Cooperative scheduling method and system of AGV group, and storage medium

Abstract

The invention provides a cooperative scheduling method and system for an AGV group and a storage medium, and the method comprises the steps: carrying out the distribution of a scheduling task according to the state parameters of each AGV in the AGV group and an established global map, and obtaining a task distribution scheme of the AGV group; the task allocation scheme comprises an initial planning path and initial driving parameters of the AGV; determining whether the AGV group has a conflict based on the initial driving parameters and the initial planning path, and determining the type of the conflict to be solved; determining a target conflict avoidance strategy corresponding to the to-be-solved conflict type based on a pre-constructed mapping relationship between the conflict type and the conflict avoidance strategy; and optimizing the initial planning path based on the target conflict avoidance strategy to obtain a conflict avoidance path, and controlling the AGV group to run along the conflict avoidance path. According to the method, the overall scheduling of the AGV group is realized, different target conflict avoidance strategies are finely set for different to-be-solved conflict types, and the environmental adaptability is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of workshop scheduling, and in particular to a collaborative scheduling method, system and storage medium for an AGV group. Background Art

[0002] An automated guided vehicle (AGV) is a wheeled robot equipped with an electromagnetic or optical automatic guidance system that uses a navigation controller to calculate the AGV's posture and path, allowing it to travel along fixed tracks or free paths to perform handling tasks. Multiple AGVs can be centrally dispatched under the control of a scheduling system to accomplish complex handling tasks.

[0003] Scheduling systems typically assign routes to specific AGVs based on the shortest travel time from a starting point to a destination. In other words, existing technologies plan AGV paths with the goal of minimizing vehicle travel time. However, when applied to AGV clusters consisting of multiple or large numbers of AGVs, this approach presents a technical problem: Independent path planning for each AGV fails to account for conflicts between different AGVs, leading to conflicts within the cluster and ineffective execution of transport tasks.

[0004] Therefore, there is an urgent need to provide a collaborative scheduling method, system and storage medium for an AGV group to achieve overall scheduling of the AGV group, avoid conflicts, and improve the reliability and effective execution of handling tasks. Summary of the Invention

[0005] In view of this, it is necessary to provide a collaborative scheduling method, system and storage medium for an AGV group to solve the technical problem in the prior art that conflicts in the paths of the AGV group are not considered, resulting in the inability to effectively complete the handling task.

[0006] In a first aspect, in order to solve the above technical problems, the present invention provides a collaborative scheduling method for an AGV group, comprising: Allocate scheduling tasks based on the status parameters of each AGV in the AGV group and the established global map to obtain a task allocation plan for the AGV group; the task allocation plan includes the initial planned path and initial driving parameters of the AGV; Determine whether there is a conflict among the AGV group based on the initial driving parameters and the initial planned path, and determine the type of conflict to be resolved; Determining a target conflict avoidance strategy corresponding to the conflict type to be resolved based on a pre-established mapping relationship between conflict types and conflict avoidance strategies; The initial planned path is optimized based on the target conflict avoidance strategy to obtain a conflict avoidance path, and the AGV group is controlled to travel along the conflict avoidance path.

[0007] In a possible implementation, before allocating the scheduling tasks according to the status parameters of each AGV in the AGV group and the established global map, the method further includes: Obtaining the workstation locations, material storage locations, AGV waiting locations, and an initial path connecting the workstation locations, material storage locations, and AGV waiting locations in the workshop; Determine a drivable path based on the path type of the initial path; the path type includes AGV drivable and AGV non-drivable, and the drivable path includes a one-way path and a two-way path; The global map is generated by connecting the workstation locations, material storage locations, and AGV waiting locations based on the drivable path.

[0008] In one possible implementation, the scheduling task includes multiple target workstations and the workpiece processing time of each target workstation, and the AGV status parameters include the AGV's current location, driving speed, and remaining power. Then, the scheduling task is allocated based on the status parameters of each AGV in the AGV group and the established global map to obtain a task allocation plan for the AGV group, including: Determine the individual driving time and individual driving mileage of each AGV based on the current location, workstation location, material storage location, AGV waiting location and driving speed; Determine a group travel time of the AGV group based on the individual travel time, and determine a load variance of the AGV group based on the individual travel time and the individual travel mileage; A multi-objective optimization model is constructed with the goal of minimizing the group travel time and minimizing the load variance, and a power constraint condition is constructed based on the remaining power, and a path constraint condition is constructed based on the path type; The multi-objective optimization model is solved based on the power constraint condition and the path constraint condition to obtain a task allocation plan for the AGV group.

[0009] In a possible implementation, the multi-objective optimization model is:

[0010]

[0011]

[0012] Where, To optimize the target value; is the group travel time; is the time weight; is the load variance; is the load weight; is the path length of the i-th AGV between the m-th station and the n-th station, and the n-th station is the next station of the m-th station; is the driving speed of AGV; is the workpiece processing time at the nth station; is the travel time of the i-th AGV from the material storage location or current location to the first workstation; is the travel time of the i-th AGV from the last station to the AGV waiting position; is the single driving time of the i-th AGV; is the average travel time of the AGV group; is the single mileage of the i-th AGV; is the average mileage of the AGV group; is the total number of AGVs in the AGV group.

[0013] In a possible implementation, the method further includes: Obtain the collaborative scheduling scenario type. When the collaborative scheduling scenario is an emergency task scenario, the time weight is greater than the load weight. When the collaborative scheduling scenario is a long-term operation scenario, the time weight is less than or equal to the load weight.

[0014] In a possible implementation, the conflict types to be resolved include single-point conflicts, unilateral conflicts, and follow-up conflicts. Determining the conflict types to be resolved includes: Determine whether the AGVs have a common path segment at the same time based on the initial driving parameters and the initial planned path; the initial driving parameters include driving direction and driving speed; If the AGVs have a common path segment at the same time, determine whether the driving directions and driving speeds of at least two conflicting AGVs with the common path segment are the same; When at least two conflicting AGVs are traveling in the same direction and at the same speed, the conflict type to be resolved is a single-point conflict. When at least two conflicting AGVs are traveling in opposite directions and at the same speed, the conflict type to be resolved is a unilateral conflict. When at least two conflicting AGVs are traveling in the same direction and at different speeds, the conflict type to be resolved is a following conflict.

[0015] In a possible implementation, when the conflict type to be resolved is a single-point conflict or a unilateral conflict, the conflict avoidance strategy is: planning paths for conflicting AGVs except for the highest priority AGV based on the order of priority from low to high; When the type of conflict to be resolved is a following conflict, the conflict avoidance strategy is: optimize the driving speeds of at least two conflicting AGVs based on a preset minimum following distance, obtain the optimized speed, and determine whether the optimized speed is within the safe speed. If not, plan the paths of the conflicting AGVs except the highest priority one in order of priority from low to high.

[0016] In a possible implementation, the method further includes: Obtain the task urgency, task type, and remaining battery power of each AGV; The priority is determined based on a fuzzy logic algorithm, the urgency of the task, the task type, and the remaining battery power.

[0017] In a second aspect, the present invention further provides a collaborative scheduling system for an AGV group, comprising: AGV group; A collaborative scheduling module is used to allocate scheduling tasks according to the status parameters of each AGV in the AGV group and the established global map, and obtain a task allocation plan for the AGV group; the task allocation plan includes the initial planned path and initial driving parameters of the AGV; based on the initial driving parameters and the initial planned path, it is determined whether there is a conflict in the AGV group and the type of conflict to be resolved; based on the pre-constructed mapping relationship between the conflict type and the conflict avoidance strategy, a target conflict avoidance strategy corresponding to the conflict type to be resolved is determined; based on the target conflict avoidance strategy, the initial planned path is optimized to obtain a conflict avoidance path, and the AGV group is controlled to travel along the conflict avoidance path.

[0018] In a third aspect, the present invention also provides a computer-readable storage medium for storing computer-readable programs or instructions, which, when executed by a processor, can implement the steps in the collaborative scheduling method of an AGV group described in any of the above possible implementation methods.

[0019] The beneficial effects of the present invention are as follows: the collaborative scheduling method of the AGV group provided by the present invention first allocates scheduling tasks based on the status parameters of each AGV and the global map, realizes the initial path planning of the AGV group, and then determines whether there is a conflict among the AGV group based on the initial planned path and the initial driving parameters, and resolves the conflict, realizing the overall task allocation and overall path planning of the AGV group, that is, realizing the overall scheduling of the AGV group, and improving the reliability and effectiveness of the handling task.

[0020] Moreover, under the premise of determining the conflict, the present invention also determines the type of conflict to be resolved, and determines the target conflict avoidance strategy through the pre-constructed mapping relationship between the conflict type and the conflict avoidance strategy. It not only achieves the resolution of the conflict, but also accurately eliminates different types of conflicts by setting different target conflict avoidance strategies for different types of conflicts to be resolved, thereby further improving the avoidance efficiency and avoidance safety, and improving the adaptability to dynamic environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 A schematic flow chart of an embodiment of the collaborative scheduling method for an AGV group provided by the present invention; Figure 2 A schematic diagram of a process flow of an embodiment of constructing a global map provided by the present invention; Figure 3 For the present invention Figure 1 A schematic flow chart of an embodiment of S101; Figure 4 A schematic diagram of a flow chart of an embodiment of determining priority provided by the present invention; Figure 5 This is a schematic structural diagram of an embodiment of the collaborative scheduling system for an AGV group provided by the present invention. DETAILED DESCRIPTION

[0023] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0024] It should be understood that the schematic drawings are not drawn to scale. The flowcharts used in the present invention illustrate operations implemented according to some embodiments of the present invention. It should be understood that the operations of the flowcharts can be implemented out of sequence, and steps that have no logical contextual relationship can be reversed in order or implemented simultaneously. In addition, those skilled in the art, guided by the content of the present invention, can add one or more other operations to the flowcharts or remove one or more operations from the flowcharts. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and / or processor systems and / or microcontroller systems.

[0025] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0026] The present invention provides a collaborative scheduling method, system and storage medium for an AGV group, which are described below respectively.

[0027] Figure 1 A flow chart of an embodiment of the collaborative scheduling method of an AGV group provided by the present invention is shown as follows: Figure 1 As shown in FIG, the collaborative scheduling method of the AGV group includes: S101. Distribute scheduling tasks according to the status parameters of each AGV in the AGV group and the established global map to obtain a task allocation plan for the AGV group; the task allocation plan includes the initial planned path and initial driving parameters of the AGV.

[0028] Specifically, an AGV group refers to a cluster consisting of multiple AGVs.

[0029] AGV status parameters include but are not limited to: current location, driving speed, and remaining battery power.

[0030] A global map refers to a map that represents the area in which the AGV group operates, and usually refers to a map that can represent the location distribution of equipment in the workshop.

[0031] The scheduling task includes multiple target stations and the workpiece processing time of each target station.

[0032] The initial planned path refers to the initial planned path of the AGV assigned to the scheduling task in the AGV group. Similarly, the initial driving parameters refer to the initial driving parameters of the AGV assigned to the scheduling task in the AGV group.

[0033] Specifically, the initial driving parameters include but are not limited to: driving direction and real-time location.

[0034] S102: Determine whether there is a conflict among the AGV groups based on the initial driving parameters and the initial planned path, and determine the type of conflict to be resolved.

[0035] Among them, conflict refers to the situation where at least two AGVs are located in the same spatial position at the same time (moment or time window), that is, the two AGVs overlap in both time and space.

[0036] S103: Determine a target conflict avoidance strategy corresponding to the conflict type to be resolved based on a pre-established mapping relationship between conflict types and conflict avoidance strategies.

[0037] It should be understood that the goal conflict avoidance strategy refers to a strategy for eliminating conflicts.

[0038] S104: Optimize the initial planned path based on the target conflict avoidance strategy to obtain a conflict avoidance path, and control the AGV group to travel along the conflict avoidance path.

[0039] It should be noted that if the scheduled task is updated, it is necessary to return to step S101, re-determine the task allocation plan, and execute S102 to S104.

[0040] It should be understood that the collaborative scheduling method for AGV groups in the embodiments of the present invention can be implemented in any device based on the collaborative scheduling method for AGV groups, such as a scheduling device. Specifically, the collaborative scheduling method for AGV groups is stored in the aforementioned device as a pre-programmed program. When the device is started, the program is called and the collaborative scheduling method for AGV groups is implemented.

[0041] Compared with the existing technology, the collaborative scheduling method of the AGV group provided by the embodiment of the present invention first allocates scheduling tasks based on the status parameters of each AGV and the global map, realizing the initial path planning of the AGV group, and then determines whether there is a conflict among the AGV group based on the initial planned path and the initial driving parameters, and resolves the conflict, realizing the overall task allocation and overall path planning of the AGV group, that is, realizing the overall scheduling of the AGV group, and improving the reliability and effectiveness of the handling task.

[0042] Moreover, under the premise of determining the conflict, the embodiment of the present invention also determines the type of conflict to be resolved, and determines the target conflict avoidance strategy through the pre-constructed mapping relationship between the conflict type and the conflict avoidance strategy. It not only achieves the resolution of the conflict, but also accurately eliminates different types of conflicts by setting different target conflict avoidance strategies for different types of conflicts to be resolved, thereby further improving the avoidance efficiency and avoidance safety, and improving the adaptability to dynamic environments.

[0043] Since an accurate and comprehensive global map is conducive to obtaining an accurate task allocation solution, in some embodiments of the present invention, before step S101, as shown in FIG. Figure 2 As shown in FIG, the collaborative scheduling method of the AGV group includes: S201. Acquire the workstation location, material storage location, AGV waiting location, and the initial path connecting the workstation location, material storage location, and AGV waiting location in the workshop.

[0044] Among them, the workstation location refers to the location where materials are processed, the material storage location refers to the storage location of the materials required for workpiece processing, and the AGV waiting area refers to the location when the AGV is not performing a scheduling task and is in an idle state.

[0045] The transportation process of AGV is: if the AGV is in the AGV waiting position, the AGV must first move from the AGV waiting position to the material storage position to obtain the material. If the AGV is not in the AGV waiting position, the AGV needs to move from the current position to the storage position to obtain the material, and then transport the material to different workstations. After the transportation is completed, the AGV returns to the AGV waiting area.

[0046] The initial path refers to a possible driving path, which can be determined based on the electronic fence.

[0047] S202: Determine a drivable path based on the path type of the initial path; the path types include AGV drivable and AGV non-drivable, and the drivable path includes a one-way path and a two-way path.

[0048] A one-way path refers to an initial path that allows traffic in only one direction, and a two-way path refers to a path that allows traffic in both directions.

[0049] S203: Generate a global map by connecting workstation locations, material storage locations, and AGV waiting locations based on the drivable path.

[0050] The global map of the embodiment of the present invention obtains the workstation location, material storage location, and AGV waiting location based on the entire process of AGV from idleness, loading, transportation, and return, taking all factors into consideration. At the same time, the drivable path is determined based on the actual situation in the workshop, providing a reference for subsequent task allocation plans, thereby ensuring the accuracy of the determined task allocation plans.

[0051] In the traditional task allocation process, since the path planning is performed for a single AGV, only the factor of minimizing travel time is considered. For AGV groups, group stability is also one of the important indicators. Existing technologies do not consider load balancing. Although it is possible to achieve a local optimum on a single path, it will cause global problems, such as: resource waste and low group efficiency.

[0052] In order to solve the above technical problems, in some embodiments of the present invention, Figure 3 As shown, step S101 includes: S301, determining the individual driving time and individual driving mileage of each AGV based on the current location, workstation location, material storage location, AGV waiting location, and driving speed; Specifically, the single-unit travel time refers to the time it takes for the AGV to obtain the material from its current location, transport the material to the workstation, and return from the workstation to the AGV's waiting position. Similarly, the single-unit travel distance refers to the distance traveled in this entire process.

[0053] S302: Determine the group travel time of the AGV group based on the individual travel time, and determine the load variance of the AGV group based on the individual travel time and the individual travel mileage.

[0054] The load variance includes mileage load variance and time complexity variance.

[0055] S303: With the goal of minimizing group travel time and minimizing load variance, a multi-objective optimization model is constructed, and power constraints are constructed based on the remaining power, and path constraints are constructed based on the path type.

[0056] The power constraint condition means that the remaining power must be sufficient for the AGV to travel the determined single driving range. The path constraint condition means that the driving path must conform to the set path type. For example, the AGV can only travel on a drivable path. If the initial path is a one-way path, only one-way passage is allowed. If the initial path is a two-way path, two-way passage is allowed.

[0057] S304: Solve the multi-objective optimization model based on the power constraint condition and the path constraint condition to obtain a task allocation plan for the AGV group.

[0058] This embodiment of the present invention determines a task allocation plan for the AGV group as a whole. Furthermore, it considers load variance based on travel time and constructs a multi-objective optimization model with the goals of minimizing group travel time and load variance. This results in a task allocation plan that simultaneously minimizes group travel time and balances load, significantly improving the rationality of the determined task allocation plan. Furthermore, the load variance in this embodiment of the present invention encompasses both mileage and time, further achieving multi-dimensional load balancing and improving the long-term operational stability of the AGV.

[0059] In a specific embodiment of the present invention, the multi-objective optimization model is:

[0060]

[0061]

[0062] Where, To optimize the target value; is the group travel time; is the time weight; is the load variance; is the load weight; is the path length of the i-th AGV between the m-th station and the n-th station, and the n-th station is the next station of the m-th station; is the driving speed of AGV; is the workpiece processing time at the nth station; is the travel time of the i-th AGV from the material storage location or current location to the first workstation; is the travel time of the i-th AGV from the last station to the AGV waiting position; is the single driving time of the i-th AGV; is the average travel time of the AGV group; is the single mileage of the i-th AGV; is the average mileage of the AGV group; is the total number of AGVs in the AGV group.

[0063] In order to further improve the adaptability of the task allocation scheme determined by the multi-objective optimization model to the actual scenario, in some embodiments of the present invention, the collaborative scheduling method of the AGV group further includes: Get the collaborative scheduling scenario type. When the collaborative scheduling scenario is an emergency task scenario, the time weight is greater than the load weight. When the collaborative scheduling scenario is a long-term operation scenario, the time weight is less than or equal to the load weight.

[0064] The embodiment of the present invention can adaptively adjust the time weight and load weight according to the different types of collaborative scheduling scenarios by setting the time weight and load weight, thereby realizing dynamic adjustment of the time weight and load weight, and then setting different weight values ​​for different collaborative scheduling scenarios, thereby improving the adaptability of the determined weight value to the collaborative scheduling scenario.

[0065] In some embodiments of the present invention, the conflict types to be resolved include single-point conflict, unilateral conflict, and follower conflict.

[0066] Among them, single-point conflict refers to a situation where multiple AGVs attempt to occupy the same node (waypoint) at the same time (or within a time window), resulting in a potential collision risk.

[0067] A unilateral conflict refers to a situation where multiple AGVs attempt to occupy the same path segment within the same time period and travel in opposite directions, resulting in a potential collision risk.

[0068] Following conflict refers to the risk of rear-end collision caused by speed differences when multiple AGVs are traveling in the same direction on the same path segment.

[0069] In a specific embodiment of the present invention, determining the type of conflict to be resolved is specifically: Based on the initial driving parameters and the initial planned path, it is determined whether the AGVs have a common path segment at the same time; the initial driving parameters include driving direction and driving speed; If the AGVs have a common path segment at the same time, determine whether the driving directions and driving speeds of at least two conflicting AGVs with the common path segment are the same; When at least two conflicting AGVs are traveling in the same direction and at the same speed, the conflict type to be resolved is a single-point conflict. When at least two conflicting AGVs are traveling in opposite directions and at the same speed, the conflict type to be resolved is a unilateral conflict. When at least two conflicting AGVs are traveling in the same direction and at different speeds, the conflict type to be resolved is a following conflict.

[0070] Based on the above-mentioned conflict types to be resolved, in a specific embodiment of the present invention, when the conflict type to be resolved is a single-point conflict or a unilateral conflict, the conflict avoidance strategy is: path planning is performed for the conflicting AGVs except the highest priority one in descending order of priority; When the conflict type to be resolved is a following conflict, the conflict avoidance strategy is: optimize the driving speeds of at least two conflicting AGVs based on the preset minimum following distance, obtain the optimized speed, and determine whether the optimized speed is within the safe speed. If not, plan the paths of the conflicting AGVs except the highest priority one in order from low to high priority.

[0071] In the embodiment of the present invention, when the type of conflict to be resolved is a single-point conflict or a unilateral conflict, the path is planned directly based on the priority order to achieve conflict elimination. When the type of conflict to be resolved is a following conflict, the speed is first judged. If the optimized speed is within the safe speed, there is no need to re-plan the path. The conflict can be eliminated by simply controlling the conflicting AGV to travel at the optimized speed, thereby simplifying the conflict elimination process and achieving the purpose of improving the efficiency of completing the scheduling task.

[0072] From the above description, it can be seen that accurate determination of priority is crucial. Priorities in the prior art are all set in advance and will not be dynamically adjusted as the scheduling task is executed, resulting in a mismatch between the priority and the actual scenario, which in turn causes the technical problem of inaccurate conflict avoidance paths. To solve this technical problem, in some embodiments of the present invention, such as Figure 4 As shown, the collaborative scheduling method of the AGV group also includes: S401: Obtain the task urgency, task type, and remaining battery power of each AGV.

[0073] Among them, the urgency of the task can be divided according to the degree of impact on the production line. Specifically, the material transportation that causes the risk of production line shutdown has the highest priority, while the AGV returning to the AGV waiting area has a lower priority.

[0074] Among them, task types include but are not limited to: pickup, delivery, and charging, etc. The priority of the delivery task is higher than the priority of the charging task.

[0075] Among them, the priority of the AGV with a lower remaining battery power is higher than the priority of the AGV with a higher remaining battery power.

[0076] S402 : Determine the priority based on the fuzzy logic algorithm, the urgency of the task, the task type, and the remaining battery power.

[0077] Specifically, step S402 includes: first obtaining the membership of the specific input to the fuzzy set according to the membership function, then obtaining the fuzzy conclusion from the fuzzy rules and the membership of the input to the relevant fuzzy set, and finally converting the fuzzy conclusion into a specific and accurate output.

[0078] In order to better implement the collaborative scheduling method of the AGV group in the embodiment of the present invention, based on the collaborative scheduling method of the AGV group, the embodiment of the present invention also provides a collaborative scheduling system for the AGV group, such as Figure 5 As shown, the collaborative scheduling system 500 of the AGV group includes: AGV group 501; The collaborative scheduling module 502 is used to allocate scheduling tasks according to the status parameters of each AGV in the AGV group and the established global map, and obtain a task allocation plan for the AGV group; the task allocation plan includes the initial planned path and initial driving parameters of the AGV; based on the initial driving parameters and the initial planned path, it is determined whether there is a conflict in the AGV group and the type of conflict to be resolved is determined; based on the pre-constructed mapping relationship between the conflict type and the conflict avoidance strategy, the target conflict avoidance strategy corresponding to the conflict type to be resolved is determined; based on the target conflict avoidance strategy, the initial planned path is optimized to obtain a conflict avoidance path, and the AGV group is controlled to travel along the conflict avoidance path.

[0079] The collaborative scheduling module 502 in the collaborative scheduling system 500 of the AGV group provided in the above embodiment can implement the technical solution described in the collaborative scheduling method embodiment of the above AGV group. The specific implementation principle of the above collaborative scheduling module 502 can refer to the corresponding content in the collaborative scheduling method embodiment of the above AGV group, which will not be repeated here.

[0080] Accordingly, an embodiment of the present invention also provides a computer-readable storage medium, which is used to store computer-readable programs or instructions. When the program or instructions are executed by a processor, it can implement the steps or functions in the collaborative scheduling method of the AGV group provided by the above-mentioned method embodiments.

[0081] Those skilled in the art will appreciate that all or part of the process flow of the above-described method embodiment can be implemented by instructing related hardware (such as a processor, controller, etc.) through a computer program, and the computer program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a magnetic disk, an optical disk, a read-only memory, or a random access memory.

[0082] The above is a detailed introduction to the collaborative scheduling method, system and storage medium of an AGV group provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A collaborative scheduling method for an AGV group, characterized in that: include: Allocate scheduling tasks based on the status parameters of each AGV in the AGV group and the established global map to obtain a task allocation plan for the AGV group; the task allocation plan includes the initial planned path and initial driving parameters of the AGV; Determine whether there is a conflict among the AGV group based on the initial driving parameters and the initial planned path, and determine the type of conflict to be resolved; Determining a target conflict avoidance strategy corresponding to the conflict type to be resolved based on a pre-established mapping relationship between conflict types and conflict avoidance strategies; The initial planned path is optimized based on the target conflict avoidance strategy to obtain a conflict avoidance path, and the AGV group is controlled to travel along the conflict avoidance path.

2. The collaborative scheduling method of an AGV group according to claim 1, characterized in that: Before allocating the scheduling tasks according to the status parameters of each AGV in the AGV group and the established global map, the method further includes: Obtaining the workstation locations, material storage locations, AGV waiting locations, and an initial path connecting the workstation locations, material storage locations, and AGV waiting locations in the workshop; Determine a drivable path based on the path type of the initial path; the path type includes AGV drivable and AGV non-drivable, and the drivable path includes a one-way path and a two-way path; The global map is generated by connecting the workstation locations, material storage locations, and AGV waiting locations based on the drivable path.

3. The collaborative scheduling method of AGV group according to claim 2, characterized in that: The scheduling task includes multiple target workstations and the workpiece processing time of each target workstation, and the state parameters of the AGV include the current location, driving speed and remaining power of the AGV. Then, the scheduling task is allocated according to the state parameters of each AGV in the AGV group and the established global map to obtain the task allocation plan of the AGV group, including: Determine the individual driving time and individual driving mileage of each AGV based on the current location, workstation location, material storage location, AGV waiting location and driving speed; Determine a group travel time of the AGV group based on the individual travel time, and determine a load variance of the AGV group based on the individual travel time and the individual travel mileage; A multi-objective optimization model is constructed with the goal of minimizing the group travel time and minimizing the load variance, and a power constraint condition is constructed based on the remaining power, and a path constraint condition is constructed based on the path type; The multi-objective optimization model is solved based on the power constraint condition and the path constraint condition to obtain a task allocation plan for the AGV group.

4. The collaborative scheduling method of AGV group according to claim 3, characterized in that: The multi-objective optimization model is: Where, To optimize the target value; is the group travel time; is the time weight; is the load variance; is the load weight; is the path length of the i-th AGV between the m-th station and the n-th station, and the n-th station is the next station of the m-th station; is the driving speed of AGV; is the workpiece processing time at the nth station; is the travel time of the i-th AGV from the material storage location or current location to the first workstation; is the travel time of the i-th AGV from the last station to the AGV waiting position; is the single driving time of the i-th AGV; is the average travel time of the AGV group; is the single mileage of the i-th AGV; is the average mileage of the AGV group; is the total number of AGVs in the AGV group.

5. The collaborative scheduling method of AGV group according to claim 4, characterized in that: The method further comprises: Obtain the collaborative scheduling scenario type. When the collaborative scheduling scenario is an emergency task scenario, the time weight is greater than the load weight. When the collaborative scheduling scenario is a long-term operation scenario, the time weight is less than or equal to the load weight.

6. The collaborative scheduling method of an AGV group according to claim 1, characterized in that: The types of conflicts to be resolved include single-point conflicts, unilateral conflicts, and follow-up conflicts. Determining the types of conflicts to be resolved includes: Determine whether the AGVs have a common path segment at the same time based on the initial driving parameters and the initial planned path; the initial driving parameters include driving direction and driving speed; If the AGVs have a common path segment at the same time, determine whether the driving directions and driving speeds of at least two conflicting AGVs with the common path segment are the same; When at least two conflicting AGVs are traveling in the same direction and at the same speed, the conflict type to be resolved is a single-point conflict. When at least two conflicting AGVs are traveling in opposite directions and at the same speed, the conflict type to be resolved is a unilateral conflict. When at least two conflicting AGVs are traveling in the same direction and at different speeds, the conflict type to be resolved is a following conflict.

7. The collaborative scheduling method of an AGV group according to claim 6, characterized in that: When the conflict type to be resolved is a single-point conflict or a unilateral conflict, the conflict avoidance strategy is: planning paths for the conflicting AGVs except the highest priority one in descending order of priority; When the type of conflict to be resolved is a following conflict, the conflict avoidance strategy is: optimize the driving speeds of at least two conflicting AGVs based on a preset minimum following distance, obtain the optimized speed, and determine whether the optimized speed is within the safe speed. If not, plan the paths of the conflicting AGVs except the highest priority one in order of priority from low to high.

8. The collaborative scheduling method of an AGV group according to claim 7, characterized in that: The method further comprises: Obtain the task urgency, task type, and remaining battery power of each AGV; The priority is determined based on a fuzzy logic algorithm, the urgency of the task, the task type, and the remaining battery power.

9. A collaborative scheduling system for an AGV group, characterized in that: include: AGV group; A collaborative scheduling module is configured to allocate scheduling tasks based on the status parameters of each AGV in the AGV group and the established global map, and obtain a task allocation plan for the AGV group; the task allocation plan includes the initial planned path and initial driving parameters of the AGV; based on the initial driving parameters and the initial planned path, determine whether there is a conflict in the AGV group and determine the type of conflict to be resolved; Determining a target conflict avoidance strategy corresponding to the conflict type to be resolved based on a pre-established mapping relationship between conflict types and conflict avoidance strategies; The initial planned path is optimized based on the target conflict avoidance strategy to obtain a conflict avoidance path, and the AGV group is controlled to travel along the conflict avoidance path.

10. A computer-readable storage medium, characterized in that Used to store computer-readable programs or instructions, which, when executed by a processor, can implement the steps of the collaborative scheduling method for an AGV group as described in any one of claims 1 to 8.

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