CN107885198A - AGV dispatching methods - Google Patents
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- CN107885198A CN107885198A CN201710873089.6A CN201710873089A CN107885198A CN 107885198 A CN107885198 A CN 107885198A CN 201710873089 A CN201710873089 A CN 201710873089A CN 107885198 A CN107885198 A CN 107885198A
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- 238000011022 operating instruction Methods 0.000 claims description 8
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0217—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with energy consumption, time reduction or distance reduction criteria
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Abstract
The invention provides a kind of AGV dispatching methods, this method comprises the following steps：Control system generates AGV maps and stored；AGV is subjected to Initialize installation；Optimal path computation between any two website；Data base optimization；AGV execution is transferred to run to the task of another website from a website and update run time；Deep learning is carried out by genetic algorithm.Compared with correlation technique, AGV dispatching methods maintenance cost provided by the invention is low and operating efficiency is high.
Description
Technical field
The present invention relates to AGV technical fields, more particularly to a kind of AGV dispatching methods.
Background technology
AGV (Automated Guided Vehicle, automatical pilot transportation vehicle) refers to that equipment is provided with electricity magnetically or optically etc.
The transport vehicle of homing guidance device, it can be travelled along defined guide path.
In correlation technique, the path management that AGV is highly run mainly is repaiied by technical staff in upper scheduling system
Change and safeguard route, the information such as work station, then by way of being wirelessly transferred newest routing information informing each AGV come
Realize AGV management and running.
AGV routing information mainly includes station information and running route information, and current AGV dispatching methods use certainly
Scheduling mode under above, i.e. the increase and decrease of work station and the change of route will inform each AGV by uploading scheduling system, be
The maintenance of system relies primarily on artificial progress.Its top-down scheduling mode, it can also increase the maintenance period and dimension of path management
Protect cost.
In addition, the AGV scheduling in correlation technique generally performs task along fixed course, lack and independently select appropriate circuit
Intellectuality so that its operational efficiency reduces, in particular with the development of ecommerce, the increase of logistics warehouse increases,
AGV utilizations are more extensive, and AGV operational efficiency is then particularly significant, and business efficiency can be greatly improved.
Solved the above problems therefore, it is necessary to provide a kind of new AGV dispatching methods.
The content of the invention
The technical problem to be solved in the invention is to provide the AGV dispatching parties that a kind of maintenance cost is low and operating efficiency is high
Method.
In order to solve the above technical problems, the invention provides a kind of AGV dispatching methods, this method comprises the following steps：
Step S1, control system generation and is stored AGV maps, and the AGV maps are digraph, it include website,
Path and virtual traffic information signal lamp between website, the virtual traffic signal lamp are used to control the AGV each described
The prevailing state of website, on the path between its approaching side for including being separately positioned on each website and adjacent two website
Identification code, the identification code includes positional information and direction information；
Step S2, AGV is subjected to Initialize installation, AGV is transported with command speed according to the AGV maps by predefined paths
OK, the AGV realizes triggering operation and positioning by reading the identification code, by two websites of arbitrary neighborhood passed through
Operating time log is simultaneously uploaded to database；
Step S3, the optimal path computation between any two website, according to the data run Floyd algorithms of database, meter
The most short path of run time between website described in any two is calculated, the path is defined as optimal path and is stored in data
Storehouse；
Step S4, transfer AGV execution to run to the task of another website from a website, pass through the database
Optimal path between two websites is transferred, the AGV is run by optimal path, and by arbitrary neighborhood in running
The operating time log of two websites is simultaneously uploaded to database update；
Step S5, data base optimization, Floyd algorithms are reruned according to the data of database, calculates any two institute
State the new optimal path between website and be updated to current optimal path；
Preferably, in addition to：Step S6, repeat step S4~S5, by genetic algorithm carry out deep learning, by it is default when
Between interval to the data run Floyd algorithms of the database, calculate between any two websites it is newest it is current most
Shortest path simultaneously updates and replaces its last time described current optimal path for calculating of operation Floyd algorithms.
Preferably, in the step S2, the AGV realizes that triggering operation and positioning include by reading the identification code
Following steps：
Step S21, described AGV reads the identification code and sends current request when running to any identification code, simultaneously
The real time position of the AGV is sent by the location information of the identification code；
Step S22, after described control system receives current request and the real time position of the AGV, according to the real-time of other AGV
Position and running status judge position corresponding to identification code where the AGV whether P Passable, and send corresponding operation
Instruct to the AGV；
Step S23, described AGV is run according to the direction information of the operating instruction and the identification code received.
Preferably, in step S22, the operating instruction includes current and waits the X seconds to pass through：
When the control system judge the corresponding position of the current request for can prevailing state when, then the operation sent refers to
It is current to make；
When the control system judges the corresponding position of the current request to need wait state, then sending instruction is
The X seconds are waited to pass through.
Preferably, the direction information of the identification code includes turning to and turning to counterclockwise clockwise.
Preferably, the direction information also includes steering angle, and the steering angle includes 0 degree, 30 degree, 45 degree, 60 degree
And at least one of 90 degree.
Preferably, in step s3, run time includes AGV in two websites between website described in any two
Between run duration, the stand-by period of any one website and the turnaround time sum of any one website.
Preferably, the identification code is ring code.
Preferably, the ring code includes central core, boundary layer and the number between the central core and the boundary layer
According to layer, wherein frame centered on the central core；The ring code is rounded, square, rectangle or triangle.
Preferably, the sensor for being used to detect distance of obstacle immediately ahead of the AGV that the AGV includes being arranged on, when
When the AGV detects that preceding object distance is less than preset value by the sensor in the process of running, the AGV stops fortune
Go and wait.
Compared with correlation technique, AGV dispatching methods of the invention by generating oriented AGV maps by AGV operational sites,
And approaching side in each website, the identification code including positional information and direction information is respectively provided with the path between website, it is described
Identification code is used to control the AGV in the prevailing state of each website as virtual traffic information signal lamp, by AGV in institute
State operation between each website on AGV maps and upload the run time between any two adjacent sites to control system, form number
According to storehouse, the control system according to the data run Floyd algorithms of database calculate any two website between run time most
Short path is simultaneously stored after confirming as optimal path, and the control system passes through that genetic algorithm carries out deep learning and dynamic updates
Database so that dispatch when the AGV performs the operation task of any two website and its selection is controlled by the control system all the time
Optimal path, intelligent control is realized, the maintenance cost not only dispatched is low and operating efficiency, intelligence degree are high.
Brief description of the drawings
Fig. 1 is the FB(flow block) of AGV dispatching methods of the present invention；
Fig. 2 is the step S2 of AGV dispatching methods of the present invention sub-process block diagram；
Fig. 3 is the operating structure schematic diagram of AGV adjustment methods of the present invention.
Embodiment
Below in conjunction with drawings and embodiments, the invention will be further described.
Please refer to Fig. 1-2, wherein, Fig. 1 is the FB(flow block) of AGV dispatching methods of the present invention；Fig. 2 is AGV of the present invention
The step S2 of dispatching method sub-process block diagram.The invention provides a kind of AGV dispatching methods, this method comprises the following steps：
Step S1, control system generates AGV maps and stored：
Control system generates AGV maps according to AGV operational sites, and the AGV maps are digraph, and it includes website, stood
Path and virtual traffic information signal lamp between point, the virtual traffic signal lamp are used to control the AGV at each station
The prevailing state of point, on the path between its approaching side for including being separately positioned on each website and adjacent two website
Identification code, the identification code include positional information and direction information.
Specifically, the direction information of the identification code includes turning to and turning to counterclockwise clockwise.It is such as solid by setting
Determine crank degree turn to and turn to counterclockwise clockwise.More excellent, the identification code also includes steering angle, you can control
The steering angle realizes that more complicated path is set.For example the steering angle includes 0 degree, 30 degree, 45 degree, 60 degree and 90
At least one of degree.Certainly, above-mentioned angle, any one angle of 0-360 degree scopes are also not limited to.
In present embodiment, the identification code is ring code.The ring code includes central core, boundary layer and positioned at the center
Data Layer between layer and the boundary layer, wherein frame centered on the central core；The ring code is rounded, square, rectangular
Shape or triangle etc..
Step S2, AGV is subjected to Initialize installation, AGV is transported with command speed according to the AGV maps by preset path
OK, the AGV realizes triggering operation and positioning by reading the identification code, by two websites of arbitrary neighborhood passed through
Operating time log is simultaneously uploaded to database.
In this step, the AGV realizes that triggering operation and positioning specifically comprise the following steps by reading the identification code：
Step S21, described AGV reads the identification code and sends current request when running to any identification code, simultaneously
The real time position of the AGV is sent by the location information of the identification code.
Step S22, after described control system receives current request and the real time position of the AGV, according to the real-time of other AGV
Position and running status judge position corresponding to identification code where the AGV whether P Passable, and send corresponding operation
Instruct to the AGV.
Specifically, the operating instruction includes current and waits the X seconds to pass through：
When the control system judge the corresponding position of the current request for can prevailing state when, then the operation sent refers to
It is current to make.
When the control system judges the corresponding position of the current request to need wait state, then sending instruction is
The X seconds are waited to pass through, the specific stand-by period is that the control system is calculated.Equivalent to the effect of road traffic lamp, but therewith
Difference is that the virtual traffic information signal lamp is virtual lamp, and controls current when giving lamp of " red signal " and " green light signals "
Between do not fix, by judging and providing in fact during the control system, avoid empty wait and phenomenon that the stand-by period is long.
Step S23, described AGV is run according to the direction information of the operating instruction and the identification code received.
Directly continue to run with by direction information, or continued to run with again by direction information after stopping a stand-by period.
Step S3, the optimal path computation between any two website：According to the data run Floyd algorithms of database, meter
The most short path of run time between website described in any two is calculated, the path is defined as optimal path and is stored in data
Storehouse.
In present embodiment, run time includes AGV between two websites between website described in any two
Run duration, the stand-by period of any one website and the turnaround time sum of any one website.
Step S4, transfer AGV execution to run to the task of another website from a website, pass through the database
Optimal path between two websites is transferred, the AGV is run by optimal path, and by arbitrary neighborhood in running
The operating time log of two websites is simultaneously uploaded to database update.
Step S5, data base optimization, Floyd algorithms are reruned according to the data of database, calculates any two institute
State the new optimal path between website and be updated to current optimal path；
Step S6, repeat step S4~S5, deep learning is carried out by genetic algorithm, is spaced at preset timed intervals to the number
According to the data run Floyd algorithms in storehouse, calculate the newest current optimal path between any two websites and update and replace
Change the current optimal path that its last time operation Floyd algorithm calculates.This step is preferred steps, you can realizes intelligence
Change control.
It is further described below with one embodiment, AGV connects with control system communication.Fig. 3 is please referred to, is
The operating structure schematic diagram of AGV adjustment methods of the present invention.
The more AGV 1 with it is uniform and automatic operation preset time after, obtain AVG maps on any two adjacent sites it
Between run time and preservation be uploaded to the database of control system 10.Run time 50S, website E such as website B to website E
60S, website E to website C run time 10S, website C when the operation of run time 30S, website B to website C to website D
Run time 20S and website E to website A run time 30S to website D etc..
Control system logical 10 calculates any two excessively to the data run Floyd algorithms such as run time in database
The most short path of the run time of website, and the path is defined as optimal path to store to the database.
For example calculating website B to website C optimal path, then path includes B-C and B-E-C.
Path B-C run times：Path run 60S, the position stand-by period of identification code 001 assume be calculated as 60S itself and turn
0 degree of angle time 0S, then common 120S；
Path B-E-C run times：B-E paths run time 40S, the position of identification code 002 without waiting for and its corner 120
Spend time 3S, E-C paths run time 30S, the position of identification code 003 without waiting for and its 0 degree of time 0S of corner, then common 73S.
Therefore, control system 10 calculate website B to the most short path of website C run times be path B-E-C, i.e., optimal road
Footpath, and the path is saved as into website B to website C optimal path, so as to update the data the data in storehouse.
That is, said process is progress in real time so that the data dynamic of the database updates, when resetting default
Between to the data run Floyd algorithms of the database, recalculate most website B to website C optimal path.And the control
System 10 realizes the dynamic optimization of database then by the deep learning for realizing above-mentioned circulation of genetic algorithm.
Certainly, above-mentioned website B to website C is only one and illustrated, and also running optimizatin principle is same between other websites.
It should be noted that the biography for being used to detect distance of obstacle immediately ahead of the AGV that the AGV 1 includes being arranged on
Sensor (not shown), when the AGV detects that preceding object distance is less than preset value by the sensor in the process of running
When, the AGV is out of service and waits.
Compared with correlation technique, AGV dispatching methods of the invention by generating oriented AGV maps by AGV operational sites,
And approaching side in each website, the identification code including positional information and direction information is respectively provided with the path between website, it is described
Identification code is used to control the AGV in the prevailing state of each website as virtual traffic information signal lamp, by AGV in institute
State operation between each website on AGV maps and upload the run time between any two adjacent sites to control system, form number
According to storehouse, the control system according to the data run Floyd algorithms of database calculate any two website between run time most
Short path is simultaneously stored after confirming as optimal path, and the control system passes through that genetic algorithm carries out deep learning and dynamic updates
Database so that dispatch when the AGV performs the operation task of any two website and its selection is controlled by the control system all the time
Optimal path, intelligent control is realized, the maintenance cost not only dispatched is low and operating efficiency, intelligence degree are high.
Claims (10)
1. a kind of AGV dispatching methods, it is characterised in that this method comprises the following steps：
Step S1, control system generates AGV maps and stored, and the AGV maps are digraph, and it includes website, website
Between path and virtual traffic information signal lamp, the virtual traffic signal lamp be used for control the AGV in each website
Prevailing state, the knowledge on path between its approaching side for including being separately positioned on each website and adjacent two website
Other code, the identification code include positional information and direction information；
Step S2, AGV is subjected to Initialize installation, AGV is run with command speed according to the AGV maps by predefined paths,
The AGV realizes triggering operation and positioning by reading the identification code, by the fortune of two websites of arbitrary neighborhood passed through
The row time records and is uploaded to database；
Step S3, the optimal path computation between any two website, according to the data run Floyd algorithms of database, calculate
The most short path of run time between website described in any two, the path is defined as optimal path and is stored in database；
Step S4, transfer AGV execution to run to the task of another website from a website, transferred by the database
Optimal path between two websites, makes the AGV be run by optimal path, and by arbitrary neighborhood in running two
The operating time log of the website is simultaneously uploaded to database update；
Step S5, data base optimization, Floyd algorithms are reruned according to the data of database, calculates station described in any two
Point between new optimal path and be updated to current optimal path.
2. AGV dispatching methods according to claim 1, it is characterised in that also include：
Step S6, repeat step S4~S5, deep learning is carried out by genetic algorithm, is spaced at preset timed intervals to the database
Data run Floyd algorithms, calculate the newest current optimal path between any two websites and update replacement its
The current optimal path that last time operation Floyd algorithms calculate.
3. AGV dispatching methods according to claim 1, it is characterised in that in the step S2, the AGV passes through reading
The identification code is taken to realize that triggering operation and positioning comprise the following steps：
Step S21, described AGV reads the identification code and sends current request when running to any identification code, pass through simultaneously
The location information of the identification code sends the real time position of the AGV；
Step S22, after described control system receives current request and the real time position of the AGV, according to other AGV real time position
Judge with running status position corresponding to the identification code where the AGV whether P Passable, and send corresponding operating instruction
To the AGV；
Step S23, described AGV is run according to the direction information of the operating instruction and the identification code received.
4. AGV dispatching methods according to claim 3, it is characterised in that in step S22, the operating instruction includes
The current and wait X seconds pass through：
The control system judge the corresponding position of the current request for can prevailing state when, then the operating instruction sent is logical
OK；
When the control system judges the corresponding position of the current request to need wait state, then instruction is sent to wait X
Second is current.
5. according to the AGV dispatching methods described in claim 1 or 2 or 3, it is characterised in that the direction information bag of the identification code
Include and turn to and turn to counterclockwise clockwise.
6. AGV dispatching methods according to claim 5, it is characterised in that the direction information also includes steering angle, institute
Stating steering angle includes at least one of 0 degree, 30 degree, 45 degree, 60 degree and 90 degree.
7. AGV dispatching methods according to claim 6, it is characterised in that in step s3, website described in any two it
Between run time include run durations of the AGV between two websites, stand-by period of any one website and any one
The turnaround time sum of website.
8. AGV dispatching methods according to claim 7, it is characterised in that the identification code is ring code.
9. AGV dispatching methods according to claim 8, it is characterised in that the ring code includes central core, boundary layer and position
Data Layer between the central core and the boundary layer, wherein frame centered on the central core；The ring code is rounded, just
Square, rectangle or triangle.
10. AGV dispatching methods according to claim 1, it is characterised in that what the AGV included being arranged on is used to examine
The sensor of distance of obstacle immediately ahead of the AGV is surveyed, when the AGV detects front by the sensor in the process of running
When distance of obstacle is less than preset value, the AGV is out of service and waits.
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Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108681322A (en) * | 2018-04-13 | 2018-10-19 | 汇专科技集团股份有限公司 | AGV carriage walkings and avoidance obstacle method and system |
| CN108983779A (en) * | 2018-07-24 | 2018-12-11 | 安徽库讯自动化设备有限公司 | A kind of AGV trolley traffic control regulation method based on path analysis |
| CN109557886A (en) * | 2018-12-28 | 2019-04-02 | 芜湖哈特机器人产业技术研究院有限公司 | A kind of grating map and more AGV dispatching methods based on grating map |
| CN110942203A (en) * | 2019-12-03 | 2020-03-31 | 青岛港国际股份有限公司 | Automatic container terminal AGV path optimization method |
| CN110980084A (en) * | 2019-12-13 | 2020-04-10 | 灵动科技（北京）有限公司 | Warehousing system and related method |
| CN111091238A (en) * | 2019-12-03 | 2020-05-01 | 青岛港国际股份有限公司 | Automatic container terminal AGV intelligent scheduling method |
| CN111275370A (en) * | 2018-12-04 | 2020-06-12 | 北京京东尚科信息技术有限公司 | AGV dynamic scheduling method, system, equipment and storage medium |
| CN111523789A (en) * | 2020-04-20 | 2020-08-11 | 厦门大学嘉庚学院 | Multi-AGV real-time scheduling method based on step length |
| CN112388627A (en) * | 2019-08-19 | 2021-02-23 | 维布络有限公司 | Method and system for executing tasks in dynamic heterogeneous robot environment |
| CN113570103A (en) * | 2020-09-14 | 2021-10-29 | 宁波舜宇智能科技有限公司 | Path control method, path control device, electronic device and storage medium |
| CN113870602A (en) * | 2021-09-28 | 2021-12-31 | 湖南大学 | Method and system for dispatching multiple AGV parking |
| CN114148959A (en) * | 2021-12-13 | 2022-03-08 | 哈尔滨工业大学芜湖机器人产业技术研究院 | Laser forklift path searching method |
| CN114463979A (en) * | 2022-02-10 | 2022-05-10 | 骁越科技（青岛）有限公司 | AGV traffic avoiding method and device for outdoor non-fixed communication network |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102708698A (en) * | 2012-06-12 | 2012-10-03 | 北京理工大学 | Vehicle optimal-path navigation method based on vehicle internet |
| CN102800243A (en) * | 2012-07-18 | 2012-11-28 | 湖南大学科技园有限公司 | Anti-counterfeiting annular code and encoding method thereof |
| EP2677384A1 (en) * | 2012-06-19 | 2013-12-25 | Ricoh Company, Ltd. | Remote control of an autonomous vehicle |
| CN103488176A (en) * | 2013-09-29 | 2014-01-01 | 中国科学院深圳先进技术研究院 | Automatic guided vehicle scheduling method and system |
| CN106444791A (en) * | 2016-12-20 | 2017-02-22 | 南阳师范学院 | Design method of multiple AGV (Automatic Guided Vehicle) unified dispatching system by upper computer |
| CN106556406A (en) * | 2016-11-14 | 2017-04-05 | 北京特种机械研究所 | Many AGV dispatching methods |
| CN106650873A (en) * | 2016-12-21 | 2017-05-10 | 深圳若步智能科技有限公司 | Identification code, and automatic guiding vehicle rapid navigation method and system |
| CN107036618A (en) * | 2017-05-24 | 2017-08-11 | 合肥工业大学(马鞍山)高新技术研究院 | A kind of AGV paths planning methods based on shortest path depth optimization algorithm |
-
2017
- 2017-09-25 CN CN201710873089.6A patent/CN107885198A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102708698A (en) * | 2012-06-12 | 2012-10-03 | 北京理工大学 | Vehicle optimal-path navigation method based on vehicle internet |
| EP2677384A1 (en) * | 2012-06-19 | 2013-12-25 | Ricoh Company, Ltd. | Remote control of an autonomous vehicle |
| CN102800243A (en) * | 2012-07-18 | 2012-11-28 | 湖南大学科技园有限公司 | Anti-counterfeiting annular code and encoding method thereof |
| CN103488176A (en) * | 2013-09-29 | 2014-01-01 | 中国科学院深圳先进技术研究院 | Automatic guided vehicle scheduling method and system |
| CN106556406A (en) * | 2016-11-14 | 2017-04-05 | 北京特种机械研究所 | Many AGV dispatching methods |
| CN106444791A (en) * | 2016-12-20 | 2017-02-22 | 南阳师范学院 | Design method of multiple AGV (Automatic Guided Vehicle) unified dispatching system by upper computer |
| CN106650873A (en) * | 2016-12-21 | 2017-05-10 | 深圳若步智能科技有限公司 | Identification code, and automatic guiding vehicle rapid navigation method and system |
| CN107036618A (en) * | 2017-05-24 | 2017-08-11 | 合肥工业大学(马鞍山)高新技术研究院 | A kind of AGV paths planning methods based on shortest path depth optimization algorithm |
Non-Patent Citations (6)
| Title |
|---|
| UMAR ALI UMAR: "Hybrid multiobjective genetic algorithms for integrated dynamic scheduling and routing of jobs and automated-guided vehicle (AGV) in flexible manufacturing systems (FMS) environment", 《INT J ADV MANUF TECHNOL》 * |
| 夏谦: "遗传算法在AGV全局路径优化中的应用", 《四川大学学报(自然科学版)》 * |
| 杨小明: "《自动集装箱码头设计与仿真》", 31 January 2016 * |
| 王佳溶: "基于改进的两阶段控制策略的AGV路径", 《机械科学与技术》 * |
| 王福利: "《2001中国控制与决策学术年会论文集》", 31 December 2001 * |
| 黄静云: "《自动化里立体仓库一本通》", 30 November 2010 * |
Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108681322A (en) * | 2018-04-13 | 2018-10-19 | 汇专科技集团股份有限公司 | AGV carriage walkings and avoidance obstacle method and system |
| CN108983779B (en) * | 2018-07-24 | 2021-12-21 | 合肥哈工库讯智能科技有限公司 | AGV trolley traffic control regulation and control method based on path analysis |
| CN108983779A (en) * | 2018-07-24 | 2018-12-11 | 安徽库讯自动化设备有限公司 | A kind of AGV trolley traffic control regulation method based on path analysis |
| CN111275370A (en) * | 2018-12-04 | 2020-06-12 | 北京京东尚科信息技术有限公司 | AGV dynamic scheduling method, system, equipment and storage medium |
| CN109557886A (en) * | 2018-12-28 | 2019-04-02 | 芜湖哈特机器人产业技术研究院有限公司 | A kind of grating map and more AGV dispatching methods based on grating map |
| CN112388627A (en) * | 2019-08-19 | 2021-02-23 | 维布络有限公司 | Method and system for executing tasks in dynamic heterogeneous robot environment |
| CN110942203A (en) * | 2019-12-03 | 2020-03-31 | 青岛港国际股份有限公司 | Automatic container terminal AGV path optimization method |
| CN111091238A (en) * | 2019-12-03 | 2020-05-01 | 青岛港国际股份有限公司 | Automatic container terminal AGV intelligent scheduling method |
| CN110942203B (en) * | 2019-12-03 | 2023-11-10 | 青岛港国际股份有限公司 | Automatic container terminal AGV path optimization method |
| CN111091238B (en) * | 2019-12-03 | 2023-08-25 | 青岛港国际股份有限公司 | Automatic container terminal AGV intelligent scheduling method |
| CN110980084A (en) * | 2019-12-13 | 2020-04-10 | 灵动科技（北京）有限公司 | Warehousing system and related method |
| CN111523789A (en) * | 2020-04-20 | 2020-08-11 | 厦门大学嘉庚学院 | Multi-AGV real-time scheduling method based on step length |
| CN113570103A (en) * | 2020-09-14 | 2021-10-29 | 宁波舜宇智能科技有限公司 | Path control method, path control device, electronic device and storage medium |
| CN113870602A (en) * | 2021-09-28 | 2021-12-31 | 湖南大学 | Method and system for dispatching multiple AGV parking |
| CN114148959A (en) * | 2021-12-13 | 2022-03-08 | 哈尔滨工业大学芜湖机器人产业技术研究院 | Laser forklift path searching method |
| CN114463979A (en) * | 2022-02-10 | 2022-05-10 | 骁越科技（青岛）有限公司 | AGV traffic avoiding method and device for outdoor non-fixed communication network |
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