CN114330831A - AGV scheduling method based on task bidding mechanism and storage medium - Google Patents

AGV scheduling method based on task bidding mechanism and storage medium Download PDF

Info

Publication number
CN114330831A
CN114330831A CN202111438010.XA CN202111438010A CN114330831A CN 114330831 A CN114330831 A CN 114330831A CN 202111438010 A CN202111438010 A CN 202111438010A CN 114330831 A CN114330831 A CN 114330831A
Authority
CN
China
Prior art keywords
agv
task
cost
winning
bid
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.)
Pending
Application number
CN202111438010.XA
Other languages
Chinese (zh)
Inventor
田华亭
陈明
董海英
李兵
韩德昱
杨进
罗蒙
段东昌
马贤朋
孙志斌
俞沛齐
唐艳英
李瑞康
陈燕林
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Yunnan Ksec Intelligent Equipment Co ltd
Original Assignee
Yunnan Ksec Intelligent Equipment Co ltd
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 Yunnan Ksec Intelligent Equipment Co ltd filed Critical Yunnan Ksec Intelligent Equipment Co ltd
Priority to CN202111438010.XA priority Critical patent/CN114330831A/en
Publication of CN114330831A publication Critical patent/CN114330831A/en
Pending legal-status Critical Current

Links

Images

Landscapes

  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention discloses an AGV scheduling method and a storage medium based on a task bidding mechanism, wherein the method comprises the following steps: s1, the upper dispatching system issues the bidding notification sheet to each AGV; s2, each AGV calculates the task cost of the AGV according to the bidding notification sheet, and notifies the task cost of the AGV to an upper dispatching system and other AGVs; s3, selecting the AGV with the lowest task cost as a candidate AGV for winning the bid, and marking the candidate AGV as a pre-winning state; s4, the upper scheduling system gives the execution authority of the bidding notification sheet to the winning bid candidate AGV according to the bidding result, and modifies the pre-winning bid state into the winning bid state; s5, after the AGV in the winning bid state is bound with the corresponding task, executing the task; the task cost is calculated by each AGV, the load of an upper dispatching system is greatly reduced, the AGV with lower cost is called to execute the task by adopting a bidding mode, the reasonability of task allocation is ensured, and the task completion speed is favorably improved.

Description

AGV scheduling method based on task bidding mechanism and storage medium
Technical Field
The invention relates to the technical field of AGV scheduling, in particular to an AGV scheduling method and a storage medium based on a task bidding mechanism.
Background
In the field of industrial logistics, an Automated Guided Vehicle (AGV) is a high-efficiency unmanned handling device, which is now used in multiple industries, and will play an indispensable role in a logistics automation system in the future. However, the existing AGV dispatching system integrates AGV path planning, task management, traffic management and other functions, and is deployed in an upper computer or a server, and each AGV single machine is not in communication connection with each other and cannot interact data, so that the AGVs are only responsible for executing mechanism control and motion control, and the degree of intelligence is low.
In the existing AGV system, the distribution method between the AGV and the task is as follows: the method comprises the steps that an AGV dispatching system deployed in an upper computer calculates the time cost of each idle AGV reaching a task starting point, one idle AGV with the lowest time cost is selected to execute the task, paths are calculated and distributed for the AGV executing the task, all calculation is completed by the dispatching system in the upper computer, the load of the upper computer is heavier and heavier, particularly the load is obvious in a large-scale AGV dispatching system, more importantly, the time cost of each AGV calculated by the upper dispatching system to the task starting point is only instant time cost, dynamic adjustment can not be carried out according to time and the running state of each AGV, and the distribution of the task can not be optimized.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides an AGV scheduling method based on a task bidding mechanism, which adopts a mode that an upper scheduling system initiates bidding, and each AGV independently calculates the cost of executing tasks to bid, thereby greatly reducing the load of an upper computer and being beneficial to improving the working efficiency.
According to a first aspect, the present invention provides an AGV scheduling method based on a task bidding mechanism, including: the system comprises an upper dispatching system and a plurality of interconnected AGVs, wherein each AGV is connected with the upper dispatching system, and the method comprises the following steps:
s1: the upper dispatching system issues the bidding notification sheet to each AGV;
s2: each AGV calculates the task cost of the AGV according to the bidding notification sheet, and notifies the task cost of the AGV to an upper dispatching system and other AGVs;
s3: selecting the AGV with the lowest task cost as a candidate AGV for winning the bid, and marking the candidate AGV as a pre-winning state;
s4: the upper scheduling system delivers the execution authority of the bidding notification sheet to the winning bid candidate AGV according to the bidding result, and modifies the pre-winning bid state of the bidding notification sheet into a winning bid state;
s5: binding the AGV in the winning bid state with the corresponding task, and then executing the task;
the bid notification sheet includes: the method comprises the steps of task numbering, task attributes, task priority, the number of required AGV, task starting time and an operation list;
the task cost refers to the time cost required for the AGV to complete the list of operations in the bid order.
Further, the task cost includes a static cost and a dynamic cost; the static cost includes: theoretical time cost required by an AGV traveling path, predictable acceleration and deceleration time cost of the AGV during traveling and other time cost spent on executing operation; the dynamic cost of the tasks comprises unpredictable time cost caused by obstacles, traffic jam, AGV faults and human intervention in the driving process of the AGV.
Further, in step S2, the theoretical time cost CtheorypathComprises the following steps:
Figure BDA0003382437800000021
where n is the total number of all path segments that need to be traversed, LiFor each path segment distance, ViFor the speed of each of the path segments,
Figure BDA0003382437800000022
is the calibration factor for each path segment.
Further, step S3 further includes:
s31: the AGV becoming the candidate AGV for winning the bid simultaneously broadcasts and sends a pre-winning notice to other AGVs and an upper dispatching system;
s32: the AGV state is updated in real time, the AGV which receives the notice of pre-winning bid judges whether to propose an objection according to the updated state, if the objection is proposed, the AGV simultaneously informs an upper dispatching system and a candidate AGV for winning bid, and the upper dispatching system organizes secondary bidding; if there is no objection, the process proceeds to step S4.
Further, in step S32, the condition of determining whether an objection is proposed is:
and in the current state, if the task cost of the AGV executing the task is superior to that of the winning candidate AGV, an objection is proposed, otherwise, no objection exists.
Further, the AGVs are in communication connection with each other; each AGV comprises a task list with the same activity state in the current AGV system, and each AGV automatically calculates the task cost of executing the tasks which are not bound in the task list at regular intervals and informs other AGVs;
the step S5 further includes:
s51: before the AGV in the winning bid state is bound with the task, the AGV in the winning bid state exchanges the task or transfers the task with other AGVs;
s52: after the AGV in the winning bid state is bound with the task, the AGV and the task can not be divided until the task is executed.
Further, in step S51, task switching occurs between two AGVs in the winning bid state, and the first winning bid AGV and the second winning bid AGV need to satisfy the condition:
C1>C12+Cswap
C2>C21+Cswap
in the formula, C1Representing the cost of the first winning AGV executing the own winning bid task;
C12representing the cost of the first winning AGV executing the second winning AGV task;
C2representing the cost of the second winning AGV for executing the own winning bid task;
C21representing the cost of the second winning AGV executing the first winning AGV task;
Cswaprepresents the cost of the task exchange;
the task transfer occurs between the AGV in the winning bid state and the AGV in the not winning bid state, and the conditions are required to be met:
C1>C31+Ctransfer
wherein, C31Representing the cost of executing winning AGV tasks by winning AGVs;
C1representing the cost of the successful winning AGV for executing the task;
Ctransferrepresenting the cost of the task transfer.
According to a second aspect, the invention also provides a computer-readable storage medium having stored thereon a computer program executable by a processor for carrying out the steps of the method as described above.
Compared with the prior art, the invention has the beneficial effects that:
(1) according to the AGV scheduling method based on the task bidding mechanism, the task cost is calculated by each AGV, and the load of an upper scheduling system is greatly reduced.
(2) And the real-time task cost is still calculated after the AGVs bid the winning bid, and the tasks can be transferred or exchanged to the AGVs with lower cost before the tasks are not bound, so that the reasonability of task allocation is ensured, and the task completion speed is favorably improved.
Drawings
FIG. 1 is a flowchart of an AGV scheduling method based on a task bidding mechanism according to the present invention;
fig. 2 is a flow chart of a task bidding mechanism.
Detailed Description
The present invention will be described in further detail with reference to the following detailed description and accompanying drawings.
Example 1
As shown in fig. 1-2, the present invention provides an AGV scheduling method based on a task bidding mechanism, which includes: the AGV comprises an upper dispatching system and a plurality of AGVs which are in communication connection with each other based on a 5G communication technology, wherein each AGV is in communication connection with the upper dispatching system, and the method comprises the following steps:
s1: and the upper dispatching system issues the bidding notification sheet to each AGV, and triggers the task bidding process of each AGV. The bid notification sheet includes: the method comprises the steps of task numbering, task attributes, task priority, the number of AGV required, task starting time and an operation list; the operation list includes a plurality of operations, each operation including: an operating station, an opcode, and an opcode attribute; the opcode attributes include: operation code type, lifting height and speed of the actuating mechanism; the opcode types include: asynchronous operation and synchronous operation.
S2: and each AGV analyzes the bidding notification sheet, identifies the task type and judges the legality of the order, then calculates the task cost of executing the task by itself under the current state, and reports the task cost of itself to an upper dispatching system and other AGVs, namely, the AGVs bid in a way of disclosing base price.
The task cost of the AGV is derived from a combination of static and dynamic costs. Specifically, the static cost includes a theoretical time cost required for the AGV to travel the route after completing the route planning, a predictable acceleration and deceleration time cost of the AGV during the travel, and a time cost taken to perform the operation. Wherein, the theoretical time cost is as follows:
Figure BDA0003382437800000041
where n is the total number of all path segments that need to be traversed, LiFor each path segment distance, ViFor the speed of each of the path segments,
Figure BDA0003382437800000042
is the calibration factor for each path segment.
The task dynamic cost comprises unpredictable time cost caused by obstacles, traffic jam, AGV faults, human intervention and the like in the driving process of the AGV, most of the task dynamic cost is unpredictable, and the task dynamic cost prediction is accumulated according to the average time spent by each AGV at a certain node. Such as: traffic congestion takes on average two minutes at a node, and when an AGV performing a task encounters traffic congestion at that node, the task dynamic cost will increase by two minutes. Human intervention takes on average three minutes in a certain path segment, and when an AGV executing a task encounters human intervention in the path segment, the dynamic cost of the task is accumulated for three minutes. And by analogy, the dynamic cost of the task is predicted by accumulation.
The task cost is a decisive factor from static cost under the condition of less interference of external conditions, the dynamic cost is taken as an auxiliary factor, more dynamic costs are referred in the motion process, judgment is made in real time according to the conditions of obstacles, traffic jam, AGV faults, human intervention and the like reported by the AGV, the task execution cost is predicted, and the task execution cost is taken as bidding cost.
S3: and each AGV autonomously sequences the task cost of each AGV, and according to the principle of winning the bid in the lowest cost, the AGV with the lowest cost is selected to be a candidate AGV for winning the bid and is marked as a pre-winning state.
S31: and the AGV becoming the candidate AGV for winning the bid simultaneously broadcasts and sends a pre-winning bid notice to other AGVs and the upper dispatching system.
S32: the AGV state is updated in real time, and in principle, the AGV receiving the pre-winning bid notification agrees to the pre-winning bid task. However, when the task cost of the AGV executing the task in the current state is superior to the task cost of the winning candidate AGV, an objection is proposed, the upper scheduling system and the winning candidate AGV are simultaneously informed, and then the upper scheduling system restarts the secondary bidding of the task.
S4: if no objection exists, the upper dispatching system gives the execution authority of the bidding notification sheet to the winning bid candidate AGV according to the bidding result, and modifies the pre-winning bid state into the winning bid state.
Each AGV comprises a task order list with the same activity state in the current AGV system, and each AGV automatically calculates the task cost of executing tasks which are not bound in the task list at regular intervals.
Before the AGVs in the winning bid state are bound with the corresponding tasks, if the cost for executing the tasks by any AGV is lower, the tasks are transferred when the following conditions are met:
C1>C31+Ctransfer
wherein, C31Representing the cost of executing winning AGV tasks by winning AGVs; c1Representing the cost of the successful winning AGV for executing the task; ctransferRepresenting the cost of the task transfer.
In addition, before the tasks are bound, if two AGVs in winning bid state exchange winning bid tasks, the saved time cost is larger than the time cost loss caused by task exchange, that is, the exchange is performed when the following conditions are satisfied:
C1>C12+Cswap
C2>C21+Cswap
in the formula, C1Representing the cost of the first winning AGV executing the own winning bid task; c12Representing the cost of the first winning AGV executing the second winning AGV task; c2Representing the cost of the second winning AGV for executing the own winning bid task; c21Representing the cost of the second winning AGV executing the first winning AGV task; cswapRepresenting the cost of the task exchange.
S5: after the AGV in the winning bid state is bound with the corresponding task, the AGV in the winning bid state executes the task according to the task order.
Example 2
If AGV1 wins task 1 and AGV2 wins task 4, the list of tasks is shown in Table 1.
The method comprises the following steps that 1, 2, at most, one task is won in the AGV1, 3, 4 and 5, and the tasks are found in the AGV3, 3 and 2.
TABLE 1
Figure BDA0003382437800000051
After a period of time has elapsed and task 1 has been completed by AGV1, the pre-winning task 2 of AGV1 is converted to the winning task, as shown in table 2 below.
TABLE 2
Figure BDA0003382437800000061
Before the AGV1 binds with the task 2, that is, before the AGV reaches the operating point, and at the same time, the task 3 is not bound with the AGV3, as shown in tables 3 and 4, since the cost 13 of the AGV1 executing the task 3 is lower than the cost 16 of the AGV3 executing the task 3, the cost of the AGV3 executing the task 2 is 8, and is also lower than the cost 15 of the AGV1 executing the task 2, and the cost difference between the two is larger than the time loss caused by task exchange, at this time, the tasks of any AGV1 and the AGV3 can be directly exchanged, and the updated task table is shown in table 5.
TABLE 3
Figure BDA0003382437800000062
TABLE 4
Figure BDA0003382437800000063
TABLE 5
Figure BDA0003382437800000064

Claims (8)

1. An AGV scheduling method based on a task bidding mechanism comprises the following steps: the system comprises an upper dispatching system and a plurality of AGV which are mutually communicated and connected, wherein each AGV is communicated and connected with the upper dispatching system; characterized in that the method comprises:
s1: the upper dispatching system issues the bidding notification sheet to each AGV;
s2: each AGV calculates the task cost of the AGV according to the bidding notification sheet, and notifies the task cost of the AGV to an upper dispatching system and other AGVs;
s3: selecting the AGV with the lowest task cost as a candidate AGV for winning the bid, and marking the candidate AGV as a pre-winning state;
s4: the upper scheduling system delivers the execution authority of the bidding notification sheet to the winning bid candidate AGV according to the bidding result, and modifies the pre-winning bid state of the bidding notification sheet into a winning bid state;
s5: the AGV in the winning bid state starts to go to the target platform and executes the task;
the bid notification sheet includes: the method comprises the steps of task numbering, task attributes, task priority, the number of required AGV, task starting time and an operation list;
the task cost refers to the time cost required for the AGV to complete the list of operations in the bid order.
2. The method of claim 1, wherein the task cost comprises a static cost and a dynamic cost; the static cost includes: theoretical time cost required by an AGV traveling path, predictable acceleration and deceleration time cost of the AGV during traveling and other time cost spent on executing operation; the dynamic cost of the tasks comprises unpredictable time cost caused by obstacles, traffic jam, AGV faults and human intervention in the driving process of the AGV.
3. The method of claim 2, wherein the theoretical time cost CtheorypathComprises the following steps:
Figure FDA0003382437790000011
where n is the total number of all path segments that need to be traversed, LiFor each path segment distance, ViFor the speed of each of the path segments,
Figure FDA0003382437790000012
is the calibration factor for each path segment.
4. The method of claim 1, wherein step S3 further comprises:
s31: the AGV becoming the candidate AGV for winning the bid simultaneously broadcasts and sends a pre-winning notice to other AGVs and an upper dispatching system;
s32: the AGV state is updated in real time, the AGV which receives the notice of pre-winning bid judges whether to propose an objection according to the updated state, if the objection is proposed, the AGV simultaneously informs an upper dispatching system and a candidate AGV for winning bid, and the upper dispatching system organizes secondary bidding; if there is no objection, the process proceeds to step S4.
5. The method of claim 4, wherein in step S32, the condition for determining whether to raise an objection is:
and in the current state, if the task cost of the AGV executing the task is superior to that of the winning candidate AGV, an objection is proposed, otherwise, no objection exists.
6. The method of claim 1,
the AGVs are in communication connection with each other; each AGV comprises a task list with the same activity state in the current AGV system, and each AGV automatically calculates the task cost of executing the tasks which are not bound in the task list at regular intervals and informs other AGVs;
the step S5 further includes:
s51: before the AGV in the winning bid state is bound with the task, the AGV in the winning bid state exchanges the task or transfers the task with other AGVs;
s52: after the AGV in the winning bid state is bound with the task, the AGV and the task can not be divided until the task is executed.
7. The method of claim 6, wherein in step S51,
the task exchange takes place between the AGVs of two winning bid states, and the first winning bid AGV and the second winning bid AGV need satisfy the condition:
C1＞C12+Cswap
C2＞C21+Cswap
in the formula, C1Representing the cost of the first winning AGV executing the own winning bid task;
C12representing the cost of the first winning AGV executing the second winning AGV task;
C2representing the cost of the second winning AGV for executing the own winning bid task;
C21representing the cost of the second winning AGV executing the first winning AGV task;
Cswaprepresents the cost of the task exchange;
the task transfer occurs between the AGV in the winning bid state and the AGV in the not winning bid state, and the conditions are required to be met:
C1＞C31+Ctransfer
wherein, C31Representing the cost of executing winning AGV tasks by winning AGVs;
C1representing the cost of the successful winning AGV for executing the task;
Ctransferrepresenting the cost of the task transfer.
8. A computer-readable storage medium, on which a computer program is stored, characterized in that the program is executable by a processor to implement the steps of the method according to any of claims 1-7.
CN202111438010.XA 2021-11-30 2021-11-30 AGV scheduling method based on task bidding mechanism and storage medium Pending CN114330831A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111438010.XA CN114330831A (en) 2021-11-30 2021-11-30 AGV scheduling method based on task bidding mechanism and storage medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111438010.XA CN114330831A (en) 2021-11-30 2021-11-30 AGV scheduling method based on task bidding mechanism and storage medium

Publications (1)

Publication Number Publication Date
CN114330831A true CN114330831A (en) 2022-04-12

Family

ID=81047619

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111438010.XA Pending CN114330831A (en) 2021-11-30 2021-11-30 AGV scheduling method based on task bidding mechanism and storage medium

Country Status (1)

Country Link
CN (1) CN114330831A (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114326608A (en) * 2021-11-30 2022-04-12 云南昆船智能装备有限公司 AGV group system based on multi-agent
CN114326608B (en) * 2021-11-30 2024-05-31 云南昆船智能装备有限公司 AGV group system based on multiple agents

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114326608A (en) * 2021-11-30 2022-04-12 云南昆船智能装备有限公司 AGV group system based on multi-agent
CN114326608B (en) * 2021-11-30 2024-05-31 云南昆船智能装备有限公司 AGV group system based on multiple agents

Similar Documents

Publication Publication Date Title
CN111596658A (en) Multi-AGV collision-free operation path planning method and scheduling system
CN110989582B (en) Multi-AGV automatic avoiding type intelligent scheduling method based on path pre-occupation
RU2589869C2 (en) Method and system for efficient scheduling for plurality of automated nonholonomic vehicles using scheduler of coordinated routes
CN113074728B (en) Multi-AGV path planning method based on jumping point routing and collaborative obstacle avoidance
CN110503260A (en) A kind of AGV dispatching method based on active path planning
CN108764579B (en) Storage multi-robot task scheduling method based on congestion control
CN111882215B (en) Personalized customization flexible job shop scheduling method containing AGV
CN108268038B (en) Dispatching method and system for multiple mobile robots
CN111563672B (en) Method for configuring number of multiple AGV
CN114489062B (en) Workshop logistics-oriented multi-automatic guided vehicle distributed dynamic path planning method
CN111857149B (en) Autonomous path planning method combining A-algorithm and D-algorithm
CN113516429B (en) Multi-AGV global planning method based on network congestion model
CN113919543A (en) AGV dispatching path optimization method based on 5G Internet of things
CN114330830A (en) Operation method for cooperatively transporting same goods by multiple AGV and storage medium
CN114529073A (en) Traveling path planning method for container approaching process of loading and unloading handling system
Zhang et al. Real-time conflict-free task assignment and path planning of multi-AGV system in intelligent warehousing
CN116205452A (en) Scheduling method, system, storage medium and processor for avoiding multiple crown blocks
CN114330831A (en) AGV scheduling method based on task bidding mechanism and storage medium
CN114911255A (en) Heterogeneous multi-unmanned aerial vehicle collaborative track planning method for communication relay guarantee
Liu et al. Holonic manufacturing system for distributed control of automated guided vehicles
KR20040072250A (en) Material control system
CN111832965B (en) Unmanned same-span multi-crown block cooperative scheduling method and system, medium and terminal
CN116719282A (en) Workshop integrated dynamic scheduling method considering path planning and disturbance response
CN114326608B (en) AGV group system based on multiple agents
Fu et al. Cooperatively scheduling hundreds of fetch and freight robots in an autonomous warehouse

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