CN110264120A - A kind of intelligent storage route planning system and method based on more AGV - Google Patents
A kind of intelligent storage route planning system and method based on more AGV Download PDFInfo
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- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
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- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
- G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
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- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06316—Sequencing of tasks or work
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
Abstract
The invention discloses a kind of intelligent storage route planning methods and system based on more AGV, include the following steps: S100, tasks clear distributes rule, priority when generating to storage task divides, S200, dynamic priority adjusting, dynamic priority adjusting is carried out to the task of the AGV load in transport, S300, optimum path planning, route is reset by dynamically being accurately calculated and being locked to time window to avoid conflict, and countermeasure research is carried out to predictable vertically meet conflict and conflict of meeting in opposite directions respectively, it is eventually found the shortest path of Lothrus apterus；S400, Conflict Strategies are predictably avoided, the detection and processing of traffic rules formulation and unpredictable conflict have been done for more AGV traffic problems being likely encountered in the transportational process in storage workshop, the time cost for Task of storing in a warehouse is preferably minimized by system, ensure that can make AGV path cost minimum under the conditions of vehicle is collisionless, while improve the efficiency of task schedule and vehicle scheduling.
Description
Technical field
The present invention relates to intelligent storage route planning technical field, specially a kind of intelligent storage route based on more AGV
Planning system and method.
Background technique
Along with the extensive use of flexible manufacturing system, attention of the people to manufacturing industry cost further, because in manufacturing industry
Lathe and the working efficiencies of automation tools directly decide our manufacturing cost, including time cost, machine cost, people
Work cost.In flexible manufacturing, reasonable AGV (Automated Guided Vehicle) task schedule and rule how are carried out
The working efficiency for drawing, improving automation tools or lathe, the core competitiveness of development and raising enterprise to manufacture field
Important in inhibiting.
As the country is that leading electric business business is risen with Jingdone district Ali, the convenience of shopping at network is also fade-in the popular feeling, small
To snacks toy, sofa furniture is arrived greatly, and the shopping at network consciousness of people is gradually reinforced, so quickly goods sorting, automation
Intelligent workshop transport is to we have proposed new requirements.Traditional storage workshop and transportation system occupy a large amount of worker, manually
It carries, a large amount of manpower and material resources of artificial counting consuming, it is also very high to link up cost, and production capacity is low.Gradually mechanization and automatic
Compound stream slowly rises, and original intention is to increase productivity, and reduces manpower people sheet, but the workshop transportation dispatching strategy of early stage is single, accounts for
With a large amount of vehicle, task deadlock and task it is hungry it occur frequently that, although reducing artificial landed cost, also increase
To the labor management cost of these vehicles.The forward position intelligent logistics system risen in recent years is arrived again, from user at electric business platform app
After good order, then system meeting intelligent positioning notifies AGV that shelf location is gone to take commodity, most descendant to shelf location where commodity
Work checks commodity, and then order can be completed in packing.In recent years, domestic Jingdone district logistics and green hand's logistics emerge rapidly,
" big country's treasure " documentary film also has many introductions, from logistics sorting analogue system to intelligent AGV job scheduling system, can accomplish
Vehicle orderly and efficiently operation, can effectively mitigate the working strength of shop worker.In fact, from host computer dispatch system to
Slave computer AGV design realizes worker, scheduling system, the zero defect cooperation between AGV, generates task with performing effectively and reaches
Seamless link is still very difficult, we are also faced with a series of about the task schedule of intelligent and high-efficiency, vehicle scheduling, lathe etc.
The problems such as the path planning of automation tools.
Demand with material flows automation, intellectualizing system increasingly increases, and the domestic demand to automatic guided vehicle is also gradually
Cumulative length.In entire automated warehousing logistics, AGV transportation cost accounts for that totle drilling cost specific gravity is higher, the accurate scheduling for task of storing in a warehouse
And the good Path Planning of AGV, to improving, logistics operation efficiency, reduction transportation cost are significant.
But existing intelligent storage route planning system and method based on more AGV has the following deficiencies:
The task complexity in warehouse logistics workshop, which determines, mostly uses greatly more AGV work compounds, but compares single AGV system
It will appear various problems:
(1) how Mission Operations rationally distribute, which task highest preferentially distributes, and how to promote job execution sequence, can
So that task will not generate hunger phenomenon；
(2) after system appointed task, which AGV is selected to go execution task, workshop needs how many AGV efficiency highests；
(3) in more AGV systems, multiple AGV may cause to interfere with each other by a crossroad simultaneously.
(4) how during multiple AGV execution tasks, avoidance or road is planned again when encountering free vehicle or fault car
The problems such as line, load AGV power-off.
Summary of the invention
In order to overcome the shortcomings of that prior art, the present invention provide a kind of intelligent storage route planning based on more AGV
Method and system, the present invention is different from traditional route planning method, i.e., is preferably minimized the time cost for Task of storing in a warehouse,
By the specified and dynamic adjustment and the locking of node time window to priority, it is minimum to find path cost, time cost
Path and deadlock freedom, the scheduling strategy without starvation, ensure that can make AGV path cost minimum under the conditions of vehicle is collisionless, together
When improve the efficiency of task schedule and vehicle scheduling.
The technical solution adopted by the present invention to solve the technical problems is:
A kind of intelligent storage route planning method based on more AGV, includes the following steps:
S100, tasks clear distribute rule, and priority when generating to storage task divides, for each storage
Task carries out the sequence of vehicle match degree, and the storage task of each vehicle to be allocated corresponds to an available vehicle platoon, simultaneously
These vehicles itself also have the queue for having distributed pending task；
S200, dynamic priority adjusting carry out dynamic priority adjusting to the task of the AGV load in transport, according to weight
Then folded time window and topological map lock the time window of each node to determine which kind of node conflict will occur；
S300, optimum path planning are heuristically multiple using A-Star algorithm for practical workshop traffic condition
Searching route obtains interim shortest path to AGV respectively, the time of counting of carriers access storage node, crosses dynamically to time window
It is accurately calculated and is locked to reset route to avoid conflict, and predictable vertically meet is conflicted and met in opposite directions respectively
Conflict carries out countermeasure research, is eventually found the shortest path of Lothrus apterus；
S400, Conflict Strategies are predictably avoided, the friendship being likely encountered in the transportational process in storage workshop for more AGV
Topic of corresponding has done the detection and processing of traffic rules formulation and unpredictable conflict.
Further, time window algorithm concrete operations in the S200 are as follows:
S201, in directed connection network G=(V, E), it is assumed that there is x vehicle to participate in task execution, then the AGV set of task
For A={ a1, a2, a3... ax}；
S202, if the point set of task and the set of terminal are denoted as S and D respectively, andThen lead automatically
The time window for drawing the passed through node of vehicle can be defined as；
In formulaIndicate that automatic guided vehicle retains the time of node i,Indicate automatic guided vehicle release node i when
Between, i ∈ V；
S203 is converted into each section of the hole AGV curve rail by AGV vehicle along the time shortest optimization of entire path locus
Time h needed for mark movementiSubsection optimization, total time fragment bit certain time interval [t1,t2], [t2,t3]…[tn-2,
tn-1], [tn-1,tn], then the length of adjacent two periods node are as follows:
hi=ti-1-ti(i=1,2,3 ... n-1)
Wherein, tiAt the time of moving to i point for AGV.
Further, in the S203, the length of adjacent two periods node is optimized as follows:
S2031, the solution of optimal path is first encoded into chromosome required for genetic algorithm；
S2032, initialization of population, and in hiA certain number of individuals are randomly generated in time interval；
S2033, for each of population individual, adaptive value size are as follows:
Wherein,It is the maximum value that can be taken in its given range；
S2034, selection, duplication, enabling fitness value individual in population is fi, the probability of selected population are as follows:
S2035, intersection or genetic recombination, crossover operator remember a using the form that counts1, a2It is individual for two Geju City, by counting
Crossover operator operation, generates new individual a '1, a '2, generate the calculation formula of new individual are as follows:
Wherein,Indicate the bound of ak, g is current to select generation number.
Further, AGV optimum path planning algorithms in the S300 are as follows:
S301, task definition is carried into a storage are as follows:
carryk(T)={ PQk(T), btk, Sk, Dk, LBk(T), }
Wherein PQkIndicate the real-time priority of k-th of task, parameter is bigger, and priority is lower；btkIndicate k-th of task
Time started, SkAnd DkRespectively indicate the gatehead and unloading point of k-th of task, and Sk∈V、Dk∈V；The parameter of enumeration type
LBk(t) the carrying state of k-th of task is indicated；
S302, for LBk(t) carrying state distributes different priority to task, respectively high-priority queue, in
Priority query and Low Priority Queuing.
Further, calculating realizing route ruleization of the S300 also according to the priority distribution and time window of task.
Further, path planning step in the S300 are as follows:
S3021, warehouse logistics parameter is inputted according to master system administrator, task is divided into priority, insertion is preferential
Grade queue, and initialize multiple transport task carryk(t)；
S3022, vehicle scheduling is carried out according to the priority orders of task: according to the most short principle of task waiting time to can bear
It carries AGV to be ranked up, selects head of the queue AGV to execute task, be inserted into the pending queue of this AGV；
S3023, heuristic search is carried out to path with A-Star algorithm, obtains interim most short driving path；
S3024, counting of carriers reach the time t of each path nodei, then calculate automatic guided vehicle and retain node i
TimeWith the time of automatic guided vehicle release node i
S3025, initialization node time window ωk, make if there is task p and qIllustrate node time
Window occurs overlapping phenomenon because not yet locking, i.e., some retention time window in there are other vehicles
S3026, according to overlapping time window and topological map, to determine which kind of node conflict will occur, then to each
The time window of node locks；
S3027, executable task (specified AGV, specify route) is generated, after AGV has executed the task free time, due to the AGV
It is likely to become the obstacle of other tasks, so preferentially to distribute the vehicle, S3021 is repeated and administrator's dynamic is waited to distribute newly
Demand.
Further, traffic rules are provided as follows to warehouse logistics workshop AGV vehicle in the S400 are as follows:
S401, AGV advance in storage workshop according to the magnetic stripe or tape that are laid in advance, it is specified can be between delivery nodes
Load cargo traveling；
S402, when by time window arrangement predict generation vertically meet conflict when, in strict accordance with task priority arrange
Sequence, the vehicle for carrying high-priority task preferentially pass through crossing, same priority according to task at the beginning of parameter btkTo arrange
Sequence, parameter value time earlier one preferentially pass through crossing；
S403, AGV vehicle remain a constant speed traveling when workshop is advanced, and have fixed turning time and AGV can only be born every time
A task is carried, is not influenced by cargo is how many；
If S404, a1 node need AGV transport three times to f1 node, then three carryings should be arranged in system manager
Task, may distribute to the same vehicle may also distribute to different vehicle；
S405, when encountering idle companion AGV during the vehicle transport of loading commissions, the position for investigating vehicle is planned again
Route.
In addition the present invention also provides a kind of intelligent storage route planning system for claim 1 the method, packets
Include master system, Processing in Barcode Recognizing System and piece-supply system；
The master system include management map module, monitoring display module, path planning module, AGV control module,
Scheduler module, data management module and communication module, the signal end and Processing in Barcode Recognizing System of the master system interconnect；
The Processing in Barcode Recognizing System is responsible for acquiring the image of object, and bar code letter is obtained after handling by image processor
Breath, is sent to master system finally by communication module；
The piece-supply system is responsible for AGV vehicle and carries out piece supplying；
The data management module is responsible for system data being persisted to database.
Compared with prior art, the beneficial effects of the present invention are:
The present invention is different from traditional route planning method, i.e., is preferably minimized the time cost for Task of storing in a warehouse, and leads to
The specified and dynamic adjustment and the locking of node time window to priority are crossed, the minimum road of path cost, time cost is found
Diameter and deadlock freedom, the scheduling strategy without starvation, ensure that can make AGV path cost minimum under the conditions of vehicle is collisionless, simultaneously
Improve the efficiency of task schedule and vehicle scheduling.
Detailed description of the invention
Fig. 1 is flow chart of the method for the present invention；
Fig. 2 is structural block diagram of the invention；
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
As shown in Fig. 2, the present invention provides a kind of intelligent storage route planning systems for claim 1 the method
System, including master system, Processing in Barcode Recognizing System and piece-supply system；The master system includes management map module, monitoring
Display module, path planning module, AGV control module, scheduler module, data management module and communication module, the host computer
The signal end and Processing in Barcode Recognizing System of system interconnect；The Processing in Barcode Recognizing System is responsible for acquiring the image of object, and passes through
Bar code information is obtained after image processor processing, is sent to master system finally by communication module；The piece-supply system is negative
Duty is that AGV vehicle carries out piece supplying；The data management module is responsible for system data being persisted to database.
In the present embodiment, in master system, management map module establishes distribution place path network electronically
Graph model, monitoring display module are responsible for monitoring the operating status of vehicle to be allocated, and path planning module is responsible for load vehicle rule
Path is drawn, AGV control module is responsible for managing and controlling the remote operating status of each vehicle, and scheduler module is responsible for appointing to vehicle allocation
Business, data management module is responsible for for system data being persisted to database, communication module be responsible for master system and all AGV it
Between communication.
In the present embodiment, as shown in Figure 1, the method for intelligent storage route planning includes the following steps:
S100, tasks clear distribute rule, and priority when generating to storage task divides, for each storage
Task carries out the sequence of vehicle match degree, and the storage task of each vehicle to be allocated corresponds to an available vehicle platoon, simultaneously
These vehicles itself also have the queue for having distributed pending task；
S200, dynamic priority adjusting carry out dynamic priority adjusting to the task of the AGV load in transport, according to weight
Then folded time window and topological map lock the time window of each node to determine which kind of node conflict will occur；
S300, optimum path planning are heuristically multiple using A-Star algorithm for practical workshop traffic condition
Searching route obtains interim shortest path to AGV respectively, the time of counting of carriers access storage node, crosses dynamically to time window
It is accurately calculated and is locked to reset route to avoid conflict, and predictable vertically meet is conflicted and met in opposite directions respectively
Conflict carries out countermeasure research, is eventually found the shortest path of Lothrus apterus；
S400, Conflict Strategies are predictably avoided, the friendship being likely encountered in the transportational process in storage workshop for more AGV
Topic of corresponding has done the detection and processing of traffic rules formulation and unpredictable conflict.
In the present embodiment, by storage task generate when priority divide and transport in AGV load task into
The adjustment of Mobile state priority realizes that different types of task carries out priority and draws in detail:
For the demand of the preferential outbound of VIP user's order cargo, initializing this carrying task is high-priority task；For
It is common to carry task in real time, it is initialized as low priority task；It is less than the charging tasks of particular value for electricity, is initialized as
Priority tasks；For the task dynamic priority adjusting aspect of the AGV load in transport, there is task non-loaded for working as
AGV trolley (trolley is gone in the road of gatehead) encounters the failures such as urgently charging or burst power-off and working as has task to have load
AGV encounter the failures two such as the power-off aspect that urgently charges or happen suddenly and discussed respectively.
Time window algorithm concrete operations in the S200 are as follows:
S201, in directed connection network G=(V, E), it is assumed that there is x vehicle to participate in task execution, then the AGV set of task
For A={ a1, a2, a3... ax}；
S202, if the point set of task and the set of terminal are denoted as S and D respectively, andThen lead automatically
The time window for drawing the passed through node of vehicle can be defined as；
In formulaIndicate that automatic guided vehicle retains the time of node i,Indicate automatic guided vehicle release node i when
Between, i ∈ V；
S203 is converted into AGV along each section of curve rail by AGV vehicle along the time shortest optimization of entire path locus
Time h needed for mark movementiSubsection optimization, total time fragment bit certain time interval [t1,t2], [t2,t3]…[tn-2,
tn-1], [tn-1,tn], then the length of adjacent two periods node are as follows:
hi=ti-1-ti(i=1,2,3 ... n-1)
Wherein, tiAt the time of moving to i point for AGV.
In the S203, the length of adjacent two periods node is optimized as follows:
S2031, the solution of optimal path is first encoded into chromosome required for genetic algorithm；
S2032, initialization of population, and in hiA certain number of individuals are randomly generated in time interval；
S2033, for each of population individual, adaptive value size are as follows:
Wherein,It is the maximum value that can be taken in its given range, which ensure that the smallest adaptation
Value is all assigned to the individual for being unsatisfactory for constraint condition；
S2034, selection, duplication, enabling fitness value individual in population is fi, the probability of selected population are as follows:
S2035, intersection or genetic recombination, crossover operator remember a using the form that counts1, a2It is individual for two Geju City, by counting
Crossover operator operation, generates new individual a '1, a '2, generate the calculation formula of new individual are as follows:
Wherein,Indicate the bound of ak, g is current to select generation number.
AGV optimum path planning algorithms in the S300 are as follows:
S301, task definition is carried into a storage are as follows:
carryk(T)={ PQk(T), btk, Sk, Dk, LBk(T), }
Wherein PQkIndicate the real-time priority of k-th of task, parameter is bigger, and priority is lower；btkIndicate k-th of task
Time started, SkAnd DkRespectively indicate the gatehead and unloading point of k-th of task, and Sk∈V、Dk∈V；The parameter of enumeration type
LBk(t) the carrying state of k-th of task is indicated；
S302, for LBk(t) carrying state distributes different priority to task, respectively high-priority queue, in
Priority query and Low Priority Queuing.
Calculating realizing route ruleization of the S300 also according to the priority distribution and time window of task, the Road S300
Diameter planning step are as follows:
S3021, warehouse logistics parameter is inputted according to master system administrator, task is divided into priority, insertion is preferential
Grade queue, and initialize multiple transport task carryk(t)；
S3022, vehicle scheduling is carried out according to the priority orders of task: according to the most short principle of task waiting time to can bear
It carries AGV to be ranked up, selects head of the queue AGV to execute task, be inserted into the pending queue of this AGV；
S3023, heuristic search is carried out to path with A-Star algorithm, obtains interim most short driving path；
S3024, counting of carriers reach the time t of each path nodei, then calculate automatic guided vehicle and retain node i
TimeWith the time of automatic guided vehicle release node i
S3025, initialization node time window ωk, make if there is task p and qIllustrate node time
Window occurs overlapping phenomenon because not yet locking, i.e., some retention time window in there are other vehicles
S3026, according to overlapping time window and topological map, to determine which kind of node conflict will occur, then to each
The time window of node locks；
In the present embodiment S3026, node conflict is conflict of meeting in opposite directions if it exists, then selects PQk(t) biggish task weight
New scheduling, since the interim optimal route planned according to heuritic approach will appear collision, it is therefore desirable to the routine weight value be arranged
Optimal path is re-searched for for infinity, returns and executes S3023, if conflict one can all occur in last all routes, then returning
It executes S3022 and replaces AGV vehicle；If conflict type only remains conflict of vertically meeting, the AGV for carrying low priority task is former
Ground waits the time of a node time window, until overlaid windows disappears；If two kinds of conflicts all exist, first according to opposite phase
Meet clash handle.
S3027, executable task (specified AGV, specify route) is generated, after AGV has executed the task free time, due to the AGV
It is likely to become the obstacle of other tasks, so preferentially to distribute the vehicle, S3021 is repeated and administrator's dynamic is waited to distribute newly
Demand.
In the present embodiment, task allocating module includes the strategy that logistics task scheduling strategy and AGV vehicle are chosen in S300,
Administrator first is spent according to the attribute of storage task and it is pressed for time to determine gatehead, unloading point and priority, but is had
When order be urgent or VIP user, we can preferentially complete outbound task, this is just to need administrator customized preferential
The urgent outbound of grade, customized that steps are as follows is described:
(1) it urgently charges and carries task, redistribute using allocated task as high priority, then appoint charging
Priority is redistributed in business conduct；
(2) urgently charging and it is non-loaded without task, by charging tasks be used as in priority redistribute；
(3) general real-time task is distributed as low priority；
(4) same priority is distributed according to the method for prerequisite variable.
As administrator appointed task carryk(t)={ PQk(t), btk, Sk, Dk, LBk(t), after parameters },
Vehicle will be entered and choose the stage, the selection of AGV vehicle generally can take into account the position of free vehicle and i.e. by idle vehicle
Position, system can automatically generate after all vehicles arrive complete current task from now on, to the completing vehicle to be allocated of task
Estimated time, system can automatically be ranked up vehicle, and the final AGV vehicle for choosing queue foremost completes this task,
Certainly during transportation, it is also contemplated that transporting detailed problem below:
(1) when vehicle electricity is less than particular value, vehicle should notify immediately master system after completing current task,
Task in the allocated task queue of this vehicle is redistributed, to avoid task starvation.
(2) AGV vehicle itself has the pending queue of task, after the unloading point of a upper task leaves, horse
On task executed according to the route of scheduling planning of system, if encountering unpredictable conflict in the process of implementation, immediately
Master system is notified to choose suitable strategy to carry out dynamic and avoid conflicting.
(3) it is at this moment just filled automatically to charging zone when AGV vehicle because the system free time causes vehicle not to be arranged task for a long time
Electricity, to area's waiting system scheduling of awaiting orders after being full of.
It (4) in general, is that the AGV free vehicle that selected distance is nearest in vehicle platoon goes to execute current task, with
Guarantee that time cost is minimum, but nearest idle AGV vehicle its task to be done of distance is relatively more sometimes, need future very
Long a period of time could execute, or all task completion time points of arrangement are far apart from the now time, so needing
AGV Priority Queues can be distributed according to waiting time cost to generate one in algorithm, the vehicle for coming queue foremost is to execute
The time most short person of task starting point to be allocated is reached in complete own task queue after all tasks, last Auto-matching is minimum
The AGV vehicle of time delay cost.
Traffic rules are provided as follows to warehouse logistics workshop AGV vehicle in the S400 are as follows:
S401, AGV advance in storage workshop according to the magnetic stripe or tape that are laid in advance, it is specified can be between delivery nodes
Load cargo traveling；
S402, when by time window arrangement predict generation vertically meet conflict when, in strict accordance with task priority arrange
Sequence, the vehicle for carrying high-priority task preferentially pass through crossing, same priority according to task at the beginning of parameter btkTo arrange
Sequence, parameter value time earlier one preferentially pass through crossing；
S403, AGV vehicle remain a constant speed traveling when workshop is advanced, and have fixed turning time and AGV can only be born every time
A task is carried, is not influenced by cargo is how many；
If S404, a1 node need AGV transport three times to f1 node, then three carryings should be arranged in system manager
Task, may distribute to the same vehicle may also distribute to different vehicle；
S405, when encountering idle companion AGV during the vehicle transport of loading commissions, the position for investigating vehicle is planned again
Route.
In this implementation S405 mode, if the free vehicle position in workshop is not the unloading point position of task, it should set
Setting this stage of route weight is infinity, again programme path；If the free vehicle in workshop is exactly that other vehicle loads are appointed
The unloading point of business, then this free time mono- simple task of AGV should be assigned, the starting point of task is the unloading point, and terminating point is
Why a nearest node nearby, select this mode to handle idle AGV, is because idle AGV is removed every time
Region and having time cost are carried, and the probability of vehicle idle is very low in busy transport workshop, free vehicle is just
Fortunately this probability of the unloading point of other vehicle load tasks is lower, in order to make to store in a warehouse workshop transport total time cost desired value more
It is small, that is, this strategy is chosen to handle temporary standby AGV.
In the present embodiment, main feature is as follows:
The present invention is different from traditional paths planning method, creates the priority query of task first, according to storage task
Pressing degree, dynamic priority dynamic allocation are carried out to storage task, can effectively avoid task hungry and deadlock, and utilize A-
Star algorithm is heuristically that searching route obtains interim shortest path, counting of carriers access storage node to multiple AGV respectively
Time, route is reset by dynamically being accurately calculated and being locked to time window to avoid conflict, not only ensure that vehicle
AGV path cost can be made minimum under the conditions of collisionless, while improve the efficiency of task schedule and vehicle scheduling, to mention
The high conevying efficiency of whole system.
It is obvious to a person skilled in the art that invention is not limited to the details of the above exemplary embodiments, Er Qie
In the case where without departing substantially from spirit or essential attributes of the invention, the present invention can be realized in other specific forms.Therefore, no matter
From the point of view of which point, the present embodiments are to be considered as illustrative and not restrictive, and the scope of the present invention is by appended power
Benefit requires rather than above description limits, it is intended that all by what is fallen within the meaning and scope of the equivalent elements of the claims
Variation is included within the present invention.Any reference signs in the claims should not be construed as limiting the involved claims.
Claims (8)
1. a kind of intelligent storage route planning method based on more AGV, characterized by the following steps:
S100, tasks clear distribute rule, and priority when generating to storage task divides, for each task of storing in a warehouse
The sequence of vehicle match degree is carried out, the storage task of each vehicle to be allocated corresponds to an available vehicle platoon, while these
Vehicle itself also has the queue for having distributed pending task；
S200, dynamic priority adjusting carry out dynamic priority adjusting to the task of the AGV load in transport, when according to overlapping
Between window and topological map then the time window of each node is locked to determine which kind of node conflict will occur；
S300, optimum path planning are heuristically multiple AGV points using A-Star algorithm for practical workshop traffic condition
Other searching route obtains interim shortest path, the time of counting of carriers access storage node, crosses and dynamically carries out to time window
It accurately calculates and locks to reset route to avoid conflict, and respectively to predictable vertically meet conflict and opposite conflict of meeting
Countermeasure research is carried out, the shortest path of Lothrus apterus is eventually found；
S400, Conflict Strategies are predictably avoided, is asked for more AGV traffic being likely encountered in the transportational process in storage workshop
Topic has done the detection and processing of traffic rules formulation and unpredictable conflict.
2. a kind of intelligent storage route planning method based on more AGV according to claim 1, it is characterised in that: described
Time window algorithm concrete operations in S200 are as follows:
S201, in directed connection network G=(V, E), it is assumed that there is x vehicle to participate in task execution, then the AGV collection of task is combined into A
={ a1, a2, a3... ax}；
S202, if the point set of task and the set of terminal are denoted as S and D respectively, andThen automatic guided vehicle institute
It can be defined as by the time window of node；
In formulaIndicate that automatic guided vehicle retains the time of node i,Indicate the time of automatic guided vehicle release node i, i
∈V；
S203 is converted into AGV and transports along each section of curvilinear path by AGV vehicle along the time shortest optimization of entire path locus
Move required time hiSubsection optimization, total time fragment bit certain time interval [t1,t2], [t2,t3]…[tn-2,
tn-1], [tn-1,tn], then the length of adjacent two periods node are as follows:
hi=ti-1-ti(i=1,2,3 ... n-1)
Wherein, tiAt the time of moving to i point for AGV.
3. a kind of intelligent storage route planning method based on more AGV according to claim 2, it is characterised in that: described
In S203, the length of adjacent two periods node is optimized as follows:
S2031, the solution of optimal path is first encoded into chromosome required for genetic algorithm；
S2032, initialization of population, and in hiA certain number of individuals are randomly generated in time interval；
S2033, for each of population individual, adaptive value size are as follows:
Wherein,It is the maximum value that can be taken in its given range；
S2034, selection, duplication, enabling fitness value individual in population is fi, the probability of selected population are as follows:
S2035, intersection or genetic recombination, crossover operator remember a using the form that counts1, a2For two Geju City individual, intersected by counting
Operator operation, generates new individual a '1, a '2, generate the calculation formula of new individual are as follows:
Wherein,Indicate akBound, g is current to select generation number.
4. a kind of intelligent storage route planning method based on more AGV according to claim 1, it is characterised in that: described
AGV optimum path planning algorithms in S300 are as follows:
S301, task definition is carried into a storage are as follows:
carryk(t)={ PQk(t), btk, Sk, Dk, LBk(t), }
Wherein PQkIndicate the real-time priority of k-th of task, parameter is bigger, and priority is lower；btkIndicate the beginning of k-th of task
Time, SkAnd DkRespectively indicate the gatehead and unloading point of k-th of task, and Sk∈V、Dk∈V；The parameter LB of enumeration typek(t)
Indicate the carrying state of k-th of task；
S302, for LBk(t) carrying state distributes different priority to task, respectively high-priority queue, in it is preferential
Grade queue and Low Priority Queuing.
5. a kind of intelligent storage route planning method based on more AGV according to claim 1, it is characterised in that: described
Calculating realizing route ruleization of the S300 also according to the priority distribution and time window of task.
6. a kind of intelligent storage route planning method based on more AGV according to claim 5, it is characterised in that: described
Path planning step in S300 are as follows:
S3021, warehouse logistics parameter is inputted according to master system administrator, task is divided into priority, is inserted into priority team
Column, and initialize multiple transport task carryk(t)；
S3022, vehicle scheduling is carried out according to the priority orders of task: according to the most short principle of task waiting time to can load
AGV is ranked up, and is selected head of the queue AGV to execute task, is inserted into the pending queue of this AGV；
S3023, heuristic search is carried out to path with A-Star algorithm, obtains interim most short driving path；
S3024, counting of carriers reach the time t of each path nodei, then calculate the time that automatic guided vehicle retains node iWith the time of automatic guided vehicle release node i
S3025, initialization node time window ωk, make if there is task p and qIllustrate node time window because
Not yet lock and overlapping phenomenon occur, i.e., some retention time window in there are other vehicles
S3026, according to overlapping time window and topological map, to determine which kind of node conflict will occur, then to each node
Time window lock；
S3027, executable task (specified AGV, specify route) is generated, after AGV has executed the task free time, since the AGV may
As the obstacle of other tasks, so preferentially to distribute the vehicle, repeats S3021 and administrator's dynamic is waited to distribute new need
It asks.
7. a kind of intelligent storage route planning method based on more AGV according to claim 1, it is characterised in that: described
Traffic rules are provided as follows to warehouse logistics workshop AGV vehicle in S400 are as follows:
S401, AGV advance in storage workshop according to the magnetic stripe or tape being laid in advance, can load between delivery nodes in specified
Cargo traveling；
S402, when by time window arrangement predict generation vertically meet conflict when, in strict accordance with the priority ranking of task, take
Vehicle with high-priority task preferentially passes through crossing, same priority according to task at the beginning of parameter btkIt sorts, joins
Numerical value time earlier one preferentially passes through crossing；
S403, AGV vehicle remain a constant speed traveling when workshop is advanced, and have fixed turning time and AGV can only load one every time
A task is not influenced by cargo is how many；
If S404, a1 node need AGV transport three times to f1 node, appoint then three carryings should be arranged in system manager
Business, may distribute to the same vehicle may also distribute to different vehicle；
S405, when encountering idle companion AGV during the vehicle transport of loading commissions, road is planned in the position for investigating vehicle again
Line.
8. a kind of intelligent storage route planning system for claim 1 the method, it is characterised in that: including host computer system
System, Processing in Barcode Recognizing System and piece-supply system；
The master system includes management map module, monitoring display module, path planning module, AGV control module, scheduling
Module, data management module and communication module, the signal end and Processing in Barcode Recognizing System of the master system interconnect；
The Processing in Barcode Recognizing System is responsible for acquiring the image of object, and obtains bar code information after handling by image processor, most
Master system is sent to by communication module afterwards；
The piece-supply system is responsible for AGV vehicle and carries out piece supplying；
The data management module is responsible for system data being persisted to database.
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