CN114611896A

CN114611896A - Robot task scheduling method, computer device and storage medium

Info

Publication number: CN114611896A
Application number: CN202210193269.0A
Authority: CN
Inventors: 王迎新; 王欢欢; 张朝辉; 许瑨
Original assignee: Youibot Robotics Co ltd
Current assignee: Youibot Robotics Co ltd
Priority date: 2022-02-28
Filing date: 2022-02-28
Publication date: 2022-06-10

Abstract

The application provides a robot task scheduling method, computer equipment and a storage medium, wherein the method comprises the following steps: acquiring a task list to be executed and AGV information; extracting tasks to be distributed with the same priority in the task list, and determining the number of the tasks to be distributed and the number of idle AGVs; if the number of the idle AGVs is larger than the number of the tasks to be distributed, distributing the tasks to be distributed to the idle AGVs which are closest to the idle AGVs and can execute the tasks in the cross-region mode; and if the number of the idle AGVs is less than or equal to the number of the tasks to be distributed, the idle AGVs select the task to be distributed which is closest to the area in which the idle AGVs are located to execute. By selecting different distribution modes according to the difference between the number of idle AGVs and the number of tasks, the problem of vehicle cross-workshop scheduling under the condition of AGV redundancy is solved, the use of high-pass cost areas is effectively reduced, the production efficiency is improved, and the task execution time consumption and system blockage are reduced.

Description

Robot task scheduling method, computer device and storage medium

Technical Field

The application relates to the technical field of multiple AGV scheduling, in particular to a robot task scheduling method, computer equipment and a storage medium.

Background

As one of the intelligent logistics core devices, the operation of a robot, including but not limited to an AGV (Automated Guided Vehicle), needs to be adapted to various factory field environments. The task assignment is to assign tasks to idle vehicles according to a task list. Task selection of an AGV can cause frequent use of the AGV in high traffic cost areas, resulting in local congestion.

The defects and shortcomings of the existing task selection AGV causing partial blockage are as follows:

1. the task assignment scheme based on the task issuing type can distribute tasks to the AGVs in other workshops, so that the AGVs transfer across workshops, and frequent occupation of high-traffic cost areas is caused.

2. The cross-workshop task assignment can cause the blockage of high-traffic-cost areas under the scene of short task execution period and high frequency.

The flow of AGVs across the bay can consume a significant amount of time, reducing the efficiency of the system and productivity.

4. The vehicles and tasks between different workshops cannot be balanced, that is, there is a situation that tasks of one workshop are stacked and AGV redundancy occurs in another workshop.

Disclosure of Invention

The application provides a robot task scheduling method, computer equipment and a storage medium, different distribution modes can be selected according to the difference between the number of AGV and the number of tasks, the use of high-pass-through cost areas is effectively reduced, the production efficiency is improved, and the system blockage is reduced.

In a first aspect, the present application provides a robot task scheduling method, including:

acquiring a task list to be executed and AGV information;

extracting tasks to be distributed with the same priority in the task list, and determining the number of the tasks to be distributed and the number of idle AGVs;

if the number of the idle AGVs is larger than the number of the tasks to be distributed, distributing the tasks to be distributed to the idle AGVs which are closest to the idle AGVs and can execute the tasks in the cross-region mode; and if the number of the idle AGVs is less than or equal to the number of the tasks to be distributed, the idle AGVs select the task to be distributed which is closest to the area in which the idle AGVs are located to execute.

In a second aspect, the present application further provides a computer device, comprising:

a memory and a processor;

wherein the memory is connected with the processor and used for storing programs;

the processor is configured to implement the steps of the robot task scheduling method according to any one of the embodiments of the present application by running the program stored in the memory.

In a third aspect, the present application further provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the processor is caused to implement the steps of the robot task scheduling method according to any one of the embodiments of the present application.

According to the robot task scheduling method, the computer equipment and the storage medium, different distribution modes are selected according to the difference between the number of idle AGVs and the number of tasks; when the number of the idle AGVs is larger than that of the tasks, the tasks select the nearby idle AGVs, so that the number of the AGVs in different workshops is increased, the task balance is improved, the AGVs can be quickly scheduled to reach different workshops for working, and the problem of inter-workshop scheduling of vehicles under the condition of AGV redundancy is solved; when the number of the tasks is larger than that of the idle AGVs, the idle AGVs select to execute the tasks nearby and avoid the AGV cross-workshop flowing under the condition of task accumulation, the use of high-pass cost areas is effectively reduced, the production efficiency is improved, and the task execution time consumption and system blockage are reduced.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a flowchart of a robot task scheduling method provided by an embodiment of the present application;

FIG. 2 is a flow chart of another robot task scheduling method provided by an embodiment of the present application;

FIG. 3 is a schematic diagram of another robot task scheduling method provided by an embodiment of the present application;

FIG. 4 is a schematic diagram illustrating steps in a robot task scheduling method according to an embodiment of the present application;

FIG. 5 is a schematic diagram illustrating steps of a robot task scheduling method according to an embodiment of the present application;

FIG. 6 is a schematic diagram illustrating steps of a robot task scheduling method according to an embodiment of the present application;

FIG. 7 is a schematic diagram illustrating steps of another robot task scheduling method provided by an embodiment of the present application;

fig. 8 is a schematic block diagram of a computer device provided by an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

The flow diagrams depicted in the figures are merely illustrative and do not necessarily include all of the elements and operations/steps, nor do they necessarily have to be performed in the order depicted. For example, some operations/steps may be decomposed, combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.

It is to be understood that the terminology used in the description of the present application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the specification of the present application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

It should be understood that, for the convenience of clearly describing the technical solutions of the embodiments of the present application, the words "first", "second", and the like are used in the embodiments of the present application to distinguish the same items or similar items with basically the same functions and actions. For example, the first callback function and the second callback function are only used for distinguishing different callback functions, and the order of the callback functions is not limited. Those skilled in the art will appreciate that the terms "first," "second," etc. do not denote any order or quantity, nor do the terms "first," "second," etc. denote any order or importance.

The term "computer device", also called "computer" in this context, refers to an intelligent electronic device that can execute predetermined processes such as numerical calculation and/or logic calculation by running predetermined programs or instructions, and may include a processor and a memory, wherein the processor executes a pre-stored instruction stored in the memory to execute the predetermined processes, or the predetermined processes are executed by hardware such as ASIC, FPGA, DSP, or a combination thereof. Computer devices include, but are not limited to, servers, personal computers, laptops, tablets, smart phones, and the like.

The computer equipment comprises user equipment and network equipment. Wherein the user equipment includes but is not limited to computers, smart phones, PDAs, etc.; the network device includes, but is not limited to, a single network server, a server group consisting of a plurality of network servers, or a Cloud Computing (Cloud Computing) based Cloud consisting of a large number of computers or network servers, wherein Cloud Computing is one of distributed Computing, a super virtual computer consisting of a collection of loosely coupled computers. Wherein, the computer equipment can be operated alone to realize the invention, and also can be accessed into the network and realize the invention through the interactive operation with other computer equipment in the network. The network in which the computer device is located includes, but is not limited to, the internet, a wide area network, a metropolitan area network, a local area network, a VPN network, and the like.

It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

When an AGV adapts to a variety of factory site environments, different types of tasks have different priorities. Generally, a task with a high priority executes a task with a higher priority than a task with a low priority, and the tasks with the same priority have no priority order, but an ordered task issuing list exists along with the difference of the entering time of the tasks with the same priority. The assignment of tasks is to assign tasks to the idle vehicles according to a task list. Task selection of an AGV can cause frequent use of the AGV in high traffic cost areas, resulting in local congestion.

For example, for a task assignment scheme based on a task-based dispatch scheme, when distributing tasks to AGVs in other workshops, the AGVs are caused to transfer across the workshops, and frequent occupation of high traffic cost areas such as elevators, automatic doors and showers is caused.

Moreover, the cross-workshop task assignment can cause the blockage of a high-traffic-cost area under the scene of short task execution period and high frequency; meanwhile, the flow of the AGV across the workshop consumes a large amount of time, the execution efficiency of the system is reduced, and the productivity is reduced; moreover, the vehicles and tasks between different workshops cannot be balanced, that is, there is a case that tasks of one workshop are stacked and AGV redundancy occurs in another workshop.

To this end, embodiments of the present application provide a robot task scheduling method, a computer device, and a storage medium. Different distribution modes can be selected according to the difference between the number of the AGVs and the number of the tasks, so that the use of high-pass-cost areas is effectively reduced, the production efficiency is improved, and the system blockage is reduced.

It should be noted that the present embodiment will be described by taking the multi-AGV system task allocation between factory workshops as an example. Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The embodiments described below and the features of the embodiments can be combined with each other without conflict.

Referring to fig. 1, fig. 1 is a flowchart illustrating a robot task scheduling method according to an embodiment of the present disclosure, and as shown in fig. 1, the robot task scheduling method includes steps S10 to S30.

S10, acquiring a task list to be executed and AGV information;

s20, extracting tasks to be distributed with the same priority in the task list, and determining the number of the tasks to be distributed and the number of the idle AGVs;

s30, if the number of the idle AGVs is larger than the number of the tasks to be distributed, distributing the tasks to be distributed to the idle AGVs which are closest to the idle AGVs and can execute the tasks in the cross-region mode; and if the number of the idle AGVs is less than or equal to the number of the tasks to be distributed, the idle AGVs select the task to be distributed which is closest to the area in which the idle AGVs are located to execute.

When acquiring a task list to be executed, an execution subject of the embodiment of the present application is a device that executes a robot task scheduling method, and the device may be a device having display and processing functions, such as a PC, a portable computer, and a mobile terminal.

It should be understood that, in acquiring the list of tasks to be executed, the list of tasks to be executed is acquired by the device executing the robot task scheduling method through interaction with the task collection system of the factory workshop.

For example, when acquiring a task list to be executed, taking a task acquisition system of a factory workshop as an Manufacturing Execution System (MES) as an example, the MES system is used as a link for information integration of an enterprise CIMS (Computer/systematic Integrated Manufacturing Systems), and the task MES system of the task list acquires tasks in the factory workshop.

In some embodiments, referring to fig. 2, the acquiring AGV information includes steps S101 to S102.

S101, constructing a regional digital map under a current scene;

and S102, obtaining the current position information of the AGV and the position information of the work station of the workpiece carrying point based on the regional digital map.

In the embodiment of the application, when the digital map is established for each workshop area in the current scene of the factory workshop, the digital map of the factory workshop is established and positioned by using the SLAM (simultaneous localization and mapping) technology.

Specifically, a digital map of the environment in which the AGV is located is generated based on SLAM technology. Based on the digital map, the AGV executes tasks such as path planning, autonomous positioning, navigation and the like. The implementation process is to establish digital map positioning by using the coupling of a laser radar and an IMU (inertial odometer), and the SLAM can establish a digital map under the current scene.

When the AGV is positioned and navigated, the laser radar can determine information such as the distance and the direction of a target according to a signal reflected by the target (such as the AGV), the IMU can measure the acceleration and the rotation motion of the AGV, and the current pose (position and attitude) information of the AGV can be obtained through the integration of the IMU on the acceleration and the coupling of the laser radar data in the running process of the AGV, so that the high-precision positioning and navigation are carried out.

When the AGV is positioned and navigated, the laser radar calculates the relative movement distance and posture change of the laser radar through matching and comparison of the laser radar data point clouds at different moments, so that the AGV is positioned.

It should be noted that the obtained AGV information includes current position information and attitude information of the AGV.

In some embodiments, when acquiring the list of tasks to be executed based on the constructed regional digital map, the method further includes: collecting the tasks in the current scene to obtain a task list to be executed; each collected task corresponds to one station.

In the embodiment of the application, after the task MES system of the task list collects tasks in a factory workshop, each task corresponds to a work station. Since the positions of the workstations are different, the AGV needs to go to different positions for workpiece acquisition.

Meanwhile, as the collection of tasks is carried out in the whole factory, and the factory has different workshops, the AGV is required to operate in different workshops, the passing modes among different workshops comprise an automatic door, an air shower door, an elevator and the like, and the passing of the positions needs a large amount of time. Therefore, the tasks in the task list are executed between different workshops and different positions.

For this reason, different distribution modes need to be selected according to the difference between the number of AGVs and the number of tasks.

In some embodiments, referring to fig. 3, before determining the number of tasks to be allocated, the method further includes steps S201 to S203.

S201, performing priority ordering according to priority information in the task list to be executed, and acquiring a priority ordering result;

s202, extracting tasks with the same priority each time to form tasks to be distributed according to the priority sorting result;

s203, counting the number of the extracted tasks to be distributed with the same priority, and determining the number of the tasks to be distributed.

After the equipment for executing the robot task scheduling method acquires a task list to be executed through interaction with an MES (manufacturing execution system), priority ordering is required to be carried out according to the priority of the acquired task list, after the priority ordering is finished, tasks with the same priority are extracted, the tasks with the same priority are used as tasks to be distributed with the same priority, and the number of the tasks to be distributed is counted and determined.

In some embodiments, when comparing the number of tasks to be allocated to the number of free AGVs, there are two cases:

the number of the tasks to be distributed is larger than that of the idle AGVs, the idle AGVs execute the tasks adjacent to the positions to be executed in the tasks to be distributed, the AGV selects the nearest task to execute, cross-workshop flowing can be reduced, and task execution consumption and task congestion are reduced.

And secondly, the number of the idle AGVs is larger than that of the tasks to be distributed, and the tasks to be distributed are distributed by selecting adjacent idle AGVs, so that the AGV can flow across the workshop, and the rapid cross-workshop deployment of the AGV is realized.

Specifically, when the number of tasks to be distributed is larger than the number of idle AGVs, the tasks are accumulated at the moment, when the idle AGVs appear in a fully loaded AGV list, the AGV retrieves the task closest to the AGV in the task list and executes the task, so that inter-vehicle flow of the AGV is reduced, and the operating efficiency of the system is improved.

In the embodiment of the application, the task to be allocated selects the allocation mode of the AGVs, so that the AGVs are deployed quickly and the task is solved efficiently. The AGV selection task aims to reduce the cross-workshop flow of the AGV under the condition of task accumulation, reduces time consumption and improves the operation efficiency of the whole system.

Specifically, when the number of free AGVs is greater than the number of tasks to be allocated, the AGVs are redundant. And selecting the nearest AGV to execute the task in the task list. In order to improve the quantity and the task balance of the AGVs in different workshops, the AGVs in another workshop can be selected to execute the task, so that the enough AGVs can be quickly dispatched to one workshop to quickly solve the task, and the problem of inter-workshop dispatching of vehicles under the condition of AGV redundancy is solved.

In some embodiments, referring to FIG. 4, the method further includes steps S310 through S312 when assigning the task to be assigned to the nearest free AGV that can execute the task across the area.

S310, extracting target tasks from the tasks to be distributed with the same priority according to the determined number of the tasks to be distributed;

s311, planning a path according to the position information of the target task and the current pose information of the idle AGV in the current scene to obtain a path planning result;

and S312, distributing the target task to the idle AGV with the shortest path in the path planning result for execution.

When the task allocation is executed, the number of the AGVs in the idle state is larger than the number of the tasks to be allocated, and the AGVs are in the redundant state, so that the tasks need to be solved quickly by the AGVs in time, and the AGVs are required to run across the workshop. Each time tasks with the same priority are extracted and combined to form a task list, each time one task is taken out of the task list, the AGV closest to the task is retrieved and the task is distributed to the AGV, and the distribution formula is similar to a centralized strength solution task.

It will be appreciated that the AGV closest to the task is determined based on the distance between the current position of the AGV and the position of the task (i.e., the target position of the AGV). In the embodiment of the application, the distance between the current position of the AGV and the task point can be calculated by adopting a mode of constructing an AGV operation network and using a graph optimization algorithm, a path planning algorithm and the like. And constructing an AGV operation network close to a road network. When the path planning is carried out, the idle AGV with the shortest path in the total path planning result is selected to be executed.

In some embodiments, referring to FIG. 5, the method further includes steps S320 through S322 when the free AGV selects the closest task to be allocated in the area in which it is located for execution.

S320, acquiring pose information of the current idle AGV based on the regional digital map;

s321, traversing all the determined position information of the tasks to be distributed, distributing the task with the shortest path to the current idle AGV, and counting all full-load AGVs to obtain a full-load AGV list;

and S322, acquiring the empty AGV in the full-load AGV list, and distributing the residual tasks with the shortest path in the tasks to be distributed.

When the task allocation is executed, the number of the tasks to be allocated is larger than the number of the AGVs in the idle state, and at the moment, the tasks belong to the accumulation state, so that the time loss is generated when the AGVs perform inter-vehicle flow again. When the AGV finishes executing the tasks, the AGV calculates the distances from the AGV to all the tasks, and selects the task with the shortest distance (also called as the minimum cost) to execute, so that the probability of crossing the workshop is reduced, and the efficiency is improved.

In the embodiment of the present application, referring to fig. 6, when allocating the task with the shortest route, the idle AGV includes steps S3201 to S322.

S3201, acquiring current pose information of the idle AGV and position information of each task in the tasks to be distributed;

s3202, planning a path according to the position information of each task and the current pose information of the idle AGV to obtain a path planning result;

s3203, sorting the path planning results based on the high-pass cost values to obtain path pass cost sorting results;

s3204, sorting the path passing cost sorting results according to path length to obtain an optimal path sorting result;

s3205, determining the task with the shortest path distributed to the idle AGV according to the optimal path sequencing result.

It should be noted that the acquired position information of each task in the tasks to be allocated includes position information of a station corresponding to each task and destination position information of each task transportation.

When the path is planned, the obtained path planning result is a path which starts from the idle AGV, passes through the position of the corresponding station of each task and finally reaches the destination position of the transportation of each task. Since all AGVs have consistent paths from the location of the workstation to the destination location for each task transport. Therefore, in the embodiment of the present application, the path planning result considers the shortest path from the idle AGV to the position of the corresponding workstation of each task.

Because the number of the tasks to be distributed is larger than the number of the AGV in the idle state, the tasks belong to the accumulation state. In order to reduce the cross-vehicle flow of the AGVs, a sequencing standard based on high-passing cost value is added, namely the number of times that the positions of the corresponding stations of each task from the idle AGVs pass through a high-passing cost area, namely the cross-vehicle flow value, is reached, and a path planning result with a high passing cost value and a small passing cost value in a path passing cost sequencing result is preferably selected. Secondly, a path length sequencing mode is introduced, in the path planning results with high running cost values consistent, the path planning result with the shortest path is selected as the optimal path sequencing result, and the task corresponding to the optimal path sequencing result is distributed to the idle AGV.

In some embodiments, when the determination result is that the number of the tasks to be allocated is greater than the number of the idle AGVs, and after the idle AGVs select the task to be allocated closest to the area where the idle AGVs are located to execute, the robot task scheduling method further includes:

detecting whether the extracted tasks to be distributed have residual tasks or not;

if yes, adding the rest tasks to a task list, and re-performing priority sequencing according to priority information in the task list;

if not, ending the task allocation operation of the current priority.

In the embodiment of the present application, referring to fig. 7, the specific implementation steps include step 1) to step 7).

Step 1): and acquiring a task list to be executed through interaction with an external system.

Step 2): and carrying out priority sequencing according to the priority of the obtained task list.

Step 3): and after the priority sorting is finished, extracting tasks with the same priority at the position.

And step 4): and judging whether the number of the idle AGVs is larger than the number of the tasks or not. If greater than divert step 5), if not greater than divert step 6).

Step 5): and assigning the tasks to the vehicles closest to the tasks according to the sequence of the task list, so that the AGV can perform the cross-vehicle task execution.

Step 6): the AGV selects the nearest task to execute, and the AGV is restricted to perform the job between itself.

Step 7): and detecting whether the residual tasks exist, if so, adding the extracted unexecuted tasks into the task list, and executing the step 1). If not, the task assignment execution ends.

According to the robot task scheduling method disclosed by the embodiment, different distribution modes are selected according to different numbers of idle AGVs and different numbers of tasks; when the number of the idle AGVs is larger than that of the tasks, the tasks select the nearby idle AGVs, so that the number of the AGVs in different workshops is increased, the task balance is improved, the AGVs can be quickly scheduled to reach different workshops for working, and the problem of inter-workshop scheduling of vehicles under the condition of AGV redundancy is solved; when the number of the tasks is larger than that of the idle AGVs, the idle AGVs select to execute the tasks nearby and avoid the AGV cross-workshop flowing under the condition of task accumulation, the use of high-pass cost areas is effectively reduced, the production efficiency is improved, and the task execution time consumption and system blockage are reduced.

Referring to fig. 8, fig. 8 is a schematic block diagram of a computer device according to an embodiment of the present application. As shown in fig. 8, the computer device 500 includes one or more processors 501 and a memory 502, and the processors 501 and the memory 502 are connected by a bus, such as an I2C (Inter-integrated Circuit) bus.

Wherein the one or more processors 501, working individually or collectively, are configured to perform the steps of the robot task scheduling method provided by the above embodiments:

acquiring a task list to be executed and AGV information;

Specifically, the Processor 501 may be a Micro-controller Unit (MCU), a Central Processing Unit (CPU), a Digital Signal Processor (DSP), or the like.

Specifically, the Memory 502 may be a Flash chip, a Read-Only Memory (ROM) magnetic disk, an optical disk, a usb disk, or a removable hard disk.

The processor 501 is configured to run a computer program stored in the memory 502, and when executing the computer program, implement the steps of the robot task scheduling method provided in the foregoing embodiments.

Illustratively, the processor 501 is configured to run a computer program stored in the memory 502 and, when executing the computer program, to implement the steps of:

acquiring a task list to be executed and AGV information; extracting tasks to be distributed with the same priority in the task list, and determining the number of the tasks to be distributed and the number of idle AGVs; if the number of the idle AGVs is larger than that of the tasks to be distributed, distributing the tasks to be distributed to the idle AGVs which are closest to the idle AGVs and can execute the tasks in the cross-region manner; if the number of the idle AGVs is less than or equal to the number of the tasks to be distributed, the idle AGVs select the task to be distributed which is closest to the area in which the idle AGVs are located to execute; and detecting whether the extracted tasks to be distributed have residual tasks, if so, adding the residual tasks to a task list, re-sequencing the priorities according to the priority information in the task list, and if not, finishing the task distribution operation of the current priority.

In some embodiments, the UI thread obtains a control pointer corresponding to a control of the application program, and stores the control pointer in a first queue; and the first thread acquires the control pointer from the first queue and acquires control data according to the control pointer.

In some embodiments, before acquiring AGV information, a regional digital map in a current scene is constructed; and acquiring the current pose information of the AGV and the position information of the work piece carrying point station based on the regional digital map.

In some embodiments, when constructing the regional digital map in the current scenario, a digital map of the environment in which the AGVs are located is generated based on SLAM technology. Based on the digital map, the AGV executes tasks such as path planning, autonomous positioning, navigation and the like. The implementation process is to establish digital map positioning by coupling the laser radar and the IMU, and the SLAM can establish a digital map under the current scene.

In some embodiments, based on the constructed regional digital map, when acquiring the task list to be executed, the method further includes: collecting the tasks in the current scene to obtain a task list to be executed; each collected task corresponds to one station.

In some embodiments, before determining the number of tasks to be allocated, a task list to be executed is obtained, priority ranking is performed according to priority information in the task list to be executed, a priority ranking result is obtained, according to the priority ranking result, tasks with the same priority are extracted each time to form tasks to be allocated, the number of the extracted tasks to be allocated with the same priority is counted, and the number of the tasks to be allocated is determined.

In some embodiments, when comparing the number of tasks to be allocated to the number of free AGVs, there are two situations, including:

And secondly, the number of the idle AGVs is larger than that of the tasks to be distributed, and the tasks to be distributed are distributed by selecting the adjacent idle AGVs, so that the AGV can flow across the workshop, and the AGV can be rapidly deployed across the workshop.

In some embodiments, when the tasks to be allocated are allocated to the nearest idle AGVs capable of executing the tasks across the regions, according to the determined number of the tasks to be allocated, target tasks are extracted from the tasks to be allocated with the same priority, path planning is performed according to the position information of the target tasks and the current pose information of the idle AGVs in the current scene, a path planning result is obtained, and the target tasks are allocated to the idle AGVs with the shortest paths in the path planning result to be executed.

In some embodiments, when the idle AGVs select the nearest task to be allocated in the area to execute, the position and posture information of the current idle AGVs is acquired based on the area digital map, the position information of all determined tasks to be allocated is traversed, the task with the shortest path is allocated to the current idle AGVs, all full-load AGVs are counted, a full-load AGV list is acquired, the idle AGVs appearing in the full-load AGV list are acquired, and the remaining tasks with the shortest path in the tasks to be allocated are allocated.

In some embodiments, the robot task scheduling method further comprises: step 1), acquiring a task list to be executed through interaction with an external system. And step 2), carrying out priority sequencing according to the priority of the obtained task list. And 3) after the priority sorting is finished, extracting tasks with the same priority. And 4) judging whether the number of the idle AGVs is larger than the number of the tasks. If greater than divert step 5), if not greater than divert step 6). And 5) assigning the tasks to the vehicles closest to the AGV according to the sequence of the task list, so that the AGV can perform the inter-vehicle task spanning execution. And 6), the AGV selects the nearest task to execute, and is limited to work in the workshop. And 7) detecting whether the residual tasks exist, if so, adding the extracted unexecuted tasks into a task list, executing the step 1, and if not, finishing the task distribution and execution.

An embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the processor is enabled to implement the steps of the robot task scheduling method provided in the foregoing embodiment.

The computer-readable storage medium may be an internal storage unit of the computer device according to any of the foregoing embodiments, for example, a hard disk or a memory of the terminal device. The computer readable storage medium may also be an external storage device of the terminal device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the terminal device.

The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think of various equivalent modifications or substitutions within the technical scope of the present application, and these modifications or substitutions should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A robot task scheduling method, the method comprising:

acquiring a task list to be executed and AGV information;

extracting the tasks to be distributed with the same priority level in the task list, and determining the number of the tasks to be distributed and the number of idle AGVs;

if the number of the idle AGVs is larger than the number of the tasks to be distributed, distributing the tasks to be distributed to the idle AGVs which are closest to the idle AGVs and can execute the tasks in the cross-region mode;

and if the number of the idle AGVs is less than or equal to the number of the tasks to be distributed, the idle AGVs select the task to be distributed which is closest to the area in which the idle AGVs are located to execute.

2. The method of claim 1, further comprising:

detecting whether the task to be distributed has residual tasks;

if the task to be distributed has residual tasks, adding the residual tasks to a task list, and re-performing priority sequencing according to priority information in the task list;

and if the task to be distributed has no residual task, ending the task distribution operation of the current priority.

3. The method of claim 1, wherein said obtaining AGV information comprises:

constructing a regional digital map under the current scene;

and obtaining the current position and attitude information of the AGV and the position information of the workpiece carrying point station based on the regional digital map.

4. The method of claim 1, wherein obtaining the list of tasks to be performed comprises: collecting tasks in a current scene to obtain a task list to be executed; each collected task corresponds to one station.

5. The method of claim 1, wherein prior to determining the number of tasks to be assigned, the method further comprises:

performing priority ordering according to the priority information in the task list to be executed to obtain a priority ordering result;

according to the priority ranking result, extracting tasks with the same priority each time to form tasks to be distributed;

and counting the number of the extracted tasks to be distributed with the same priority, and determining the number of the tasks to be distributed.

6. The method of claim 1, wherein upon assigning said task to be assigned to an idle AGV that is closest to a transregional executable task, said method further comprises:

extracting target tasks from the tasks to be distributed with the same priority according to the determined number of the tasks to be distributed;

performing path planning according to the position information of the target task and the current pose information of the idle AGV in the current scene to obtain a path planning result;

and distributing the target task to the idle AGV with the shortest path in the path planning result for execution.

7. The method of claim 1 wherein, when the free AGV selects the task to be allocated that is closest in the area in which it is located for execution, the method further comprises:

acquiring pose information of the current idle AGV based on the regional digital map;

traversing all the determined position information of the tasks to be distributed, distributing the task with the shortest path to the current idle AGV, counting all full-load AGV and obtaining a full-load AGV list;

and acquiring the empty AGV in the full-load AGV list, and distributing the residual tasks with the shortest path in the tasks to be distributed.

8. The method of claim 7 wherein said free AGV assigning the shortest task comprises:

acquiring current pose information of the idle AGV and position information of each task in the tasks to be distributed;

performing path planning according to the position information of each task and the current pose information of the idle AGV to obtain a path planning result;

sorting the path planning results based on the high-passing cost value to obtain path passing cost sorting results;

sorting the path passing cost sorting results according to the path length to obtain an optimal path sorting result;

and determining the task with the shortest path distributed to the idle AGV according to the optimal path sequencing result.

9. A computer device, characterized in that the computer device comprises:

a memory and a processor;

the processor is adapted to carry out the steps of the robot task scheduling method according to any of claims 1-8 by running a program stored in the memory.

10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by a processor, causes the processor to carry out the steps of the robot task scheduling method according to any one of claims 1-8.