WO2020103298A1 - 分布式机器人的调度决策方法、装置、系统与电子设备和存储介质 - Google Patents

分布式机器人的调度决策方法、装置、系统与电子设备和存储介质

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Publication number
WO2020103298A1
WO2020103298A1 PCT/CN2018/125150 CN2018125150W WO2020103298A1 WO 2020103298 A1 WO2020103298 A1 WO 2020103298A1 CN 2018125150 W CN2018125150 W CN 2018125150W WO 2020103298 A1 WO2020103298 A1 WO 2020103298A1
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WO
WIPO (PCT)
Prior art keywords
task
robot
robots
package
decision
Prior art date
Application number
PCT/CN2018/125150
Other languages
English (en)
French (fr)
Inventor
杨志钦
Original Assignee
炬星科技（深圳）有限公司
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by 炬星科技（深圳）有限公司 filed Critical 炬星科技（深圳）有限公司
Priority to SG11202105245VA priority Critical patent/SG11202105245VA/en
Priority to KR1020207028540A priority patent/KR102431055B1/ko
Priority to EP18940929.5A priority patent/EP3885858A4/en
Priority to CA3120404A priority patent/CA3120404C/en
Priority to US17/294,428 priority patent/US11504847B2/en
Publication of WO2020103298A1 publication Critical patent/WO2020103298A1/zh

Links

Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
    • B25J9/1661—Programme controls characterised by programming, planning systems for manipulators characterised by task planning, object-oriented languages
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
    • G05B19/4189—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by the transport system
    • G05B19/41895—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by the transport system using automatic guided vehicles [AGV]
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J13/00—Controls for manipulators
    • B25J13/006—Controls for manipulators by means of a wireless system for controlling one or several manipulators
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1679—Programme controls characterised by the tasks executed
    • B25J9/1682—Dual arm manipulator; Coordination of several manipulators
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/31—From computer integrated manufacturing till monitoring
    • G05B2219/31002—Computer controlled agv conveys workpieces between buffer and cell
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Definitions

  • the invention belongs to the technical field of cluster robot control, and particularly relates to a scheduling decision method, device, system, electronic equipment and storage medium of a distributed robot.
  • a central control server and multiple AGV Auto Guided Vehicle: An AGV dispatching decision system composed of logistics management of receiving, transportation and unloading of goods.
  • the central control server uniformly schedules and decides the actions of multiple AGVs, so as to achieve the purpose of logistics management.
  • AGV itself does not have computing power, only has motion control capabilities, which leads to the need for a central control server for its path selection and action planning, and because the composition of cluster AGVs is similar to a "star" topology, that is, each AGV is connected to the central The control server, so the bombing of requests from the cluster AGV makes the central control server exceed the load capacity and cannot achieve unified scheduling decisions.
  • AGVs do not have computing power, there is no communication between AGVs, so if the central control server fails, the cluster AGV will systematically collapse.
  • the existing AGV scheduling decision system has technical problems that the AGVs cannot communicate and are easily overloaded by the central control server.
  • the object of the present invention is to provide a dispatching decision method, device, system and electronic equipment and storage medium of a distributed robot, to solve the AGV dispatching decision system where there is no communication between AGVs and it is easy to overload the central control server technical problem.
  • the scheduling decision method for distributed robots includes:
  • the present invention also provides an electronic device, including a memory and a processor, where the memory stores a computer program, and the computer program is executed in the processor to implement any of the above methods.
  • the present invention also provides a storage medium that stores a computer program, and the computer program is executed in a processor to implement any of the above methods.
  • the invention also provides a distributed robot scheduling decision system, including:
  • the cluster robot includes at least two robots, and the robots communicate with each other through a communication interface;
  • the server side communicates with the cluster robot and is configured to transmit a task package containing at least one task to any one of the cluster robots for transmission to other robots;
  • the cluster robot decides to receive tasks suitable for execution in the task package according to the decision variables, and executes the tasks suitable for execution.
  • the invention also provides a scheduling decision-making device for distributed robots, including:
  • a receiving and transmitting module configured to receive a task package containing at least one task, and transmit the task package to other robots in the cluster robot;
  • the decision receiving module decides to receive the tasks suitable for execution in the task package according to the decision variables
  • the execution module is configured to execute the task suitable for execution.
  • the scheduling decision method of the distributed robot provided by the present invention, by receiving a task package containing at least one task, and then transmitting the task package to other robots in the cluster robot, and then according to the decision variables to receive the tasks suitable for execution in the task package, Then perform tasks that are suitable for execution, so that the cluster robots can communicate with each other for task transmission, and can receive the task execution suitable for execution in the task package according to the decision-making variable decision, so that not only can the cluster robot make independent decisions, no longer rely on centralized decision
  • the central control decision-making method effectively avoids the technical effect that the server is easily overloaded, and reaches the technical effect of intelligently selecting execution tasks and improving execution efficiency.
  • FIG. 1 is a flowchart of a distributed robot scheduling decision method provided by an embodiment
  • FIG. 2 is a flowchart of an improved method for the method in FIG. 1 provided by an embodiment
  • FIG. 3 is a flowchart of an improved method for the method in FIG. 2 provided by an embodiment
  • FIG. 4 is a structural diagram of an electronic device provided by an embodiment
  • FIG. 5 is an architectural diagram of a distributed robot scheduling decision system provided by an embodiment
  • FIG. 6 is an architecture diagram of a scheduling decision device for a distributed robot provided by an embodiment
  • FIG. 7 is a diagram of an improved device architecture of the device in FIG. 6;
  • FIG. 8 is an improved device architecture diagram of the device in FIG. 7.
  • the term “storage medium” may be various media that can store computer programs, such as ROM, RAM, magnetic disk, or optical disk.
  • the term "processor” can be CPLD (Complex Programmable Logic Device), FPGA (Field-Programmable Gate Array), MCU (Microcontroller Unit), PLC (Programmable Logic Controller: programmable logic controller) and CPU (Central Processing Unit (Central Processing Unit) and other chips or circuits with data processing functions.
  • the term "electronic device” may be any device having a data processing function and a storage function, and may generally include a fixed terminal and a mobile terminal.
  • Fixed terminals such as desktop computers.
  • Mobile terminals such as mobile phones, PAD and mobile robots.
  • the technical features involved in different embodiments of the present invention described later can be combined as long as they do not conflict with each other.
  • a central control server and multiple AGV Auto Guided Vehicle: An AGV dispatching decision system composed of logistics management of receiving, transportation and unloading of goods.
  • the central control server uniformly schedules and decides the actions of multiple AGVs, so as to achieve the purpose of logistics management.
  • the existing AGV scheduling decision system can achieve logistics management, the communication between AGVs is not easy to cause the central control server to be overloaded.
  • AGV itself does not have computing power, only has motion control capabilities, which leads to the need for a central control server for its path selection and action planning, and because the composition of cluster AGVs is similar to a "star" topology, that is, each AGV is connected to the central The control server, so the bombing of requests from the cluster AGV makes the central control server exceed the load capacity and cannot achieve unified scheduling decisions.
  • AGVs do not have computing power, there is no communication between AGVs, so if the central control server fails, the cluster AGV will systematically collapse.
  • the existing AGV scheduling and decision-making system has the technical problem that the AGV cannot communicate with each other and easily overload the central control server. This technical problem can also be understood as the defect of centralized decision-making and central control.
  • FIG. 1 is a flowchart of a distributed robot scheduling decision method provided by an embodiment, showing a distributed robot scheduling decision method to solve the above technical problems.
  • a scheduling decision method for a distributed robot includes:
  • Step S10 Receive a task package containing at least one task, and transmit the task package to other robots in the cluster robot;
  • Step S11 according to the decision-making variable decision to receive tasks suitable for execution in the task package
  • step S12 a task suitable for execution is executed.
  • the distributed robot scheduling decision method provided in this embodiment can be used to schedule cluster robots in any field to perform tasks.
  • the method can be used in the logistics field to schedule logistics cluster robots to perform tasks.
  • This method can be used in logistics warehouses to dispatch logistics cluster robots to perform tasks.
  • the cluster robot includes but is not limited to two robots.
  • each robot can be a mobile robot with autonomous computing capabilities and autonomous navigation capabilities centered on a small computer.
  • multiple communication interfaces can be installed inside each robot, and each robot can communicate with the communication interfaces on other robots through one communication interface on each robot.
  • the task packet is a data packet containing at least one task, and the data packet can be transmitted via the network.
  • the task package includes at least one task. It can be understood that the task package is a task group, and the task group may include a navigation task, a mobile task, a picking task, and a prompt task.
  • multiple communication interfaces may include a WiFi network interface and 4G IoT network interface.
  • the WiFi network interface can be used for connection and communication between multiple robots.
  • 4G The IoT network interface can be used to connect and communicate with any one of multiple robots on the server side.
  • any one of the cluster robots receives a task packet containing at least one task from the server, and then transmits the task packet to other robots in the cluster robot.
  • the transmission mode may be broadcast transmission or point-to-point transmission.
  • point-to-point transmission is preferred to implement the transmission of the task packet. For example, after the task package is transmitted from the server to one of the cluster robots, the receiver is transmitted to the receiver afterwards. In this way, the task package is transmitted in order so that the cluster robot can receive the task package.
  • the cluster robots receive the task packages and store them separately. At this time, the task status of the task packages received by each robot is completely consistent.
  • step S11 and step S12 the decision-making variables received include but are not limited to the robot's stopped state, working state, current position state, own vehicle or container, power status, task status of the task received, and The task status of the task to be collected.
  • the task that is suitable for execution is that the robot judges whether the content of the task in the task package is more suitable for itself to receive and execute according to the decision-making variable.
  • a task content in a task package read by a robot in a cluster robot is: go to a nearby picking point to pick the goods. At this time, the robot extracts its current position and judges whether the distance between its current position and the nearby picking point exceeds the threshold. If it does not exceed it, the task is considered to be suitable for its execution.
  • the cluster robots receive their own tasks that are suitable for execution according to their own decision variables, so that not only can the cluster robots make independent decisions, no longer rely on centralized decision-making and central control, and effectively avoid the technical effect of easily overloading the server. , And achieve the technical effect of intelligently selecting execution tasks and improving execution efficiency.
  • FIG. 2 is a flowchart of an improved method for the method in FIG. 1 provided by an embodiment, which shows an improved method for scheduling decision of a distributed robot to solve the technical problem of task execution conflict.
  • the method in FIG. 1 further includes:
  • Step S20 marking the task suitable for execution as the task package where the received task is stored locally and receiving the update package transmitted by other robots to mark the task package that has received the task;
  • Step S21 judging the priority of the same received task in the task package according to the updated decision variable, and updating the task package with the high reserved priority;
  • Step S22 transmit the task packet with high priority to other robots.
  • the task that is suitable for execution is stored as a task that has been collected locally, and then receives the update of the task package of the received task transmitted by other robots, and then determines the same received task in different task packages according to the update decision variables Priority, update and retain the task package with high priority, and then transmit the task package with high priority to other robots, so as to achieve the technical effect of solving the task conflict through priority judgment.
  • the cluster robots all receive their own tasks that are suitable for execution according to their own decision variables, when at least two of the cluster robots receive the same task content, for example, any three robots All of them have received the task of picking the goods at the nearby picking point, which will lead to the conflict of task execution.
  • each robot in the cluster robot marks the task it has collected as a received task, and transmits the task package after the marked task is received to other robots. Therefore, each The robots have stored the task packages after they have received the marked tasks (for convenience, the "task packages after the self-marked tasks are received” are local task collection packages) and the task packages after the marked tasks received by other robots ( For the convenience of explanation, the "task package after receiving the marked tasks transmitted by other robots" is referred to as the transmission task receiving package).
  • the updated decision variables include, but are not limited to: the current position status of the robot, the power status, and the task receiving time.
  • different task packages are local task receiving packages and transmission task receiving packages.
  • the priority of the same received tasks in different task packages is judged according to the updated decision variables, and the task packages with high reserved priority are updated.
  • the priority judgment can be performed by updating decision variables including but not limited to the following.
  • the priority is judged by the time the task is received.
  • the robot reads the T1 task receiving time of the local task receiving package as t1, and the T1 task receiving time of the transmission task receiving package as t2. If t1 comes first and t2 comes after, it can be judged that the T1 task is received later The priority is higher than the priority of the previous T1 task.
  • the priority is determined by the current position status.
  • the robot reads its current position from the T1 task to travel the distance s1
  • the other robot's current position from the T1 task to travel the distance s2 if s1 is greater than s2, it can be judged that the T1 task priority of the transmission task to receive the package.
  • the transmission mode may be broadcast transmission or point-to-point transmission.
  • point-to-point transmission is preferred to implement the transmission of the task packet.
  • the task package is transmitted from one robot to another robot, and then transmitted from another robot to other robots, and then transferred in this order, and finally the cluster robots receive the task package.
  • FIG. 3 is a flowchart of an improved method for the method in FIG. 2 provided by an embodiment, which shows an improved method for scheduling decision of a distributed robot to solve the technical problem of how to obtain a new task package from the server.
  • the method in FIG. 2 further includes:
  • Step S30 Determine whether the tasks in the task package have been collected
  • Step S31 when the task in the task package has been received, request to transmit a new task package.
  • any one of the robots in the cluster reads that the tasks in the transmission task receiving package it has received are all marked as received, and the task in the task package is judged to have been After being received, the robot can transmit the transmission task collection package to other robots through broadcast transmission or point-to-point transmission until it is transmitted to the robot that communicates with the server to communicate with the server to request the transmission of a new task package.
  • the technical effect of controlling the continuous work of the cluster robot is realized.
  • FIG. 4 is a structural diagram of an electronic device provided by an embodiment, showing an electronic device for storing and processing a computer program.
  • an electronic device includes a memory and a processor.
  • the memory stores a computer program, and the computer program is executed in the processor to implement any of the methods shown in FIGS. 1-3.
  • a storage medium is also provided.
  • the storage medium stores a computer program, and the computer program is executed in a processor to implement any of the methods shown in FIGS. 1-3.
  • FIG. 5 is an architectural diagram of a distributed robot scheduling decision system provided by an embodiment, showing a distributed robot scheduling decision system.
  • the distributed robot scheduling decision system includes:
  • the cluster robot 50 includes at least two robots, and the robots communicate with each other through a communication interface;
  • the server 51 communicates with the cluster robot 50 to transmit a task package containing at least one task to any one of the cluster robots 50 for transmission to other robots;
  • the cluster robot 50 decides to receive the tasks suitable for execution in the task package according to the decision-making variables and executes the tasks suitable for execution.
  • the cluster robots 50 can communicate with each other for task transmission, and can receive the task execution suitable for execution in the task package according to the decision-making variable decision, so that not only can the cluster robot 50 be able to make independent decisions, and no longer rely on centralized decision making and central control decision making In this way, the technical effect of easily avoiding the overload of the server is effectively avoided, and the technical effect of intelligently selecting execution tasks and improving execution efficiency is reached.
  • the distributed robot scheduling decision method provided in this embodiment can be used to schedule cluster robots 50 in any field to perform tasks.
  • this method can be used in the logistics field to schedule logistics cluster robots 50 to perform tasks.
  • the method can be used in a logistics warehouse to schedule a logistics cluster robot 50 to perform tasks.
  • the cluster robot 50 includes but is not limited to two robots.
  • each robot can be a mobile robot with autonomous computing capabilities and autonomous navigation capabilities centered on a small computer.
  • multiple communication interfaces can be installed inside each robot, and each robot can communicate with the communication interfaces on other robots through one communication interface on each robot.
  • the task packet is a data packet containing at least one task, and the data packet can be transmitted via the network.
  • the task package includes at least one task. It can be understood that the task package is a task group, and the task group may include a navigation task, a mobile task, a picking task, and a prompt task.
  • multiple communication interfaces may include a WiFi network interface and 4G IoT network interface.
  • the WiFi network interface can be used for connection and communication between multiple robots.
  • 4G The IoT network interface can be used for connection and communication between the server 51 and any one of multiple robots.
  • any one of the cluster robots 50 receives a task package containing at least one task from the server 51, and then transmits the task package to other robots in the cluster robot 50.
  • the transmission mode may be broadcast transmission or point-to-point transmission.
  • point-to-point transmission is preferred to implement the transmission of the task packet. For example, after the task package is transmitted to one of the cluster robots 50 from the server side, the receiver is transmitted first to the post-receiver, and the task packages are transmitted in this order to realize that the task packets are received by the cluster robots 50.
  • cluster robots 50 each receive the task package and store it separately. At this time, the task status of the task package received by each robot is completely consistent.
  • decision-making variables include but are not limited to the robot's stop status, working status, current position status, own vehicle or container, power status, task status of the task received and task status of the task to be received.
  • the task that is suitable for execution is that the robot judges whether the content of the task in the task package is more suitable for itself to receive and execute according to the decision-making variable.
  • one task content in a task package read by one robot in the cluster robot 50 is: go to a nearby picking point to pick the goods. At this time, the robot extracts its current position and judges whether the distance between its current position and the nearby picking point exceeds the threshold. If it does not exceed it, the task is considered to be suitable for its execution.
  • the cluster robot 50 receives its own tasks suitable for execution according to its own decision variables, so as to not only achieve that the cluster robot 50 can make independent decisions, and no longer rely on centralized decision-making and central control decision-making methods, effectively avoiding server overload.
  • Technical effect and achieve the technical effect of intelligently selecting execution tasks and improving execution efficiency.
  • FIG. 6 is an architectural diagram of a distributed robot scheduling decision device provided by an embodiment, showing a distributed robot scheduling decision device, the distributed robot scheduling decision device includes:
  • the receiving and transmitting module 60 is configured to receive a task package containing at least one task and transmit the task package to other robots in the cluster robot;
  • the decision-taking module 61 according to the decision-making variable decision-making to receive tasks suitable for execution in the task package;
  • the execution module 62 is used to execute tasks suitable for execution.
  • the distributed robot scheduling decision method provided in this embodiment can be used to schedule cluster robots in any field to perform tasks.
  • the method can be used in the logistics field to schedule logistics cluster robots to perform tasks.
  • This method can be used in logistics warehouses to dispatch logistics cluster robots to perform tasks.
  • the cluster robot includes but is not limited to two robots.
  • each robot can be a mobile robot with autonomous computing capabilities and autonomous navigation capabilities centered on a small computer.
  • multiple communication interfaces can be installed inside each robot, and each robot can communicate with the communication interfaces on other robots through one communication interface on each robot.
  • the task packet is a data packet containing at least one task, and the data packet can be transmitted via the network.
  • the task package includes at least one task. It can be understood that the task package is a task group, and the task group may include a navigation task, a mobile task, a picking task, and a prompt task.
  • multiple communication interfaces may include a WiFi network interface and 4G IoT network interface.
  • the WiFi network interface can be used for connection and communication between multiple robots.
  • 4G The IoT network interface can be used to connect and communicate with any one of multiple robots on the server side.
  • any one of the cluster robots receives a task package containing at least one task from the server, and then transmits the task package to other robots in the cluster robot.
  • the transmission mode may be broadcast transmission or point-to-point transmission.
  • point-to-point transmission is preferred to implement the transmission of the task packet. For example, after the task package is transmitted from the server to one of the cluster robots, the receiver is transmitted to the receiver afterwards. In this way, the task package is transmitted in order so that the cluster robot can receive the task package.
  • the cluster robots receive the task packages and store them separately. At this time, the task status of the task packages received by each robot is completely consistent.
  • decision-making variables include but are not limited to the robot's stop status, working status, current position status, own vehicle or container, power status, task status of the task received and task status of the task to be received.
  • the task that is suitable for execution is that the robot judges whether the content of the task in the task package is more suitable for itself to receive and execute according to the decision-making variable.
  • a task content in a task package read by a robot in a cluster robot is: go to a nearby picking point to pick the goods. At this time, the robot extracts its current position and judges whether the distance between its current position and the nearby picking point exceeds the threshold. If it does not exceed it, the task is considered to be suitable for its execution.
  • the cluster robots receive their own tasks that are suitable for execution according to their own decision variables, so that not only can the cluster robots make independent decisions, no longer rely on centralized decision-making and central control, and effectively avoid the technical effect of easily overloading the server. , And achieve the technical effect of intelligently selecting execution tasks and improving execution efficiency.
  • FIG. 7 is an improved device architecture diagram of the device in FIG. 6, showing an improved scheduling decision-making device of a distributed robot.
  • the scheduling decision device of the distributed robot in FIG. 6 further includes:
  • the mark receiving module 70 is used to mark a task that is suitable for execution as the received task is stored locally, and receives the update of the task package of the received task transmitted by other robots;
  • the update decision module 71 is used to judge the priority of the same received tasks in different task packages according to the update decision variables, and update the task packages with high reserved priority;
  • the transmission module 72 is used to transmit task packets with high priority to other robots.
  • the task that is suitable for execution is stored as a task that has been collected locally, and then receives the update of the task package of the received task transmitted by other robots, and then determines the same received task in different task packages according to the update decision variables Priority, update and retain the task package with high priority, and then transmit the task package with high priority to other robots, so as to achieve the technical effect of solving the task conflict through priority judgment.
  • the cluster robots all receive their own tasks that are suitable for execution according to their own decision variables, when at least two of the cluster robots receive the same task content, for example, any three robots All of them have received the task of picking the goods at the nearby picking point, which will lead to the conflict of task execution.
  • each robot in the cluster robot marks its own task as a received task, and transmits the task package after the marked task is received to other robots, each robot stores its own The task package after the marked task is received (for convenience, referred to as “the task package after the self-marked task is received” is the local task collection package) and the task package after the marked task received by other robots (for convenience, abbreviated for short) "The task package after receiving the marked tasks transmitted by other robots is the transmission task receiving package).
  • updated decision variables include, but are not limited to: the current position status of the robot, the power status, and the task receiving time.
  • different task packages are local task receiving packages and transmission task receiving packages.
  • the priority of the same received tasks in different task packages is judged according to the updated decision variables, and the task packages with high reserved priority are updated.
  • the priority judgment can be performed by updating decision variables including but not limited to the following.
  • the priority is judged by the time the task is received.
  • the robot reads the T1 task receiving time of the local task receiving package as t1, and the T1 task receiving time of the transmission task receiving package as t2.
  • the priority is higher than the priority of the previous T1 task.
  • the priority is determined by the current position status.
  • the robot reads its current position from the T1 task to travel the distance s1
  • the other robot's current position from the T1 task to travel the distance s2 if s1 is greater than s2, it can be judged that the T1 task priority of the transmission task to receive the package.
  • the transmission mode may be broadcast transmission or point-to-point transmission.
  • point-to-point transmission is preferred to implement the transmission of the task packet.
  • the task package is transmitted from one robot to another robot, and then transmitted from another robot to other robots, and then transferred in this order, and finally the cluster robots receive the task package.
  • FIG. 8 is an improved device architecture diagram of the device in FIG. 7, showing an improved scheduling decision-making device of a distributed robot.
  • the scheduling decision device of the distributed robot in FIG. 7 further includes:
  • the finishing judgment module 80 is used to judge whether the tasks in the task package have been received
  • the request module 81 is used to request the transmission of a new task package based on the judgment result that the task in the task package has been received.
  • any one of the robots in the cluster reads that the tasks in the transmission task receiving package it has received are all marked as received, and the task in the task package is judged to have been After being received, the robot can transmit the transmission task collection package to other robots through broadcast transmission or point-to-point transmission until it is transmitted to the robot that communicates with the server to communicate with the server to request the transmission of a new task package.
  • the technical effect of controlling the continuous work of the cluster robot is realized.
  • the scheduling decision method of a distributed robot provided by an embodiment of the present invention, by receiving a task package containing at least one task, and then transmitting the task package to other robots in the cluster robot, and then according to the decision variable to receive the task package suitable for execution Tasks, and then perform tasks that are suitable for execution, so that the cluster robots can communicate with each other for task transmission, and can receive the task execution suitable for execution in the task package according to the decision-making variable decision, so that not only can the cluster robot make independent decisions, and no longer rely on centralized
  • the decision-making and central control decision-making method effectively avoids the technical effect that the server is easily overloaded, and reaches the technical effect of intelligently selecting execution tasks and improving execution efficiency.

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Abstract

一种分布式机器人的调度决策方法，通过接收包含至少一个任务的任务包，再传输任务包给集群机器人中的其他机器人（S10），再根据领取决策变量决策领取任务包中的适合执行的任务(S11)，再执行适合执行的任务(S12)，使得集群机器人可以彼此通信进行任务传输，并可根据领取决策变量决策领取任务包中的适合执行的任务执行，从而不仅达到集群机器人能够独立决策，不再依赖集中决策、中央控制的决策方式，有效避免服务器易超负载的技术效果，而且到达智能选择执行任务，提高执行效率的技术效果。

Description

分布式机器人的调度决策方法、装置、系统与电子设备和存储介质 技术领域
本发明属于集群机器人控制技术领域，尤其涉及一种分布式机器人的调度决策方法、装置、系统与电子设备和存储介质。
 背景技术
在货物流通领域，经常使用到由一个中央控制服务器和多个AGV（Auto Guided Vehicle：自动导引运输车）组成的AGV调度决策系统对货物进行接收、运输以及卸载的物流管理。其中，中央控制服务器统一调度和决策多个AGV的动作，从而达到物流管理的目的。
虽然，现有的AGV调度决策系统达到物流管理的目的，但是，AGV之间无法通信易造成中央控制服务器超负载。
由于AGV本身不具有计算能力，仅具有运动控制能力，这导致其路径选择和动作规划均需要中央控制服务器，又由于集群AGV的构成类似于“星型”拓扑结构，即每一个AGV均连接中央控制服务器，因此来自集群AGV的请求轰炸使得中央控制服务器超出负载能力而无法实现统一调度决策。另外，由于AGV不具有计算能力，AGV之间无法进行通信，因此如果中央控制服务器发生故障，集群AGV将系统性崩溃。
综上，现有的AGV调度决策系统存在AGV之间无法通信易造成中央控制服务器超负载的技术问题。
 技术问题
有鉴于此，本发明的目的在于提供一种分布式机器人的调度决策方法、装置、系统与电子设备和存储介质，以解决AGV调度决策系统存在AGV之间无法通信易造成中央控制服务器超负载的技术问题。
 技术解决方案
为解决上述技术问题，本发明提供一种分布式机器人的调度决策方法，该分布式机器人的调度决策方法，包括：
接收包含至少一个任务的任务包，传输所述任务包给集群机器人中的其他机器人；
根据领取决策变量决策领取所述任务包中的适合执行的任务；
执行所述适合执行的任务。
本发明还提供一种电子设备，包括存储器和处理器，所述存储器存储计算机程序，所述计算机程序在所述处理器中执行可实现上述任一种方法。
本发明还提供一种存储介质，存储计算机程序，所述计算机程序在处理器中执行可实现上述任一种方法。
本发明还提供一种分布式机器人的调度决策系统，包括：
集群机器人，包含至少两台机器人，所述机器人之间通过通信接口彼此通信；
服务器端，与所述集群机器人通信，设置为将包含至少一个任务的任务包传输给所述集群机器人中的任意一台以传输给其他机器人；
所述集群机器人根据领取决策变量决策领取所述任务包中的适合执行的任务，并执行所述适合执行的任务。
本发明还提供一种分布式机器人的调度决策装置，包括：
接收传输模块，设置为接收包含至少一个任务的任务包，传输所述任务包给集群机器人中的其他机器人；
决策领取模块，根据领取决策变量决策领取所述任务包中的适合执行的任务；
执行模块，设置为执行所述适合执行的任务。
 有益效果
本发明提供的分布式机器人的调度决策方法，通过接收包含至少一个任务的任务包，再传输任务包给集群机器人中的其他机器人，再根据领取决策变量决策领取任务包中的适合执行的任务，再执行适合执行的任务，使得集群机器人可以彼此通信进行任务传输，并可根据领取决策变量决策领取任务包中的适合执行的任务执行，从而不仅达到集群机器人能够独立决策，不再依赖集中决策、中央控制的决策方式，有效避免服务器易超负载的技术效果，而且到达智能选择执行任务，提高执行效率的技术效果。
 附图说明
图1为一实施例提供的分布式机器人的调度决策方法的流程图；
图2为一实施例提供的对图1中方法的改进方法流程图；
图3为一实施例提供的对图2中方法的改进方法流程图；
图4为一实施例提供的电子设备的结构图；
图5为一实施例提供的分布式机器人的调度决策系统的架构图；
图6为一实施例提供的分布式机器人的调度决策装置的架构图；
图7为图6中装置的改进装置架构图；
图8为图7中装置的改进装置架构图。
 本发明的实施方式
为了使本发明的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本发明进行进一步详细说明。应当理解，在本发明的描述中，除非另有明确的规定和限定，术语“存储介质”可以是ROM、RAM、磁碟或者光盘等各种可以存储计算机程序的介质。术语“处理器”可以是CPLD（Complex Programmable Logic Device：复杂可编程逻辑器件）、FPGA（Field－Programmable Gate Array：现场可编程门阵列）、MCU（Microcontroller Unit：微控制单元）、PLC（Programmable Logic Controller：可编程逻辑控制器）以及CPU（Central Processing Unit：中央处理器）等具备数据处理功能的芯片或电路。术语“电子设备”可以是具有数据处理功能和存储功能的任何设备，通常可以包括固定终端和移动终端。固定终端如台式机等。移动终端如手机、PAD以及移动机器人等。此外，后续所描述的本发明不同实施方式中所涉及的技术特征只要彼此之间未构成冲突就可以相互结合。
下面，本发明提出部分优选实施例以教导本领域技术人员实现。
为突出本发明的创新，帮助本领域技术人员理解本发明，在说明本发明的具体实施方式之前，先介绍与本发明最接近的现有技术，本发明正是在该最接近的现有技术的基础上进行智慧贡献得出。
在货物领域，经常使用到由一个中央控制服务器和多个AGV（Auto Guided Vehicle：自动导引运输车）组成的AGV调度决策系统对货物进行接收、运输以及卸载的物流管理。其中，中央控制服务器统一调度和决策多个AGV的动作，从而达到物流管理的目的。
虽然，现有的AGV调度决策系统能够实现物流管理，但是AGV之间无法通信易造成中央控制服务器超负载。
由于AGV本身不具有计算能力，仅具有运动控制能力，这导致其路径选择和动作规划均需要中央控制服务器，又由于集群AGV的构成类似于“星型”拓扑结构，即每一个AGV均连接中央控制服务器，因此来自集群AGV的请求轰炸使得中央控制服务器超出负载能力而无法实现统一调度决策。另外，由于AGV不具有计算能力，AGV之间无法进行通信，因此如果中央控制服务器发生故障，集群AGV将系统性崩溃。
综上，现有的AGV调度决策系统存在AGV之间无法通信易造成中央控制服务器超负载的技术问题，该技术问题也可以理解为集中决策、中央控制的缺陷问题。
图1为一实施例提供的分布式机器人的调度决策方法的流程图，展现了一种分布式机器人的调度决策方法，用以解决上述技术问题。
参见图1，一种分布式机器人的调度决策方法，包括：
步骤S10，接收包含至少一个任务的任务包，传输任务包给集群机器人中的其他机器人；
步骤S11，根据领取决策变量决策领取任务包中的适合执行的任务；
步骤S12，执行适合执行的任务。
本实施例中，通过接收包含至少一个任务的任务包，再传输任务包给集群机器人中的其他机器人，再根据领取决策变量决策领取任务包中的适合执行的任务，再执行适合执行的任务，使得集群机器人可以彼此通信进行任务传输，并可根据领取决策变量决策领取任务包中的适合执行的任务执行，从而不仅达到集群机器人能够独立决策，不再依赖集中决策、中央控制的决策方式，有效避免服务器易超负载的技术效果，而且到达智能选择执行任务，提高执行效率的技术效果。
需要说明的是，本实施例中提供的分布式机器人的调度决策方法可以用于调度任何领域的集群机器人执行任务，优选地，可将该方法用在物流领域调度物流集群机器人执行任务，进一步地，可将该方法用在物流仓库调度物流集群机器人执行任务。
需要说明的是，集群机器人包括但不限于两台机器人。其中，每台机器人可以为一种以小型计算机为核心的具有自主计算能力和自主导航能力的移动机器人。另外，每台机器人的内部可以安装多个通信接口，每台机器人都能通过其身上的一个通信接口与其他机器人身上的通信接口彼此通信。
还需要说明的是，任务包是一种包含至少一个任务的数据包，数据包可以借助网络进行传输。其中，任务包包含至少一个任务可以理解为任务包是一个任务组，该任务组中可以有导航任务、移动任务、拣货任务以及提示任务等。
还需要说明的是，多个通信接口可以包括WiFi网络接口和4G IoT网络接口。其中，WiFi网络接口可以用于多台机器人之间连接通信。另外，4G IoT网络接口可以用于服务器端与多台机器人中的任意一台连接通信。
还需要说明的是，在步骤S10中，集群机器人中的任意一台从服务器端接收包含至少一个任务的任务包，然后传输任务包给集群机器人中的其他机器人。
其中，传输方式可以是广播传输或者点对点传输，本实施例中，优选点对点传输实现任务包的传输。例如，任务包由服务器端传输至集群机器人中的一台后，先接收者再传给后接受者，如此依次传递从而实现集群机器人均接收的到任务包。
另外，集群机器人均接收的到任务包后各自存储，此时每个机器人接收到的任务包的任务状态完全一致。
还需要说明的是，在步骤S11和步骤S12中，领取 决策变量包括但不限于机器人的停止状态、工作状态、当前位置状态、自身载具或容器、电量状态、已领取任务的任务量状态以及待领取任务的任务量状态。
另外，适合执行的任务是机器人根据领取决策变量判断任务包中的任务内容是否比较适合自己来领取执行。例如，集群机器人中的一台机器人读取的任务包中的一个任务内容为：前往附近拣货点拣货。此时，该机器人便提取自身当前位置，判断自身当前位置距离附近拣货点的距离是否超出阈值，若没有超出，则认为该任务为自己适合领取执行的任务。
另外，集群机器人均根据自身的决策变量领取自身的适合领取执行的任务执行，从而不仅达到集群机器人能够独立决策，不再依赖集中决策、中央控制的决策方式，有效避免服务器易超负载的技术效果，而且达到智能选择执行任务，提高执行效率的技术效果。
图2为一实施例提供的对图1中方法的改进方法流程图，展现了一种分布式机器人的调度决策方法的改进方法，用以解决任务执行冲突的技术问题。
参见图2，图1中的方法还包括：
步骤S20，标记适合执行的任务为已领取任务存于本地存于本地，接收其他机器人传输的更新标记已领取任务的任务包；
步骤S21，根据更新决策变量判断任务包中的相同的已领取任务的优先级，更新保留优先级高的任务包；
步骤S22，传输优先级高的任务包至其他机器人。
本实施例中，通过标记适合执行的任务为已领取任务存于本地，再接收其他机器人传输的更新标记已领取任务的任务包，再根据更新决策变量判断不同任务包中的相同的已领取任务的优先级，更新保留优先级高的任务包，再通过传输优先级高的任务包至其他机器人，从而实现通过优先级判断解决执行任务冲突的技术效果。
需要说明的是，由于集群机器人均根据自身的决策变量领取自身的适合领取执行的任务执行，因此，在集群机器人中至少两台决策领取了相同的任务内容的情况下，例如，任意三台机器人均领取了前往附近拣货点拣货的任务，便会导致任务执行冲突的情况发生。
还需要说明的是，在步骤S20中，由于集群机器人中的每台机器人均将自己领取的任务标记为已领取任务，并将已标记任务领取后的任务包传输给其他机器人，因此，每台机器人都存储了自身已标记任务领取后的任务包（为方便说明，简称“自身已标记任务领取后的任务包”为本地任务领取包）和其他机器人传输的已标记任务领取后的任务包（为方便说明，简称“其他机器人传输的已标记任务领取后的任务包”为传输任务领取包）。
还需要说明的是，在步骤S21和步骤S22中，更新决策变量包括但不限于：机器人的当前位置状态、电量状态以及任务领取时间。
另外，不同任务包为本地任务领取包和传输任务领取包。
另外，根据更新决策变量判断不同任务包中的相同的已领取任务的优先级，更新保留优先级高的任务包。以本地任务领取包和传输任务领取包均领取了T1任务为例，可以通过包括但不限于如下的更新决策变量进行优先级判断。
第一，通过任务领取时间判断优先级。
假设机器人读取到本地任务领取包的T1任务领取时间为t1，读取到传输任务领取包的T1任务领取时间为t2，若t1在先，t2在后，则可判断在后领取T1任务的优先级高于在先领取T1任务的优先级。
第二，通过当前位置状态判断优先级。
假设机器人读取到其当前位置距离执行T1任务的行程为s1，其他机器人的当前位置距离执行T1任务的行程为s2，如果s1大于s2，则可判断传输任务领取包的T1任务优先级高。
另外，更新保留优先级高的任务包，然后传输优先级高的任务包至其他机器人，可以实现逐一判断决策优先级，确定最终某一台机器人执行T1任务的完成情况，不仅避免任务执行冲突，而且实现优化资源配置的技术效果。其中，传输方式可以是广播传输或者点对点传输，本实施例中，优选点对点传输实现任务包的传输。例如，任务包由一台机器人传输给另一台机器人，再由另一台机器人传输给其他机器人，如此依次传递，最终实现集群机器人均收到任务包。
图3为一实施例提供的对图2中方法的改进方法流程图，展现了一种分布式机器人的调度决策方法的改进方法，用以解决如何从服务器端获取新任务包的技术问题。
参见图3，图2中的方法还包括：
步骤S30，判断任务包中的任务是否已被领取完；
步骤S31，当任务包中任务已被领取完毕时，请求传输新的任务包。
本实施例中，通过判断任务包中的任务是否已被领取完，再于任务领取完后向服务器端请求传输新的任务包，从而实现控制集群机器人连续工作的技术效果。
需要说明的是，在步骤S31和步骤S31中，集群机器人中的任意一台机器人读取到其接收到的传输任务领取包中的任务全部被标记为已领取，则判断任务包中的任务已被领取完，此时该机器人可以通过是广播传输或者点对点传输的方式将该传输任务领取包传输给其他机器人，直到传至与服务器端通信的机器人以与服务器端通信请求传输新的任务包，从而实现控制集群机器人连续工作的技术效果。
图4为一实施例提供的电子设备的结构图，展现了一种电子设备，用于存储处理计算机程序。
参见图4，一种电子设备，包括存储器和处理器，存储器存储计算机程序，计算机程序在处理器中执行可实现图1-3中展示的任一种方法。
在一实施例中，还提供一种存储介质，该存储介质存储计算机程序，该计算机程序在处理器中执行可实现图1-3中展示的任一种方法。
图5为一实施例提供的分布式机器人的调度决策系统的架构图，展现了一种分布式机器人的调度决策系统，该分布式机器人的调度决策系统包括：
集群机器人50，包含至少两台机器人，机器人之间通过通信接口彼此通信；
服务器端51，与集群机器人50通信，用于将包含至少一个任务的任务包传输给集群机器人50中的任意一台以传输给其他机器人；
集群机器人50根据领取决策变量决策领取任务包中的适合执行的任务，并执行适合执行的任务。
本实施例中，通过接收包含至少一个任务的任务包，再传输任务包给集群机器人50中的其他机器人，再根据领取决策变量决策领取任务包中的适合执行的任务，再执行适合执行的任务，使得集群机器人50可以彼此通信进行任务传输，并可根据领取决策变量决策领取任务包中的适合执行的任务执行，从而不仅达到集群机器人50能够独立决策，不再依赖集中决策、中央控制的决策方式，有效避免服务器易超负载的技术效果，而且到达智能选择执行任务，提高执行效率的技术效果。
需要说明的是，本实施例中提供的分布式机器人的调度决策方法可以用于调度任何领域的集群机器人50执行任务，优选地，可将该方法用在物流领域调度物流集群机器人50执行任务，进一步地，可将该方法用在物流仓库调度物流集群机器人50执行任务。
需要说明的是，集群机器人50包括但不限于两台机器人。其中，每台机器人可以为一种以小型计算机为核心的具有自主计算能力和自主导航能力的移动机器人。另外，每台机器人的内部可以安装多个通信接口，每台机器人都能通过其身上的一个通信接口与其他机器人身上的通信接口彼此通信。
还需要说明的是，任务包是一种包含至少一个任务的数据包，数据包可以借助网络进行传输。其中，任务包包含至少一个任务可以理解为任务包是一个任务组，该任务组中可以有导航任务、移动任务、拣货任务以及提示任务等。
还需要说明的是，多个通信接口可以包括WiFi网络接口和4G IoT网络接口。其中，WiFi网络接口可以用于多台机器人之间连接通信。另外，4G IoT网络接口可以用于服务器端51与多台机器人中的任意一台连接通信。
还需要说明的是，集群机器人50中的任意一台从服务器端51接收包含至少一个任务的任务包，然后传输任务包给集群机器人50中的其他机器人。
其中，传输方式可以是广播传输或者点对点传输，本实施例中，优选点对点传输实现任务包的传输。例如，任务包由服务器端传输至集群机器人50中的一台后，先接收者再传给后接受者，如此依次传递从而实现集群机器人50均接收的到任务包。
另外，集群机器人50均接收的到任务包后各自存储，此时每个机器人接收到的任务包的任务状态完全一致。
还需要说明的是，领取决策变量包括但不限于机器人的停止状态、工作状态、当前位置状态、自身载具或容器、电量状态、已领取任务的任务量状态以及待领取任务的任务量状态。
另外，适合执行的任务是机器人根据领取决策变量判断任务包中的任务内容是否比较适合自己来领取执行。例如，集群机器人50中的一台机器人读取的任务包中的一个任务内容为：前往附近拣货点拣货。此时，该机器人便提取自身当前位置，判断自身当前位置距离附近拣货点的距离是否超出阈值，若没有超出，则认为该任务为自己适合领取执行的任务。
另外，集群机器人50均根据自身的决策变量领取自身的适合领取执行的任务执行，从而不仅达到集群机器人50能够独立决策，不再依赖集中决策、中央控制的决策方式，有效避免服务器易超负载的技术效果，而且达到智能选择执行任务，提高执行效率的技术效果。
图6为一实施例提供的分布式机器人的调度决策装置的架构图，展现了一种分布式机器人的调度决策装置，该分布式机器人的调度决策装置包括：
接收传输模块60，用于接收包含至少一个任务的任务包，传输任务包给集群机器人中的其他机器人；
决策领取模块61，根据领取决策变量决策领取任务包中的适合执行的任务；
执行模块62，用于执行适合执行的任务。
本实施例中，通过接收包含至少一个任务的任务包，再传输任务包给集群机器人中的其他机器人，再根据领取决策变量决策领取任务包中的适合执行的任务，再执行适合执行的任务，使得集群机器人可以彼此通信进行任务传输，并可根据领取决策变量决策领取任务包中的适合执行的任务执行，从而不仅达到集群机器人能够独立决策，不再依赖集中决策、中央控制的决策方式，有效避免服务器易超负载的技术效果，而且到达智能选择执行任务，提高执行效率的技术效果。
需要说明的是，本实施例中提供的分布式机器人的调度决策方法可以用于调度任何领域的集群机器人执行任务，优选地，可将该方法用在物流领域调度物流集群机器人执行任务，进一步地，可将该方法用在物流仓库调度物流集群机器人执行任务。
需要说明的是，集群机器人包括但不限于两台机器人。其中，每台机器人可以为一种以小型计算机为核心的具有自主计算能力和自主导航能力的移动机器人。另外，每台机器人的内部可以安装多个通信接口，每台机器人都能通过其身上的一个通信接口与其他机器人身上的通信接口彼此通信。
还需要说明的是，任务包是一种包含至少一个任务的数据包，数据包可以借助网络进行传输。其中，任务包包含至少一个任务可以理解为任务包是一个任务组，该任务组中可以有导航任务、移动任务、拣货任务以及提示任务等。
还需要说明的是，多个通信接口可以包括WiFi网络接口和4G IoT网络接口。其中，WiFi网络接口可以用于多台机器人之间连接通信。另外，4G IoT网络接口可以用于服务器端与多台机器人中的任意一台连接通信。
还需要说明的是，集群机器人中的任意一台从服务器端接收包含至少一个任务的任务包，然后传输任务包给集群机器人中的其他机器人。
其中，传输方式可以是广播传输或者点对点传输，本实施例中，优选点对点传输实现任务包的传输。例如，任务包由服务器端传输至集群机器人中的一台后，先接收者再传给后接受者，如此依次传递从而实现集群机器人均接收的到任务包。
另外，集群机器人均接收的到任务包后各自存储，此时每个机器人接收到的任务包的任务状态完全一致。
还需要说明的是，领取决策变量包括但不限于机器人的停止状态、工作状态、当前位置状态、自身载具或容器、电量状态、已领取任务的任务量状态以及待领取任务的任务量状态。
另外，适合执行的任务是机器人根据领取决策变量判断任务包中的任务内容是否比较适合自己来领取执行。例如，集群机器人中的一台机器人读取的任务包中的一个任务内容为：前往附近拣货点拣货。此时，该机器人便提取自身当前位置，判断自身当前位置距离附近拣货点的距离是否超出阈值，若没有超出，则认为该任务为自己适合领取执行的任务。
另外，集群机器人均根据自身的决策变量领取自身的适合领取执行的任务执行，从而不仅达到集群机器人能够独立决策，不再依赖集中决策、中央控制的决策方式，有效避免服务器易超负载的技术效果，而且达到智能选择执行任务，提高执行效率的技术效果。
图7为图6中装置的改进装置架构图，展现了一种改进后的分布式机器人的调度决策装置。
参见图7，图6中分布式机器人的调度决策装置还包括：
标记接收模块70，用于标记适合执行的任务为已领取任务存于本地，接收其他机器人传输的更新标记已领取任务的任务包；
更新决策模块71，用于根据更新决策变量判断不同任务包中的相同的已领取任务的优先级，更新保留优先级高的任务包；
传输模块72，用于传输优先级高的任务包至其他机器人。
本实施例中，通过标记适合执行的任务为已领取任务存于本地，再接收其他机器人传输的更新标记已领取任务的任务包，再根据更新决策变量判断不同任务包中的相同的已领取任务的优先级，更新保留优先级高的任务包，再通过传输优先级高的任务包至其他机器人，从而实现通过优先级判断解决执行任务冲突的技术效果。
需要说明的是，由于集群机器人均根据自身的决策变量领取自身的适合领取执行的任务执行，因此，在集群机器人中至少两台决策领取了相同的任务内容的情况下，例如，任意三台机器人均领取了前往附近拣货点拣货的任务，便会导致任务执行冲突的情况发生。
还需要说明的是，由于集群机器人中的每台机器人均将自己领取的任务标记为已领取任务，并将已标记任务领取后的任务包传输给其他机器人，因此，每台机器人都存储了自身已标记任务领取后的任务包（为方便说明，简称“自身已标记任务领取后的任务包”为本地任务领取包）和其他机器人传输的已标记任务领取后的任务包（为方便说明，简称“其他机器人传输的已标记任务领取后的任务包”为传输任务领取包）。
还需要说明的是，更新决策变量包括但不限于：机器人的当前位置状态、电量状态以及任务领取时间。
另外，不同任务包为本地任务领取包和传输任务领取包。
另外，根据更新决策变量判断不同任务包中的相同的已领取任务的优先级，更新保留优先级高的任务包。以本地任务领取包和传输任务领取包均领取了T1任务为例，可以通过包括但不限于如下的更新决策变量进行优先级判断。
第一，通过任务领取时间判断优先级。
假设机器人读取到本地任务领取包的T1任务领取时间为t1，读取到传输任务领取包的T1任务领取时间为t2，若t1在先，t2在后，则可判断在后领取T1任务的优先级高于在先领取T1任务的优先级。
第二，通过当前位置状态判断优先级。
假设机器人读取到其当前位置距离执行T1任务的行程为s1，其他机器人的当前位置距离执行T1任务的行程为s2，如果s1大于s2，则可判断传输任务领取包的T1任务优先级高。
另外，更新保留优先级高的任务包，然后传输优先级高的任务包至其他机器人，可以实现逐一判断决策优先级，确定最终某一台机器人执行T1任务的完成情况，不仅避免任务执行冲突，而且实现优化资源配置的技术效果。其中，传输方式可以是广播传输或者点对点传输，本实施例中，优选点对点传输实现任务包的传输。例如，任务包由一台机器人传输给另一台机器人，再由另一台机器人传输给其他机器人，如此依次传递，最终实现集群机器人均收到任务包。
图8为图7中装置的改进装置架构图，展现了一种改进后的分布式机器人的调度决策装置。
参见图8，图7中分布式机器人的调度决策装置还包括：
领完判断模块80，用于判断任务包中的任务是否已被领取完；
请求模块81，用于根据任务包中的任务已被领取完的判断结果，请求传输新的任务包。
本实施例中，通过判断任务包中的任务是否已被领取完，再于任务领取完后向服务器端请求传输新的任务包，从而实现控制集群机器人连续工作的技术效果。
需要说明的是，在步骤S31和步骤S31中，集群机器人中的任意一台机器人读取到其接收到的传输任务领取包中的任务全部被标记为已领取，则判断任务包中的任务已被领取完，此时该机器人可以通过是广播传输或者点对点传输的方式将该传输任务领取包传输给其他机器人，直到传至与服务器端通信的机器人以与服务器端通信请求传输新的任务包，从而实现控制集群机器人连续工作的技术效果。
以上所述仅为本发明的较佳实施例而已，并不用以限制本发明，凡在本发明的精神和原则之内所作的任何修改、等同替换和改进等，均应包含在本发明的保护范围之内。
 工业实用性
本发明实施例提供的分布式机器人的调度决策方法，通过接收包含至少一个任务的任务包，再传输任务包给集群机器人中的其他机器人，再根据领取决策变量决策领取任务包中的适合执行的任务，再执行适合执行的任务，使得集群机器人可以彼此通信进行任务传输，并可根据领取决策变量决策领取任务包中的适合执行的任务执行，从而不仅达到集群机器人能够独立决策，不再依赖集中决策、中央控制的决策方式，有效避免服务器易超负载的技术效果，而且到达智能选择执行任务，提高执行效率的技术效果。

Claims (10)

  1. 一种分布式机器人的调度决策方法，包括：
    接收包含至少一个任务的任务包，传输所述任务包给集群机器人中的其他机器人；
    根据领取决策变量决策领取所述任务包中的适合执行的任务；
    执行所述适合执行的任务。
  2. 如权利要求1所述的方法，其中，还包括：
    标记所述适合执行的任务为已领取任务存于本地，接收所述其他机器人传输的更新标记已领取任务的任务包；
    根据更新决策变量判断不同任务包中的相同的已领取任务的优先级，更新保留优先级高的任务包；
    传输所述优先级高的任务包至所述其他机器人。
  3. 如权利要求2所述的方法，其中，还包括：
    判断所述任务包中的任务是否已被领取完；
    当所述任务包中任务已被领取完毕时，请求传输新的所述任务包。
  4. 如权利要求1所述的方法，其中，所述领取决策变量包括：机器人的停止和工作状态和/或机器人的当前位置状态和/或自身载具及容器和/或机器人的已领取任务或待领取任务的任务量。
  5. 如权利要求2所述的方法，其中，所述更新决策变量包括：机器人的当前位置状态和/或机器人的电量状态和/或任务领取时间。
  6. 一种电子设备，包括存储器和处理器，所述存储器存储计算机程序，所述计算机程序在所述处理器中执行可实现权利要求1-5中任一种方法。
  7. 一种存储介质，存储计算机程序，所述计算机程序在处理器中执行可实现权利要求1-5中任一种方法。
  8. 一种分布式机器人的调度决策系统，包括：
    集群机器人，包含至少两台机器人，所述机器人之间通过通信接口彼此通信；
    服务器端，与所述集群机器人通信，设置为将包含至少一个任务的任务包传输给所述集群机器人中的任意一台以传输给其他机器人；
    所述集群机器人根据领取决策变量决策领取所述任务包中的适合执行的任务，并执行所述适合执行的任务。
  9. 一种分布式机器人的调度决策装置，包括：
    接收传输模块，设置为接收包含至少一个任务的任务包，传输所述任务包给集群机器人中的其他机器人；
    决策领取模块，根据领取决策变量决策领取所述任务包中的适合执行的任务；
    执行模块，设置为执行所述适合执行的任务。
  10. 如权利要求9所述的装置，其中，还包括：
    标记接收模块，设置为标记所述适合执行的任务为已领取任务存于本地，接收所述其他机器人传输的更新标记已领取任务的任务包；
    更新决策模块，设置为根据更新决策变量判断不同任务包中的相同的已领取任务的优先级，更新保留优先级高的任务包；
    传输模块，设置为传输所述优先级高的任务包至所述其他机器人；
    领完判断模块，设置为判断所述任务包中的任务是否已被领取完；
    请求模块，设置为根据所述任务包中的任务已被领取完的判断结果，请求传输新的所述任务包。
PCT/CN2018/125150 2018-11-19 2018-12-29 分布式机器人的调度决策方法、装置、系统与电子设备和存储介质 WO2020103298A1 (zh)

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