based on the test data fitting, and scores the time priority at the current time point. The real-time acquisition process ensures the real-time priority judgment of AGV transportation tasks. Through the calculation logic in the mathematical model, the time priority value can be dynamically adjusted according to the remaining time and the value of the waiting time. When the remaining time is low, the waiting time factor is ignored, and the priority is adjusted according to the length of the remaining time. The less the remaining time, the higher the priority value. When the remaining time is sufficient, the time priority value is adaptively adjusted according to the waiting time of the AGV transportation task. The longer the waiting time, the higher the priority value. Through the time priority scoring mathematical model in this embodiment, the accuracy and real-time performance of the time priority scoring are guaranteed.

[0030] Furthermore, in step three, a dynamic path optimization process is performed based on the improved spatiotemporal A* algorithm and the priority value of each AGV's transport task. The improved spatiotemporal A* algorithm is a core advanced algorithm for solving dynamic path planning and obstacle avoidance for multiple AGVs. By introducing a time dimension and conflict prediction mechanism into the traditional A* algorithm, it achieves a key leap from "spatial path planning" to "spatiotemporal collaborative scheduling." In this embodiment, the improved spatiotemporal A* cost function is: F(n) = G(n) + H(n) + C(n), where G(n) represents the actual cost from the starting point to the current node n (including travel time and energy consumption); H(n) represents the heuristically estimated cost from the current node n to the destination (including Manhattan distance and Euclidean distance); C(n) represents the cost from the current node n to the destination (including Manhattan distance and Euclidean distance); and C(n) represents the cost from the starting point to the current node n. The conflict cost is represented by a penalty based on the probability or severity of a conflict between the current path (x, y, t) and other planned paths of AGVs. This guides the algorithm to actively select spatiotemporal nodes with no or low conflict. Therefore, during the dynamic path optimization process, the improved spatiotemporal A* algorithm dynamically adjusts the weight of the conflict cost c(n) according to the priority range of the AGV transport tasks. The conflict cost (c(n)) weight of high-priority tasks should be reduced so that they tend to follow the "shortest time path" rather than the "least conflict path" during planning. Thus, the higher the priority value, the lower the weight of the conflict cost c(n), ensuring the rationality of the order of AGV transport tasks of different priorities during AGV scheduling.

[0031] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for dynamic path optimization in an AGV scheduling system, characterized in that, include: Step 1: Identify and obtain the material model and batch number transported by AGV, and obtain the material production data based on the production management system, product model and batch number; Step 2: Perform priority analysis on the materials based on the production data updated at preset time intervals, and obtain the priority values ​​of all AGV transport tasks based on the priority analysis results; Step 3: Dynamic path optimization based on the improved spatiotemporal A* algorithm and the priority value of each AGV transport task.

2. The method for dynamic path optimization in an AGV scheduling system according to claim 1, characterized in that, The production data includes material type, material transportation task type, waiting time, demand time point, order type involved, transportation information, and transportation congestion coefficient. The material types include critical materials, important materials, and ordinary materials; The material transport task types include emergency material replenishment tasks, non-emergency material replenishment tasks, and regular material delivery tasks. The order types involved include trial production orders, VIP customer orders, replenishment orders, and regular orders.

3. The method for dynamic path optimization in an AGV scheduling system according to claim 2, characterized in that, The process of prioritization analysis includes: Different priority scores are pre-set according to different material types, material transportation task types, and related order types, and priority scores are obtained according to material type, material transportation task type, and related order type respectively; Based on transportation information and transportation congestion coefficient, the delivery time is predicted, and the time priority is calculated and divided according to the predicted delivery time, the waiting time, and the demand time. The priority score of the AGV transportation task is obtained by summing the priority scores of the material type, material transportation task type, and related order type with the time priority score.

4. The method for dynamic path optimization in an AGV scheduling system according to claim 3, characterized in that, The process of predicting delivery times includes: The origin and destination of material transportation are obtained from the transportation information. The transportation route area is obtained based on the location of the origin and destination. The transportation route area is divided into n regions. Obtain the reference transport distance for each segmented region. and route congestion coefficient , i∈[1,n]; through mathematical model Calculate and obtain the predicted delivery time .

5. The method for dynamic path optimization in an AGV scheduling system according to claim 3, characterized in that, The process of calculating the time priority equally includes: Through mathematical models Calculate the time priority score at the current time point. ;in, To preset the base score, The predicted delivery time is given by t, where t is the current time. For the required time point, For the waiting time, and All are defined functions, when When <1, , ;when When ≥1, , , For preset fixed coefficients, <1.

6. The method for dynamic path optimization in an AGV scheduling system according to claim 4, characterized in that, The process for determining the transportation congestion coefficient includes: Obtain the critical value of the number of AGVs in each division area and the actual number of AGVs in the current division area. Obtain the location points of all AGVs in the current division area. Calculate the AGV distribution concentration based on the location points of all AGVs. Obtain the transportation congestion coefficient based on the ratio of the actual number of AGVs to the critical value of the number of AGVs and the AGV distribution concentration.

7. The method for dynamic path optimization in an AGV scheduling system according to claim 3, characterized in that, The process of performing dynamic path optimization includes: In the improved spatiotemporal A* algorithm, the weight of the conflict cost c(n) in the algorithm is dynamically adjusted according to the priority value of the AGV transport task. The higher the priority value, the lower the weight of the conflict cost c(n).

8. A path dynamic optimization system for an AGV scheduling system, characterized in that, The system adopts a path dynamic optimization method for AGV scheduling system as described in any one of claims 1-7, including a production data docking module, an AGV transport task priority rating module, and a path dynamic optimization module; The production data docking module is used to identify and obtain the material model and batch number transported by AGV, and obtain the production data of the material based on the production management system, product model and batch number; The AGV transport task priority rating module is used to perform priority analysis on materials based on production data updated at preset time intervals, and obtain the priority value of all AGV transport tasks based on the priority analysis results. The path dynamic optimization module is used to perform a path dynamic optimization process based on the improved spatiotemporal A* algorithm and the priority value of each AGV transport task.

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