CN105740979A - Intelligent dispatching system and method for multi-AGV (Automatic Guided Vehicle) of automatic container terminal - Google Patents
Intelligent dispatching system and method for multi-AGV (Automatic Guided Vehicle) of automatic container terminal Download PDFInfo
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Abstract
The invention relates to an intelligent dispatching system and method for a multi-AGV (Automatic Guided Vehicle) of an automatic container terminal. The two-stage distribution control dispatching mode of an operation plan and dispatching decoupling separation is realized through a container terminal management information system and an AGV group intelligent dispatching system. A knowledge learning method is used; an AGV dispatching knowledge management module is built; an AGV task case base, a path case base and a knowledge base are continuously enriched and optimized; and the AGV self learning, self organization and self decision capability are improved. Through the rolling prediction on traffic flow in a road network space of the automatic container terminal and the calling of the knowledge base, the AGV task case base and the path case base, the collaborative optimization is performed on the task dispatching and the path planning of AGVs. The invention provides an AGV path planning strategy including the optional path and free path mode; the path plan in the conventional task can be ensured; and the fast response measures can also be provided for emergency tasks.
Description
Technical field
The present invention relates to the intelligent dispatching system of the many automatic guided vehicles of a kind of automatic dock and method.
Background technology
Automatic dock operation scheduling mainly includes bank bridge operation, horizontal transport operation and three links of storage yard operation.Automatic guided vehicle (AutomaticGuidedVehicle is called for short AGV) horizontal transport is the important step of linking bank bridge operation and field bridge operation, is the main way of current automatic dock horizontal transport.Different from the bottleneck of tradition harbour An Qiao, field bridge-type operating system, on the one hand, in modern automation harbour, container operation amount and horizontal transport distance are greatly increased, An Qiao, field bridge handling capacity are existing significantly to be promoted；On the other hand, due to the uncertain disturbances of mission bit stream, road network information and status information so that many AGV produce the space-times such as node conflict, traffic congestion, deadlock under multi-state, multitask, multiobject complex job environment to be interfered.Therefore, horizontal transport link has become automatic dock and has improved the new bottleneck of working performance.
Automatic dock operation process has the uncertain features such as dynamic, random, burst.Bank bridge loading/unloading case, Chang Qiao unload/case the uncertain meeting of time and cause the uncertain of AGV queue waiting time；The average running time of AGV is subject to the impact of the intrinsic uncertain parameter of AGV system and the impact of harbour real-time traffic condition (i.e. the uncertainty of traffic flow)；Additionally, also include the sudden of contingency tasks and fault；Etc..
Current dock operation plan and management and running adopt concentrating decision-making mechanism, consider complexity and the impact of various unascertained information of system, meeting the high-efficient homework requirement under the high security of following automatic dock, the dynamic optimization of the overall situation can not or can not control because the decision-making time is long in real time.Owing to AGV lacks " brain ", without making decisions on one's own, self study, self organization ability, it is impossible to prediction harbour traffic flow conditions, cause being primarily present problems with in AGV job scheduling:
(1) static scheduling of AGV is made it cannot adapt to uncertainty by concentrating decision-making mechanism.At present, AGV horizontal transport link does not also have colony intelligence, it is impossible to Self-organizing Coordinated task is distributed, and every AGV manages system from dock operation and passively receives an assignment and routing instruction.The hypotheses that the task of AGV is distributed is time of advent of each task and the activity duration can accurately be predicted, and in fact lift van activity duration such as An Qiao, field bridge has uncertainty, therefore under this static task scheduling mode, AGV queue waiting time is long.In a word, this scheduling method is static, and the efficiency of decision-making is low, hinders the raising of working performance.
(2) static path planning is difficult to avoid that space-time is interfered, it is easy to cause traffic congestion and deadlock.Simply accept the path planning overall situation, static passively due to AGV, the planning of this fixed route is left out real-time traffic states to the AGV impact travelled, it is impossible to carry out path dynamic optimization and adjustment.Lack self-learning capability due to AGV and Self-organization Mechanism, its self adaptation and behavior adjustment capability are also not enough to avoid conflict to interfere.Therefore, in the urgent need to promoting the intelligent level of AGV, the information between AGV of setting up is mutual, " being certainly in harmony " mechanism of conflict coordination.
(3) lacking contingency tasks priority allocation mechanism, fault, accident emergency response capability are not enough.At present, mainly still moved ahead by pre-decelerating and stopping after ultrasonic ranging or laser acquisition, and the hard measure installing buffer protection device carrys out pre-anticollision.For fault and accident, mainly afterwards by artificial emergency processing, still lacking contingency tasks override mechanism, functionally but without realizing, AGV is independently collaborative to be dodged and the quick executive measure of contingency tasks (such as free path traveling).
Summary of the invention
It is an object of the invention to provide the intelligent dispatching system of the many automatic guided vehicles of a kind of automatic dock and method, realize the safety of AGV operation, real-time, accuracy, shorten its operation waiting time, improve its work efficiency, AGV is made to have the adaptation self-decision of unascertained information disturbance, self study, self-organizing (intelligence) ability under Complex System Environment, to solve the dynamic dispatching technical problem (i.e. the collaborative optimization problem of dynamic task allocation and active path planning) of many AGV work compound.
In order to achieve the above object, a technical scheme of the present invention is to provide the intelligent dispatching method of the many automatic guided vehicles of a kind of automatic dock, wherein:
Production plan, according to operation real time status information individual for each AGV, is assigned to AGV colony intelligence dispatching patcher with the form of task-set by harbour management information system, assigns contingency tasks depending on feelings, and the task of receiving described AGV colony intelligence dispatching patcher feedback completes information；
Described AGV colony intelligence dispatching patcher completes information according to the task of operation real time status information individual for each AGV and each AGV individuality previous task, carry out task distribution or task reassigns to each AGV individuality dynamic dispatching, it is achieved the two-stage distributed controll scheduling method that production plan separates with scheduling decoupling；
Described AGV colony intelligence dispatching patcher generates arithmetic for real-time traffic flow distributed intelligence according to described operation real time status information and is supplied to each AGV individuality, and generate the rolling forecast information of future transportation flow distribution by the traffic flow in intelligent harbour limitation working space is carried out rolling forecast, it is used for realizing the collaborative optimization of the distribution of the task to many AGV and path planning；
Wherein, when making AGV individuality according to optional path mode to tackle normal work to do, described AGV colony intelligence dispatching patcher calls conventional scheduling path examples from case library, form AGV individuality and complete the optional path collection of current task, and disposable for the rolling forecast information of the future transportation flow distribution AGV of being supplied to individuality is carried out current task path planning for it, in order to this AGV individuality obtains the active path that can complete normal work to do；
Or, when making AGV individuality according to free path pattern to tackle contingency tasks, for carrying out free path planning by AGV individuality, and carried out traffic flow dynamic prediction by described AGV colony intelligence dispatching patcher and be supplied to process individual for AGV by predicting the outcome and repeat several times, until this AGV individuality obtains the free driving path that can complete contingency tasks；
Described AGV colony intelligence dispatching patcher receives each AGV individuality and completes information to the task of its feedback, completes path optimization's information during task according to each AGV individuality, case library and knowledge base are optimized.
Preferably, the sampling time interval from each AGV individuality acquisition operation real time status information ist 2；
Real time status information is acquired, transmits, stores and merges by described AGV colony intelligence dispatching patcher, generates current timeT 0Arithmetic for real-time traffic flow distributed intelligence；
Described AGV colony intelligence dispatching patcher will be to futureT fThe traffic flow distributed intelligence prediction of durationpSecondary, andT 0+△T 1Before moment, the optional path collection generated was supplied to each AGV individual；Wherein, △T 1=t 1×p,t 1It it is the forecasting traffic flow time once；Further,t 2<t 1；
Under optional path mode, AGV individuality existsT 0+△T 1+△T 2In the moment, the routing information completing current task is fed back to AGV colony intelligence dispatching patcher；Wherein, △T 2It is that AGV individuality path planning calculates the time；
?T 0+△T 1+△T 2+△T 3In the moment, the individual task to AGV colony intelligence dispatching patcher feedback current task of AGV completes information；△T 3It it is the AGV individuality travel time that completes current task.
Preferably, whenT f≠△T 3Time, for prediction durationT fInterior task performance and the uncertain AGV in path are individual, and AGV colony intelligence dispatching patcher generates virtual route by calling history similar tasks and traffic example, completes the rolling forecast of future transportation flow distribution.
Preferably, under free path pattern, the AGV being assigned with contingency tasks is individual in the momentT 0Carry out first time free path planning；The iterative computation time of AGV individual freedom path planning is δt 2；
At T0+ δt 2In the moment, perform first time forecasting traffic flow；The sampling interval of forecasting traffic flow is the forecasting traffic flow time oncet 1；
Afterwards, several times are alternately performed the process of free path planning and forecasting traffic flow, until the AGV individuality being assigned with contingency tasks obtains required free driving path.
Preferably, the variable communication topological network based on bionic self-organization auto-negotiation mechanism is set up, it is achieved Real-Time Scheduling between many AGV individuality and decentralised control:
Using individual for an AGV in AGV group as manager, multiple AGV are individual as leader, and additionally multiple AGV are individual as follower；
Wherein, the information that described manager is responsible between dock operation management system and AGV colony intelligence dispatching patcher is mutual, the Real-Time Scheduling of the dynamic task scheduling of AGV group, Collaborative Control task, single AGV individuality enter or exit the management of communication topology network；
Two-way communication between described manager and leader；Described leader carries out periodical communication between follower；Described follower carries out real-time Communication for Power between leader.
Preferably, set up AGV traffic rules and anticollision discretionary security control strategy, make the individuality of each AGV in AGV group complete the real-time status feedback information with fault case of emergency according to task, following four classification adjusts its respective priority；
The priority of four kinds, comprises from high to low:
First priority is perform the AGV individuality of contingency tasks；
Second priority is be carrying out task and the individuality of the AGV with load；
Third priority is be carrying out the AGV individuality of task and zero load；
4th priority is that the AGV without task and zero load is individual；
When there is conflict, the AGV individuality low by priority is given way to the AGV individuality that priority is high；During conflict between the AGV individuality of same priority, the AGV individuality that speed is low give way to fast AGV individuality.
Another technical scheme of the present invention is to provide the intelligent dispatching system of the many automatic guided vehicles of a kind of automatic dock, wherein comprises:
Harbour management information system, receives the individual operation real time status information of each AGV, assigns production plan with the form of task-set, and assign contingency tasks depending on feelings；
AGV colony intelligence dispatching patcher, receive task-set and contingency tasks that described harbour management information system is assigned, many AGV individuality is carried out task distribution or task reassigns, it is achieved the collaborative optimization of dynamic dispatching and path planning, and task-set performance is fed back to dock operation management system；
Wherein, described AGV colony intelligence dispatching patcher comprises Real-time Task Dispatch module, road network traffic flow prediction module, scheduling knowledge management module and AGV path planning module further；
Described scheduling knowledge management module is provided with the path examples storehouse and task instances storehouse called in the past, and excavation was called data in the past and obtained the knowledge base of evolution data；
Described Real-time Task Dispatch module manages calling task example and knowledge module from scheduling knowledge, and task allocation information is sent to AGV path planning module；
Described road network traffic flow prediction module generates arithmetic for real-time traffic flow distributed intelligence then rolling forecast road network traffic flow in future distributed intelligence according to operation real time status information；
The optional path collection provided based on road network traffic flow distributed intelligence and the scheduling knowledge management module of prediction and knowledge, described AGV path planning module realizes optional path planning, it is thus achieved that make the AGV individuality being assigned with normal work to do can complete the active path of current task；
Or, it is alternately performed the process of free path planning and forecasting traffic flow, it is achieved the real-time rolling optimization of free path, it is thus achieved that make the AGV individuality being assigned with contingency tasks can complete the free driving path of current task described AGV path planning module several times；
Whether executable for task information is fed back to Real-time Task Dispatch module by described AGV path planning module: if feedback task can perform information, then the corresponding AGV of Real-time Task Dispatch module schedules is individual has walked distributing of task according to the path planned；If the feedback not executable information of task, then carried out task reassignment alternately by Real-time Task Dispatch module and path planning module.
Preferably, described AGV path planning module is planned that path optimization's information of the task that completes obtained is sent to scheduling knowledge management module, described scheduling knowledge manage module and carry out data-optimized to case library and knowledge base.
Preferably, individual for the uncertain AGV of task performance and path, described scheduling knowledge management module calls history similar tasks and traffic example generates virtual route to road network traffic flow prediction module, described road network traffic flow prediction module complete the rolling forecast of future transportation flow distribution.
Compared with prior art, it is an advantage of the current invention that:
(1) concentrating decision-making mechanism is changed, adopt complication system decoupling method, build the scheduling new model of dock operation management information system (upper system) and the two-stage distributed controll of AGV colony intelligence dispatching patcher (i.e. " brain " of AGV), it is achieved the separation of work plan and schedule.
(2) adopt knowledge learning method, set up AGV scheduling knowledge management system, enrich constantly and optimize AGV task instances storehouse, path examples storehouse and knowledge base, improving the self study of AGV, self-organizing, self-decision ability.
(3) passing through the traffic flow rolling forecast in intelligent harbour road network space, and call knowledge base, AGV task instances storehouse and path examples storehouse, task scheduling and path planning to AGV carry out collaborative optimization.Propose the AGV Path Planning comprising optional path and free path pattern, both can guarantee that the path planning under normal work to do, quick responsive measures can be provided for contingency tasks again.
Accompanying drawing explanation
Fig. 1 is the Organization Chart of AGV colony intelligence dispatching patcher in the present invention；
Fig. 2 is the schematic diagram of the information interaction mechanism that many AGV task is collaborative with path in the present invention.
Detailed description of the invention
In view of the task of many AGV distributes the complicated coupling interactive relation with path planning (in time, space, information), the present invention utilizes the thought of complication system decoupling by each cargo handling operation decomposition module, be intended to separate with scheduling, by two-level scheduler new model, AGV job task collection is transferred, AGV job status information Real-time Feedback and traffic flow rolling forecast, the impact of the unascertained information of elimination or minimizing harbour complex job environment.AGV intelligent level is promoted by self-decision, self-organizing and self study, realize many AGV work compound dynamic dispatching (the collaborative optimization of dynamic task allocation and active path planning), to solve the problems such as many AGV space-times such as current concentrating decision-making mechanism, static task scheduling and static path planning insurmountable node conflict, traffic congestion, deadlock are interfered and job task is preferential, fault is emergent.
Specifically, dock operation management system issues task-set to AGV group after collaborative for the production plan of other links such as AGV and An Qiao, field bridge optimization, depending on feelings (because of AGV fault or long time block up and cause task to complete, the emergency such as dangerous materials container accident) assign contingency tasks；The present invention provides a kind of AGV colony intelligence dispatching patcher, is responsible for the dynamic dispatching of AGV, and task-set performance feeds back to dock operation management system.
Described AGV colony intelligence dispatching patcher, mainly comprise Real-time Task Dispatch, road network traffic flow prediction, scheduling knowledge management and four modules of AGV path planning, system architecture as shown in Figure 1:
In order to set up the collaborative optimization of many AGV task scheduling and path planning, Real-time Task Dispatch module manages calling task example and knowledge module from scheduling knowledge, by task allocation information to AGV path planning module.The arithmetic for real-time traffic flow distributed intelligence that road network traffic flow prediction module generates according to AGV real time status information, then rolling forecast road network traffic flow in future distributed intelligence.In normal work to do situation, road grid traffic stream information based on prediction, AGV path planning module realizes optional path planning according to optional path collection and the knowledge of scheduling knowledge management module offer, and whether task can be performed information feed back to Real-time Task Dispatch module, if task can perform, then AGV performs corresponding path planning；Otherwise, Real-time Task Dispatch module and path planning module task reassignment is carried out alternately.In contingency tasks situation, with normal work to do the difference is that, realized the real-time rolling optimization of free path of AGV " forecasting traffic flow-freely plan-predict again-plan again " by traffic flow dynamic prediction.Completing after task until AGV, the information that task completed feeds back to AGV colony intelligence dispatching patcher in time, and path optimization's information is supplied to scheduling knowledge management module.
As in figure 2 it is shown, in the intelligent dispatching system of the present invention and method, mainly have following characteristics:
1) concentrating decision-making mechanism is changed, adopt complication system decoupling method, build the scheduling new model of dock operation management information system (upper system) and the two-stage distributed controll of AGV colony intelligence dispatching patcher (i.e. " brain " of AGV), it is achieved work plan and schedule separates.
Consider that the dynamic realtime of the overall situation of All Jobs link optimizes hardly possible, by cargo handling operation modularity, by two-level scheduler new model by production plan and scheduling decoupling.Redistribute function and the authority of dock operation management information system and AGV colony intelligence dispatching patcher, set up the mission bit stream between two-stage system (task-set distribution and task feedback) interaction mechanism: upper system only need to consider production plan and task-set distribution, assign contingency tasks depending on feelings (AGV fault, security incident, emergency), optimize production plan such that it is able to collaborative with other cargo handling operations better；AGV colony intelligence dispatching patcher then becomes AGV task and distributes (reassignment) and Dispatch and Command Center, it is actually and gives AGV self-decision ability, such that it is able to realize the task Real-time and Dynamic distribution such as priority of task, fault and accident be emergent, for establishing framework basis towards probabilistic dynamic dispatching.
2) by the traffic flow rolling forecast in intelligent harbour limitation working space, calling AGV and dispatched path examples in the past, based on intelligent optimization algorithm, the task of AGV is distributed and path planning carries out collaborative optimization, it is achieved dynamic route real-time optimization.
Generate the rolling forecast information of arithmetic for real-time traffic flow distributed intelligence and future transportation flow distribution according to operation real time status information (including the orientation of every AGV, speed, task status, fault and security alarm etc.), call conventional path examples from management and running knowledge system and form optional path collection by data mining.Path optimization problems is converted to the static optimization problem that can (quickly) solve in real time under discrete time, and AGV individuality is respectively directed to normal work to do according to optional path and free path pattern and contingency tasks carries out path and quickly plans.Under normal work to do (optional path) pattern, disposable for the rolling forecast information of the future transportation flow distribution AGV of being supplied to individuality is carried out current task path planning for it；Under contingency tasks (free path) pattern, realized the free path real-time optimization of AGV " forecasting traffic flow-freely plan-predict again-plan again " by traffic flow dynamic prediction.
3) adopt knowledge learning method, set up AGV scheduling knowledge management system, enrich constantly and optimize AGV Scheduling instances storehouse and knowledge base, improving the self study of AGV, self-organizing, self-decision ability.
It is extremely difficult owing to setting up dynamic task allocation and the active path planning Combinatorial Optimization Model of many AGV, constantly trains AGV intelligent capability mainly through the method for data acquisition and knowledge learning.Utilize the path optimization's information architecture case library having completed task, carry out data mining generation and supplementary knowledge base, make AGV possess self-learning capability.Reference bionic self-organization mechanism, according to AGV with or without task, whether band load, whether meeting an urgent need divides job priority (such as contingency tasks can give the highest job priority).What propose the AGV anticollision based on job priority rank height and anti-self-locking is in harmony traffic rules and discretionary security control strategy certainly, makes AGV have self organization ability.
Although present disclosure has been made to be discussed in detail already by above preferred embodiment, but it should be appreciated that the description above is not considered as limitation of the present invention.After those skilled in the art have read foregoing, multiple amendment and replacement for the present invention all will be apparent from.Therefore, protection scope of the present invention should be limited to the appended claims.
Claims (9)
1. the intelligent dispatching method of the many automatic guided vehicles of automatic dock, it is characterised in that
Production plan, according to operation real time status information individual for each AGV, is assigned to AGV colony intelligence dispatching patcher with the form of task-set by harbour management information system, assigns contingency tasks depending on feelings, and the task of receiving described AGV colony intelligence dispatching patcher feedback completes information；
Described AGV colony intelligence dispatching patcher completes information according to the task of operation real time status information individual for each AGV and each AGV individuality previous task, carry out task distribution or task reassigns to each AGV individuality dynamic dispatching, it is achieved the two-stage distributed controll scheduling method that production plan separates with scheduling decoupling；
Described AGV colony intelligence dispatching patcher generates arithmetic for real-time traffic flow distributed intelligence according to described operation real time status information and is supplied to each AGV individuality, and generate the rolling forecast information of future transportation flow distribution by the traffic flow in intelligent harbour limitation working space is carried out rolling forecast, it is used for realizing the collaborative optimization of the distribution of the task to many AGV and path planning；
Wherein, when making AGV individuality according to optional path mode to tackle normal work to do, described AGV colony intelligence dispatching patcher calls conventional scheduling path examples from case library, form AGV individuality and complete the optional path collection of current task, and disposable for the rolling forecast information of the future transportation flow distribution AGV of being supplied to individuality is carried out current task path planning for it, in order to this AGV individuality obtains the active path that can complete normal work to do；
Or, when making AGV individuality according to free path pattern to tackle contingency tasks, for carrying out free path planning by AGV individuality, and carried out traffic flow dynamic prediction by described AGV colony intelligence dispatching patcher and be supplied to process individual for AGV by predicting the outcome and repeat several times, until this AGV individuality obtains the free driving path that can complete contingency tasks；
Described AGV colony intelligence dispatching patcher receives each AGV individuality and completes information to the task of its feedback, completes path optimization's information during task according to each AGV individuality, case library and knowledge base are optimized.
2. intelligent dispatching method as claimed in claim 1, it is characterised in that
The sampling time interval obtaining operation real time status information from each AGV individuality ist 2；
Real time status information is acquired, transmits, stores and merges by described AGV colony intelligence dispatching patcher, generates current timeT 0Arithmetic for real-time traffic flow distributed intelligence；
Described AGV colony intelligence dispatching patcher will be to futureT fThe traffic flow distributed intelligence prediction of durationpSecondary, andT 0+△T 1Before moment, the optional path collection generated was supplied to each AGV individual；Wherein, △T 1=t 1×p,t 1It it is the forecasting traffic flow time once；Further,t 2<t 1；
Under optional path mode, AGV individuality existsT 0+△T 1+△T 2In the moment, the routing information completing current task is fed back to AGV colony intelligence dispatching patcher；Wherein, △T 2It is that AGV individuality path planning calculates the time；
?T 0+△T 1+△T 2+△T 3In the moment, the individual task to AGV colony intelligence dispatching patcher feedback current task of AGV completes information；△T 3It it is the AGV individuality travel time that completes current task.
3. intelligent dispatching method as claimed in claim 2, it is characterised in that
WhenT f≠△T 3Time, for prediction durationT fInterior task performance and the uncertain AGV in path are individual, and AGV colony intelligence dispatching patcher generates virtual route by calling history similar tasks and traffic example, completes the rolling forecast of future transportation flow distribution.
4. intelligent dispatching method as claimed in claim 2, it is characterised in that
Under free path pattern, the AGV being assigned with contingency tasks is individual in the momentT 0Carry out first time free path planning；The iterative computation time of AGV individual freedom path planning is δt 2；
At T0+ δt 2In the moment, perform first time forecasting traffic flow；The sampling interval of forecasting traffic flow is the forecasting traffic flow time oncet 1；
Afterwards, several times are alternately performed the process of free path planning and forecasting traffic flow, until the AGV individuality being assigned with contingency tasks obtains required free driving path.
5. intelligent dispatching method as claimed in claim 1, it is characterised in that
Set up the variable communication topological network based on bionic self-organization auto-negotiation mechanism, it is achieved Real-Time Scheduling between many AGV individuality and decentralised control:
Using individual for an AGV in AGV group as manager, multiple AGV are individual as leader, and additionally multiple AGV are individual as follower；
Wherein, the information that described manager is responsible between dock operation management system and AGV colony intelligence dispatching patcher is mutual, the Real-Time Scheduling of the dynamic task scheduling of AGV group, Collaborative Control task, single AGV individuality enter or exit the management of communication topology network；
Two-way communication between described manager and leader；
Described leader carries out periodical communication between follower；Described follower carries out real-time Communication for Power between leader.
6. intelligent dispatching method as claimed in claim 1, it is characterised in that
Set up AGV traffic rules and anticollision discretionary security control strategy, make the individuality of each AGV in AGV group complete the real-time status feedback information with fault case of emergency according to task, following four classification adjusts its respective priority；
The priority of four kinds, comprises from high to low:
First priority is perform the AGV individuality of contingency tasks；
Second priority is be carrying out task and the individuality of the AGV with load；
Third priority is be carrying out the AGV individuality of task and zero load；
4th priority is that the AGV without task and zero load is individual；
When there is conflict, the AGV individuality low by priority is given way to the AGV individuality that priority is high；During conflict between the AGV individuality of same priority, the AGV individuality that speed is low give way to fast AGV individuality.
7. the intelligent dispatching system of the many automatic guided vehicles of automatic dock, it is characterised in that comprise:
Harbour management information system, receives the individual operation real time status information of each AGV, assigns production plan with the form of task-set, and assign contingency tasks depending on feelings；
AGV colony intelligence dispatching patcher, receive task-set and contingency tasks that described harbour management information system is assigned, many AGV individuality is carried out task distribution or task reassigns, it is achieved the collaborative optimization of dynamic dispatching and path planning, and task-set performance is fed back to dock operation management system；
Wherein, described AGV colony intelligence dispatching patcher comprises Real-time Task Dispatch module, road network traffic flow prediction module, scheduling knowledge management module and AGV path planning module further；
Described scheduling knowledge management module is provided with the path examples storehouse and task instances storehouse called in the past, and excavation was called data in the past and obtained the knowledge base of evolution data；
Described Real-time Task Dispatch module manages calling task example and knowledge module from scheduling knowledge, and task allocation information is sent to AGV path planning module；
Described road network traffic flow prediction module generates arithmetic for real-time traffic flow distributed intelligence then rolling forecast road network traffic flow in future distributed intelligence according to operation real time status information；
The optional path collection provided based on road network traffic flow distributed intelligence and the scheduling knowledge management module of prediction and knowledge, described AGV path planning module realizes optional path planning, it is thus achieved that make the AGV individuality being assigned with normal work to do can complete the active path of current task；
Or, it is alternately performed the process of free path planning and forecasting traffic flow, it is achieved the real-time rolling optimization of free path, it is thus achieved that make the AGV individuality being assigned with contingency tasks can complete the free driving path of current task described AGV path planning module several times；
Whether executable for task information is fed back to Real-time Task Dispatch module by described AGV path planning module: if feedback task can perform information, then the corresponding AGV of Real-time Task Dispatch module schedules is individual has walked distributing of task according to the path planned；If the feedback not executable information of task, then carried out task reassignment alternately by Real-time Task Dispatch module and path planning module.
8. intelligent dispatching system as claimed in claim 7, it is characterised in that
Described AGV path planning module is planned that path optimization's information of the task that completes obtained is sent to scheduling knowledge management module, described scheduling knowledge manage module and carry out data-optimized to case library and knowledge base.
9. intelligent dispatching system as claimed in claim 7, it is characterised in that
Individual for the uncertain AGV of task performance and path, described scheduling knowledge management module calls history similar tasks and traffic example generates virtual route to road network traffic flow prediction module, described road network traffic flow prediction module complete the rolling forecast of future transportation flow distribution.
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|---|---|---|---|---|
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101944201A (en) * | 2010-07-27 | 2011-01-12 | 昆明理工大学 | Multi-agent-based steelmaking workshop crane scheduling simulation method |
| CN102495612A (en) * | 2011-12-24 | 2012-06-13 | 长春艾希技术有限公司 | Electrical automatic control system device of automatic guided vehicle adopting non-contact power supply technology |
| CN103020792A (en) * | 2012-11-20 | 2013-04-03 | 上海海事大学 | Low-bridging electric trolley dispatching method for automatic container terminals and system thereof |
| CN104679004A (en) * | 2015-02-09 | 2015-06-03 | 上海交通大学 | Flexible path and fixed path combined automated guided vehicle and guide method thereof |
| CN105204462A (en) * | 2015-08-17 | 2015-12-30 | 国家电网公司 | AGV quantity and work task matching method in AGV production scheduling system |
-
2016
- 2016-01-29 CN CN201610062279.5A patent/CN105740979B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101944201A (en) * | 2010-07-27 | 2011-01-12 | 昆明理工大学 | Multi-agent-based steelmaking workshop crane scheduling simulation method |
| CN102495612A (en) * | 2011-12-24 | 2012-06-13 | 长春艾希技术有限公司 | Electrical automatic control system device of automatic guided vehicle adopting non-contact power supply technology |
| CN103020792A (en) * | 2012-11-20 | 2013-04-03 | 上海海事大学 | Low-bridging electric trolley dispatching method for automatic container terminals and system thereof |
| CN104679004A (en) * | 2015-02-09 | 2015-06-03 | 上海交通大学 | Flexible path and fixed path combined automated guided vehicle and guide method thereof |
| CN105204462A (en) * | 2015-08-17 | 2015-12-30 | 国家电网公司 | AGV quantity and work task matching method in AGV production scheduling system |
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