CN110146088A - Indoor positioning air navigation aid and navigation model in a kind of intelligent warehouse management system - Google Patents

Indoor positioning air navigation aid and navigation model in a kind of intelligent warehouse management system Download PDF

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Publication number
CN110146088A
CN110146088A CN201910523313.8A CN201910523313A CN110146088A CN 110146088 A CN110146088 A CN 110146088A CN 201910523313 A CN201910523313 A CN 201910523313A CN 110146088 A CN110146088 A CN 110146088A
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vehicle
cargo
picking
path
intelligent
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CN110146088B (en
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徐磊
王子泰
任远
张红伟
方红雨
李晓辉
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Anhui Nongdao Intelligent Technology Co ltd
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安徽大学
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations
    • G01C21/206—Instruments for performing navigational calculations specially adapted for indoor navigation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
    • G01S15/02—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems using reflection of acoustic waves
    • G01S15/06—Systems determining the position data of a target
    • G01S15/08—Systems for measuring distance only
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
    • G01S15/88—Sonar systems specially adapted for specific applications

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Acoustics & Sound (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Automation & Control Theory (AREA)

Abstract

The invention discloses the indoor positioning air navigation aid and navigation model in a kind of intelligent warehouse management system, which includes: to acquire the vacancy information of the volume information of cargo, the location information of intelligent vehicle, warehouse；The intelligent vehicle being in idle condition is judged whether there is, is, cargo is judged whether there is and needs to store, is then determining target area and shelf, determines inventory-vehicle；It plans stock path, and inventory-vehicle is driven to be advanced according to path；Judge whether inventory-vehicle reaches stock point, be, target goods are stored in stock point；Judge whether to need to take out cargo, be to judge cargo whether in current region, is then using inventory-vehicle as picking vehicle.It plans picking path, and drives picking vehicle according to picking path to picking point；It plans shipment path, drives picking vehicle to output port.The present invention improves the utilization rate of storage, can reduce the access time of intelligent vehicle, improves storage efficiency, convenient to navigate to intelligent vehicle in storage.

Description

Indoor positioning air navigation aid and navigation model in a kind of intelligent warehouse management system
Technical field
The present invention relates to a kind of indoor positioning air navigation aid of Warehouse Management System technical field more particularly to a kind of intelligence Indoor positioning air navigation aid in Warehouse Management System further relates to the indoor positioning navigation mould in a kind of intelligent warehouse management system Type.
Background technique
In tradition storage work, manpower has inborn disadvantage in terms of transportation management of storing in a warehouse, and operator's labour is strong Spend inevitably will appear greatly it is tired out so as to cause sorting, check error, meanwhile, have in some dark, low temperature, pollution and storing easy Under the storage environment with security risk for firing explosives, the safety of field operation is difficult to be protected.Therefore, Intelligent standing Body Warehouse Management System has a multiple functions such as information processing, system control, system monitoring and system administration, collection information flow and Logistics is the important component of modern enterprise logistics and Information Flow Management in one.But existing Warehouse Management System In, the storage utilization rate and storage efficiency when multipoint access are all relatively low, are unfavorable for navigating to intelligent vehicle in storage.
Summary of the invention
Problem in view of the prior art, the present invention provide the indoor positioning air navigation aid in a kind of intelligent warehouse management system And navigation model, the storage utilization rate and efficiency of storing in a warehouse when solving multipoint access in existing Warehouse Management System are all relatively low The problem of.
The present invention is implemented with the following technical solutions: the indoor positioning air navigation aid in a kind of intelligent warehouse management system, It is used to navigate to the intelligent vehicle of access cargo in warehouse comprising following steps:
Step S1 acquires the vacancy letter in the volume information of the cargo, the location information of the intelligent vehicle, the warehouse Breath；
Step S2 judges whether there is the intelligent vehicle being in idle condition；Wherein, the intelligent vehicle being in idle condition is defined For idle intelligent vehicle；
If it exists when at least one idle intelligent vehicle, step S3 is executed, judges the inlet in the warehouse with the presence or absence of goods Object needs to store；
If the inlet needs to store there are at least one target goods one, step S4 is executed, according to the vacancy Information and the volume information determine target area and target shelf that the target goods one are gone to, and determine a sky Not busy intelligent vehicle is as inventory-vehicle；
Step S5 plans the inventory-vehicle from the inlet to the target area and the multiple target shelf Stock path, and the inventory-vehicle is driven to be advanced according to the stock path；
Step S6, judges whether the inventory-vehicle reaches the stock point；
When the inventory-vehicle reaches the stock point, step S7 is executed, the target goods one are sequentially stored in The stock point；
Step S8 judges whether to need to take out cargo in the warehouse；
When if desired taking out multiple target goods two in the warehouse, step S9 is executed, judges the target goods two Whether in the current region where the inventory-vehicle；
If the target goods two are in the current region, using the inventory-vehicle as picking vehicle；
If the target goods two are not in the current region, step S10 is executed, is judged whether there is in the free time The intelligent vehicle of state；When the intelligent vehicle being in idle condition if it exists, step S11 is executed, according to the location information of picking point, really A fixed idle intelligent vehicle is the picking vehicle；
Step S12 plans that the picking vehicle from its current location to the picking path of the picking point, and drives described Picking vehicle marches to the picking point according to the picking path, to obtain the target goods two；
Step S13 plans that the picking vehicle from the picking point to the shipment path of output port, drives the picking vehicle The output port is marched to according to the shipment path；
Step S14, judges whether the picking vehicle reaches the output port；
When the picking vehicle reaches the output port, step S15 is executed, it will be the multiple in the picking vehicle Target goods two take out, and the picking vehicle is made to be back to the inlet；
If do not need to take out cargo in the warehouse, execute step S16, make the inventory-vehicle be back to it is described enter Hatch Opening；
Step S17, the state for updating the inventory-vehicle or the picking vehicle that are located at the inlet is idle shape State.
As a further improvement of the foregoing solution, multiple rectangular areas are arranged in the warehouse, are arranged in each rectangular area In a plurality of road of mesh arrangement, adjacent two road meet at a node；The quantity of the intelligent vehicle be it is multiple, definition is every A intelligent vehicle is an ant, and multiple intelligent vehicles constitute an ant colony；The planning side in the stock path or the picking path Method determines fitness function the following steps are included: step S501, initialization population, coding and genetic variance；Step S502, Individual in parent group is ranked up according to fitness size, the probability for selecting the biggish individual of fitness is greater than fitness The probability that lesser individual is selected；Step S503 judges whether to need to carry out mutation operation to the ant colony；It is needing to institute When stating ant colony progress mutation operation, step S504 is executed, the ant colony is carried out to reverse variation and to new individual and parent individuality It is selected according to fitness size；When not needing to carry out mutation operation to the ant colony or complete step S504, step is executed Rapid S505 judges whether to need to carry out crossover operation to the ant colony；When needing to carry out crossover operation to the ant colony, execute Step S506 carries out crossover operation to the ant colony, and selects new individual and parent individuality according to fitness size；? When not needing to carry out the ant colony crossover operation or complete step S506, step S507 is executed, judges whether the ant colony is full The preset fusion conditions of foot；When the ant colony meets the fusion conditions, step S508 is executed, will be obtained by genetic algorithm The initial information element Distribution Value that is converted on the path of the ant colony of solution and limit routing information element range；The ant colony not When meeting the fusion conditions, step S502 is executed.
Further, the planing method is further comprising the steps of: step S509, initializes what the ant colony needed to be traversed for Interstitial content, ant number, cycle-index and pheromones volatilization factor, are placed on each node for the ant at random, clearly The taboo list of the empty ant colony；Step S510, judges whether the population scale of the ant colony is not less than maximum cycle；Institute When stating the loop iteration number of population not less than the cycle-index, step S511 is executed, exports search result；In the population Loop iteration number when being less than the cycle-index, execute step S512, every ant is public according to following state movement rule Formula selects next node:
Wherein, τijIt (t) is pheromones track, tablekFor the taboo list, allowedkFor candidate collection；λjIt is described The urgency level of cargo, mjFor cargo total weight, d required for node jijFor the distance of present node to next node；Step Rapid S513, after kth ant one week node of traversal returns to its starting point, local updating is located at kth ant by path Pheromones；Step S512 and step S513 are repeated, until all ants traverse one week node and return to its starting point；Step Rapid S514, according to fitness maximum fmaxWith minimum fminUpdate current iteration optimal path length and the pheromones it includes section And current iteration worst path and the pheromones it includes section；Step S515, the position reset by the m ant are Point empties taboo list tablek, and judge whether to need the value of adjustment information volatilization factor；Needing adjustment information volatilization factor Value when, execute step S516, according to after the value of following formula adjustment information volatilization factor ρ execute step S510:
When not needing the value of adjustment information volatilization factor, directly execution step S510.
Still further, the intelligent vehicle stock or picking used time most in short-term, have:
Wherein, f is the fitness of the fitness function；
When the intelligent vehicle stock energy consumption is minimum, have:
When the intelligent vehicle picking energy consumption is minimum, have:
When the intelligent vehicle stock need to consider expense, used time and cargo emergency, have:
When the intelligent vehicle picking need to consider expense, used time and cargo emergency, have:
Still further, in step S508, the restriction formula of routing information element range are as follows:
In step S513, also the range of pheromone is defined, and limits formula are as follows:
τ(r,s)←ρ·τ(r,s)+(1-ρ)·Δτ(r,s)
In step S514, also the range of pheromone is updated；Wherein,
(1) formula of Pheromone update is carried out to optimal path length are as follows:
τ(r,s)←τ(r,s)+δ·Δτ(r,s)
In formula, LgbFor the path length that current iteration is optimal；
(2) formula of Pheromone update is carried out to worst path length are as follows:
In formula, (r, s) is to belong to worst path and be not belonging to the side of optimal path, LworstThe worst path of diameter is sought for this Length, LbestSeek the optimal path length of diameter for this, ε is every time after circulation terminates, to worst path and to be not belonging to optimal road The attenuation coefficient of the pheromones on side on diameter, and value range is [0,1].
As a further improvement of the foregoing solution, the indoor positioning air navigation aid is further comprising the steps of:
When the inventory-vehicle does not reach the stock point, step S18 is executed, according to the real-time position of the inventory-vehicle Set with the stock path, adjust the operating status of the inventory-vehicle in real time；
When the picking vehicle does not reach the output port, step S19 is executed, according to the real-time position of the picking vehicle Set with the picking path, adjust the operating status of the picking vehicle in real time.
As a further improvement of the foregoing solution, the volume information of the cargo acquisition method the following steps are included:
Step S101, measurement are located at the length L of the cargo of the inlet；On the length direction of the cargo, pass through A pair of cargo of one ultrasonic wave transmitting terminal carries out ultrasonic distance measurement and starts timing；Judge one phase of ultrasonic wave transmitting terminal Whether adjacent ultrasonic distance measurement poor one twice is greater than a threshold value one；It is greater than the threshold value one in the ultrasonic distance measurement poor one When, terminate timing and obtains sweep time；Calculate the cargo rate travel in the longitudinal direction and the sweep time Product, obtain the length L of the cargo；
Step S102, measurement are located at the width W of the cargo of the inlet；By rotating a ultrasonic wave transmitting terminal two, A fan-shaped detection faces one are generated in the width direction of the cargo to carry out ultrasonic distance measurement to the cargo；Described in judgement Whether the adjacent ultrasonic distance measurement twice of ultrasonic wave transmitting terminal two poor two is greater than a threshold value two；In the ultrasonic distance measurement poor two When greater than the threshold value two, the boundary that the cargo is arrived in the scanning of ultrasonic wave transmitting terminal two is defined；Calculate the ultrasonic wave hair It penetrates end two and scans the time difference Δ t's, ultrasonic wave transmitting terminal two and two boundary for arriving two opposite boundaries of the cargo Interval SαAnd Sβ；Calculate the angle theta of the fan-shaped detection faces oneL；Wherein, θL=v × Δ t, v is the ultrasonic wave transmitting terminal two Angular velocity of rotation；Calculate the width W of the cargo；Wherein,
Step S103, measurement are located at the height H of the cargo of the inlet；By rotating a ultrasonic wave transmitting terminal three, A fan-shaped detection faces two are generated in the short transverse of the cargo to carry out ultrasonic distance measurement to the cargo；Described in judgement Whether the ultrasonic distance measurement poor three of ultrasonic wave transmitting terminal three adjacent twice is greater than a threshold value three；In the ultrasonic wave transmitting terminal three Greater than the threshold value three, the boundary that the cargo is arrived in the scanning of ultrasonic wave transmitting terminal three is defined；Calculate the ultrasonic wave transmitting The interval S at end three and the boundary of the cargoψ, the ultrasonic wave transmitting terminal three and the cargo vertical range Sζ；Described in calculating The height H of cargo:Wherein, H2Height for the ultrasonic wave transmitting terminal three apart from ground, H1 Height for the cargo apart from ground；
Step S104 calculates volume V:V=L × W × H of the cargo.
As a further improvement of the foregoing solution, multiple rectangular areas are arranged in the warehouse, are arranged in each rectangular area In a plurality of road of mesh arrangement, adjacent two road meet at a node；The acquisition side of the location information of the intelligent vehicle Method places two locating modules at the diagonal line both ends of the rectangular area the following steps are included: step S105 respectively, described fixed Position module is communicated wirelessly with the intelligent vehicle；Step S106, when according to the communication of the intelligent vehicle and two locating modules Between, calculate the intelligent vehicle and two locating module distance S1And S2；Step S107, according to the coordinate of two locating modules, meter Calculate the coordinate of two pre- punctuates；Wherein, each pre- punctuate is respectively S at a distance from two locating modules1And S2；Step S108, Judge whether two pre- punctuates are located on the travel path of the intelligent vehicle respectively；A pre- punctuate is located at the traveling wherein On path and when the pre- punctuate of another one is not located on the travel path, determine the pre- punctuate on the travel path for institute State the measurement position of intelligent vehicle；It is previous according to the intelligent vehicle when two pre- punctuates are respectively positioned on the travel path Measurement position does not appear in the pre- punctuate on the travel path once before selecting as the measurement position of the intelligent vehicle.
As a further improvement of the foregoing solution, the acquisition method of the vacancy information in the warehouse is the following steps are included: step Rapid S109 places two wireless moulds corresponding with two locating modules respectively at the diagonal line both ends of the rectangular area respectively Block；Step S110 sends to the wireless module of each rectangular area and wakes up when the intelligent vehicle receives picking and stock is instructed Information acquires the location information of the intelligent vehicle to wake up the wireless module by corresponding locating module；Step S111, When the intelligent vehicle leaves the rectangular area, the wireless module suspend mode is driven.
The present invention also provides the indoor positioning navigation models in a kind of intelligent warehouse management system, and it is right in warehouse to be used for The intelligent vehicle of access cargo navigates comprising data acquisition subsystem, data transmission sub-system and data processing subsystem System, wherein the data acquisition subsystem is for acquiring the volume information of the cargo, the location information of the intelligent vehicle, institute State the vacancy information in warehouse；The data transmission sub-system is used for the information acquired according to the data acquisition subsystem, in real time Update the location information, the volume information and the vacancy information；The data process subsystem is for executing above-mentioned The step S2- step S17 of the meaning air navigation aid.
Indoor positioning air navigation aid and navigation model in intelligent warehouse management system of the invention, the air navigation aid pass through The volume information of cargo is acquired, and determines region and the shelf of cargo storage according to the volume of cargo, in stock according to goods Object product size and vacancy information brush select multiple vacancy shelf as target shelf, and when picking can filter out multiple picking points works For target shelf, and determine most short transport path, the cooperation two-point locating intelligent vehicle that is used to navigate realizes the more of indoor storage environment Point accessing operation in order to improve the utilization rate of storage, while passing through the vacancy letter of the location information of acquisition intelligent vehicle and warehouse Breath, and then the idle intelligent vehicle close to cargo is selected to be accessed, it can reduce the access time of intelligent vehicle, to improve storage Efficiency, it is convenient to navigate to intelligent vehicle in storage.
When in the present invention, to the planning in stock path or picking path, first with the random search of genetic algorithm, entirely Office's convergence and rapidity generate the initial information element distribution of relevant issues.Then, the concurrency, just of ant group algorithm is made full use of Feedback mechanism and higher efficiency are solved.Finally merged, and fused algorithm is better than ant colony in time efficiency Algorithm is better than genetic algorithm on solution efficiency, forms a kind of time efficiency and heuritic approach that solution efficiency is taken into account.And And the present invention limits candidate collection, only can directly arrive intelligent vehicle in view of the track limitation of practical storage environment Candidate collection is added in several trunk nodes reached, and the improvement of candidate collection further improves the efficiency of solution.Meanwhile this Invention considers that the mechanism that ant group algorithm can effectively be avoided Premature Convergence with one kind is combined together, and will obtain optimality Can ant group algorithm improve precision, avoid search from stagnating, optimal solution enhanced to a greater extent, and to the progress of worst solution Weaken so that belong to optimal path while belong to worst path while between pheromones difference increase, make search more concentrate Near optimal solution, the automatic adjusument mechanism of pheromones volatilization factor is in addition introduced, using minimal path energy consumption as main target, Comprehensively consider distribution time, solves the dispatching road that energy consumption is minimum in the case where considering goods part urgency level and the time is shorter Diameter.
In existing Warehouse Management System for cargo size acquisition mainly using image procossing method, pass through by The image information of acquisition is transferred to processor and is handled, and information content is bigger, higher cost and can not be to the ruler of cargo The detection of little progress Mobile state, and present invention determine that transport the shape information of cargo and the location information of intelligent vehicle, pass through ultrasound Wave module dynamic scan measurement of cargo size is obtained the location information of intelligent vehicle by two-point locating, combines ultrasonic wave in this way Precision, the cost advantage of ranging, information content is smaller, and can reduce cost, while can also carry out to the size of cargo Dynamic detection.
Detailed description of the invention
Fig. 1 is the flow chart of the indoor positioning air navigation aid in the intelligent warehouse management system of the embodiment of the present invention 1；
Fig. 2 be Fig. 1 in air navigation aid applied by warehouse zoning plan；
Fig. 3 is the floor map of region 5. in Fig. 2；
Fig. 4 is the schematic diagram of the device of the air navigation aid acquisition volume information in Fig. 1；
Fig. 5 is that the ultrasonic sensor B of the device in Fig. 4 detects the schematic diagram of cargo；
Fig. 6 is the angle schematic diagram of the ultrasonic sensor B detection cargo in Fig. 5；
Fig. 7 is the front view of the device in Fig. 4；
Fig. 8 is the work flow diagram of the device in Fig. 4；
Fig. 9 be air navigation aid in Fig. 1 region 5. in the Position-Solving mathematical model figure of intelligent vehicle；
Figure 10 is the schematic diagram of the shelf in the warehouse in Fig. 2；
Figure 11 is a part of flow chart of the air navigation aid planning intelligent vehicle driving path in Fig. 1；
Figure 12 is another part flow chart of the air navigation aid planning intelligent vehicle driving path in Fig. 1；
Figure 13 is the system function of the indoor positioning navigation model in the intelligent warehouse management system of the embodiment of the present invention 1 Figure.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to the accompanying drawings and embodiments, right The present invention is further elaborated.It should be appreciated that described herein, specific examples are only used to explain the present invention, not For limiting the present invention.
Embodiment 1
Fig. 1, Fig. 2 and Fig. 3 are please referred to, the present invention provides the indoor positioning navigation in a kind of intelligent warehouse management system Method is used to navigate to the intelligent vehicle 7 of access cargo 5 in warehouse comprising these following step (step S1- steps Rapid S19), it may also include step S20 and step S21.In the present embodiment, multiple rectangular areas, Mei Geju is arranged in the warehouse Setting is in a plurality of road of mesh arrangement in shape region, and adjacent two road meet at a node.Meanwhile the intelligent vehicle 7 Quantity be it is multiple, defining each intelligent vehicle 7 in this way is an ant, and multiple intelligent vehicles 7 constitute an ant colony.
Step S1 acquires the vacancy letter in the volume information of the cargo 5, the location information of the intelligent vehicle 7, the warehouse Breath.
One, in the present embodiment, the acquisition method of the volume information of the cargo 5 includes the following steps (step S101- step Rapid S104).
Step S101, measurement are located at the length L of the cargo 5 of the inlet: 1. on the length direction of the cargo 5, Ultrasonic distance measurement is carried out by a pair of cargo 5 of a ultrasonic wave transmitting terminal and starts timing；2. judging the ultrasonic wave hair Penetrate whether end one adjacent ultrasonic distance measurement twice poor one is greater than a threshold value one；3. being greater than institute in the ultrasonic distance measurement poor one It states threshold value for the moment, terminate timing and obtains sweep time；4. calculating rate travel in the longitudinal direction and the institute of the cargo 5 The product for stating sweep time obtains the length L of the cargo 5；That is L=vTT, vTFor the rate travel of cargo 5, T is when scanning Between.
Referring to Fig. 4, specifically, ultrasonic sensor 1 (ultrasonic sensor A) fixed placement is in 5 form of cargo On testing stand 2, it is in constant testing state, judges whether adjacent ranging difference twice is less than threshold value m1.If ranging difference is less than threshold Value m1, then it is assumed that do not scan 5 boundary FH of cargo, i.e. cargo 5 is transmitted to object Morphology observation frame not yet；If ranging difference is big In threshold value m1, then it is assumed that be scanning to 5 boundary FH of cargo, i.e. cargo 5 reaches testing stand.
Cargo 5 reaches testing stand, and opening timing device counts.It carries out ranging always during counting, judges adjacent two Whether secondary ranging difference is less than threshold value m1.If ranging difference is less than threshold value m1, then it is assumed that do not scan 5 boundary EG of cargo, i.e. goods Object 5 leaves testing stand not yet, and timer continues to count；If ranging difference is greater than threshold value m1, then it is assumed that scanned 5 side of cargo Boundary EG, i.e. cargo 5 have been moved off shelf, read timer current value, obtain sweep time T and initialization timer counts. 6 constant rate of intelligent vehicle is v againT, then the length of available cargo 5.
Step S102, measurement are located at the width W of the cargo 5 of the inlet: 1. passing through one ultrasonic wave transmitting terminal of rotation Two, a fan-shaped detection faces one are generated in the width direction of the cargo 5 to carry out ultrasonic distance measurement to the cargo 5；② Judge whether the adjacent ultrasonic distance measurement twice of ultrasonic wave transmitting terminal two poor two is greater than a threshold value two；3. in the ultrasound When wave ranging poor two is greater than the threshold value two, the boundary that the cargo 5 is arrived in the scanning of ultrasonic wave transmitting terminal two is defined；Calculate institute State the scanning of ultrasonic wave transmitting terminal two to the time difference Δ t on opposite two boundaries of the cargo 5, the ultrasonic wave transmitting terminal two with The interval S on two boundariesαAnd Sβ；4. calculating the angle theta of the fan-shaped detection faces oneL；Wherein, θL=v × Δ t, v is described super The angular velocity of rotation of sound wave transmitting terminal two；5. calculating the width W of the cargo 5；Wherein,
Referring to Fig. 5, specifically, having detected cargo 5 to arriving in ultrasonic sensor 23 (ultrasonic sensor B) When, just starting the revolving speed that ultrasonic sensor 23 is scanned steering engine with ultrasonic sensor 34 will affect scanning acquisition object The length of time and precision of size.Under normal conditions, the revolving speed of steering engine is smaller, scanning obtain size time is longer, precision It is higher；The revolving speed of steering engine is bigger, and the time that scanning obtains size is shorter, but in contrast precision will receive influence.Cause This, will comprehensively consider length of time and required precision, it is assumed that steering engine revolving speed is v, and actual use only needs that steering engine is allowed to rotate 180 ° (size in one face of cube is scanned within the scope of 180 °) turns round 180 ° to initial bit after rotating 180 ° to a direction again It sets.Object boundary is continued to scan on during revolution, is equivalent to and has been carried out twice sweep, it can will calculated object edge twice The long length for being averaged one side as present scan.
When ultrasonic sensor 23 combines the horizontal revolving stage rotation of steering engine control, ultrasonic sensor 23 is located always In working condition, real-time detection encounters two-way time t when barrier, it is assumed that speed immobilizes as c, then can count as the following formula Calculate ultrasonic wave module and barrier distance S:
By taking the control of ultrasonic sensor 23 as an example, the scanning effect picture of actual use is as shown in Figure 5.Supersonic sensing Device 23 goes to the direction og from the direction oa counterclockwise under the action of the vertical rotation axis B that steering engine controls, and corotation is 180 ° dynamic. After system initialization, when starting turning when ultrasonic sensor A has detected that cargo 5 arrives, starting timer is used to count The time t of rotation can calculate the angle, θ turned under uniform rotation according to the time；In the course of rotation, ultrasonic wave mould Block calculates ultrasonic wave module and barrier distance s always in transmitting and received ultrasonic signal in real time.
The distance of initial position oa orientation measurement is calculated as S1, the data measured next time are calculated as S2, and so on, it counts every time Calculate the difference of this measurement distance with last measurement distance.During from the scanning of the direction oa to the direction oc, measurement Adjacent distance difference twice is less than threshold value m and (is set according to actual conditions, with placement location and warehouse of the intelligent vehicle 7 in warehouse Size is related), it is believed that do not scan 5 boundary AD of cargo.Assuming that the α times calculated distance is Sα, α -1 times calculated Distance is calculated as Sα-1, α -2 times calculated distance is calculated as Sα-2, if there is relationship as follows:
Then illustrate α -1 times without scanning to boundary AD, the α times scanning has arrived boundary AD, recorded timer at this time Time tαWith two-way time tα1, tα1The distance S that can be calculated at this time is brought in above formula intoα, and by turn over angle, θ and revolving speed v and Relationship between time t: θ=vt
Know the angle, θ turned over when boundary is arrived in the α times scanningα=vtα。
Similarly, during going to the direction oc and the direction oe, it is consistently less than threshold value m per range difference adjacent twice, is recognized For the boundary BC for scanning out the face ABCD not yet.Assuming that the β times calculated distance is Sβ, β -1 times calculated distance is calculated as Sβ-1, β -2 times calculated distance is calculated as Sβ-2, if there is relationship shown in following formula:
Then illustrate β -1 times without scanning to boundary B C, the β times scanning has arrived boundary B C, recorded timer at this time Time tβWith two-way time tβ1, tβ1Distance S at this time can be calculated after bringing intoβ, and by turning over angle, θ and revolving speed v and time t Between relationship, it is known that when the β times scanning is to boundary, the angle, θ that turns overβ=vtβ, which sweeps on the face ABCD It is X-Y in Fig. 5 that the path crossed is practical.
By being analyzed above it can be concluded that four parameter θsα、θβ、Sα、Sβ, have it is following shown in relational expression, tα1With tβ1It is respectively Two-way time when scanning to two boundaries, tαWith tβThe time on two boundaries is respectively turned to, c is the propagation speed of ultrasonic wave Degree, v are the revolving speed of steering engine, can be had:
Known four parameter θs are converted by practical problemα、θβ、Sα、Sβ, the problem of seeking a side length W of a triangle, As shown in the mathematical model figure of Fig. 6.The length S on two sides of known triangleα、SβWith corner dimension θL, can be fixed using cosine Manage acquire angle pair side side length W, which is the width of the cargo 5, be shown below:
Step S103, measurement are located at the height H of the cargo 5 of the inlet: 1. passing through one ultrasonic wave transmitting terminal of rotation Three, a fan-shaped detection faces two are generated in the short transverse of the cargo 5 to carry out ultrasonic distance measurement to the cargo 5；② Judge whether the ultrasonic distance measurement poor three of the ultrasonic wave transmitting terminal three adjacent twice is greater than a threshold value three；3. in the ultrasound Wave transmitting terminal three is greater than the threshold value three, defines the boundary that the cargo 5 is arrived in the scanning of ultrasonic wave transmitting terminal three；Described in calculating The interval S on the boundary of ultrasonic wave transmitting terminal three and the cargo 5ψ, the ultrasonic wave transmitting terminal three it is vertical with the cargo 5 away from From Sζ；4. calculating the height H of the cargo 5:Wherein, H2For the ultrasonic wave transmitting terminal three away from Height from the ground, H1Height for the cargo 5 apart from ground.
Referring to Fig. 7, specifically, elevation information can hang down with ultrasonic sensor 34 (ultrasonic sensor C) Straight shaft gets to scan the right side of object, practical only to need to scan the size of top half (from horizontal position to 90 ° of models Enclose), lower half portion can be equivalent to the specific installation site of ultrasonic sensor 34 and the difference in height of intelligent vehicle 6, and two parts are high Degree is added the height H that cargo 5 can be obtained, and scanning effect picture is as shown in Figure 7.
Only when ultrasonic sensor A has detected that cargo 5 reaches, just starting sensor C is scanned.Wherein SζFor Initial position ultrasonic sensor 34 is emitted to received round-trip distance, SψTo scan the round-trip distance for arriving boundary position, tζFor The two-way time of initial position, tψTo reach two-way time when boundary B C.HhWith SψAnd SζCollectively constitute a right angle trigonometry Shape, practical problem are converted into the bevel edge and a right-angle side of known right angled triangle, can be in the hope of another straight using Pythagorean theorem The arm of angle, just like the relational expression of following formula:
Step S104 calculates volume V:V=L × W × H of the cargo 5.So far, the length and width of cargo 5 on intelligent vehicle 7, High dimension information is all got by sensor scanning, it is assumed that the volume of cargo 5 is calculated as V, unit cm3, then have following institute The relational expression shown: V=L × W × H (cm3)
The V that this relational expression calculates is the volume of cargo 5, facilitates back to be sent to according to different volumes different size of The realization of the function of counter layer.In addition, in the present embodiment, the course of work of ultrasonic sensor A, B, C are as shown in Figure 8.
Two, in the present embodiment, the acquisition method of the location information of intelligent vehicle 7 is the following steps are included: step S105, in institute Two locating modules are placed at the diagonal line both ends for stating rectangular area respectively, and the locating module and the intelligent vehicle 7 carry out wirelessly Communication；Step S106 calculates the intelligent vehicle 7 and two fixed according to the communication time of the intelligent vehicle 7 and two locating modules The distance S of position module1And S2；Step S107 calculates the coordinate of two pre- punctuates according to the coordinate of two locating modules；Its In, each pre- punctuate is respectively S at a distance from two locating modules1And S2；Whether step S108 judges two pre- punctuates respectively On the travel path of the intelligent vehicle 7；A pre- punctuate is located on the travel path wherein and another one are pre- When punctuate is not located on the travel path, determine that the pre- punctuate on the travel path is the measurement position of the intelligent vehicle 7； It is primary before selecting according to the previous measurement position of the intelligent vehicle 7 when two pre- punctuates are respectively positioned on the travel path The pre- punctuate not appeared on the travel path is the measurement position of the intelligent vehicle 7.
Consider the 7 position real-time monitoring of intelligent vehicle in complicated storage environment, storage region is divided.With wherein one A region 5. for, to illustrate that 7 location information acquisition method of intelligent vehicle, the storage environment schematic diagram of region 5. are as shown in Figure 3.Circle Circle represents main roads node, and shelf are placed sequentially on linear position.
Place 4463 wireless modules at two endpoints on the diagonal line 5. of storage region, i.e. in Fig. 9 X1(x1,y1) and X2(x2,y2), two 4463 wireless modules wake up after the signal for receiving the transmission of 7 end of intelligent vehicle and to intelligence Vehicle 7 sends signal.After 4463 wireless transport modules on intelligent vehicle 7 receive signal, each 4463 positioning label is recorded respectively The signal of sending reaches the time of intelligent vehicle 7.That is X1Issue the arrival time t of signal1, X2Position the arrival that label issues signal Time t2。
The constant airspeed v for the signal that each 4463 positioning label is sentx, then intelligent vehicle 7 is calculated separately out from two 4463 Position the distance of label.Assuming that intelligent vehicle 7 is from X1Distance be S1, intelligent vehicle 7 is from X2Distance be S2, between two labels Distance S3, 7 coordinate of intelligent vehicle be set as P (x, y) then and have following shown in relational expression.
Problem is converted into the coordinate of known three edge lengths and two of them point, can use two-point locating method, calculate The position of intelligent vehicle 7.7 position coordinates of intelligent vehicle found out according to two-point locating method are not unique, and there are two to meet condition Solution, such as the P in Fig. 91(x3,y3) and P2(x4,y4)。
In order to determine that 7 position of intelligent vehicle is the P in Fig. 91Or P2, need to combine the optimal path of the traveling of intelligent vehicle 7 To judge.Assuming that intelligent vehicle 7 is according to A → B in Fig. 9 → C → D route, if because of only P1Traveling of the point in intelligent vehicle 7 On path, then P1Point is the current location of intelligent vehicle 7；If P1And P2All on driving path, then last intelligent vehicle 7 is combined Measurement position judgement be located at P1Or P2。
Three, in the present embodiment, the acquisition method of the vacancy information in the warehouse is the following steps are included: step S109, Two wireless modules corresponding with two locating modules respectively are placed respectively in the diagonal line both ends of the rectangular area；Step S110 sends to the wireless module of each rectangular area when the intelligent vehicle 7 receives picking and stock is instructed and wakes up information, The location information of the intelligent vehicle 7 is acquired to wake up the wireless module by corresponding locating module；Step S111, described When intelligent vehicle 7 leaves the rectangular area, the wireless module suspend mode is driven.
Wherein, shelf are successively placed on the road of connection main node as shown in Figure 3 (straight line in figure), each shelf It is divided into three layers, is followed successively by large cargo 5, medium-sized cargo 5 and small freight 5, the schematic diagram of single shelf such as Figure 10 from top to bottom It is shown.For save the cost, it is not used to transmit vacancy information in every layer of upper wireless module of placing of each shelf, but will be each The prior logging data processing subsystem (server) of goods layer vacancy information of shelf, intelligent vehicle 7 is in the accessing operation for carrying out cargo 5 When, it is not required to upload all vacancy information, only need to timely update specific vacancy information to server.
When initial, the diagonal position in warehouse shown in Fig. 9 is respectively placed with 4463 wireless module (known to position), intelligent vehicle 7 can just send wake-up information, wake on wireless module only after receiving picking or stock instruction to the wireless module in the region Carry out location information acquisition and in time upload location information (with a distance from two positioning labels)；Intelligent vehicle 7 is leaving the region Dormancy instruction is sent to the wireless module in the region afterwards, so that wireless module suspend mode, to reduce power consumption.The shape information of cargo 5 After being obtained by the ultrasonic sensor group scanning on 5 Morphology observation frame of cargo, storehouse is sent to by the wireless module of affiliated MCU carry Library extension set, then upload server.
In the present embodiment, the number of intelligent vehicle 7 is i (i=1,2,3 ..., 8), state si, si=0 shows that trolley is Idle state, si=1 shows that trolley has task arrangement.For the same task, gives priority in arranging for and number minimum (highest priority) Idle trolley go to complete, after a trolley completion task, be updated to idle state, be added in queue queue, etc. To mission dispatching next time.
Step S2 judges whether there is the intelligent vehicle 7 being in idle condition；Wherein, the intelligence being in idle condition is defined Vehicle 7 is idle intelligent vehicle 7.
If it exists when at least one idle intelligent vehicle 7, step S3 is executed, judges the inlet in the warehouse with the presence or absence of goods Object 5 needs to store.
If the inlet needs to store there are at least one target goods one, step S4 is executed, according to the vacancy Information and the volume information determine target area and multiple target shelf that the target goods one are gone to, and determine one A free time intelligent vehicle 7 is used as inventory-vehicle.
Step S5 plans the inventory-vehicle from the inlet to the target area and the multiple target shelf Stock path, and the inventory-vehicle is driven to be advanced according to the stock path.
Step S6, judges whether the inventory-vehicle reaches the stock point.
When the inventory-vehicle reaches the stock point, step S7 is executed, the multiple target goods one are successively deposited Storage is in the stock point.
Step S8 judges whether to need to take out cargo 5 in the warehouse.
When if desired taking out target goods two in the warehouse, step S9 is executed, judges the multiple target goods two Whether in the current region where the inventory-vehicle.
If the target goods two are in the current region, using the inventory-vehicle as picking vehicle.
If the target goods two are not in the current region, step S10 is executed, is judged whether there is in the free time The intelligent vehicle 7 of state；When the intelligent vehicle 7 being in idle condition if it exists, step S11 is executed, according to the location information of picking point, Determine that an idle intelligent vehicle 7 is the picking vehicle.
Step S12 plans that the picking vehicle from its current location to the picking path of the picking point, and drives described Picking vehicle marches to the picking point according to the picking path, to obtain the target goods two.
In the present embodiment, it is also provided with step S20, which executes after step S12.Step S12 takes described in judgement Whether lorry reaches picking point, is to perform the next step (step S13), no to then follow the steps S21, according to the position of picking vehicle The relationship with picking path is set, the operating status of picking vehicle is adjusted.
Step S13 plans that the picking vehicle from the picking point to the shipment path of output port, drives the picking vehicle The output port is marched to according to the shipment path.
Step S14, judges whether the picking vehicle reaches the output port.
When the picking vehicle reaches the output port, step S15 is executed, by the target in the picking vehicle Cargo two takes out, and the picking vehicle is made to be back to the inlet.
If do not need to take out cargo 5 in the warehouse, step S16 is executed, is back to the inventory-vehicle described Inlet.
Step S17, the state for updating the inventory-vehicle or the picking vehicle that are located at the inlet is idle shape State.
When the inventory-vehicle does not reach the stock point, step S18 is executed, according to the real-time position of the inventory-vehicle Set with the stock path, adjust the operating status of the inventory-vehicle in real time.
When the picking vehicle does not reach the output port, step S19 is executed, according to the real-time position of the picking vehicle Set with the picking path, adjust the operating status of the picking vehicle in real time.
In existing Warehouse Management System, although carrying out optimum path planning using genetic algorithm has easy, quick, appearance The strong feature of mistake, but there is also following problems demands to solve:
(1) Parametric optimization problem of algorithm itself, it is difficult to which algorithm is improved according to actual running environment adjusting parameter Performance；(2) it is easily trapped into " precocity " convergence, it is difficult to avoid prematurely concentrating near extremely excellent solution in search, cause to be difficult to send out It now preferably solves, cannot ensure the accuracy travelled in actually transporting according to optimal path；(3) efficiency of algorithm is lower, reaches It is needed to be implemented for a long time to convergence, complexity is higher, is poorly suitable for the timeliness requirement of storage indoor positioning navigation；(4) Genetic algorithm merges problem with other optimization algorithms, it is difficult to improve algorithm performance with other algorithm fusions.
Although and its experimental performance has quality height, initial robust performance when simulated annealing being used to carry out path planning By force, the characteristics of general easy realization, but there is also following shortcomings:
(1) there are the contradictions between effect of optimization and calculating time, as long as theoretically calculating time long enough can guarantee Convergence with probability 1 is in globally optimal solution, but in actual use due to the limitation of calculating speed and time, it is difficult to guarantee to calculate knot Fruit is global optimum, and effect of optimization is not satisfactory；(2) it is difficult to determine whether to have reached equilibrium state at each temperature, The number of Metropolis process is not easy to control；(3) two kinds of annealing way in simulated annealing, T always according to giving before Fixed rule variation is not modified, and cannot carry out parameter adjustment according to the actual operation to improve algorithm performance.
Although on solving point-to-point optimum path problems be using traditional dijkstra's algorithm can find it is numerous Point-to-point optimal path in path, and the shortest path of source node any one node into path can be saved, but It is that there is certain limitations.It is only applicable to solve point-to-point shortest route problem, is not suitable for solving multiple nodes Shortest route problem is traversed, and is searched for traversal, requires to traverse again once in source node frequent switching, successively acquires two Shortest path between a node, but cannot be guaranteed that total path is most short in traversal, algorithm complexity is higher.
Based on above-mentioned these problems, the present embodiment is on planning stock path or picking path using high efficiency, high-precision The paths planning method of degree and lower complexity calculates the optimal driving path from starting point to target shelf for navigating Intelligent vehicle 7.The improved ant group algorithm of the present embodiment proposed adoption carries out multipoint access path planning, to calculate from departure place to mesh Ground optimal driving path.Ant group algorithm has the advantage that 1. ant group algorithm is that one kind combines distributed computing, positive and negative The algorithm of infeed mechanism and Greedy search, the ability with the very strong more excellent solution of search.Positive feedback can rapidly find more excellent Solution, distributed computing largely reduce appearance " precocity " convergent probability, and Greedy search helps searching for Acceptable solution is found out in journey in early days；2. ant group algorithm has very strong concurrency, each ant finds path simultaneously, Solution efficiency is higher；3. system has good expandability, by direct communication between individual but information can not be passed through Element is cooperated so that due to in system individual increase and increased system communication expense herein will be very small.With mould Quasi- annealing algorithm compares with genetic algorithm, the quality highest for the solution that ant group algorithm is found out and reaches iteration required for convergence Number is less, convergence rate faster, but there is also following some disadvantages for the algorithm: 1. due to the information on each side of early stage Plain difference is unobvious, although can be evolved towards optimal path by information exchange, when population size is larger, is difficult A preferable path is found out from a large amount of rambling paths in short time；Information positive feedback adjustment mechanism, so that shorter Information content on path is gradually increased, and the information content on preferable path can be made to be apparently higher than by long period of time Information content on other paths is the progress with this process, and difference is more and more obvious, to finally converge on preferable road Diameter.This process generally requires consuming longer time；2. the presence meeting of pheromones volatilization factor ρ (0 < ρ < 1) so that never by The information content on path searched is gradually less, so that the probability being searched is gradually reduced to nearly zero, reduces complete Office's search capability, is easy to miss optimal solution；ρ value is excessive will to will lead to a possibility that path being searched in the past is selected again Increase, influencing the randomness of algorithm and ability of searching optimum leads to " precocity ", influences the required precision of location navigation；ρ value is too small It will lead to convergence rate reduction, influence the timeliness requirement of location navigation.
For the above two o'clock disadvantage, the present embodiment proposes the following solution of meter:
(1) in order to improve solution efficiency, the implementation complexity of algorithm is reduced.
1. genetic algorithm (GA) is merged with ant group algorithm (ACA).Genetic algorithm has quick global search capability, But without often leading to redundancy iteration, solution efficiency is low using the feedback information in system；Although ant group algorithm is that can pass through letter The positive feedback mechanism of breath element makes the pheromones of shorter path be apparently higher than other paths, but initial stage pheromones are deficient, cause Algorithm speed is slow.
Consideration has complementary advantages, and has first with the random search, global convergence and rapidity of genetic algorithm The initial information element of pass problem is distributed.Then, make full use of concurrency, positive feedback mechanism and the higher efficiency of ant group algorithm into Row solves.Fused algorithm is better than ant group algorithm in time efficiency, is better than genetic algorithm on solution efficiency, forms one The heuritic approach that kind time efficiency and solution efficiency are taken into account.
2. the track in view of practical storage environment limits, server limits candidate collection, only by 7 energy of intelligent vehicle Enough several trunk nodes addition candidate collections for directly reaching (having connection attribute in Fig. 2), the improvement of candidate collection, further Improve the efficiency of solution.
(2) in order to solve prematurely to fall into local optimum in ant group algorithm actual search, there is search stagnation behavior Problem.Consider that the mechanism that ant group algorithm can effectively be avoided Premature Convergence with one kind is combined together, will obtain optimal The ant group algorithm of performance improves precision.
1. search is avoided to stagnate, consider that it directly depends on pheromones from influencing for selecting the probability of next solution to start with Track and heuristic information.Heuristic information will not change depending on problem, but can be avoided with the influence of restricted information element track Difference during algorithm operation between each pheromones track is excessive.Introduce max-min ant system (MMAS) as a result, In mechanism that the maxima and minima of pheromones track is limited so as to the pheromones track τ on all sidesij(t), have τmin< τij(t) < τmax.Reach convergent Rule of judgment are as follows: the track amount on each selected element, one of solution element For τmax, the every other selectable track amount solved on element is τmin。
2. the worst ant pheromones overall situation being introduced into optimal-worst ant system updates rule.Optimal solution is carried out more The enhancing of limits, and worst solution is weakened so that belong to optimal path while belong to worst path while between Pheromones difference increases, and focuses more on search near optimal solution.If (r, s) is a line on worst ant path, and not It is the side in optimal ant path, then carries out pheromone amount adjustment as the following formula:
3. introducing the automatic adjusument mechanism of pheromones volatilization factor ρ.The keeping optimization after each circulation, when what is acquired Optimal solution does not significantly improve in n times circulation, and ρ adjustment reduces 0.1, and minimum value is set as ρmin, prevent too small reduction convergence speed Degree, it may be assumed that
The present embodiment considers that dispensing vehicle loading gage limited mass comprehensively considers dispatching using minimal path energy consumption as main target Time solves the Distribution path that energy consumption is minimum in the case where considering goods part urgency level and the time is shorter.The present embodiment is examined Ant group algorithm and genetic algorithm are merged in worry, the accumulation of initial stage pheromones are quickly generated using genetic algorithm, and being capable of root Adaptive adjustment fitness function and transition probability are required according to different dispatchings, the rear positive feedback using ant group algorithm is searched for special Property, it solves and meets the optimal Distribution path that different dispatchings require.Please refer to Figure 11 and Figure 12, the stock path or described The planing method in picking path includes the following steps (step S501- step S516).
Step S501, initialization population, coding and genetic variance, determines fitness function.In the present embodiment, initially Change crossover probability pc, mutation probability pm, and maximum evolutionary generation Gmax, minimum evolutionary generation Gmin, minimum evolution rate Gratio, into Changing terminates algebra Gend.It is N that population scale, which is arranged, in the present embodiment, obtains initial population G, makes Gmin< G < Gmax, according to practical problem It is encoded, determines fitness function f (s), calculate fitness value f individual in populationi.Each distributing node is with one when coding A byte representation, such as 01,03.If 5 total weight of cargo is M, 5 total weight of cargo that each distributing node i needs is mi, work as prosthomere The distance of point to next distributing node i are di, dispense cost and be denoted as s.Then there is following relational expression:Define fitness
It then needs to ask:So that fitness f is up to
Step S502 is ranked up individual in parent group according to fitness size, keeps the biggish individual of fitness selected The probability selected is greater than the probability that the lesser individual of fitness is selected.This step completes selection operation, so that fitting in parent group The probability that the biggish individual of response is selected is larger, and the probability that the lesser individual of fitness is selected is smaller, so that preferably a Physical efficiency is by the selection of greater probability.
Step S503 judges whether to need to carry out mutation operation to the ant colony；It is needing to make a variation to the ant colony When operation, step S504 is executed, the ant colony is carried out to reverse variation and to new individual and parent individuality according to fitness size It is selected；
When not needing to carry out mutation operation to the ant colony or complete step S504, step S505 is executed, is judged whether It needs to carry out crossover operation to the ant colony.The process of crossover operation is as follows:
1. two father's strings of selection, randomly choose a mating region, as follows:
Old1=12 | 3456 | 789
Old2=98 | 7654 | 321
2. the region of old2 is added to before old1, the region of old1 is added to before old2:
Old1^=7654 | 123456789
Old2^=3456 | 987654321
3. successively deleting old1^ and number identical with the region that mates in old2^, final two substring is obtained:
New1=765412389
New2=345698721
Wherein, mutation operation: variation method, such as 1-2-3-4-5-6 is reversed to be broken between section 2-3 and 5-6, with anti- To being sequentially inserted into, become 1-2-5-4-3-6.
When needing to carry out crossover operation to the ant colony, step S506 is executed, crossover operation is carried out to the ant colony, and Select new individual and parent individuality according to fitness size；It is not needing to carry out crossover operation or completion to the ant colony When step S506, step S507 is executed, judges whether the ant colony meets preset fusion conditions.In order to enable population is towards suitable The higher direction of response is evolved, and the individual better than parent after intersecting, making a variation can just retain into new parent group, adaptive value Individual lower than father tape will be eliminated, and not place into father tape group.And the present embodiment compares new individual and former parent population In individual, replaced according to the superiority and inferiority that result carries out individual, select the high individual of fitness as next-generation new sub individual.
When the ant colony meets the fusion conditions, step S508 is executed, the solution obtained by genetic algorithm is converted For the initial information element Distribution Value on the path of the ant colony and limit routing information element range.It is generated with genetic algorithm more excellent Solve the initial value of the pheromones in initialization with Ant colony algorithm on path, in order to prevent on path pheromones early stage difference it is larger so that Search falls into precocity, cannot find preferably to solve, be defined according to the following formula to the pheromone concentration on path:
When the ant colony is unsatisfactory for the fusion conditions, step S502 is executed.Even Gmin< G < GmaxAnd evolution rate Gend> Gratio, then step S502 is turned to, (i.e. the evolution rate of constant generations is higher than the termination threshold value set), is otherwise carried out next Step.
Step S509 initializes interstitial content, ant number, cycle-index and pheromones that the ant colony needs to be traversed for The ant is placed on each node at random by volatilization factor, empties the taboo list of the ant colony；
Step S510, judges whether the loop iteration number of the population of the ant colony is not less than maximum cycle；
When the loop iteration number of the population is not less than the cycle-index, step S511, output search knot are executed Fruit；
The population loop iteration number be less than the cycle-index when, execute step S512, every ant according to Following state movement rule formula selects next node:
That is:
Wherein, τijIt (t) is pheromones track, tablekFor the taboo list, allowedkFor candidate collection；λjIt is described The urgency level of cargo 5, mjFor 5 total weight of cargo, d required for node jijFor the distance of present node to next node. The effect of taboo list is that repeated accesses node, the strategy of candidate collection are to limit can be used as and move mesh next time in order to prevent The selectable range for marking node, avoids a large amount of redundancy iteration, reduces the complexity of algorithm.And only consider in traditional ant group algorithm To the factor of distance, in 5 access procedure of physicals, it can only guarantee that the total time transported is most short (assuming that at the uniform velocity with delivery car It transports, and does not consider to access the time difference of cargo 5 in each 5 access point of cargo).
The present embodiment improves of both having done herein:
1. comprehensively considering Distribution path and dispatching energy consumption, needs loadage amount also to take into account each distributing node, protecting In the case that card Distribution path is shorter, intelligent vehicle 7 can preferentially store heavier cargo 5 when carrying out multiple spot stock, intelligent vehicle 7 exists When carrying out multiple spot picking can the preferential lighter cargo 5 of pickup reduce energy consumption.
2. consider the urgency level (priority) of cargo 5, to need the node of the urgent cargo 5 for carrying out accessing operation into Row weighting considers the urgent cargo 5 of priority memory access while considering energy consumption.
Fitness function, state transition probability can adaptively be adjusted according to following three kinds of actual conditions, intelligence Vehicle 7 is when carrying out multiple spot stock:
1), 7 stock of intelligent vehicle or picking used time most in short-term, have:
Wherein, f is the fitness of the fitness function；
2), when the 7 stock energy consumption of intelligent vehicle is minimum, have:
When the 7 picking energy consumption of intelligent vehicle is minimum, have:
3), when 7 stock of intelligent vehicle need to consider 5 emergency of expense, used time and cargo, have:
When 7 picking of intelligent vehicle need to consider 5 emergency of expense, used time and cargo, have:
λiMore big then cargo 5 is more urgent, for 5 λ of general cargoi=1, it can be with λ according to the urgency level of the storage of cargo 5i =2,3 ....
Step S513, after kth ant one week node of traversal returns to its starting point, local updating is located at kth ant By the pheromones on path.In the present embodiment, also the range of pheromone is updated, and limits formula are as follows:
τ (r, s) ← ρ τ (r, s)+(1- ρ) Δ τ (r, s) (formula 4)
Step S512 and step S513 are repeated, until all ants traverse one week node and return to its starting point.
Step S514, according to fitness maximum fmaxWith minimum fminIt updates current iteration optimal path length and it includes roads The pheromones and current iteration worst path of section and the pheromones it includes section.Equally, the present embodiment is in step S514, Also the range of pheromone is defined；Wherein,
(1) formula of Pheromone update is carried out to optimal path length are as follows:
τ(r,s)←τ(r,s)+δ·Δτ(r,s)
In formula, LgbFor the path length that current iteration is optimal；
(2) formula of Pheromone update is carried out to worst path length are as follows:
In formula, (r, s) is to belong to worst path and be not belonging to the side of optimal path, LworstThe worst path of diameter is sought for this Length, LbestSeek the optimal path length of diameter for this, ε is every time after circulation terminates, to worst path and to be not belonging to optimal road The attenuation coefficient of the pheromones on side on diameter, and value range is [0,1].ε is the parameter introduced, and expression is following every time After ring, to the worst path chosen and the attenuation coefficient of pheromones being not belonging on the side on optimal path.The coefficient Value is bigger, illustrates that the pheromones decaying in this time recycling for the side being pertaining only on worst path is larger；The value value is smaller, It is smaller for the pheromones attenuation degree on the side that is pertaining only on worst path in illustrating this time to recycle.
The position reset of the m ant is starting point, empties taboo list table by step S515k, and judge whether to need The value of adjustment information volatilization factor；
It (if continuous 3 iteration all do not find more preferably path, is adjusted in the value for needing adjustment information volatilization factor The value of information volatilization factor) when, step S516 is executed, executes step after the value according to following formula adjustment information volatilization factor ρ S510:
When not needing the value of adjustment information volatilization factor, directly execution step S510.
In conclusion the indoor positioning air navigation aid in the intelligent warehouse management system of the present embodiment has the advantage that
The air navigation aid determines the area that cargo 5 stores according to the volume of cargo 5 by acquiring the volume information of cargo 5 Domain and shelf select multiple vacancy shelf as target shelf according to 5 volume size of cargo and vacancy information brush in stock, Multiple picking points can be filtered out when picking as target shelf, and determine most short transport path, and cooperation two-point locating is used to navigate Intelligent vehicle 7 realizes the multipoint access operation of indoor storage environment, in order to improve the utilization rate of storage, while passing through acquisition intelligence The location information of vehicle 7 and the vacancy information in warehouse, and then the idle intelligent vehicle 7 close to cargo 5 is selected to be accessed, it can reduce The access time of intelligent vehicle 7, so that storage efficiency is improved, it is convenient to navigate to intelligent vehicle 7 in storage.
When in the present embodiment, to the planning in stock path or picking path, first with the random search of genetic algorithm, Global convergence and rapidity generate the initial information element distribution of relevant issues.Then, make full use of ant group algorithm concurrency, Positive feedback mechanism and higher efficiency are solved.Finally merged, and fused algorithm is better than ant in time efficiency Group's algorithm, is better than genetic algorithm on solution efficiency, forms a kind of time efficiency and heuritic approach that solution efficiency is taken into account.And And the present embodiment limits candidate collection in view of the track limitation of practical storage environment, it only can be straight by intelligent vehicle 7 It is connected to several trunk nodes reached and candidate collection is added, the improvement of candidate collection further improves the efficiency of solution.Together When, the present embodiment considers that the mechanism that ant group algorithm can effectively be avoided Premature Convergence with one kind is combined together, and will obtain The ant group algorithm of optimal performance is obtained to improve precision, avoids search from stagnating, optimal solution is enhanced to a greater extent, and to most Poor solution is weakened so that belong to optimal path while belong to worst path while between pheromones difference increase, make to search Rope focuses more near optimal solution, in addition introduces the automatic adjusument mechanism of pheromones volatilization factor, is with minimal path energy consumption Main target comprehensively considers distribution time, solves the energy consumption minimum in the case where considering goods part urgency level and the time is shorter Distribution path.
The acquisition of 5 size of cargo is passed through mainly using the method for image procossing in existing Warehouse Management System The image information of acquisition is transferred to processor to handle, information content is bigger, higher cost and can not be to cargo 5 Size carries out dynamic detection, and the location information of the shape information and intelligent vehicle 7 that transport cargo 5 has been determined in the present embodiment, leads to 5 volume size of ultrasonic wave module dynamic scan cargo is crossed, the location information of intelligent vehicle 7 is obtained by two-point locating, is combined in this way The precision of ultrasonic distance measurement, cost advantage, information content is smaller, and can reduce cost, while can also be to cargo 5 Size carries out dynamic detection.
Embodiment 2
Figure 13 is please referred to, the indoor positioning navigation model in a kind of intelligent warehouse management system is present embodiments provided, it should Model is for navigating to the intelligent vehicle 7 of access cargo 5 in warehouse comprising data acquisition subsystem, data transmission System and data process subsystem.
The data acquisition subsystem be used to acquire the volume information of the cargo 5, the intelligent vehicle 7 location information, The vacancy information in the warehouse.In the present embodiment, data acquisition subsystem includes 7 location information acquisition module of intelligent vehicle and goods 5 shape information acquisition module of object.5 shape information acquisition module of cargo includes ultrasound microphones A, ultrasound microphones B, ultrasound Wave microphone C, STM32 single-chip microcontroller, 5 Morphology observation frame of cargo, 4463 wireless modules, the STM32 single-chip microcontroller are received and are analyzed Length information that ultrasound microphones A, B, C are detected simultaneously generates volume information.5 Morphology observation frame of cargo is used for for cargo 5 pass through, and are provided with the intelligent vehicle 6 passed through for cargo 5.The data acquisition subsystem of the present embodiment can be used in embodiment 1 The acquisition method of the volume information of cargo 5 carries out the acquisition of data.
The data transmission sub-system is used for the information acquired according to the data acquisition subsystem, can be according to the two of foundation The low power consumption transmission mechanism of point location model, location information, the volume information and the vacancy information described in real-time update.This The low power consumption transmission advantage of the data transmission sub-system combination Si4463 module of embodiment obtains intelligence by two-point locating method 7 real-time coordinates of vehicle provide Basis for whether intelligent vehicle 7 deviates most short access path.Specific data transmission method can be adopted The method described in embodiment 1, and warehouse extension set receives and uploads above-mentioned volume information and range information.
Content of the data process subsystem for the step S2 to step S21 in embodiment 1, and it is settable multiple discrete Unit goes to execute each step.Data process subsystem is selected in stock according to 5 volume size of cargo and vacancy information brush more Multiple picking points can be filtered out when a vacancy shelf are as target shelf, picking as target shelf, and determine most short transport road Diameter, cooperation two-point locating realize the multipoint access operation of indoor storage environment for the intelligent vehicle 7 that navigates.Wherein, extension set will be described Signal is transferred to server, and server is to filtering out multiple target shelf and optimal Distribution path and navigate after the information analysis The accessing operation of the progress multiple spot cargo 5 of intelligent vehicle 7.Data process subsystem is directed to the path planning problem of multipoint access, in conjunction with The advantage of ant group algorithm is accelerated the accumulation of initial information element by the introducing of genetic algorithm, promotes the quick global of ant group algorithm Convergence, to obtain most short access path.
Embodiment 3
The present embodiment provides a kind of terminals comprising memory, processor and storage are on a memory and can The computer program run on a processor.Processor is realized in the intelligent warehouse management system of embodiment 1 when executing program The step of indoor positioning air navigation aid.The method of embodiment 1 is such as designed in use, can be applied in the form of software Independently operated program, installation on computer terminals, terminal can be computer, smart phone, control system and Other internet of things equipment etc..The method of embodiment 1 can also be designed to the program of embedded operation, be mounted on terminal On, such as it is mounted on single-chip microcontroller.
Embodiment 4
The present embodiment provides a kind of computer readable storage mediums, are stored thereon with computer program.Program is by processor When execution, realize embodiment 1 intelligent warehouse management system in indoor positioning air navigation aid the step of.The method of embodiment 1 In use, can be applied in the form of software, be such as designed to computer readable storage medium can independently operated program, meter Calculation machine readable storage medium storing program for executing can be USB flash disk, be designed to U-shield, be designed to start the journey of entire method by external triggering by USB flash disk Sequence.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all in essence of the invention Made any modifications, equivalent replacements, and improvements etc., should all be included in the protection scope of the present invention within mind and principle.

Claims (10)

1. the indoor positioning air navigation aid in a kind of intelligent warehouse management system is used in warehouse to the intelligence of access cargo Vehicle navigates, which is characterized in that itself the following steps are included:
Step S1 acquires the vacancy information of the volume information of the cargo, the location information of the intelligent vehicle, the warehouse；
Step S2 judges whether there is the intelligent vehicle being in idle condition；Wherein, defining the intelligent vehicle being in idle condition is sky Not busy intelligent vehicle；
If it exists when at least one idle intelligent vehicle, step S3 is executed, judges that the inlet in the warehouse is needed with the presence or absence of cargo It stores；
If the inlet needs to store there are at least one target goods one, step S4 is executed, according to the vacancy information With the volume information, target area and target shelf that the target goods one are gone to are determined, and determine an idle intelligence Energy vehicle is as inventory-vehicle；
Step S5 plans the inventory-vehicle from the inlet to the target area and the stock road of the target shelf Diameter, and the inventory-vehicle is driven to be advanced according to the stock path；
Step S6, judges whether the inventory-vehicle reaches the stock point；
When the inventory-vehicle reaches the stock point, step S7 is executed, the target goods one are stored in the stock Point；
Step S8 judges whether to need to take out cargo in the warehouse；
When if desired taking out target goods two in the warehouse, step S9 is executed, judges the target goods two whether in institute It states in the current region where inventory-vehicle；
If the target goods two are in the current region, using the inventory-vehicle as picking vehicle；
If the target goods two are not in the current region, step S10 is executed, judges whether there is and is in idle condition Intelligent vehicle；When the intelligent vehicle being in idle condition if it exists, step S11 is executed according to the location information of picking point and determines one A free time intelligent vehicle is the picking vehicle；
Step S12 plans that the picking vehicle from its current location to the picking path of the picking point, and drives the picking Vehicle marches to the picking point according to the picking path, to obtain the target goods two；
Step S13 plans the picking vehicle from the picking point to the shipment path of output port, drive the picking vehicle by The output port is marched to according to the shipment path；
Step S14, judges whether the picking vehicle reaches the output port；
When the picking vehicle reaches the output port, step S15 is executed, by the target goods in the picking vehicle Two take out, and the picking vehicle is made to be back to the inlet；
If do not need to take out cargo in the warehouse, step S16 is executed, the inventory-vehicle is made to be back to described put in storage Mouthful；
Step S17, the state for updating the inventory-vehicle or the picking vehicle that are located at the inlet is idle state.
2. the indoor positioning air navigation aid in intelligent warehouse management system as described in claim 1, which is characterized in that the storehouse The multiple rectangular areas of lab setting, setting is in a plurality of road of mesh arrangement in each rectangular area, and adjacent two road are met at One node；The quantity of the intelligent vehicle be it is multiple, defining each intelligent vehicle is an ant, and multiple intelligent vehicles constitute an ant Group；The planing method in the stock path or the picking path the following steps are included:
Step S501, initialization population, coding and genetic variance, determines fitness function；
Step S502 is ranked up individual in parent group according to fitness size, selects the biggish individual of fitness Probability is greater than the probability that the lesser individual of fitness is selected；
Step S503 judges whether to need to carry out mutation operation to the ant colony；
When needing to carry out mutation operation to the ant colony, step S504 is executed, the ant colony is carried out to reverse variation and to new Individual and parent individuality are selected according to fitness size；
When not needing to carry out mutation operation to the ant colony or complete step S504, step S505 is executed, judges whether to need Crossover operation is carried out to the ant colony；
When needing to carry out crossover operation to the ant colony, step S506 is executed, crossover operation is carried out to the ant colony, and is made new Individual and parent individuality are selected according to fitness size；
When not needing to carry out crossover operation to the ant colony or complete step S506, step S507 is executed, judges the ant colony Whether preset fusion conditions are met；
When the ant colony meets the fusion conditions, step S508 is executed, converts institute for the solution obtained by genetic algorithm It states the initial information element Distribution Value on the path of ant colony and limits routing information element range；The fusion is unsatisfactory in the ant colony When condition, step S502 is executed.
3. the indoor positioning air navigation aid in intelligent warehouse management system as claimed in claim 2, which is characterized in that the rule The method of drawing is further comprising the steps of:
Step S509 initializes interstitial content, ant number, cycle-index and pheromones volatilization that the ant colony needs to be traversed for The ant is placed on each node at random by the factor, empties the taboo list of the ant colony；
Step S510, judges whether the loop iteration number of the ant colony is not less than maximum cycle；
When the loop iteration number of the population is not less than the maximum cycle, step S511, output search knot are executed Fruit；
When the loop iteration number of the population is less than the cycle-index, step S512 is executed, every ant is according to following State movement rule formula selects next node:
Wherein, τijIt (t) is pheromones track, tablekFor the taboo list, allowedkFor candidate collection；λjFor the cargo Urgency level, mjFor cargo total weight, d required for node jijFor the distance of present node to next node；
Step S513, after kth ant one week node of traversal returns to its starting point, local updating is located at kth ant process Pheromones on path；
Step S512 and step S513 are repeated, until all ants traverse one week node and return to its starting point；
Step S514, according to fitness maximum fmaxWith minimum fminIt updates current iteration optimal path length and it includes sections Pheromones and current iteration worst path and the pheromones it includes section；
The position reset of the m ant is starting point, empties taboo list table by step S515k, and judge whether to need to adjust letter Cease the value of volatilization factor；
When needing the value of adjustment information volatilization factor, step S516 is executed, according to following formula adjustment information volatilization factor ρ's Step S510 is executed after value:
When not needing the value of adjustment information volatilization factor, directly execution step S510.
4. the indoor positioning air navigation aid in intelligent warehouse management system as claimed in claim 3, which is characterized in that described Intelligent vehicle stock or picking used time most in short-term, have:
λj=1, mj=1,
Wherein, f is the fitness of the fitness function；
When the intelligent vehicle stock energy consumption is minimum, have:
m0=0
When the intelligent vehicle picking energy consumption is minimum, have:
m0=0
When the intelligent vehicle stock need to consider expense, used time and cargo emergency, have:
m0=0
When the intelligent vehicle picking need to consider expense, used time and cargo emergency, have:
m0=0.
5. the indoor positioning air navigation aid in intelligent warehouse management system as claimed in claim 3, which is characterized in that in step In S508, the restriction formula of routing information element range are as follows:
In step S513, also the range of pheromone is defined, and limits formula are as follows:
τ(r,s)←ρ·τ(r,s)+(1-ρ)·Δτ(r,s)
In step S514, also the range of pheromone is defined；Wherein,
(1) formula of Pheromone update is carried out to optimal path length are as follows:
τ(r,s)←τ(r,s)+δ·Δτ(r,s)
In formula, LgbFor the path length that current iteration is optimal；
(2) formula of Pheromone update is carried out to worst path length are as follows:
In formula, (r, s) is to belong to worst path and be not belonging to the side of optimal path, LworstThe worst path for seeking diameter for this is long Degree, LbestSeek the optimal path length of diameter for this, ε is every time after circulation terminates, to worst path and to be not belonging to optimal path On side on pheromones attenuation coefficient, and value range be [0,1].
6. the indoor positioning air navigation aid in intelligent warehouse management system as described in claim 1, which is characterized in that the room Interior positioning navigation method is further comprising the steps of:
When the inventory-vehicle does not reach the stock point, execute step S18, according to the real time position of the inventory-vehicle and The stock path, adjusts the operating status of the inventory-vehicle in real time；
When the picking vehicle does not reach the output port, execute step S19, according to the real time position of the picking vehicle and The picking path adjusts the operating status of the picking vehicle in real time.
7. the indoor positioning air navigation aid in intelligent warehouse management system as described in claim 1, which is characterized in that the goods The acquisition method of the volume information of object the following steps are included:
Step S101, measurement are located at the length L of the cargo of the inlet；
On the length direction of the cargo, ultrasonic distance measurement is carried out by a pair of cargo of a ultrasonic wave transmitting terminal and is opened Beginning timing；
Judge whether the adjacent ultrasonic distance measurement twice of ultrasonic wave transmitting terminal one poor one is greater than a threshold value one；
It is greater than the threshold value for the moment in the ultrasonic distance measurement poor one, terminates timing and obtain sweep time；
The rate travel and the product of the sweep time in the longitudinal direction for calculating the cargo, obtains the length of the cargo Spend L；
Step S102, measurement are located at the width W of the cargo of the inlet；
By rotating a ultrasonic wave transmitting terminal two, a fan-shaped detection faces one are generated in the width direction of the cargo with right The cargo carries out ultrasonic distance measurement；
Judge whether the adjacent ultrasonic distance measurement twice of ultrasonic wave transmitting terminal two poor two is greater than a threshold value two；
When the ultrasonic distance measurement poor two is greater than the threshold value two, the scanning of ultrasonic wave transmitting terminal two is defined to the cargo Boundary；It calculates the scanning of ultrasonic wave transmitting terminal two and arrives the time difference Δ t on two opposite boundaries of the cargo, the ultrasound The interval S on wave transmitting terminal two and two boundaryαAnd Sβ；
Calculate the angle theta of the fan-shaped detection faces oneL；Wherein, θL=v × Δ t, v is the rotation angle of the ultrasonic wave transmitting terminal two Speed；
Calculate the width W of the cargo；Wherein,
Step S103, measurement are located at the height H of the cargo of the inlet；
By rotating a ultrasonic wave transmitting terminal three, a fan-shaped detection faces two are generated in the short transverse of the cargo with right The cargo carries out ultrasonic distance measurement；
Judge whether the ultrasonic distance measurement poor three of the ultrasonic wave transmitting terminal three adjacent twice is greater than a threshold value three；
It is greater than the threshold value three in the ultrasonic wave transmitting terminal three, defines the scanning of ultrasonic wave transmitting terminal three to the cargo Boundary；Calculate the interval S of the ultrasonic wave transmitting terminal three and the boundary of the cargoψ, the ultrasonic wave transmitting terminal three with it is described The vertical range S of cargoζ；
Calculate the height H of the cargo:Wherein, H2It is the ultrasonic wave transmitting terminal three apart from ground The height in face, H1Height for the cargo apart from ground；
Step S104 calculates volume V:V=L × W × H of the cargo.
8. the indoor positioning air navigation aid in intelligent warehouse management system as described in claim 1, which is characterized in that the storehouse The multiple rectangular areas of lab setting, setting is in a plurality of road of mesh arrangement in each rectangular area, and adjacent two road are met at One node；The acquisition method of the location information of the intelligent vehicle the following steps are included:
Step S105 places two locating modules, the locating module and institute at the diagonal line both ends of the rectangular area respectively Intelligent vehicle is stated to communicate wirelessly；
Step S106 calculates the intelligent vehicle and two positioning according to the communication time of the intelligent vehicle and two locating modules The distance S of module1And S2；
Step S107 calculates the coordinate of two pre- punctuates according to the coordinate of two locating modules；Wherein, each pre- punctuate with The distance of two locating modules is respectively S1And S2；
Step S108, judges whether two pre- punctuates are located on the travel path of the intelligent vehicle respectively；
A pre- punctuate is located on the travel path wherein and the pre- punctuate of another one is not located on the travel path When, determine that the pre- punctuate on the travel path is the measurement position of the intelligent vehicle；
When two pre- punctuates are respectively positioned on the travel path, according to the previous measurement position of the intelligent vehicle, before selection The pre- punctuate not appeared in once on the travel path is the measurement position of the intelligent vehicle.
9. the indoor positioning air navigation aid in intelligent warehouse management system as claimed in claim 8, which is characterized in that the storehouse The acquisition method of the vacancy information in library the following steps are included:
Step S109 places two nothings corresponding with two locating modules respectively at the diagonal line both ends of the rectangular area respectively Wire module；
Step S110 is called out when the intelligent vehicle receives picking and stock is instructed to the transmission of the wireless module of each rectangular area Awake information, the location information of the intelligent vehicle is acquired to wake up the wireless module by corresponding locating module；
Step S111 drives the wireless module suspend mode when the intelligent vehicle leaves the rectangular area.
10. the indoor positioning navigation model in a kind of intelligent warehouse management system is used in warehouse to the intelligence of access cargo Energy vehicle navigates comprising data acquisition subsystem, data transmission sub-system and data process subsystem, feature exist In,
The data acquisition subsystem is for acquiring the volume information of the cargo, the location information of the intelligent vehicle, the storehouse The vacancy information in library；
The data transmission sub-system is used for the information acquired according to the data acquisition subsystem, the letter of position described in real-time update Breath, the volume information and the vacancy information；
The data process subsystem is for executing following steps:
Step S2 judges whether there is the intelligent vehicle being in idle condition；Wherein, defining the intelligent vehicle being in idle condition is sky Not busy intelligent vehicle；
If it exists when at least one idle intelligent vehicle, step S3 is executed, judges that the inlet in the warehouse is needed with the presence or absence of cargo It stores；
If the inlet needs to store there are at least one target goods one, step S4 is executed, according to the vacancy information With the volume information, target area and target shelf that the target goods one are gone to are determined, and determine an idle intelligence Energy vehicle is as inventory-vehicle；
Step S5 plans the inventory-vehicle from the inlet to the target area and the stock of the multiple target shelf Path, and the inventory-vehicle is driven to be advanced according to the stock path；
Step S6, judges whether the inventory-vehicle reaches the stock point；
When the inventory-vehicle reaches the stock point, step S7 is executed, the multiple target goods one are sequentially stored in The stock point；
Step S8 judges whether to need to take out cargo in the warehouse；
When if desired taking out target goods two in the warehouse, step S9 is executed, whether judges the multiple target goods two In the current region where the inventory-vehicle；
If the target goods two are in the current region, using the inventory-vehicle as picking vehicle；
If the target goods two are not in the current region, step S10 is executed, judges whether there is and is in idle condition Intelligent vehicle；When the intelligent vehicle being in idle condition if it exists, step S11 is executed according to the location information of picking point and determines one A free time intelligent vehicle is the picking vehicle；
Step S12 plans that the picking vehicle from its current location to the picking path of the multiple picking point, and drives described Picking vehicle marches to the picking point according to the picking path, to obtain the target goods two；
Step S13 plans that the picking vehicle from the picking point of the last time picking to the shipment path of output port, drives The picking vehicle marches to the output port according to the shipment path；
Step S14, judges whether the picking vehicle reaches the output port；
When the picking vehicle reaches the output port, step S15 is executed, by the target goods in the picking vehicle Two take out, and the picking vehicle is made to be back to the inlet；
If do not need to take out cargo in the warehouse, step S16 is executed, the inventory-vehicle is made to be back to described put in storage Mouthful；
Step S17, the state for updating the inventory-vehicle or the picking vehicle that are located at the inlet is idle state.
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