CN112990617A - Scheduling method and scheduling device for intelligent mobile robot - Google Patents

Scheduling method and scheduling device for intelligent mobile robot Download PDF

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CN112990617A
CN112990617A CN201911212782.4A CN201911212782A CN112990617A CN 112990617 A CN112990617 A CN 112990617A CN 201911212782 A CN201911212782 A CN 201911212782A CN 112990617 A CN112990617 A CN 112990617A
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task
floor
intelligent mobile
mobile robot
scheduling
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吴剑进
张敏亮
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Hangzhou Hikrobot Technology Co Ltd
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Hangzhou Hikrobot Technology Co Ltd
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    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management

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Abstract

The invention provides a scheduling method and a scheduling device of an intelligent mobile robot. Based on the invention, the task assignment of the intelligent mobile robot can be not limited by floors, so that the cross-floor task assignment of the intelligent mobile robot can be realized, the use efficiency of the intelligent mobile robot in a working space containing at least two floors can be improved, the total deployment amount of the intelligent mobile robot in the working space can be reduced, the working efficiency is considered, and the cost can be reduced. Moreover, if the tasks to be dispatched include the pre-dispatching tasks, the use efficiency of the intelligent mobile robot in the working space comprising at least two floors can be further improved. In addition, if the maintenance resources are only deployed on local floors of the working space, cross-floor sharing of the maintenance resources can be achieved by assigning maintenance tasks to the intelligent mobile robots.

Description

Scheduling method and scheduling device for intelligent mobile robot
Technical Field
The present invention relates to the field of logistics automation, and in particular, to a method and a device for scheduling an intelligent mobile robot, and a robot control device, which are suitable for an intelligent mobile robot such as an AGV (Automated Guided Vehicle).
Background
Along with the continuous improvement of the automation degree of logistics, the intelligent mobile robot is more and more widely applied.
Some application scenarios of the intelligent mobile robot have multiple floors, and how to reasonably deploy the intelligent mobile robot on the multiple floors becomes a technical problem to be solved in the prior art.
Disclosure of Invention
In view of the above, embodiments of the present invention respectively provide a scheduling method for an intelligent mobile robot, a scheduling device for an intelligent mobile robot, and a robot control device.
In one embodiment, a scheduling method of an intelligent mobile robot is provided, including:
when the task allocation cycle time arrives, estimating the scheduling cost generated by allocating each task to be allocated in the task pool to different idle intelligent mobile robots in the working space according to the assigned position of the task to be allocated in the task pool and the real-time position of the idle intelligent mobile robot collected from the working space, wherein the working space comprises at least two floors communicated by a cross-floor channel, and the assigned position of each task to be allocated and the real-time position of each idle intelligent mobile robot are positioned on any floor of the at least two floors;
and selecting a dispatching target for each task to be dispatched from the idle intelligent mobile robot by taking the minimum sum of the scheduling costs generated by all the tasks to be dispatched as a desired target.
Optionally, further comprising: when receiving a job task issued by a service system, storing the received job task as a task to be assigned in a task pool; when receiving a pre-scheduling task issued by a service system or generating the pre-scheduling task according to the task beat of the job task, storing the received or generated pre-scheduling task as a task to be allocated in a task pool.
Optionally, the generating the pre-scheduled task according to the task tempo of the job task includes: acquiring task beats of job tasks with the same designated position; according to the acquired task tempo of the specified position, predicting the issuing time of the operation task aiming at the specified position; and generating a prescheduled task containing the specified position before the predicted assigned time arrives. Optionally, further comprising: receiving a beat configuration file issued by a service system, wherein the beat configuration file comprises an appointed position and a task beat; or counting task beats of the job tasks with the same designated position according to the historical receiving records of the job tasks. Optionally, further comprising: monitoring the use state of maintenance resources deployed on a local floor of a working space and the capability state of the intelligent mobile robot collected from the working space; when the maintenance resources with the idle use state exist in the working space and the capability state of the intelligent mobile robot is in the abnormal level, a maintenance task is generated, wherein the generated maintenance task is forbidden to be distributed to the intelligent mobile robot with the capability state in the normal level, and the designated position of the generated maintenance task is the deployment position of the idle maintenance resources; and storing the generated maintenance tasks as tasks to be dispatched in a task pool.
Optionally, estimating a scheduling cost generated by assigning each task to be assigned in the task pool to a different idle intelligent mobile robot in the working space according to the designated position of the task to be assigned in the task pool and the real-time position of the idle intelligent mobile robot collected from the working space includes: estimating the dispatching distance of each idle intelligent mobile robot from the current in-floor coordinate of the current floor to the specified in-floor coordinate of each task to be dispatched according to the specified floor and the specified in-floor coordinate of each task to be dispatched, and the current floor and the current in-floor coordinate of each idle intelligent mobile robot in the real-time position; if the current floor of the idle intelligent mobile robot is the same as the appointed floor of the task to be dispatched, the dispatching distance of the idle intelligent mobile robot comprises a flat-floor dispatching distance from the current in-floor coordinate to the appointed in-floor coordinate of the task to be dispatched; if the current floor of the idle intelligent mobile robot is different from the appointed floor of the task to be dispatched, the dispatching distance of the idle intelligent mobile robot comprises a flat dispatching distance from the current in-floor coordinate of the idle intelligent mobile robot to the traffic gate of the cross-floor passage at the current floor, a cross-floor converted distance from the current floor to the appointed floor by the idle intelligent mobile robot through the cross-floor passage, and a flat dispatching distance from the traffic gate of the cross-floor passage at the appointed floor to the appointed in-floor coordinate of the task to be dispatched; and determining the scheduling cost generated when each idle intelligent mobile robot is assigned to each task to be assigned to the idle intelligent mobile robot according to the estimated scheduling distance from the current in-layer coordinate of the current floor to the designated in-layer coordinate of the designated floor of each task to be assigned.
Optionally, estimating a scheduling cost generated by assigning each task to be assigned in the task pool to a different idle intelligent mobile robot in the working space according to the designated position of the task to be assigned in the task pool and the real-time position of the idle intelligent mobile robot collected from the working space includes: estimating the dispatching distance of each idle intelligent mobile robot from the current in-floor coordinate of the current floor to the specified in-floor coordinate of each task to be dispatched according to the specified floor and the specified in-floor coordinate of each task to be dispatched, and the current floor and the current in-floor coordinate of each idle intelligent mobile robot in the real-time position; if the current floor of the idle intelligent mobile robot is the same as the appointed floor of the task to be dispatched, the dispatching distance of the idle intelligent mobile robot comprises a flat-floor dispatching distance from the current in-floor coordinate to the appointed in-floor coordinate of the task to be dispatched; if the current floor of the idle intelligent mobile robot is different from the appointed floor of the task to be dispatched, the dispatching distance of the idle intelligent mobile robot comprises a flat dispatching distance from the current in-floor coordinate of the idle intelligent mobile robot to the traffic gate of the cross-floor passage at the current floor, a cross-floor converted distance from the current floor to the appointed floor by the idle intelligent mobile robot through the cross-floor passage, and a flat dispatching distance from the traffic gate of the cross-floor passage at the appointed floor to the appointed in-floor coordinate of the task to be dispatched; carrying out weighted compensation on the scheduling distance according to the traffic condition in the operation space; and determining the scheduling cost generated when each task to be assigned is assigned to the idle intelligent mobile robot according to the scheduling distance after the weighted compensation.
Optionally, performing weighted compensation on the scheduling distance according to the traffic condition in the working space includes: estimating the traffic flow coefficient of each floor according to the distribution quantity of the intelligent mobile robots of each floor, and performing weighted compensation on the flat scheduling distance of the floor in the scheduling distance by using the traffic flow coefficient of each floor; and estimating a cross-floor competition coefficient according to the utilization rate of the cross-floor channel obtained through monitoring, and performing weighted compensation on the cross-floor converted distance between every two floors in the scheduling distance by using the cross-floor competition coefficient.
Optionally, before selecting a dispatching target for each task to be dispatched from the idle intelligent mobile robot with a minimum sum of scheduling costs generated by all tasks to be dispatched as a desired target, the method further includes: and performing capacity compensation on the estimated scheduling cost by using the capacity state of the idle intelligent mobile robot collected from the working space, wherein the compensation amount of the capacity compensation is in inverse monotone change with the level of the capacity state of the idle intelligent mobile robot.
Optionally, before selecting a dispatching target for each task to be dispatched from the idle intelligent mobile robot with a minimum sum of scheduling costs generated by all tasks to be dispatched as a desired target, the method further includes: carrying out hierarchical deduction on the scheduling cost obtained by estimation by using the task priority of the task to be dispatched in the task pool; the higher the task priority is, the larger the deduction limit of the scheduling cost is.
In another embodiment, there is provided a scheduling apparatus of an intelligent mobile robot, including:
the system comprises a task pool, a cost estimation module, a task scheduling module and a task scheduling module, wherein the task pool comprises a task pool, a task scheduling module and a task scheduling module, the task pool comprises a task scheduling module, the cost estimation module is used for estimating scheduling cost generated by allocating each task to be allocated in the task pool to different idle intelligent mobile robots in a working space according to the assigned position of the task to be allocated in the task pool and the real-time position of the idle intelligent mobile robot collected from the working space when the periodic time of task allocation arrives, the working space comprises at least two floors communicated by a cross-floor channel, and the assigned position of each task to be allocated and the real-time position of each idle intelligent mobile robot are;
and the task assignment module is used for selecting an assignment target for each task to be assigned from the idle intelligent mobile robot by taking the minimum sum of the scheduling costs generated by all the tasks to be assigned as an expected target.
In another embodiment, there is provided a robot control apparatus including: the storage medium is used for providing storage space for the task pool; the communication module is used for collecting the real-time position, the task state and the capability state of the intelligent mobile robot from the operation space; and the processor is used for executing the steps in the scheduling method of the intelligent mobile robot in the embodiment.
In another embodiment, a non-transitory computer readable storage medium is provided, which stores instructions that, when executed by a processor, cause the processor to perform the steps in the scheduling method of the intelligent mobile robot as described above.
In another embodiment, there is provided a logistics system including a work space, a smart mobile robot deployed in the work space, and a robot control device in communication with the smart mobile robot, wherein the work space includes at least two floors in communication with a cross-floor aisle, a designated location of each task to be dispatched and a real-time location of each idle smart mobile robot are located on any floor of the at least two floors, and the robot control device includes a processor for performing the steps in the scheduling method of the smart mobile robot as described above.
Based on the embodiment, the task assignment of the intelligent mobile robot can be not limited by floors, so that the cross-floor task assignment of the intelligent mobile robot can be realized, the use efficiency of the intelligent mobile robot in a working space containing at least two floors can be improved, the total deployment amount of the intelligent mobile robot in the working space can be reduced, and the cost can be reduced while the working efficiency is considered.
Moreover, if the tasks to be dispatched include the pre-dispatching tasks, the use efficiency of the intelligent mobile robot in the working space comprising at least two floors can be further improved. In addition, if the maintenance resources are only deployed on local floors of the working space, cross-floor sharing of the maintenance resources can be achieved by assigning maintenance tasks to the intelligent mobile robots.
Drawings
The following drawings are only schematic illustrations and explanations of the present invention, and do not limit the scope of the present invention:
FIG. 1 is a schematic workspace for an intelligent mobile robot in one embodiment;
FIG. 2 is an exemplary flow diagram of a scheduling method for an intelligent mobile robot in one embodiment;
3 a-3 c are schematic diagrams illustrating the scheduling principle based on pre-scheduled tasks in one embodiment;
FIG. 4 is an expanded flow diagram of the scheduling method of FIG. 2 with the pre-scheduling mechanism;
FIG. 5 is an expanded flow diagram of the self-decision of the pre-scheduling mechanism introduced by the scheduling method shown in FIG. 2;
FIG. 6 is an expanded flow diagram illustrating the introduction of a resource sharing mechanism into the scheduling method shown in FIG. 2;
FIG. 7 is an exemplary flow chart of a scheduling cost estimation process in the scheduling algorithm shown in FIG. 2;
FIGS. 8a and 8b are diagrams illustrating an example of reducing cross-layer scheduling cost in the flow shown in FIG. 7;
FIGS. 9a and 9b are schematic diagrams of an extended process flow for introducing a conversion compensation mechanism into the process flow shown in FIG. 7;
FIG. 10 is a schematic diagram illustrating an expanded flow of the scheduling method introduced with the capability classification mechanism shown in FIG. 2;
FIG. 11 is an expanded flow diagram illustrating the task priority mechanism introduced by the scheduling method shown in FIG. 2;
fig. 12 is a schematic diagram of a frame structure of the robot controller shown in fig. 1;
fig. 13 is an exemplary structural diagram of a scheduling apparatus of an intelligent mobile robot in another embodiment;
fig. 14 is an expanded structural diagram of the scheduling apparatus shown in fig. 13;
fig. 15 is a schematic diagram of a further expanded structure of the scheduling apparatus shown in fig. 13;
fig. 16 is a schematic diagram of an extended example of the scheduling apparatus shown in fig. 13.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and examples.
Fig. 1 is a schematic view of a working space of an intelligent mobile robot in one embodiment. Referring to fig. 1, in one embodiment, a logistics system may include a workspace, a smart mobile robot 10 disposed in the workspace, and a robot control device 100 in communication with the smart mobile robot. The working space may be any one of space scenarios such as a factory (where there is a material transportation demand) or a hospital (where there is a medicine transportation demand) where the intelligent mobile robot (e.g., AGV)10 can be scheduled to work and assist, and the working space in this embodiment may include at least two floors L1-Ln communicated with a cross-floor passage Pcro, where n is a positive integer greater than 1.
In fig. 1, two floors Li and Lj are used to exemplarily represent L1 to Ln, where i and j are positive integers equal to or greater than 1 and equal to or less than n, and in fig. 1, the in-floor arrangement of different floors Li and Lj may be different, for example, the floor Li is provided with a work area Aw for the smart mobile robot 10 to perform a work task and a maintenance area Am for performing maintenance work such as charging and maintenance on the smart mobile robot 10, and the floor Lj has only the work area Aw, and the arrangement manner of the work area Aw of the floor Lj is also different from the work area Aw of the floor Li. Of course, such a diagrammatic representation does not exclude the possibility of the same floor being deployed within a floor.
Since at least two floors L1 to Ln can be communicated by the cross-floor passage Pcro, the intelligent mobile robot 10 in the working space can belong to schedulable resources shared by at least two floors L1 to Ln. Accordingly, in this embodiment, the task assignment to the smart mobile robot 10 may not be limited by the floor, and thus the task assignment across floors to the smart mobile robot 10 may be implemented, which contributes to improving the efficiency of use of the smart mobile robot 10 in the work space including at least two floors L1 to Ln, and at the same time, to reducing the total amount of deployment of the smart mobile robot 10 in the work space, thereby reducing the cost while taking into account the work efficiency.
Still referring to fig. 1, in order to implement the cross-floor task assignment for the intelligent mobile robots 10, each intelligent mobile robot 10 deployed in the working space may report a real-time position, a task state, and a capability state to the robot control device 100, so that the robot control device 100 can control the position and the state of each intelligent mobile robot 10 in real time, and thus a scheduling scheme beneficial to improving the use efficiency of the intelligent mobile robot 10 can be made during task assignment.
The real-time position reported by each intelligent mobile robot 10 may include the current floor and the current in-floor coordinates.
For example, the ground of each floor may be printed with a two-dimensional code (which may be referred to as a ground code) at a different location, and the ground code of each floor may contain a floor designation representing that floor, and in-layer coordinates representing the printed location of the two-dimensional code at the preceding floor. Accordingly, the intelligent mobile robot 10 can read the code of the floor where the intelligent mobile robot 10 is located by using its own vision system, and can obtain the current floor and the current in-floor coordinates of the intelligent mobile robot 10, and the intelligent mobile robot 10 can report the real-time position including the current floor and the current in-floor coordinates to the robot control device 100 by using its own wireless communication device, so that the robot control device 100 can determine the current floor and the current in-floor coordinates of the intelligent mobile robot 10.
In addition, the cross-floor gateway Pcro may also be printed with a ground code, for example, at the gate of the cross-floor gateway Pcro at each floor, but the floor identification contained in the ground code of the cross-floor gateway Pcro may indicate an invalid floor, and the ground code of the cross-floor gateway Pcro may not include in-floor coordinates, or the ground code of the cross-floor gateway Pcro may include in-floor coordinates that are area coordinates indicating the deployment position of the cross-floor gateway. Accordingly, after the intelligent mobile robot 10 reads the codes of the ground codes of the cross-floor channel Pcro through the own vision system and reports the real-time positions obtained by the code reading to the robot control device 100, the robot control device 100 may determine that the intelligent mobile robot 10 is located in the cross-floor channel Pcro by indicating the current floor of the invalid floor in the implementation position.
The task status reported by each intelligent mobile robot 10 to the robot controller 100 may indicate that the intelligent mobile robot 10 is currently tasked or idle. By identifying the task state, the robot controller 100 may determine which intelligent mobile robots 10 are in an idle state where tasks may be currently dispatched. In general, there is a need for cross-floor scheduling only for the intelligent mobile robots 10 to which the task has been dispatched, that is, the intelligent mobile robots 10 in the cross-floor passage Pcro are mostly in a non-idle state, and the idle intelligent mobile robots 10 are mostly located on any of the at least two floors L1 to Ln. The capability status reported by each intelligent mobile robot 10 to the robot controller 100 may include the current remaining power of the intelligent mobile robot 10, and may also include a mechanism operating status indicating whether each part mechanism (e.g., lifting mechanism, driving mechanism, vision system, etc.) is operating normally. By identifying the capability status, the robot controller 100 may determine whether the current capability of the smart mobile robot 10 matches the task to be dispatched.
A task pool may be maintained in the robot controller 100 and may store tasks to be assigned. The robot controller 100 may periodically assign the tasks to be assigned in the task pool in batches, and schedule the intelligent mobile robot 10 to move to the task designation location in the work space including at least two floors L1 to Ln.
Fig. 2 is an exemplary flow diagram of a scheduling method of an intelligent mobile robot in one embodiment. Referring to fig. 2, in this embodiment, the robot controller 100 may implement scheduling for the intelligent mobile robot 10 by executing a scheduling method including the following steps:
s210: when the task dispatching cycle time arrives, according to the assigned position of the task to be dispatched in the task pool and the real-time position of the idle intelligent mobile robot collected from the working space, the dispatching cost generated by dispatching each task to be dispatched in the task pool to different idle intelligent mobile robots in the working space is estimated. The operation space comprises at least two floors which are communicated through a cross-floor channel, and the designated position of each task to be dispatched and the real-time position of each idle intelligent mobile robot are positioned on any floor of the at least two floors.
If the current floor of the idle intelligent mobile robot is the same as the assigned floor of the task to be dispatched, the dispatching cost of the idle intelligent mobile robot comprises the flat-floor dispatching cost of the idle intelligent mobile robot from the current in-floor coordinate to the in-floor assigned position; if the current floor of the idle intelligent mobile robot is different from the appointed floor of the task to be dispatched, the dispatching distance of the idle intelligent mobile robot comprises the flat-floor dispatching cost of the idle intelligent mobile robot from the current in-floor coordinate to the traffic gate of the cross-floor passage, the cross-floor dispatching cost of the idle intelligent mobile robot from the current floor to the appointed floor through the cross-floor passage, and the flat-floor dispatching cost of the idle intelligent mobile robot from the traffic gate of the cross-floor passage to the appointed floor of the task to be dispatched.
S220: and selecting a dispatching target for each task to be dispatched from the idle intelligent mobile robot by taking the minimum sum of the scheduling costs generated by all the tasks to be dispatched as a desired target.
If M tasks to be dispatched and N idle intelligent mobile robots are provided, where M and N are positive integers greater than or equal to 1, then N dispatching possibilities exist for each task to be dispatched, and M × N dispatching costs can be obtained through S210. Accordingly, the scheduling cost sum determined in this step is the scheduling cost sum of each assignment combination for assigning M tasks to be assigned to N idle intelligent mobile robots.
In each assignment combination, one idle intelligent mobile robot can be limited to be assigned with at most one task to be assigned, one idle intelligent mobile robot can be allowed to be assigned with a plurality of tasks to be assigned, and the idle intelligent mobile robot can be allowed to have no task to be assigned. That is, any collocation of M tasks to be dispatched and N idle intelligent mobile robots can form a dispatching combination as long as there is a theoretical possibility.
By circularly executing the flow, each time the cycle time of task assignment arrives, assignment target selection can be executed once on the task to be assigned in the task pool. In addition, the tasks to be dispatched in the task pool at each arrival of the cycle time may not all be the same, the idle intelligent mobile robots in the working space at each arrival of the cycle time may not all be the same, and the number M of the tasks to be dispatched and the number N of the idle intelligent mobile robots at each arrival of the cycle time may be different.
In addition, in order to distinguish the idle and non-idle intelligent mobile robots 10, the process shown in fig. 2 may further include: when the task dispatching cycle time arrives, determining the idle intelligent mobile robots in the working space according to the task states of the intelligent mobile robots collected from the working space, wherein the task state of each intelligent mobile robot represents that the intelligent mobile robot is currently dispatched with a task or is idle.
In practical applications, the tasks to be assigned in the task pool may include job tasks issued by the business system to the robot controller 100, and after the job tasks are assigned to the intelligent mobile robot 10 by the robot controller 100, the intelligent mobile robot 10 may perform operations such as carrying, lifting, and the like according to the job tasks.
In addition to the job task, the business system may issue to the robot controller 100 a pre-scheduling task for scheduling the idle intelligent mobile robot 10 to a designated position of the job task to be issued, so that the idle intelligent mobile robot 10 having a small scheduling cost exists near the designated position of the job task when the job task is issued.
Fig. 3a to 3c are schematic diagrams illustrating a scheduling principle based on a pre-scheduled task in an embodiment.
Referring first to fig. 3a, it is assumed that when the cycle time of the p-th (p is a positive integer greater than or equal to 1) task dispatch arrives, a pre-dispatching command 310 exists in the task pool, and the designated position 300 of the pre-dispatching command 310 is located on the floor Lj. At this time, by executing the flow shown in fig. 2, in a case where the sum of the scheduling costs resulting from all the tasks currently to be dispatched in the task pool is minimized as a desired target, the pre-scheduling instruction 310 is assigned to the smart mobile robot 31 on the floor Li, and thus the smart mobile robot 31 is scheduled across floors.
Referring back to fig. 3b, it is assumed that when the cycle time of the p + q (q is a positive integer greater than or equal to 1) th task assignment arrives, one job task 320 exists in the task pool, and the designated position 300 of the job task 320 is located on the floor Lj. At this time, the smart mobile robot 31 has been scheduled from the floor Li to the floor Lj across floors, the scheduling cost of the smart mobile robot 31 being scheduled to the designated location 300 is greatly reduced compared to the state shown in fig. 3a, and thus, by executing the flow shown in fig. 2, the job task 320 is assigned to the smart mobile robot 31 of the floor Li with the sum of the scheduling costs generated by all the tasks currently to be assigned in the task pool being the minimum as the desired target.
Referring to fig. 3c again, there is another possibility that the cycle time of the p + q (q is a positive integer greater than or equal to 1) th task assignment arrives, that is, there is another intelligent mobile robot 32 near the designated location 300, and the intelligent mobile robot 32 may become idle and stop at the location just after completing other tasks, and at this time, in the case that the sum of the scheduling costs of all the tasks to be assigned currently in the task pool is the minimum as the desired target, the job task 320 may be assigned to another intelligent mobile robot 32.
That is, the pre-scheduled tasks may be considered as auxiliary tasks in order to reduce the scheduling cost for the job tasks, and the intelligent mobile robot to which the job task is assigned is not limited to the intelligent mobile robot to which the corresponding pre-scheduled task is assigned. Alternatively, the pre-scheduling task may be considered as a resource allocation task for dynamically adjusting the available intelligent mobile robots on each floor.
Fig. 4 is an extended flow diagram illustrating the scheduling method of fig. 2 introducing a pre-scheduling mechanism. Referring to fig. 4, the process shown in fig. 2 can be expanded to further include the following steps:
s410: when receiving a job task issued by the service system, storing the received job task as a task to be assigned in the task pool 400;
s420: when receiving a pre-scheduled task issued by the service system, the received pre-scheduled task is stored in the task pool 400 as a task to be assigned.
Accordingly, S210 in the flow shown in fig. 2 may obtain tasks to be dispatched from the task pool 400 maintained by performing S410 and S420.
In practical applications, the prescheduled tasks in the task pool may be generated by the robot controller 100, in addition to being issued by the service system. For example, S420 shown in fig. 4 may be further extended to: when receiving a pre-scheduled task issued by the service system or generating a pre-scheduled task according to the task beat of the job task, the received or generated pre-scheduled task is stored in the task pool 400 as a task to be assigned.
Fig. 5 is an extended flow diagram of the scheduling method shown in fig. 2, which introduces a pre-scheduling mechanism self-decision. Referring to fig. 5, in order to support the robot controller 100 to generate the pre-scheduled task according to the task tempo of the job task, the flow shown in fig. 2 may be further extended to further include the following steps:
s510: task beats of job tasks having the same designated position are acquired.
The task tempo acquisition in this step may include the following optional modes:
the robot control device 100 receives a beat configuration file issued by a service system, wherein the beat configuration file includes an assigned position and a task beat; alternatively, the task tempo of the job task having the same designated position is counted by the robot controller 100 based on the history of the job tasks.
S520: and predicting the assignment time of the job task aiming at the specified position according to the acquired task beat of the specified position.
S530: and generating a prescheduled task containing the specified position before the predicted assigned time arrives.
S540: the generated prescheduled tasks are deposited in the task pool 400 as tasks to be dispatched.
S510 to S540 described above may be executed in parallel with S410 and S420.
In the working space shown in fig. 1, maintenance resources such as charging piles, power swapping stations, maintenance stations, etc. may be deployed only on a part of at least two floors L1-Ln, that is, maintenance resources (e.g., the maintenance area Am located on the floor Li in fig. 1) may be deployed only on a local floor, and at this time, there is competition for maintenance resources for all the intelligent mobile robots. Accordingly, the tasks to be dispatched in the task pool may also further include maintenance tasks for resolving the competition of maintenance resources by cross-floor scheduling.
Fig. 6 is an extended flowchart of the scheduling method introduced with the resource sharing mechanism shown in fig. 2. Referring to fig. 6, the flow shown in fig. 2 can be further expanded to further include the following steps:
s610: the use state of maintenance resources deployed on a local floor of the working space and the capability state of the intelligent mobile robot collected from the working space are monitored.
S620: when the maintenance resources with the idle use state exist in the working space and the capacity state of the intelligent mobile robot is in the abnormal level, the maintenance task is generated, wherein the generated maintenance task is forbidden to be distributed to the intelligent mobile robot with the normal capacity state, and the designated position of the generated maintenance task is the deployment position of the idle maintenance resources. The capability state may be a horizontal section divided according to a capability index value such as an amount of electricity. The smart mobile robot whose capability state is in the relatively low level section can be an assignment target of the maintenance task, while the smart mobile robot whose capability state is in the relatively high level section is prohibited from being selected as an assignment target of the maintenance task, i.e., "no assignment" as described above.
S630: the generated maintenance tasks are stored in the task pool 400 as tasks to be assigned.
In fig. 6, S410 and S420 in the flows shown in fig. 4 and fig. 5 and S510 to S540 in the flow shown in fig. 5 are also included, which is only to indicate that the maintenance tasks generated based on S610 to S630 do not conflict with the pre-scheduled tasks described above, and is not to limit that the self-decision mechanism for generating the maintenance tasks must exist simultaneously with the pre-scheduled mechanism based on the pre-scheduled tasks. In addition, there is also a possibility that the service system may also issue a maintenance task to the robot controller, that is, for the scheduling method in this embodiment, the method may further include: when a maintenance task issued by the service system is received, the received maintenance task is stored in the task pool 400 as a task to be assigned. The estimation mode of the scheduling cost can be universal no matter which task to be dispatched is selected from the job task, the pre-scheduling task and the maintenance task. For example, the estimation of the scheduling cost may primarily take into account the scheduling distance, further in combination with other factors.
Fig. 7 is an exemplary flowchart of a scheduling cost estimation process in the scheduling algorithm shown in fig. 2. Referring to fig. 7, in this embodiment, S210 in the flow shown in fig. 2 may specifically include:
s710: estimating the dispatching distance of each idle intelligent mobile robot from the current in-floor coordinate of the current floor to the specified in-floor coordinate of each task to be dispatched according to the specified floor and the specified in-floor coordinate of each task to be dispatched, and the current floor and the current in-floor coordinate of each idle intelligent mobile robot in the real-time position;
if the current floor of the idle intelligent mobile robot is the same as the appointed floor of the task to be dispatched, the dispatching distance of the idle intelligent mobile robot comprises a flat-floor dispatching distance from the current in-floor coordinate to the appointed in-floor coordinate of the task to be dispatched; if the current floor of the idle intelligent mobile robot is different from the appointed floor of the task to be dispatched, the dispatching distance of the idle intelligent mobile robot comprises a flat dispatching distance from the current in-floor coordinate of the current floor to the traffic gate of the cross-floor passage at the current floor, a cross-floor converted distance from the current floor to the appointed floor by the idle intelligent mobile robot through the cross-floor passage, and a flat dispatching distance from the traffic gate of the cross-floor passage at the appointed floor to the appointed floor of the task to be dispatched.
The flat scheduling distance may be a length of a moving path between a start position and an end position of the intelligent mobile robot on the same floor, or may be a spatial straight distance between the start position and the end position of the intelligent mobile robot on the same floor.
The cross-layer reduced distance is a reduced value of the cost consumed by the intelligent mobile robot through the cross-layer channel according to the dimension of the flat-layer scheduling distance. For example, the conversion method of the cross-layer conversion distance can be expressed as the following equation (1):
Scro＝Tcro×Vcro×αwcalculator (1)
In equation (1):
Scrothe cross-floor converted distance represents that the intelligent mobile robot reaches the appointed floor of the task to be dispatched from the current floor through a cross-floor passage;
Tcrothe estimated time of the intelligent mobile robot from the current floor to the appointed floor in the cross-floor passage is represented and can be determined according to the product of the number of cross-floor and the preset single-floor cross-over time;
Vcrorepresents an average moving speed of the intelligent mobile robot in a cross-floor direction (e.g., a direction from a current floor to a specified floor) in the cross-floor corridor; wherein relying on intelligent movement for requirementsThe case where the robot autonomously travels to realize a cross-layer in the cross-layer passage, the movement speed described herein may be a travel speed at which the intelligent mobile robot has a travel tendency in the cross-layer direction in the cross-layer passage, or a speed degree measure in the cross-layer direction of the travel speed of the intelligent mobile robot in the cross-layer passage; in the case that the intelligent mobile robot performs a floor crossing in the floor crossing aisle by means of an auxiliary carrier (e.g., an elevator), the moving speed described herein may also be a traveling speed of the auxiliary carrier (e.g., an elevator) carrying the intelligent mobile robot in the floor crossing aisle in a floor crossing direction (e.g., a direction from a current floor to a specified floor), and a traveling speed of the intelligent mobile robot on the auxiliary carrier in a horizontal direction with respect to the auxiliary carrier may not be considered at this time;
αwthe compensation coefficient is preset according to the difference degree of the traffic capacity of the intelligent mobile robot in the cross-floor passage compared with the traffic capacity of the flat floor.
S720: and carrying out weighted compensation on the scheduling distance according to the traffic condition in the operation space.
For example, the step may estimate the traffic flow coefficient of each floor according to the distribution number of the intelligent mobile robots of the floor, and perform weighted compensation on the flat scheduling distance of the floor in the scheduling distance by using the traffic flow coefficient of each floor. The traffic flow coefficient mentioned here is intended to mean the degree of smoothness of the intelligent mobile robot moving in the same floor. Optionally, the higher the traffic flow coefficient is, the lower the smoothness of movement is, which means that the higher the scheduling cost consumed by the scheduling distance in the same layer is; conversely, the lower the traffic flow coefficient is, the higher the smoothness of movement is, which means that the scheduling cost consumed by the scheduling distance in the same layer is lower.
For another example, the step may further estimate a cross-floor competition coefficient according to the monitored utilization rate of the cross-floor channel, and perform weighted compensation on the cross-floor converted distance between every two floors in the scheduling distance by using the cross-floor competition coefficient. The cross-floor competition coefficient mentioned here is intended to represent the efficiency of the intelligent mobile robot for realizing floor crossing through a cross-floor channel. Optionally, the higher the cross-floor competition coefficient is, the lower the efficiency of the intelligent mobile robot in realizing floor crossing is, which means that the higher the scheduling cost consumed by the equivalent cross-floor reduced distance is; conversely, the lower the cross-floor competition coefficient is, the higher the efficiency of the intelligent mobile robot in realizing the floor crossing is, which means that the scheduling cost consumed by the equivalent cross-floor reduced distance is lower.
S730: and determining the scheduling cost generated when each task to be assigned is assigned to the idle intelligent mobile robot according to the scheduling distance after the weighted compensation.
The process of determining the scheduling cost in this step can be expressed as the following equation (2):
Cost＝αloc×Sloc+αcro×Scro+αobj×Sobjcalculator (2)
In equation (2):
cost represents the scheduling Cost of assigning a task to be assigned to an intelligent mobile robot;
Slocthe floor leveling dispatching distance from the current position of the current floor to the traffic gate of the cross-floor passage at the current floor is represented;
αlocthe traffic flow coefficient represents the current floor of the intelligent mobile robot;
Scrothe cross-floor converted distance represents that the intelligent mobile robot reaches the appointed floor of the task to be dispatched from the current floor through a cross-floor passage;
αcrorepresenting cross-layer competition coefficients;
Sobjthe floor-leveling dispatching distance represents that the intelligent mobile robot reaches the appointed floor of the task to be dispatched from the traffic entrance of the cross-floor passage;
αobjrepresenting the traffic flow coefficient for the designated floor to which the task is to be assigned.
In the above flow, S721 and S722 may be executed in parallel as shown in fig. 7, or may be executed serially in any order.
Optionally, the weighted compensation of S720 may also be omitted, for example, for some work spaces with a more balanced distribution of the intelligent mobile robot, the traffic condition has less influence on the scheduling cost, and omitting the weighted compensation on the scheduling distance may reduce the amount of calculation required for determining the scheduling cost and thus improve the determination efficiency of the scheduling cost. When the weighted compensation for the scheduling distance is omitted, the scheduling distance, which is estimated in S710, from the current in-floor coordinates of the current floor to the designated in-floor coordinates of the designated floor of each task to be assigned may be determined as the scheduling cost generated when each task to be assigned is assigned to the idle intelligent mobile robot.
To better understand the cross-layer reduced distance and the cross-layer competition coefficient in the above flow, two examples are listed below for further explanation. Fig. 8a and 8b are schematic diagrams illustrating an example of reducing the cross-layer scheduling cost in the flow shown in fig. 7.
Referring to fig. 8a, in this example, an elevator 81 is disposed in the cross-floor aisle Pcro, the elevator 81 is controlled by an elevator dispatching system 82, and the car floor of the elevator 81 may be printed with the aforementioned ground code indicating an invalid floor. The intelligent mobile robot 10 may initiate a call to elevator 81 to elevator dispatching system 82 by itself, or the robot control device 100 may initiate a call to elevator 81 to elevator dispatching system 82 instead of intelligent mobile robot 10 according to the task assignment result. In the example shown in fig. 8 a: v representing average moving speed of intelligent mobile robot in cross-layer channelcroCan be a constant and this constant can be assigned to the average running speed of the elevator 81; alpha representing a preset compensation coefficient according to the difference degree of the traffic capacity of the intelligent mobile robot in the cross-layer passage compared with the level passagewMay be a variable, and the variable may be assigned as a busyness of the elevator 81, wherein the busyness may embody to some extent a loss of the intelligent mobile robot 10 waiting for the elevator 81.
Referring to fig. 8b, in this example, a ramp 83 is disposed in the cross-floor channel Pcro, and the ramp 83 and the cross-floor channel Pcro are located at the ground of the joint of the through-hole of each floor, which can be printed with the above-mentioned descriptionIndicating the ground code of the invalid floor. In the example shown in fig. 8 b: v representing average moving speed of intelligent mobile robot in cross-layer channel PprocroMay be a constant, and the value of the constant may be estimated according to the inclination of the ramp 83 and the horizontal average moving speed of the intelligent mobile robot 10; alpha representing a preset compensation coefficient according to the difference degree of the traffic capacity of the intelligent mobile robot in the cross-layer passage compared with the level passagewMay be a variable, and a value of the variable may be determined according to the number of the intelligent mobile robots currently crawling on the ramp 83 in the cross-layer channel Pcro, and is intended to represent an intra-lane traffic flow of the cross-layer channel Pcro, where the intra-lane traffic flow may reflect, to some extent, a loss generated by the intelligent mobile robot 10 due to a change in traffic conditions when the ramp 83 crawls.
For the above example, the robot controller 100 may further perform weighted compensation when determining the cross-layer reduced distance. Fig. 9a and 9b are schematic diagrams of an extended process flow in which a conversion compensation mechanism is introduced into the process flow shown in fig. 7.
Referring to fig. 9a, for the case of deploying elevators in the cross-floor tunnel, if the scheduling distance estimated in S710 in fig. 7 includes the cross-floor reduced distance, the scheduling method may further include, for each cross-floor reduced distance:
s911: obtaining elevator task information of a cross-floor channel from an elevator dispatching system;
s912: estimating the cross-floor busyness of the cross-floor channel by using the acquired elevator task information;
s913: and performing weighted compensation on the cross-floor converted distance determined based on the elevator running speed and the waiting time by using the estimated cross-floor busyness.
Referring to fig. 9b, for the case of deploying ramps in the cross-layer tunnel, if the scheduling distance estimated in S710 in fig. 7 includes a cross-layer reduced distance, the scheduling method may further include, for each cross-layer reduced distance:
s921: estimating the number of intelligent mobile robots in a road which crawls on a ramp of a cross-layer channel;
for the statistics of the number of intelligent mobile robots in the lane, the number of the intelligent mobile robots with invalid floors on the current floor can be identified according to the collected real-time positions of the intelligent mobile robots; or, the number of intelligent mobile robots effective on the current floor can be subtracted from the total number of the intelligent mobile robots which is predicted according to the collected real-time positions of the intelligent mobile robots; or, a detection system can be additionally arranged at each passage of the cross-layer passage and used for monitoring the intelligent mobile robots entering and exiting the cross-layer passage, and the number of the intelligent mobile robots in the passage is obtained according to the statistics of the monitoring result.
S922: and estimating the traffic flow in the cross-layer channel by using the obtained number of the intelligent mobile robots in the channel.
S923: and performing weighted compensation on the cross-layer converted distance determined based on the ramp traffic speed by using the estimated traffic flow in the lane.
The compensation for the cross-layer reduced distance may not be limited to the above two methods, and the compensation coefficient α may be setwSet to 1 and cancel the compensation. The estimated scheduling cost may be adjusted in combination with other factors.
Fig. 10 is an expanded flow diagram of the scheduling method introduced with the capability ranking mechanism shown in fig. 2. Referring to fig. 10, in this embodiment, the robot controller 100 may implement scheduling for the intelligent mobile robot 10 by executing a scheduling method including the following steps:
s1010: when the task dispatching cycle time arrives, according to the assigned position of the task to be dispatched in the task pool and the real-time position of the idle intelligent mobile robot collected from the working space, the dispatching cost generated by dispatching each task to be dispatched in the task pool to different idle intelligent mobile robots in the working space is estimated. This step may be substantially the same as S210 as shown in fig. 2.
S1020: and performing capacity compensation on the estimated scheduling cost by using the capacity state of the idle intelligent mobile robot collected from the working space. The compensation amount of the capacity compensation changes inversely monotonically with the level of the capacity state of the idle intelligent mobile robot, and the intelligent mobile robot with high capacity state level (for example, enough electric quantity) is scheduled preferentially, so that the intelligent mobile robot is prevented from losing the operation capacity (for example, electric quantity exhaustion or fault deterioration) due to assigned tasks when the capacity state level is lower.
The scheduling cost compensated by this step can be expressed as the following equation (3) or equation (4):
Costcomp＝(αloc×Sloc+αcro×Scro+αobj×Sobj)k+βcapcalculator (3)
Costcomp＝(αloc×Sloc+αcro×Scro+αobj×Sobj)k×βcapCalculator (4)
In equation (3) or equation (4): costcompA value obtained by compensating the scheduling cost of a task to be assigned to an intelligent mobile robot by S1020, and Sloc、αloc、Scro、αcro、Sobj、αobjThe same as formula (2); beta is acapA level indicating a capability state of the smart mobile robot, which may be a horizontal section divided according to a capability index value such as an electric quantity; k is a distance gain coefficient, which may be a positive integer greater than or equal to 1, and in general, k may be set to be greater than 1 in order to make the scheduling distance occupy a higher proportion in the scheduling cost.
S1030: and selecting a dispatching target for each task to be dispatched from the idle intelligent mobile robot by taking the minimum sum of the scheduling costs generated by all the tasks to be dispatched as a desired target.
This step may be substantially the same as S220 shown in fig. 2.
Fig. 11 is an expanded flow diagram illustrating the task priority mechanism introduced by the scheduling method shown in fig. 2. Referring to fig. 11, in this embodiment, the robot controller 100 may implement scheduling for the intelligent mobile robot 10 by executing a scheduling method including the following steps:
s1110: when the task dispatching cycle time arrives, according to the assigned position of the task to be dispatched in the task pool and the real-time position of the idle intelligent mobile robot collected from the working space, the dispatching cost generated by dispatching each task to be dispatched in the task pool to different idle intelligent mobile robots in the working space is estimated. This step may be substantially the same as S210 as shown in fig. 2.
S1120: and carrying out grading deduction on the scheduling cost obtained by estimation by using the task priority of the task to be dispatched in the task pool. It can be considered that the higher the task priority is, the higher the tolerance to the scheduling cost is, so that when the scheduling cost is deducted in a grading manner, the higher the task priority is, the larger the deduction limit to the scheduling cost is.
In practical applications, the task priorities may include a base priority divided according to task types and an addition priority determined according to indicators such as task time limit and importance, for example, the base priority of a job task may be higher than the base priorities of a pre-scheduled task and a maintenance task, and the task priority of each task to be dispatched may be added on the basis of the base priorities, thereby also allowing the task priority of a part of the pre-scheduled task and the maintenance task to be higher than the task priority of a part of the job.
The scheduling cost deducted by this step can be expressed as the following equation (5) or equation (6):
Costdis＝(αloc×Sloc+αcro×Scro+αobj×Sobj)k+βpricalculator (5)
Costdis＝(αloc×Sloc+αcro×Scro+αobj×Sobj)k×βpriCalculator (6)
In equation (5) or equation (6): costdisRepresents the value of the dispatching cost of a task to be dispatched to an intelligent mobile robot after being deducted by S1120, and Sloc、αloc、Scro、αcro、Sobj、αobjFormula of and(2) the same; beta is apriIndicating the task priority, the higher the task priority, βpriThe smaller the value of (A) is; k is a distance gain coefficient, which may be a positive integer greater than or equal to 1, and in general, k may be set to be greater than 1 in order to make the scheduling distance occupy a higher proportion in the scheduling cost.
S1130: and selecting a dispatching target for each task to be dispatched from the idle intelligent mobile robot by taking the minimum sum of the scheduling costs generated by all the tasks to be dispatched as a desired target.
This step may be substantially the same as S220 shown in fig. 2.
In addition, as a further extension, the capability compensation implemented at S1020 in fig. 10 and the hierarchical discount implemented at S1120 in fig. 11 may exist at the same time, and at this time, the scheduling cost for assigning a task to be assigned to an intelligent mobile robot may become equation (7) or equation (8) as follows:
Costcomb＝(αloc×Sloc+αcro×Scro+αobj×Sobj)k+βcap+βpricalculator (7)
Costcomb＝(αloc×Sloc+αcro×Scro+αobj×Sobj)k×βcap×βpriCalculator (8)
In equation (7) or equation (8): costcombThe value of the scheduling cost of a task to be dispatched to an intelligent mobile robot after S1020 compensation and S1120 compensation is shown, Sloc、αloc、Scro、αcro、Sobj、αobjSame as equation (2), betacapBeta is the same as equation (3) or equation (4)priLike equation (5) or equation (6), k is a distance gain coefficient appearing in equations (3) to (6).
Fig. 12 is a schematic diagram of a frame structure of the robot controller shown in fig. 1. Referring to fig. 12, in order to support the robot controller 100 to execute the foregoing scheduling method, the robot controller 100 may include: a storage medium 110 for providing a storage space for the task pool; a communication module 120 for collecting the real-time location, task status and capability status of the intelligent mobile robot from the workspace, and for the example shown in fig. 8a, the communication module 120 may also be used to interact with the elevator dispatching system 82; a processor 130 for executing the steps of the scheduling method. Also, the robot control device 100 may further include a non-transitory computer-readable storage medium 140, the non-transitory computer-readable storage medium 140 may store instructions, and the instructions, when executed by the processor 130, may cause the processor 130 to perform the steps in the scheduling method described previously.
The processor 100 of the robot controller 100 may also be considered as a modular scheduler.
Fig. 13 is a schematic structural diagram of a scheduling apparatus of an intelligent mobile robot in another embodiment. Referring to fig. 13, the scheduling apparatus in this embodiment includes:
the cost estimation module 1310 is configured to estimate scheduling costs generated by allocating each task to be allocated in the task pool to different idle intelligent mobile robots in the working space according to the assigned position of the task to be allocated in the task pool and the real-time position of the idle intelligent mobile robot collected from the working space when the cycle time of task allocation arrives, where the working space includes at least two floors connected by a cross-floor channel, and the assigned position of each task to be allocated and the real-time position of each idle intelligent mobile robot are located on any floor of the at least two floors;
if the current floor of the idle intelligent mobile robot is the same as the assigned floor of the task to be assigned, the scheduling cost of the idle intelligent mobile robot comprises the flat-floor scheduling cost of the idle intelligent mobile robot from the current in-floor coordinate to the in-floor assigned position; if the current floor of the idle intelligent mobile robot is different from the assigned floor of the task to be assigned, the scheduling distance of the idle intelligent mobile robot comprises the flat scheduling cost of the idle intelligent mobile robot from the current in-layer coordinate to the traffic gate of the cross-floor passage, the cross-floor scheduling cost of the idle intelligent mobile robot from the current floor to the assigned floor through the cross-floor passage, and the flat scheduling cost of the idle intelligent mobile robot from the traffic gate of the cross-floor passage to the assigned floor of the task to be assigned;
and the task dispatching module 1320 is configured to select a dispatching target for each task to be dispatched from the idle intelligent mobile robot, where the sum of the scheduling costs generated by all the tasks to be dispatched is minimum as a desired target.
For example, the cost estimation module 1310 may estimate a scheduling distance from the current in-floor coordinate of the current floor to the designated in-floor coordinate of the designated floor of each task to be dispatched, according to the designated floor and the designated in-floor coordinate included in the designated position of each task to be dispatched, and the current floor and the current in-floor coordinate included in the real-time position of each idle intelligent mobile robot;
if the current floor of the idle intelligent mobile robot is the same as the appointed floor of the task to be dispatched, the dispatching distance of the idle intelligent mobile robot comprises a flat-floor dispatching distance from the current in-floor coordinate to the appointed in-floor coordinate of the task to be dispatched; if the current floor of the idle intelligent mobile robot is different from the appointed floor of the task to be dispatched, the dispatching distance of the idle intelligent mobile robot comprises a flat dispatching distance from the current in-floor coordinate of the current floor to the traffic gate of the cross-floor passage at the current floor, a cross-floor converted distance from the current floor to the appointed floor by the idle intelligent mobile robot through the cross-floor passage, and a flat dispatching distance from the traffic gate of the cross-floor passage at the appointed floor to the appointed floor of the task to be dispatched.
The cost estimation module 1310 may perform weighted compensation for the scheduling distance based on traffic conditions in the workspace. For example, the cost estimation module 1310 may estimate the traffic flow coefficient of each floor according to the distribution number of the intelligent mobile robots of the floor, and perform weighted compensation on the flat scheduling distance of the floor in the scheduling distance by using the traffic flow coefficient of each floor; in addition, the cost estimation module 1310 may further estimate a cross-floor competition coefficient according to the monitored utilization rate of the cross-floor channel, and perform weighted compensation on the cross-floor converted distance between every two floors in the scheduling distance by using the cross-floor competition coefficient. Moreover, the cost estimation module 1310 may determine a scheduling cost generated when each task to be assigned is assigned to the idle intelligent mobile robot according to the weighted and compensated scheduling distance. Alternatively, the cost estimation module 1310 may omit weighted compensation of the scheduling distance, and at this time, the cost estimation module 1310 may determine the estimated scheduling distance from the current in-floor coordinate of the current floor to the in-floor coordinate of the designated floor of each task to be assigned, as the scheduling cost generated when each task to be assigned is assigned to the idle intelligent mobile robot.
To estimate the cross-floor contention factor, for the example shown in fig. 8a, cost estimation module 1310 may further obtain elevator task information for the cross-floor corridor from the elevator dispatching system; estimating the cross-floor busyness of the cross-floor channel by using the acquired elevator task information; performing weighted compensation on the cross-floor converted distance determined based on the elevator running speed and the waiting time by using the estimated cross-floor busyness; for the example shown in fig. 8b, cost estimation module 1310 may further estimate the number of smart mobile robots in a lane that crawls a ramp across the floor lane; estimating the traffic flow in the cross-layer channel by using the obtained number of the intelligent mobile robots in the channel; and performing weighted compensation on the cross-layer converted distance determined based on the ramp traffic speed by using the estimated traffic flow in the lane.
Fig. 14 is an expanded structural diagram of the scheduling apparatus shown in fig. 13. Referring to fig. 14, the scheduling apparatus in this embodiment may further include at least one of the following modules:
the task collection module 1330 is configured to maintain a task pool, and when receiving a job task issued by the service system, store the received job task in the task pool as a task to be assigned; when receiving a pre-scheduling task issued by a service system, storing the received pre-scheduling task as a task to be allocated in a task pool;
the pre-scheduling decision module 1340 is configured to obtain task beats of job tasks having the same designated location; according to the acquired task tempo of the specified position, predicting the issuing time of the operation task aiming at the specified position; generating a prescheduling task containing the specified position before the predicted issuing time is reached; correspondingly, when the pre-scheduling decision module 1340 generates a pre-scheduling task according to the task beat of the job task, the task collection module 1330 may obtain the pre-scheduling task generated by the pre-scheduling decision module 1340 and store the obtained pre-scheduling task in the task pool as a task to be assigned;
a maintenance judgment module 1350, configured to monitor a use state of maintenance resources deployed on a local floor of the working space and a capability state of the intelligent mobile robot collected from the working space; when the maintenance resources with the idle use state exist in the working space and the capability state of the intelligent mobile robot is in the abnormal level, a maintenance task is generated, wherein the generated maintenance task is forbidden to be distributed to the intelligent mobile robot with the capability state in the normal level, and the designated position of the generated maintenance task is the deployment position of the idle maintenance resources; and storing the generated maintenance tasks as tasks to be dispatched in a task pool.
Fig. 15 is a schematic diagram of a further expanded structure of the scheduling apparatus shown in fig. 13. Referring to fig. 15, the scheduling apparatus in this embodiment may further include at least one of the following modules: the cost compensation module 1360 is used for acquiring the capability state of the intelligent mobile robot collected from the working space when the periodic time of task assignment arrives; performing capacity compensation on the scheduling cost obtained by estimation by using the capacity state of the idle intelligent mobile robot, wherein the compensation amount of the capacity compensation and the level of the capacity state of the idle intelligent mobile robot are in reverse monotonous change; the cost deduction module 1370 is used for identifying the task priority of the task to be dispatched in the task pool; carrying out grading deduction on the scheduling cost obtained by estimation by utilizing the identified task priority; the higher the task priority is, the larger the deduction limit of the scheduling cost is. When the scheduling apparatus includes both the cost compensation module 1360 and the cost deduction module 1370, the arrangement order of the cost compensation module 1360 and the cost deduction module 1370 between the cost estimation module 1310 and the task dispatching module 1320 may be arbitrarily set.
Fig. 16 is a schematic diagram of an extended example of the scheduling apparatus shown in fig. 13. Referring to fig. 16, the scheduling module in this embodiment may include a cost estimation module 1310 and a task assignment module 1320 shown in fig. 13, a task collection module 1330 and a pre-scheduling decision module 1340 shown in fig. 14, a maintenance decision module 1350, and a cost compensation module 1360 and a cost deduction module 1370 shown in fig. 15. In addition, in order to distinguish between idle and non-idle intelligent mobile robots, the scheduling device in this embodiment may further include a state screening module 1380, configured to determine idle intelligent mobile robots in the task space according to task states of the intelligent mobile robots collected from the job space when the cycle time of task assignment arrives, where the task state of each intelligent mobile robot indicates that the intelligent mobile robot has currently assigned a task or is idle.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (23)

1. A scheduling method of an intelligent mobile robot is characterized by comprising the following steps:
when the task allocation cycle time arrives, estimating the scheduling cost generated by allocating each task to be allocated in the task pool to different idle intelligent mobile robots in the working space according to the assigned position of the task to be allocated in the task pool and the real-time position of the idle intelligent mobile robot collected from the working space, wherein the working space comprises at least two floors communicated by a cross-floor channel, and the assigned position of each task to be allocated and the real-time position of each idle intelligent mobile robot are positioned on any floor of the at least two floors;
and selecting a dispatching target for each task to be dispatched from the idle intelligent mobile robot by taking the minimum sum of the scheduling costs generated by all the tasks to be dispatched as a desired target.
2. The scheduling method of claim 1, further comprising:
when receiving a job task issued by a service system, storing the received job task as a task to be assigned in a task pool;
when receiving a pre-scheduling task issued by a service system or generating the pre-scheduling task according to the task beat of the job task, storing the received or generated pre-scheduling task as a task to be allocated in a task pool.
3. The scheduling method of claim 2, wherein generating the pre-scheduled task according to the task tempo of the job task comprises:
acquiring task beats of job tasks with the same designated position;
according to the acquired task tempo of the specified position, predicting the issuing time of the operation task aiming at the specified position;
and generating a prescheduled task containing the specified position before the predicted assigned time arrives.
4. The scheduling method of claim 3, further comprising:
receiving a beat configuration file issued by a service system, wherein the beat configuration file comprises an appointed position and a task beat; or
And counting task beats of the job tasks with the same designated position according to the historical receiving records of the job tasks.
5. The scheduling method of claim 2, further comprising:
monitoring the use state of maintenance resources deployed on a local floor of a working space and the capability state of the intelligent mobile robot collected from the working space;
when the maintenance resources with the idle use state exist in the working space and the capability state of the intelligent mobile robot is in the abnormal level, a maintenance task is generated, wherein the generated maintenance task is forbidden to be distributed to the intelligent mobile robot with the capability state in the normal level, and the designated position of the generated maintenance task is the deployment position of the idle maintenance resources;
and storing the generated maintenance tasks as tasks to be dispatched in a task pool.
6. The scheduling method of claim 1 wherein estimating the scheduling cost of assigning each task to be assigned in the task pool to a different idle intelligent mobile robot in the workspace based on the assigned locations of the tasks to be assigned in the task pool and the real-time locations of the idle intelligent mobile robots collected from the workspace comprises:
estimating the dispatching distance of each idle intelligent mobile robot from the current in-floor coordinate of the current floor to the specified in-floor coordinate of each task to be dispatched according to the specified floor and the specified in-floor coordinate of each task to be dispatched, and the current floor and the current in-floor coordinate of each idle intelligent mobile robot in the real-time position; if the current floor of the idle intelligent mobile robot is the same as the appointed floor of the task to be dispatched, the dispatching distance of the idle intelligent mobile robot comprises a flat-floor dispatching distance from the current in-floor coordinate to the appointed in-floor coordinate of the task to be dispatched; if the current floor of the idle intelligent mobile robot is different from the appointed floor of the task to be dispatched, the dispatching distance of the idle intelligent mobile robot comprises a flat dispatching distance from the current in-floor coordinate of the idle intelligent mobile robot to the traffic gate of the cross-floor passage at the current floor, a cross-floor converted distance from the current floor to the appointed floor by the idle intelligent mobile robot through the cross-floor passage, and a flat dispatching distance from the traffic gate of the cross-floor passage at the appointed floor to the appointed in-floor coordinate of the task to be dispatched;
and determining the scheduling cost generated when each idle intelligent mobile robot is assigned to each task to be assigned to the idle intelligent mobile robot according to the estimated scheduling distance from the current in-layer coordinate of the current floor to the designated in-layer coordinate of the designated floor of each task to be assigned.
7. The scheduling method of claim 1 wherein estimating the scheduling cost of assigning each task to be assigned in the task pool to a different idle intelligent mobile robot in the workspace based on the assigned locations of the tasks to be assigned in the task pool and the real-time locations of the idle intelligent mobile robots collected from the workspace comprises:
estimating the dispatching distance of each idle intelligent mobile robot from the current in-floor coordinate of the current floor to the specified in-floor coordinate of each task to be dispatched according to the specified floor and the specified in-floor coordinate of each task to be dispatched, and the current floor and the current in-floor coordinate of each idle intelligent mobile robot in the real-time position; if the current floor of the idle intelligent mobile robot is the same as the appointed floor of the task to be dispatched, the dispatching distance of the idle intelligent mobile robot comprises a flat-floor dispatching distance from the current in-floor coordinate to the appointed in-floor coordinate of the task to be dispatched; if the current floor of the idle intelligent mobile robot is different from the appointed floor of the task to be dispatched, the dispatching distance of the idle intelligent mobile robot comprises a flat dispatching distance from the current in-floor coordinate of the idle intelligent mobile robot to the traffic gate of the cross-floor passage at the current floor, a cross-floor converted distance from the current floor to the appointed floor by the idle intelligent mobile robot through the cross-floor passage, and a flat dispatching distance from the traffic gate of the cross-floor passage at the appointed floor to the appointed in-floor coordinate of the task to be dispatched;
carrying out weighted compensation on the scheduling distance according to the traffic condition in the operation space;
and determining the scheduling cost generated when each task to be assigned is assigned to the idle intelligent mobile robot according to the scheduling distance after the weighted compensation.
8. The scheduling method of claim 7 wherein the weighted compensation of the scheduling distance according to the traffic conditions in the job space comprises:
estimating the traffic flow coefficient of each floor according to the distribution quantity of the intelligent mobile robots of each floor, and performing weighted compensation on the flat scheduling distance of the floor in the scheduling distance by using the traffic flow coefficient of each floor;
and estimating a cross-floor competition coefficient according to the utilization rate of the cross-floor channel obtained through monitoring, and performing weighted compensation on the cross-floor converted distance between every two floors in the scheduling distance by using the cross-floor competition coefficient.
9. The scheduling method of claim 8, wherein estimating the cross-layer contention factor according to the monitored usage rate of the cross-layer channel comprises:
obtaining elevator task information of a cross-floor channel from an elevator dispatching system;
estimating the elevator busyness of the cross-floor passage by using the acquired elevator task information;
and determining a cross-floor competition coefficient according to the estimated elevator busyness.
10. The scheduling method of claim 8, wherein estimating the cross-layer contention factor according to the monitored usage rate of the cross-layer channel comprises:
estimating the number of intelligent mobile robots in a road which crawls on a ramp of a cross-layer channel;
determining the ramp occupancy rate by utilizing the ratio of the obtained number of the intelligent mobile robots in the track to the predicted ramp bearing capacity;
and determining a cross-layer competition coefficient according to the ramp occupancy rate obtained by estimation.
11. The scheduling method of claim 1, wherein before selecting the assignment target for each task to be assigned from the idle intelligent mobile robot, with the minimum sum of the scheduling costs generated by all tasks to be assigned as the desired target, further comprising:
and performing capacity compensation on the estimated scheduling cost by using the capacity state of the idle intelligent mobile robot collected from the working space, wherein the compensation amount of the capacity compensation is in inverse monotone change with the level of the capacity state of the idle intelligent mobile robot.
12. The scheduling method of claim 1, wherein before selecting the assignment target for each task to be assigned from the idle intelligent mobile robot, with the minimum sum of the scheduling costs generated by all tasks to be assigned as the desired target, further comprising:
carrying out hierarchical deduction on the scheduling cost obtained by estimation by using the task priority of the task to be dispatched in the task pool; the higher the task priority is, the larger the deduction limit of the scheduling cost is.
13. The scheduling method of claim 1, further comprising:
when the task dispatching cycle time arrives, determining the idle intelligent mobile robots in the task space according to the task states of the intelligent mobile robots collected from the working space, wherein the task state of each intelligent mobile robot represents that the intelligent mobile robot is currently dispatched with a task or is idle.
14. A scheduling apparatus of an intelligent mobile robot, comprising:
the system comprises a task pool, a cost estimation module, a task scheduling module and a task scheduling module, wherein the task pool comprises a task pool, a task scheduling module and a task scheduling module, the task pool comprises a task scheduling module, the cost estimation module is used for estimating scheduling cost generated by allocating each task to be allocated in the task pool to different idle intelligent mobile robots in a working space according to the assigned position of the task to be allocated in the task pool and the real-time position of the idle intelligent mobile robot collected from the working space when the periodic time of task allocation arrives, the working space comprises at least two floors communicated by a cross-floor channel, and the assigned position of each task to be allocated and the real-time position of each idle intelligent mobile robot are;
and the task assignment module is used for selecting an assignment target for each task to be assigned from the idle intelligent mobile robot by taking the minimum sum of the scheduling costs generated by all the tasks to be assigned as an expected target.
15. The scheduling apparatus of claim 14, further comprising:
the task collection module is used for storing the received job task serving as a task to be assigned in a task pool when the job task issued by the service system is received; and when receiving a pre-scheduling task issued by a service system or acquiring the pre-scheduling task generated according to the task beat of the job task, storing the received or acquired pre-scheduling task as a task to be allocated in a task pool.
16. The scheduling apparatus of claim 15, further comprising:
the pre-scheduling judging module is used for acquiring task beats of job tasks with the same designated position; according to the acquired task tempo of the specified position, predicting the issuing time of the operation task aiming at the specified position; generating a prescheduling task containing the specified position before the predicted issuing time is reached; and storing the generated prescheduled tasks as tasks to be dispatched in a task pool.
17. The scheduling apparatus of claim 15, further comprising:
the maintenance judgment module is used for monitoring the use state of maintenance resources deployed on a local floor of the operation space and the capability state of the intelligent mobile robot collected from the operation space; when the maintenance resources with the idle use state exist in the working space and the capability state of the intelligent mobile robot is in the abnormal level, a maintenance task is generated, wherein the generated maintenance task is forbidden to be distributed to the intelligent mobile robot with the capability state in the normal level, and the designated position of the generated maintenance task is the deployment position of the idle maintenance resources; and storing the generated maintenance tasks as tasks to be dispatched in a task pool.
18. The scheduling apparatus of claim 14, further comprising:
the cost compensation module is used for acquiring the capacity state of the intelligent mobile robot collected from the working space when the periodic time of task assignment arrives; and performing capacity compensation on the scheduling cost obtained by estimation by using the capacity state of the idle intelligent mobile robot, wherein the compensation amount of the capacity compensation and the level of the capacity state of the idle intelligent mobile robot are in inverse monotonous change.
19. The scheduling apparatus of claim 14, further comprising:
the cost deduction module is used for identifying the task priority of the task to be dispatched in the task pool; carrying out grading deduction on the scheduling cost obtained by estimation by utilizing the identified task priority; the higher the task priority is, the larger the deduction limit of the scheduling cost is.
20. The scheduling apparatus of claim 14, further comprising:
and the state screening module is used for determining idle intelligent mobile robots in the task space according to the task states of the intelligent mobile robots collected from the working space when the task assignment cycle time arrives, wherein the task state of each intelligent mobile robot represents that the intelligent mobile robot is assigned with a task or is idle at present.
21. A robot control apparatus, comprising:
the storage medium is used for providing storage space for the task pool;
the communication module is used for collecting the real-time position, the task state and the capability state of the intelligent mobile robot from the operation space;
a processor for performing the steps in the scheduling method of an intelligent mobile robot of any of claims 1 to 13.
22. A non-transitory computer readable storage medium storing instructions that, when executed by a processor, cause the processor to perform the steps in the scheduling method of a smart mobile robot of any of claims 1 to 13.
23. A logistics system, comprising a smart mobile robot deployed in a work space, and a robot control device in communication with the smart mobile robot, wherein the work space includes at least two floors in communication with a cross-floor corridor, a designated location of each task to be dispatched and a real-time location of each idle smart mobile robot are located on any of the at least two floors, and wherein the robot control device comprises a processor for performing the steps in the scheduling method for the smart mobile robot as recited in any one of claims 1 to 13.
CN201911212782.4A 2019-12-02 2019-12-02 Scheduling method and scheduling device for intelligent mobile robot Pending CN112990617A (en)

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