CN110046795B - Task allocation method and device for robot - Google Patents
Task allocation method and device for robot Download PDFInfo
- Publication number
- CN110046795B CN110046795B CN201910158851.1A CN201910158851A CN110046795B CN 110046795 B CN110046795 B CN 110046795B CN 201910158851 A CN201910158851 A CN 201910158851A CN 110046795 B CN110046795 B CN 110046795B
- Authority
- CN
- China
- Prior art keywords
- task
- robot
- processed
- excitation
- target
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- 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
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Economics (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Marketing (AREA)
- Educational Administration (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Manipulator (AREA)
Abstract
The embodiment of the invention discloses a task allocation method and a task allocation device for a robot, wherein the method comprises the following steps: calculating an excitation value of the task to be processed to the target robot based on a preset excitation model; distributing corresponding tasks to be processed for the target robot according to the size of the excitation value, and changing the state information of the tasks to be processed and the target robot; and when the task to be processed needs to be assisted, distributing the task to be processed to at least two target robots for assisting processing according to a preset assisting strategy. By the method, the distribution of the tasks can be triggered in time, the task distribution efficiency of the robot is improved, the cooperative operation among different robots is realized, and the operation efficiency of the system is improved.
Description
Technical Field
The invention relates to the technical field of communication, in particular to a task allocation method and device for a robot.
Background
With the development and progress of science and technology, the intelligent robot is more and more widely applied. The multi-robot system can better realize information and resource sharing, has higher parallelism and robustness, can complete more complex tasks, is applied to a plurality of fields such as intelligent production, unknown environment detection, carrying and cleaning, service industry, search and rescue, remote communication and the like, and has good practical value. The focus of attention is on how multiple robots perform task allocation scheduling.
Most of the traditional task allocation algorithms are based on modes such as behavior mechanisms, reward mechanisms, market mechanisms, group intelligence and the like. The algorithm is only suitable for static or scenes with task information known in advance, and in the application field of multiple robots, the robots are often required to cooperatively execute tasks or execute the tasks in a 'forward way', and the traditional task allocation algorithm has great limitation.
Disclosure of Invention
The embodiment of the invention provides a task allocation method and device for a robot, which can improve the task allocation efficiency of the robot and realize cooperative operation among different robots.
A task allocation method of a robot includes:
calculating an excitation value of the task to be processed to the target robot based on a preset excitation model;
distributing corresponding tasks to be processed for the target robot according to the size of the excitation value, and changing the state information of the tasks to be processed and the target robot;
and when the task to be processed needs to be assisted, distributing the task to be processed to at least two target robots for assisting processing according to a preset assisting strategy.
Optionally, in one embodiment, the calculating an excitation value of the task to be processed to the target robot based on the preset excitation model includes:
acquiring a first excitation value of the task to be processed to a target robot;
the first excitation value is positively correlated with the capability value and the path matching degree of the target robot, and is negatively correlated with the distance between the target robot and the location of the task to be processed.
Optionally, in one embodiment, the target robot includes a first robot in a waiting state and a second robot in an idle state; the method for calculating the excitation value of the task to be processed to the target robot based on the preset excitation model further comprises the following steps:
acquiring a second excitation value of the first robot to a second robot;
the second excitation value is positively correlated with the capability value of the first robot and the capability value of the second robot, and is negatively correlated with the distance between the first robot and the second robot.
Optionally, in one embodiment, the calculating an excitation value of the task to be processed to the target robot based on the preset excitation model further includes:
acquiring a third excitation value of the task to be processed to the target robot;
the third excitation value is positively correlated with a sum of the first excitation value and the second excitation value.
Optionally, in one embodiment, the state information of the task to be processed includes a state to be allocated, a state to be assisted, an allocated state, an execution state, and a completion state.
Optionally, in one embodiment, the state information of the target robot corresponds to the state information of the task to be processed, and the state information of the target robot includes an idle state, an excited state, a waiting state, and a working state.
Optionally, in one embodiment, when the task to be processed needs to be assisted by the robot, allocating the task to be processed to at least two target robots to assist the process according to a preset assistance policy includes:
when the task to be processed needs to be assisted, changing the state information of the target robot into a waiting state, changing the state information of the task to be processed into a state to be assisted, and preferentially processing the task to be processed in the state to be assisted;
and sending the task to be processed in the state to be assisted to the target robot in the idle state.
A task assigning apparatus of a robot, comprising:
the excitation value calculation module is used for calculating an excitation value of the task to be processed on the target robot based on a preset excitation model;
the task allocation module is used for allocating corresponding tasks to be processed for the target robot according to the size of the excitation value and changing the state information of the tasks to be processed and the target robot;
and the task processing module is used for distributing the tasks to be processed to at least two target robots for assisting processing according to a preset assisting strategy when the tasks to be processed need assisting processing.
A terminal comprising a memory and a processor, the memory having stored therein a computer program which, when executed by the processor, causes the processor to carry out the steps of the method as described above.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method.
The embodiment of the invention has the following beneficial effects:
according to the method and the device for allocating the tasks of the robot, the incentive value of the tasks to be processed to the target robot is calculated based on the preset incentive model, the corresponding tasks to be processed are allocated to the target robot according to the incentive value, the state information of the tasks to be processed and the state information of the target robot are changed, and when the tasks to be processed need to be assisted, the tasks to be processed are allocated to at least two target robots to be assisted according to the preset assistance strategy. By the method, the distribution of the tasks can be triggered in time, the task distribution efficiency of the robot is improved, the cooperative operation among different robots is realized, and the operation efficiency of the system is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Wherein:
FIG. 1 is a flow diagram of a task assignment methodology for a robot in one embodiment;
FIG. 2 is a flowchart of a task assignment method of a robot in another embodiment;
FIG. 3 is a flowchart of a task assignment method of a robot in another embodiment;
FIG. 4 is a flowchart of a task assignment method of a robot in another embodiment;
FIG. 5 is a state transition diagram of a pending task in one embodiment;
FIG. 6 is a state transition diagram of a target robot in one embodiment;
FIG. 7 is a flowchart of a task assignment method of a robot in another embodiment;
FIG. 8 is a schematic workflow diagram of a robotic system in one embodiment;
FIG. 9 is a block diagram showing the construction of a task assigning apparatus of a robot according to one embodiment;
fig. 10 is a schematic diagram of the internal structure of the terminal in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. It will be understood that, as used herein, the terms "first," "second," and the like may be used herein to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish one element from another. For example, a first application may be referred to as a second application, and similarly, the second application may be the first application, without departing from the scope of the present application. The first application and the second application are both applications, but they are not the same application.
FIG. 1 is a flow diagram of a task assignment methodology for a robot in one embodiment. The task allocation method of the robot in the embodiment can be applied to logistics and warehousing industries, and the robot can independently or cooperatively operate to complete the transportation of goods. Specifically, the robot may be an Automated Guided Vehicle (AGV), which is a Vehicle equipped with an electromagnetic or optical automatic guide device, can travel along a predetermined guide path, and has safety protection and various transfer functions. Alternatively, the number of robots may be one or more, and the types of robots may be the same type or different types. The task allocation method for the robot provided by the embodiment can improve the task allocation efficiency for the robot and realize cooperative work among different robots. As shown in fig. 1, the task assignment method for a robot includes the following steps 102 to 106:
step 102: and calculating an excitation value of the task to be processed to the target robot based on a preset excitation model.
The task to be processed refers to a task waiting for the robot to process in the system, such as goods waiting for being carried in a warehouse; the target robot may be understood as a robot in the system that can perform a task. In the embodiment, the multi-robot system and the immune system are analogized to construct a preset excitation model so as to realize task allocation and cooperative operation of one or more robots.
Specifically, the task to be processed can be analogized to an antigen, the target robot can be analogized to an antibody, the robot communication network can be analogized to an immune network, the excitation between the task to be processed and the target robot can be analogized to the excitation of the antibody by the antigen, and the excitation between the robot and the robot can be analogized to the excitation between the antibodies. The excitation of the target robot through the task to be processed can cause the system to generate robot communication network response, and then the target robot can receive the task to be processed; the cooperation operation among a plurality of robots can be triggered through the excitation between the robots, so that the operation efficiency of the system is improved.
Further, the excitation value of the task to be processed to the target robot is calculated based on the preset excitation model, and specifically, the excitation value of the task to be processed to the target robot, the excitation value between the robot and the robot, the comprehensive excitation value and the like can be included.
Step 104: and distributing corresponding tasks to be processed for the target robot according to the size of the excitation value, and changing the state information of the tasks to be processed and the target robot.
In this embodiment, the system assigns the corresponding task to be processed to the target robot according to the magnitude of the excitation value. Specifically, at the initial time, task allocation among robots is mainly selected according to the excitation of a task to be processed to a target robot, and if the excitation value of a certain task to the target robot is higher, the probability that the target robot obtains the task is higher. When the robot executes a task and needs the assistance of other robots, cooperative work is carried out according to the excitation value between the robot and the robot, if the task is in a state to be assisted, the more robots waiting for the task are, the higher the probability that the task is preferentially selected is, and the higher the system efficiency is.
Further, when the task to be processed is distributed to the corresponding target robot, the state information of the task to be processed and the state information of the target robot can be changed in a same step, so that the next operation can be performed. Specifically, the state information of the task to be processed can be indicated as being changed from the state to be allocated to the allocated state, and the state information of the target robot can be indicated as being changed from the idle state to the excited state.
Step 106: and when the task to be processed needs to be assisted, distributing the task to be processed to at least two target robots for assisting processing according to a preset assisting strategy.
After the task to be processed is distributed to the target robot, judging whether the target robot can finish the task independently; if not, the task to be processed needs to be assisted, at the moment, the state information of the task to be processed and the target robot is changed, the state information of the task to be processed is changed into a state to be assisted, and the state information of the target robot is changed into a waiting state. Further, the task to be processed is distributed to at least two target robots for assisting processing according to a preset assisting strategy.
Specifically, the system adds dynamic tasks according to the tasks to be processed which need to be assisted, and matches and allocates the dynamic tasks by adopting an event-driven mechanism. When the tasks are dynamically added, the system preferentially allocates the dynamic tasks, namely preferentially processes the tasks to be processed in the state to be assisted. When the dynamic task is triggered, the system performs the operations described in step 102 to assign the dynamic task to the corresponding target robot, so as to realize the cooperative work of at least two target robots.
According to the task allocation method of the robot, the incentive value of the task to be processed to the target robot is calculated based on the preset incentive model, the corresponding task to be processed is allocated to the target robot according to the size of the incentive value, the state information of the task to be processed and the state information of the target robot are changed, and when the task to be processed needs to be assisted, the task to be processed is allocated to at least two target robots to be assisted according to the preset assistance strategy. By the method, the distribution of the tasks can be triggered in time, the task distribution efficiency of the robot is improved, the cooperative operation among different robots is realized, and the operation efficiency of the system is improved.
As shown in fig. 2, in one embodiment, the step 102 of calculating the excitation value of the task to be processed to the target robot based on the preset excitation model further includes the following steps 202 to 204:
step 202: and acquiring a first excitation value of the task to be processed to the target robot.
The first excitation value can be understood as the excitation of the target robot by the task to be processed at the initial moment.
Step 204: the first excitation value is positively correlated with the capability value and the path matching degree of the target robot, and is negatively correlated with the distance between the target robot and the location of the task to be processed.
Specifically, let m target robots AGVi (i ═ 1, 2, 3 … m) and n tasks to be processed Tj (j ═ 1, 2, 3 … n) exist in the system, and AGVi has corresponding capability value fiEach task has constraints on capabilities, constraints on completion time, and constraints on whether collaboration is required.
For example, let the j task stimulate the i AGV as gijThe excitation is defined as:
wherein, γ1、γ2、γ3For regulating ginsengNumber, R is a constant, fiTo the capacity value of AGV, deltaijMatching degree of the current path of the AGV and the path of the task to be processed, dijThe distance between the AGV and the location of the task to be processed. In particular, deltaijThe number of the planned routes of the vehicles is the same as the number of the routes to be run by the AGV when the tasks are executed.
As shown in fig. 3, in one embodiment, the target robot includes a first robot in a waiting state and a second robot in an idle state; the step 102 of calculating the excitation value of the task to be processed to the target robot based on the preset excitation model also includes the following steps 302-304:
step 302: and acquiring a second excitation value of the first robot to the second robot.
The second stimulus value can be understood as the stimulus that the robot in the waiting state sends to the robot in the idle state when the task to be processed needs to assist the processing.
Step 304: the second excitation value is positively correlated with the capability value of the first robot and the capability value of the second robot, and is negatively correlated with the distance between the first robot and the second robot.
Specifically, when the AGVs need other AGVs to assist in task execution, the AGVs need to be stimulated to assist in task execution, and the probability that the robot waiting somewhere is going to cooperate is higher as the stimulation of the idle robot is higher.
For example, let the excitation of AGVj to AGVi be mijThe excitation is defined as:
wherein, γ4、γ5、γ6To adjust the parameters, fiCapability value of AGVi, fjIs the ability value of AGVj, dijIs the euclidean distance between AGVi and AGVj.
As shown in fig. 4, in an embodiment, the calculating the excitation value of the task to be processed to the target robot based on the preset excitation model further includes the following steps 402 to 406:
step 402: and acquiring a third excitation value of the task to be processed to the target robot.
The third excitation value can be understood as a comprehensive excitation of the task to be processed to the target robot.
Step 406: the third excitation value is positively correlated with a sum of the first excitation value and the second excitation value.
For example, let AijThe integrated incentive for the ith AGV for the jth task is defined as:
wherein, alpha and beta are adjusting parameters, and N is waiting task TjAGV, mikExcitation value, g, for the k robot to the i robotijAn excitation value for the ith robot for the jth task. From this formula, if a task is in a to-be-collaborated state, the more AGVs waiting on the task, the greater the probability that the task is preferentially selected, and the higher the system efficiency.
In one embodiment, as shown in fig. 5, which is a state transition diagram of the task to be processed in one embodiment, the state information of the task to be processed includes a state to be allocated, a state to be assisted, an allocated state, an execution state, and a completion state, and the state information of the task to be processed and the state information of the target robot correspond to each other. Specifically, a task in a state to be allocated excites the robot in an idle state and then is converted into an allocated state, and if the robot can finish the task by itself, the task is converted into an execution state from the allocated state; if the robot can not finish the task by itself, the task is converted into a state to be assisted from an allocated state, the task in the state to be assisted is converted into an allocated state after being assisted, and if the robot waiting for assistance abandons the task because the robot is not assisted for a long time, the task returns to the state to be allocated again from the state to be assisted.
In one embodiment, as shown in fig. 6, which is a state transition diagram of the target robot in one embodiment, the state information of the target robot corresponds to the state information of the task to be processed, and the state information of the target robot includes an idle state, an excited state, a waiting state, and a working state. Specifically, at an initial moment, all tasks are in a state to be distributed, the robots are in an idle state, each robot is matched with a corresponding task according to an excitation value, if the task matched with each robot is not repeated, the corresponding task is directly obtained, and the robot is changed from the idle state to an excited state; if conflict occurs, the suboptimal task in the conflicting robots stimulates the lower person to obtain the optimal task, if the suboptimal task is stimulated to be the same, the optimal task is randomly distributed, and the other robots reselect the task. Optionally, during task allocation, each robot can only obtain one task at the same time, and can be excited by the task when the robot is in an idle state, and the task can be excited when the robot is in a state to be allocated and a state to be assisted.
In one embodiment, as shown in fig. 7, when the task to be processed requires an assistance process, the allocating the task to be processed to at least two target robot assistance processes according to a preset assistance strategy includes the following steps 702 to 704:
step 702: and when the task to be processed needs to be assisted, changing the state information of the target robot into a waiting state, changing the state information of the task to be processed into a state to be assisted, and preferentially processing the task to be processed in the state to be assisted.
When a task is dynamically added, a system is required to be capable of allocating the task as soon as possible, an event mechanism is introduced at this time, and specifically, when the robot is in the following situations, the task can be allocated preferentially: when the robot changes from the waiting state to the idle state, the robot changes from the working state to the idle state, and when the robot changes from the excited state to the waiting state, the robot breaks down; and when the event is triggered, performing task allocation on the robot in the idle state.
Step 704: and sending the task to be processed in the state to be assisted to the target robot in the idle state.
For example, as shown in fig. 8, a schematic workflow diagram of a robot system in an embodiment is shown, where the workflow of the robot system includes the following steps:
step 801: initializing task information and AGV information;
step 802: calculating the comprehensive excitation of the idle AGV, the tasks to be distributed and the tasks to be assisted;
step 803: selecting a proper task for the idle AGV to execute according to the magnitude of the comprehensive excitation;
step 804: judging whether the AGV receiving the task can complete the task, if so, executing step 806, and if not, executing step 805;
step 805: changing the state information of the AGVs, triggering an event and then executing step 802, and waiting for the proper AGVs to assist in executing the task;
step 806: the AGVs perform tasks either by themselves or in tandem.
Step 807: judging whether the system is terminated, if so, executing step 808, otherwise, executing step 805;
step 808: and finishing the task.
The task allocation method of the robot can trigger the allocation of the tasks in time, improve the task allocation efficiency of the robot, realize the cooperative operation among different robots and improve the operation efficiency of the system.
It should be understood that, although the steps in the above-described figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in the figures may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.
As shown in fig. 9, in one embodiment, a task assigning apparatus of a robot is provided, and the apparatus includes an incentive value calculating module 910, a task assigning module 920, and a task processing module 930.
The excitation value calculation module 910 is configured to calculate an excitation value of the task to be processed on the target robot based on a preset excitation model.
The task allocation module 920 is configured to allocate a corresponding task to be processed to the target robot according to the size of the excitation value, and change the state information of the task to be processed and the target robot.
The task processing module 930 is configured to allocate the task to be processed to at least two target robots for assisting processing according to a preset assistance strategy when the task to be processed requires assisting processing.
According to the task allocation device of the robot, the incentive value of the task to be processed to the target robot is calculated based on the preset incentive model, the corresponding task to be processed is allocated to the target robot according to the size of the incentive value, the state information of the task to be processed and the state information of the target robot are changed, and when the task to be processed needs to be assisted, the task to be processed is allocated to at least two target robots to be assisted according to the preset assistance strategy. By the aid of the device, task allocation can be triggered in time, task allocation efficiency of the robots is improved, cooperative operation among different robots is achieved, and operation efficiency of the system is improved.
For specific limitations of the task assigning apparatus of the robot, reference may be made to the above limitations of the task assigning method of the robot, and details thereof are not described herein again. The various modules in the task assigning apparatus of the robot described above may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
The implementation of the respective modules in the task assigning apparatus of the robot provided in the embodiments of the present application may be in the form of a computer program. The computer program may be run on a terminal or a server. The program modules constituted by the computer program may be stored on the memory of the terminal or the server. The computer program, when executed by a processor, implements the steps of the robot task assignment method described in the embodiments of the present application.
Fig. 10 is a schematic diagram of the internal structure of the terminal in one embodiment. As shown in fig. 2, the terminal includes a processor, a memory, and a communication module connected through a system bus. Wherein, the processor is used for providing calculation and control capability and supporting the operation of the whole terminal. The memory is used for storing data, programs and the like, and at least one computer program is stored on the memory and can be executed by the processor to realize the task allocation method of the robot suitable for the terminal provided by the embodiment of the application. The memory may include a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The computer program can be executed by a processor for implementing a task assignment method for a robot provided in the following embodiments. The internal memory provides a cached execution environment for the operating system computer programs in the non-volatile storage medium. The communication module may be a 4G communication module, a WiFi communication module, or a COFDM communication module, etc., and is configured to communicate with an external communication transmission platform. The terminal may be an automated guided vehicle.
Those skilled in the art will appreciate that the configuration shown in fig. 10 is a block diagram of only a portion of the configuration relevant to the present application, and does not constitute a limitation on the terminal to which the present application is applied, and that a particular terminal may include more or less components than those shown in the drawings, or may combine certain components, or have a different arrangement of components.
The embodiment of the application also provides a computer readable storage medium. One or more non-transitory computer-readable storage media containing computer-executable instructions that, when executed by one or more processors, cause the processors to perform a method of task assignment for a robot as described in the embodiments above.
The embodiment of the application also provides a computer program product. A computer program product comprising instructions which, when run on a computer, cause the computer to perform the method of task allocation for a robot as described in the embodiments above.
In the above embodiments, all or part of the implementation may be realized by software, hardware, firmware, or any combination thereof. When implemented using a software program, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention, and it is therefore to be understood that the invention is not limited by the scope of the appended claims.
Claims (7)
1. A task allocation method for a robot, comprising:
calculating an excitation value of the task to be processed to the target robot based on a preset excitation model, wherein the calculation of the excitation value of the task to be processed to the target robot based on the preset excitation model comprises the following steps:
acquiring a first excitation value of the task to be processed to a target robot;
the first excitation value is positively correlated with the capability value of the target robot and the path matching degree, and is negatively correlated with the distance between the target robot and the location of the task to be processed, the path matching degree is the matching degree of the current path of the target robot and the path of the task to be processed, and the path matching degree is obtained by calculating the same number of paths to be operated by the target robot in the vehicle planning path when the task is executed;
wherein, the target robot includes the first robot in waiting state and the second robot in idle state, then calculate the incentive value to the target robot of task to be processed based on presetting the incentive model, still include:
acquiring a second excitation value of the first robot to a second robot;
the second excitation value is positively correlated with the capability value of the first robot and the capability value of the second robot, and is negatively correlated with the distance between the first robot and the second robot;
calculating an excitation value of the task to be processed to the target robot based on the preset excitation model, and further comprising:
acquiring a third excitation value of the task to be processed to the target robot;
the third excitation value is positively correlated with the sum of the first excitation value and the second excitation value;
distributing corresponding tasks to be processed for the target robot according to the size of the excitation value, and changing the state information of the tasks to be processed and the target robot;
wherein the third excitation value AijAnd comprehensively exciting the ith target robot AGV for the jth task to be processed, wherein a third excitation value is defined as:
wherein alpha and beta are regulationParameter, N is waiting task TjAGV, mikSecond excitation value, g, for the kth robot to the ith robotijA first excitation value for the ith robot for the jth task;
and when the task to be processed needs to be assisted, distributing the task to be processed to at least two target robots for assisting processing according to a preset assisting strategy.
2. The method of claim 1, wherein the status information of the pending task comprises a pending status, an allocated status, an executing status, and a completing status.
3. The method according to claim 2, wherein the state information of the target robot corresponds to the state information of the task to be processed, and the state information of the target robot includes an idle state, an excited state, a waiting state, and a working state.
4. The method according to claim 1, wherein said assigning the task to be processed to at least two target robotic assistance processes according to a preset assistance strategy when the task to be processed requires assistance processing comprises:
when the task to be processed needs to be assisted, changing the state information of the target robot into a waiting state, changing the state information of the task to be processed into a state to be assisted, and preferentially processing the task to be processed in the state to be assisted;
and sending the task to be processed in the state to be assisted to the target robot in the idle state.
5. A task assigning apparatus for a robot, comprising:
the excitation value calculation module is used for calculating the excitation value of the task to be processed to the target robot based on a preset excitation model, and the excitation value of the task to be processed to the target robot is calculated based on the preset excitation model, and the excitation value calculation module comprises:
acquiring a first excitation value of the task to be processed to a target robot;
the first excitation value is positively correlated with the capability value of the target robot and the path matching degree, and is negatively correlated with the distance between the target robot and the location of the task to be processed, the path matching degree is the matching degree of the current path of the target robot and the path of the task to be processed, and the path matching degree is obtained by calculating the same number of paths to be operated by the target robot in the vehicle planning path when the task is executed;
wherein, the target robot includes the first robot in waiting state and the second robot in idle state, then calculate the incentive value to the target robot of task to be processed based on presetting the incentive model, still include:
acquiring a second excitation value of the first robot to a second robot;
the second excitation value is positively correlated with the capability value of the first robot and the capability value of the second robot, and is negatively correlated with the distance between the first robot and the second robot;
calculating an excitation value of the task to be processed to the target robot based on the preset excitation model, and further comprising:
acquiring a third excitation value of the task to be processed to the target robot;
the third excitation value is positively correlated with the sum of the first excitation value and the second excitation value, wherein the third excitation value AijAnd comprehensively exciting the ith target robot AGV for the jth task to be processed, wherein a third excitation value is defined as:
wherein, alpha and beta are adjusting parameters, and N is waiting task TjAGV, mikExcitation value, g, for the k robot to the i robotijStimulus value to ith robot for jth task；
The task allocation module is used for allocating corresponding tasks to be processed for the target robot according to the size of the excitation value and changing the state information of the tasks to be processed and the target robot;
and the task processing module is used for distributing the tasks to be processed to at least two target robots for assisting processing according to a preset assisting strategy when the tasks to be processed need assisting processing.
6. A terminal, comprising a memory and a processor, the memory having stored thereon a computer program that, when executed by the processor, causes the processor to perform the steps of the method according to any one of claims 1 to 4.
7. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910158851.1A CN110046795B (en) | 2019-03-01 | 2019-03-01 | Task allocation method and device for robot |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910158851.1A CN110046795B (en) | 2019-03-01 | 2019-03-01 | Task allocation method and device for robot |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110046795A CN110046795A (en) | 2019-07-23 |
| CN110046795B true CN110046795B (en) | 2021-11-05 |
Family
ID=67274451
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910158851.1A Active CN110046795B (en) | 2019-03-01 | 2019-03-01 | Task allocation method and device for robot |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110046795B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113341905B (en) * | 2021-08-09 | 2021-10-26 | 山东华力机电有限公司 | Multi-AGV (automatic guided vehicle) collaborative planning method and system based on artificial intelligence |
| CN113902177A (en) * | 2021-09-26 | 2022-01-07 | 上海擎朗智能科技有限公司 | Task allocation method and system |
| CN114859883A (en) * | 2022-03-18 | 2022-08-05 | 成都盛锴科技有限公司 | Maintenance robot multi-machine cooperation control method, system and storage medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101618543A (en) * | 2009-07-23 | 2010-01-06 | 重庆邮电大学 | Task allocation method of heterogeneous multi-robot system |
| CN101945492A (en) * | 2010-08-09 | 2011-01-12 | 哈尔滨工程大学 | Clustering-based multi-robot task allocation method |
| CN105045094A (en) * | 2015-08-03 | 2015-11-11 | 南通大学 | Task-search and task execution method for multiple robot groups |
| CN105729491A (en) * | 2016-04-01 | 2016-07-06 | 纳恩博（北京）科技有限公司 | Executing method, device and system for robot task |
| CN106875090A (en) * | 2017-01-09 | 2017-06-20 | 中南大学 | A kind of multirobot distributed task scheduling towards dynamic task distributes forming method |
| CN108416488A (en) * | 2017-12-21 | 2018-08-17 | 中南大学 | A kind of more intelligent robot method for allocating tasks towards dynamic task |
-
2019
- 2019-03-01 CN CN201910158851.1A patent/CN110046795B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101618543A (en) * | 2009-07-23 | 2010-01-06 | 重庆邮电大学 | Task allocation method of heterogeneous multi-robot system |
| CN101945492A (en) * | 2010-08-09 | 2011-01-12 | 哈尔滨工程大学 | Clustering-based multi-robot task allocation method |
| CN105045094A (en) * | 2015-08-03 | 2015-11-11 | 南通大学 | Task-search and task execution method for multiple robot groups |
| CN105729491A (en) * | 2016-04-01 | 2016-07-06 | 纳恩博（北京）科技有限公司 | Executing method, device and system for robot task |
| CN106875090A (en) * | 2017-01-09 | 2017-06-20 | 中南大学 | A kind of multirobot distributed task scheduling towards dynamic task distributes forming method |
| CN108416488A (en) * | 2017-12-21 | 2018-08-17 | 中南大学 | A kind of more intelligent robot method for allocating tasks towards dynamic task |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110046795A (en) | 2019-07-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109991977B (en) | Path planning method and device for robot | |
| CN110046795B (en) | Task allocation method and device for robot | |
| Rahman et al. | An integrated approach for line balancing and AGV scheduling towards smart assembly systems | |
| Erol et al. | A multi-agent based approach to dynamic scheduling of machines and automated guided vehicles in manufacturing systems | |
| Chan et al. | Analysis of dynamic dispatching rules for a flexible manufacturing system | |
| Cai et al. | Rescheduling policies for large-scale task allocation of autonomous straddle carriers under uncertainty at automated container terminals | |
| CN111400868B (en) | Distributed workshop scheduling optimization method and system with order and robot carrying functions | |
| Chawla et al. | Automatic guided vehicles fleet size optimization for flexible manufacturing system by grey wolf optimization algorithm | |
| Ichnowski et al. | Fog robotics algorithms for distributed motion planning using lambda serverless computing | |
| Lim et al. | A dispatching method for automated guided vehicles by using a bidding concept | |
| CN109683504A (en) | Outbound control method, device, electronic equipment and storage medium | |
| CN115983553A (en) | Stereoscopic warehouse task scheduling method and device, computer equipment and storage medium | |
| Hu et al. | A dynamic integrated scheduling method based on hierarchical planning for heterogeneous AGV fleets in warehouses | |
| JP2017191373A (en) | Multi-agent system, task assignment device, task assignment method, and program | |
| CN115061436A (en) | Dynamic scheduling method, system, electronic device and computer storage medium | |
| Wu et al. | Multi-agent pickup and delivery with task deadlines | |
| Chawla et al. | Material handling robots fleet size optimization by a heuristic | |
| CN113177704A (en) | Task allocation method and device for automatic guided vehicle and computer storage medium | |
| JP3189883B2 (en) | Automatic guided vehicle system | |
| KR20200078318A (en) | Situation-adapted Global Pooling System and Method for Transfer Vehicles in Automated Container Terminal | |
| US20230003753A1 (en) | Systems and methods for managing experimental requests at remote laboratories | |
| Qu et al. | Simultaneous 2D hoist scheduling and production line design for multi-recipe and multi-stage material handling processes | |
| Kim et al. | Multi-agent-based scheduling methods for hybrid cellular production lines in semiconductor industry | |
| CN115796553A (en) | AGV task scheduling method and device and AGV scheduling system | |
| CN115099666A (en) | Robot scheduling method and device and electronic equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |