CN116562481A - AGV multi-target point autonomous navigation method, system, terminal and storage medium - Google Patents
AGV multi-target point autonomous navigation method, system, terminal and storage medium Download PDFInfo
- Publication number
- CN116562481A CN116562481A CN202310361339.3A CN202310361339A CN116562481A CN 116562481 A CN116562481 A CN 116562481A CN 202310361339 A CN202310361339 A CN 202310361339A CN 116562481 A CN116562481 A CN 116562481A
- Authority
- CN
- China
- Prior art keywords
- priority value
- station
- data
- transportation
- priority
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 42
- 238000003860 storage Methods 0.000 title claims abstract description 23
- 238000004519 manufacturing process Methods 0.000 claims abstract description 45
- 238000010586 diagram Methods 0.000 claims abstract description 39
- 238000004364 calculation method Methods 0.000 claims abstract description 31
- 239000011159 matrix material Substances 0.000 claims abstract description 31
- 238000012163 sequencing technique Methods 0.000 claims abstract description 3
- 238000001514 detection method Methods 0.000 claims description 23
- 238000004590 computer program Methods 0.000 claims description 10
- 238000009434 installation Methods 0.000 claims description 10
- 238000007405 data analysis Methods 0.000 claims description 7
- 238000009826 distribution Methods 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000003247 decreasing effect Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000000052 comparative effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
- G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/40—Business processes related to the transportation industry
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/60—Electric or hybrid propulsion means for production processes
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Physics (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- Pure & Applied Mathematics (AREA)
- Computational Mathematics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Operations Research (AREA)
- Marketing (AREA)
- General Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Databases & Information Systems (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Algebra (AREA)
- Quality & Reliability (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Probability & Statistics with Applications (AREA)
- Game Theory and Decision Science (AREA)
- Evolutionary Biology (AREA)
- Development Economics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Entrepreneurship & Innovation (AREA)
- Life Sciences & Earth Sciences (AREA)
- Computing Systems (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention discloses an AGV multi-target point autonomous navigation method, a system, a terminal and a storage medium, which belong to the field of intelligent factories, wherein the scheme comprises the steps of acquiring the position and production information of each station in a production line; determining stations to be transported according to the production information of each station; constructing a priority value judging index library and determining a priority value judging index; acquiring priority value judging data of the station according to the priority value judging index, and generating a priority value judging data matrix; generating a transportation priority calculation model according to the priority judgment index, and calculating the priority of the station to be transported according to the transportation priority calculation model and the priority judgment data matrix; sequencing the transportation sequence of the stations to be transported according to the calculated priority value of the stations to be transported, and generating a transportation route diagram according to the final transportation sequence. The AGV transport control method and device based on the AGV transport control system have the advantages that priority value calculation is conducted on multiple target points, transport sequence is determined according to the priority values, reasonable transport routes are planned, and AGV transport efficiency is improved.
Description
Technical Field
The invention relates to the technical field of intelligent factories, in particular to an AGV multi-target point autonomous navigation method, an AGV multi-target point autonomous navigation system, a terminal and a storage medium.
Background
The intelligent factory is a new stage of informatization development of modern factories. On the basis of a digital factory, the information management and service are enhanced by utilizing the technology of the Internet of things and the equipment monitoring technology; clearly grasp the production and marketing flow, improve the controllability of the production process, reduce the manual intervention on the production line, timely and correctly collect the production line data, and reasonably schedule and schedule the production. And the novel technologies such as a green intelligent means and an intelligent system are integrated, so that a humanized factory which is efficient, energy-saving, environment-friendly and comfortable in environment is constructed. Is the result of the practical application of IBM "smart earth" concepts in manufacturing.
At present, the existing AGVs can acquire the transport request information of each station, plan the transport route of the AGVs according to the sending sequence of the transport request information, and transport cargoes of each station in sequence.
In carrying out the present application, the inventors have found that the above-described technique has at least the following problems: the AGV transport route planned in the order in which the transport request information was sent, there is a consideration of the optimal path, resulting in lower transport efficiency.
Disclosure of Invention
In order to solve the problem that the path planning of the existing AGV aiming at multiple target points lacks consideration of the optimal path, the application provides an AGV multi-target point autonomous navigation method, an AGV multi-target point autonomous navigation system, a terminal and a storage medium.
In a first aspect, the present application provides an AGV multi-target autonomous navigation method, which adopts the following technical scheme:
an AGV multi-target point autonomous navigation method comprises the following steps:
the method comprises the steps of obtaining the position and production information of each station in a production line, wherein the production information comprises production progress and goods information;
determining stations to be transported with transportation requirements according to the production progress of each station;
constructing a priority value judging index library, and determining a priority value judging index aiming at cargo information;
acquiring priority value judging data of the station according to the priority value judging index, and generating a priority value judging data matrix, wherein the priority value judging data matrix comprises a plurality of priority value judging sub-data;
generating a transportation priority calculation model according to the priority judgment indexes, and calculating the priority of the station to be transported according to the transportation priority calculation model and the priority judgment data matrix, wherein the transportation priority calculation model is provided with a corresponding weight ratio for each priority judgment sub-data;
sequencing the transportation sequence of the stations to be transported according to the calculated priority value of the stations to be transported, and generating a transportation route diagram according to the final transportation sequence.
Through adopting above-mentioned technical scheme, in actual transportation, there are multiple factors and can lead to the fact the influence to the transportation time, according to the priority value judgement index in the priority value judgement index storehouse, calculate the transportation priority value of each station, confirm the highest station of priority value to the transportation route of this station is planned, thereby improves AGV's actual transportation efficiency.
In a specific embodiment, the calculating the priority value of the station to be transported according to the transportation priority value calculation model and the priority value determination data matrix specifically includes:
obtaining a priority value judging data matrix, calculating the score corresponding to each priority value judging sub-data through a transportation priority value calculating model, and calculating the priority value of the station to be transported according to the weight ratio corresponding to each priority value judging sub-data:
specifically, regarding the weight ratio corresponding to the priority value judging sub-data as a coefficient, generating a corresponding coefficient matrix according to the distribution of the priority value judging sub-data in the priority value judging data matrix, wherein the coefficient matrix is Y, the priority value judging data matrix is X, and calculating according to the following formula;
X·Y T ＝(x 1 ,x 2 ,x 3 ,...,x n )·(λ 1 ,λ 2 ,λ 3 ,...,λ n ) T ，λ 1 +λ 2 +λ 3 +...+λ n ＝1；
wherein x is a score corresponding to the priority value determination sub-data, and λ is a coefficient corresponding to the priority value determination sub-data.
By adopting the technical scheme, the scores of all the priority value judging sub-data are calculated, the calculated scores are converted according to the weight ratio corresponding to the priority value judging sub-data to obtain actual scores, the actual scores of all the priority value judging sub-data in a single station are added to obtain the priority value of the station to be transported, and the score ratio of each priority value judging sub-data is increased or decreased by setting the corresponding weight ratio because the influence of all the priority value judging sub-data on the actual transportation time is different, so that the accuracy of the finally calculated priority value is improved.
In a specific embodiment, the priority value determination sub-data includes cargo quantity data, cargo weight data, and station position data.
Through adopting above-mentioned technical scheme, the time of loading and unloading goods is influenced to cargo quantity data, and cargo weight data influences the travel speed of AGV, and station position data influences the actual time of AGV to the station, through the analysis to above-mentioned three aspects, calculates the priority value of waiting to transport the station to be convenient for confirm the station that the priority value is highest, and plan the transportation route to this station, improve AGV's actual transport efficiency.
In a specific embodiment, before calculating the priority value of the station to be transported according to the transport priority value calculation model and the priority value determination data matrix, the method includes:
acquiring transportation mode information of a station to be transported;
judging whether the goods at the station to be transported are transported integrally or not according to the transportation mode information;
if the goods to be transported at the station are transported in whole, the quantity data of the goods is 1.
By adopting the technical scheme, because the transportation mode information of cargoes corresponding to each station is different, cargoes of certain stations need be put on a goods shelf for integral transportation, so that the loading and unloading time of single cargoes does not exist, the time of single loading and unloading is calculated, and the accuracy of calculation of the priority value can be maintained under different conditions.
In a specific embodiment, the method further comprises:
acquiring the actual loading quantity and the actual unloading quantity of the goods;
judging whether the actual loading quantity is consistent with the actual unloading quantity;
if the actual loading quantity is inconsistent with the actual unloading quantity, which represents the condition that the goods fall in the transportation process, acquiring an actual transportation route diagram and other AGV transportation route diagrams;
judging the overlap ratio of the actual transport line diagram and the transport line diagrams of other AGVs, and selecting the AGV corresponding to the transport line diagram with the highest overlap ratio;
and generating a cargo detection task, and synchronizing the actual transportation route diagram and the cargo detection task to the AGV corresponding to the transportation route diagram with the highest overlap ratio.
Through adopting above-mentioned technical scheme, AGVs is carrying out the in-process of goods transportation, probably has the condition that the goods dropped, because the specialty of wisdom mill, in order to improve the intellectuality of whole transportation, consequently, need other AGVs to pick up the goods that drops and handle, through the similarity of the transportation route diagram of comparing actual transportation route diagram and other AGVs, select the AGVs that the transportation route diagram that the contact ratio is highest corresponds to send goods detection task to it, thereby pick up and transport the acquisition that drops, improved the intellectuality of whole transportation.
In a specific embodiment, the method further comprises the step of judging whether the AGV receiving the cargo detection task meets the condition of executing the cargo detection task or not, and specifically comprises the following steps:
acquiring cargo volume data and cargo weight data of the dropped cargo;
acquiring actual installation conditions, and judging whether loading conditions are met according to cargo volume data, cargo weight data and the actual installation conditions, wherein the actual installation conditions comprise residual loadable space data and residual bearing capacity, and the met loading conditions comprise meeting space conditions and meeting bearing conditions;
if the cargo volume data is not greater than the remaining installable space data, then the space condition is satisfied;
if the weight data of the goods is not more than the residual bearing capacity, the bearing condition is met;
judging whether the space condition and the bearing condition are met at the same time;
if the space condition and the bearing condition are met at the same time, executing a cargo detection task;
if the space condition and the bearing condition are not met at the same time, the cargo detection task is not executed, and the road obstacle avoidance treatment is carried out.
Through adopting above-mentioned technical scheme, carrying out the ability judgement to the AGV of receiving the goods detection task, judging whether this AGV has sufficient space and has sufficient bearing capacity to pick up the goods that drop, if do not satisfy the condition of picking up, can reselect assorted AGV to pick up the goods that drop to acquire to drop and pick up and transport, improve the intellectuality of whole transportation.
In a specific embodiment, the method further comprises:
acquiring waiting time of a station to be transported and a preset waiting time threshold;
judging whether the waiting time of the station to be transported is larger than a waiting time threshold value or not;
if the waiting time of the station to be transported is larger than the waiting time threshold, planning a transport route diagram according to the position of the station to be transported, and carrying out cargo transport treatment.
Through adopting above-mentioned technical scheme, to some transport stations that wait that the transportation time is longer, if do not have AGV to transport its goods for a long time, can be waiting to when certain time, arrange the AGV to transport its goods, reduce to wait for transport station latency longer, influence the condition emergence of actual time limit for a project.
In a second aspect, the present application provides an AGV multi-target autonomous navigation system, which adopts the following technical scheme: an AGV multi-target point autonomous navigation system comprises a data acquisition module, a data analysis module, an index storage module, a priority value calculation module and a route planning module;
the data acquisition module is used for acquiring the position and production information of each station in the production line;
the data analysis module is used for determining stations to be transported, which have transportation requirements, according to the production information of each station;
the index storage module is used for storing index items for judging the station transportation priority value;
the priority value calculation module is used for constructing a transportation priority value calculation model according to the stored index items and calculating the priority value of the station to be transported;
the route planning module is used for carrying out route planning on the station to be transported with the largest priority value according to the priority value of the station to be transported.
Through adopting above-mentioned technical scheme, in actual transportation, there are multiple factors and can lead to the fact the influence to the transportation time, according to the priority value judgement index in the priority value judgement index storehouse, calculate the transportation priority value of each station, confirm the highest station of priority value to the transportation route of this station is planned, thereby improves AGV's actual transportation efficiency.
In a third aspect, the present application provides an intelligent terminal, which adopts the following technical scheme;
an intelligent terminal comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the computer program realizes the AGV multi-target point autonomous navigation method according to any one of the above when being executed by the processor.
Through adopting above-mentioned technical scheme, in actual transportation, there are multiple factors and can lead to the fact the influence to the transportation time, according to the priority value judgement index in the priority value judgement index storehouse, calculate the transportation priority value of each station, confirm the highest station of priority value to the transportation route of this station is planned, thereby improves AGV's actual transportation efficiency.
In a fourth aspect, the present application provides a computer readable storage medium, which adopts the following technical solutions;
a computer readable storage medium comprising a readable storage medium and a computer program stored for execution on the readable storage medium, the computer program loaded and executed by a processor to implement an AGV multi-target autonomous navigation method as described in any of the above.
Through adopting above-mentioned technical scheme, in actual transportation, there are multiple factors and can lead to the fact the influence to the transportation time, according to the priority value judgement index in the priority value judgement index storehouse, calculate the transportation priority value of each station, confirm the highest station of priority value to the transportation route of this station is planned, thereby improves AGV's actual transportation efficiency.
In summary, the present application includes at least one of the following beneficial technical effects:
1. in the actual transportation process, a plurality of factors influence the transportation time, the transportation priority value of each station is calculated according to the priority value judging indexes in the priority value judging index library, the station with the highest priority value is determined, and the transportation route of the station is planned, so that the actual transportation efficiency of the AGV is improved.
2. Because the transportation mode information of the cargos corresponding to each station is different, the cargos of some stations are required to be put on a goods shelf for integral transportation, so that the loading and unloading time of single cargos does not exist, only the time of single loading and unloading is calculated, and the accuracy of calculating the priority value can be maintained under different conditions.
3. AGVs are carrying out the in-process of goods transportation, probably there is the condition that the goods dropped, because the specificity of wisdom mill, in order to improve the intellectuality of whole transportation, consequently need other AGVs to pick up the processing to the goods that drops, through the similarity of the transportation route diagram of comparative actual transportation route diagram and other AGVs, select the AGVs that the transportation route diagram that the contact ratio is highest corresponds to send goods detection task to it, thereby pick up and transport the acquisition that drops, improved the intellectuality of whole transportation.
Drawings
FIG. 1 is a schematic diagram of an overall structure of an AGV multi-target autonomous navigation system according to an embodiment of the present application.
Fig. 2 is an overall flow chart of an AGV multi-target autonomous navigation method according to an embodiment of the present application.
Fig. 3 is a schematic flow chart of calculating a priority value of a station to be transported in the embodiment of the application.
Reference numerals illustrate:
1. a data acquisition module; 2. a data analysis module; 3. an index storage module; 4. a priority value calculation module; 5. and a route planning module.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
An embodiment of the application discloses an AGV multi-target point autonomous navigation system, referring to fig. 1, the system includes a data acquisition module 1, a data analysis module 2, an index storage module 3, a priority calculation module 4 and a route planning module 5, specifically:
the data acquisition module 1 is used for acquiring the position and production information of each station in the production line;
the production information comprises production progress and cargo information, and the time when the cargo on the station needs to be transported can be predicted according to the production progress.
The data analysis module 2 is used for determining stations to be transported, which have transportation requirements, according to the production information of each station; the index storage module 3 is used for storing a priority value judging index for judging the station transportation priority value;
the priority value judging indexes are mainly determined according to the cargo information, and the priority value judging indexes corresponding to different cargo information are different.
The priority value calculation module 4 is used for constructing a transportation priority value calculation model according to the stored index items and calculating the priority value of the station to be transported;
the route planning module 5 is used for carrying out route planning on the station to be transported with the largest priority value according to the priority value of the station to be transported.
In the implementation, in the actual transportation process, a plurality of factors influence the transportation time, the transportation priority value of each station is calculated according to the priority value judging indexes in the priority value judging index library, the station with the highest priority value is determined, and the transportation route of the station is planned, so that the actual transportation efficiency of the AGV is improved.
The following describes in detail the implementation of an AGV multi-target point autonomous navigation method in combination with an AGV multi-target point autonomous navigation system:
referring to fig. 2, another embodiment of the present application provides an AGV multi-target autonomous navigation method, including the following steps:
s10, acquiring the position and production information of each station in a production line;
the position of the acquired station is the relative position between the station and the AGV, and the production information comprises the production progress and the goods information.
S20, determining stations to be transported with transportation requirements according to the production progress of each station;
in this application an embodiment, through setting for the production progress threshold value, compare actual production progress and the production progress threshold value of predetermineeing, when actual production progress is greater than the production progress threshold value, represent the goods of this station need transport, when actual production progress is not greater than the production progress threshold value, can calculate the time that needs to carry out the goods transportation according to the production efficiency of station and actual production progress to send the transportation signal in advance.
S30, constructing a priority value judging index library, and determining a priority value judging index aiming at cargo information;
s40, acquiring priority value judging data of the station according to the priority value judging index, and generating a priority value judging data matrix, wherein the priority value judging data matrix comprises a plurality of priority value judging sub-data;
specifically, the priority determination sub-data includes cargo quantity data, cargo weight data, and station position data. The load and unload time is influenced by the cargo quantity data, the traveling speed of the AGV is influenced by the cargo weight data, and the actual time from the AGV to the station is influenced by the station position data.
S50, generating a transportation priority calculation model according to the priority judgment indexes, and calculating the priority of the station to be transported according to the transportation priority calculation model and the priority judgment data matrix, wherein the transportation priority calculation model is provided with a corresponding weight ratio for each priority judgment sub-data;
the accuracy of the finally calculated priority value is improved by setting corresponding weight ratio, and increasing or decreasing fraction ratio of each priority value judging sub-data.
S60, sorting the transportation sequence of the stations to be transported according to the calculated priority value of the stations to be transported, and generating a transportation route diagram according to the final transportation sequence.
In one embodiment of the present application, referring to fig. 3, calculating the priority value of the station to be transported according to the transportation priority value calculation model and the priority value determination data matrix specifically includes the following steps:
a10, acquiring a priority value judgment data matrix;
a20, calculating the score corresponding to each priority value judgment sub-data according to the transportation priority value calculation model;
a30, calculating the priority value of the station to be transported according to the weight ratio corresponding to each priority value judging sub-data.
Specifically, regarding the weight ratio corresponding to the priority value judging sub-data as a coefficient, generating a corresponding coefficient matrix according to the distribution of the priority value judging sub-data in the priority value judging data matrix, wherein the coefficient matrix is Y, the priority value judging data matrix is X, and calculating according to the following formula;
X·Y T ＝(x 1 ,x 2 ,x 3 ,...,x n )·(λ 1 ,λ 2 ,λ 3 ,...,λ n ) T ，λ 1 +λ 2 +λ 3 +...+λ n ＝1；
wherein x is a score corresponding to the priority value determination sub-data, and λ is a coefficient corresponding to the priority value determination sub-data.
In the implementation, the score of each priority value judging sub-data is calculated, the calculated score is converted according to the weight ratio corresponding to the priority value judging sub-data to obtain an actual score, the actual scores of all the priority value judging sub-data in a single station are added to obtain the priority value of the station to be transported, and the accuracy of the finally calculated priority value is improved by setting the corresponding weight ratio and improving or reducing the score ratio of each priority value judging sub-data due to the different influence of each priority value judging sub-data on the actual transportation time.
In one embodiment of the present application, because the transportation mode information of the cargos corresponding to each station is different, the cargos of some stations need to be put on the shelf for overall transportation, so that there is no loading and unloading time of a single cargo, and the calculation of the priority value for the different transportation mode information specifically includes the following steps:
b10, acquiring transportation mode information of a station to be transported;
b20, judging whether the goods at the station to be transported are transported integrally according to the transportation mode information;
if the goods to be transported at the station are transported in whole, the quantity data of the goods is 1.
In practice, the actual single loading and unloading time is negligible for the goods which do not need to be loaded and unloaded singly, and in order to improve the accuracy of the priority value, the calculation is carried out according to the loading and unloading time of 1 time.
In this application an embodiment, AGV is carrying out the in-process of cargo transportation, probably has the condition that the goods dropped, in order to reduce the influence of dropping the goods to other AGV transport routes, need pick up and transport the goods that drop, specifically include the following steps:
c10, acquiring the actual loading quantity and the actual unloading quantity of the goods;
c20, judging whether the actual loading quantity is consistent with the actual unloading quantity;
if the actual loading quantity is inconsistent with the actual unloading quantity, which represents the condition that the goods fall in the transportation process, acquiring an actual transportation route diagram and other AGV transportation route diagrams;
c30, judging the coincidence ratio of the actual transport line diagram and the transport line diagrams of other AGVs, and selecting the AGV corresponding to the transport line diagram with the highest coincidence ratio;
and C40, generating a cargo detection task, and synchronizing the actual transportation route diagram and the cargo detection task to the AGV corresponding to the transportation route diagram with the highest overlap ratio.
Wherein, because each AGV has its own transportation task to the goods installation space and the maximum bearing capacity of each AGV are limited, therefore the AGV that carries out the goods that drops that actually selects is unable to confirm whether can accomplish the picking up and transporting of the goods that drops.
In implementation, after the AGV receives the cargo detection task, it can determine whether the AGV meets the condition of executing the cargo detection task, and specifically includes the following steps:
d10, acquiring cargo volume data and cargo weight data of the dropped cargo;
d20, acquiring actual installation conditions, and judging whether the loading conditions are met according to the cargo volume data, the cargo weight data and the actual installation conditions;
the actual installation condition comprises residual loadable space data and residual bearing capacity, and the satisfied loading conditions comprise satisfied space conditions and satisfied bearing conditions.
If the cargo volume data is not greater than the remaining installable space data, then the space condition is satisfied;
if the weight data of the goods is not more than the residual bearing capacity, the bearing condition is met;
d30, judging whether the space condition and the bearing condition are met at the same time;
if the space condition and the bearing condition are met at the same time, executing a cargo detection task;
if the space condition and the bearing condition are not met at the same time, the cargo detection task is not executed, and the road obstacle avoidance treatment is carried out.
In this application, when an AGV carries out the goods and detects the task, compare the actual transportation route diagram that obtains and the actual transportation route diagram of self, to having the region of coincidence, the AGV carries out infrared scanning to the surrounding environment in the transportation, judges whether there is the goods that drop in the transportation route. If detect the goods that drop, then pick up the goods to according to current position, compare AGV and self discharge station's distance and AGV and the distance of the station that drops the goods place, if AGV and self discharge station's distance is nearer, then carry out the transportation of self goods preferentially, if AGV and the distance of the station that drops the goods place are nearer, then transport the goods that drops preferentially.
In practice, if no dropped load is found in the overlapping area, the transport of the load is preferentially performed, and after the transport is completed, the AGV travels to the non-overlapping area, scans the non-overlapping area, and detects the dropped load.
In another embodiment of the present application, when the AGV does not perform the load detection task, a request signal is generated that cannot be performed and the request signal is fed back to the AGV that dropped the load, and the AGV that dropped the load again finds a new AGV. After the AGV that the AGV will not carry out the request signal feedback that produces drops the goods compares the actual transportation circuit diagram that obtains and self actual transportation circuit diagram, to there being the region of coincidence, the AGV carries out infrared scanning to surrounding environment in the transportation, judges whether there is the goods that drop in the transportation route. If the falling goods are detected, the road conditions are analyzed, whether the AGV can normally pass through the AGV under the premise of not touching the falling goods is judged, if the AGV can normally pass through the AGV under the premise of not touching the falling goods, the current route is kept to continue running, if the AGV can not normally pass through the AGV under the premise of not touching the falling goods, the route planning is conducted again based on the current position of the AGV, and a new route is selected to go forward.
In still another embodiment of the present application, when the AGV does not execute the cargo detection task, and detects the cargo that drops in the traveling process, can preferentially judge whether the AGV bears the cargo currently, if the current AGV bears the cargo, then carry out route planning again based on the AGV current position and the unloading point that bears the cargo, if the current AGV does not bear the cargo, then select the station that the peripheral transport priority is highest based on the AGV current position, replace current transport task.
Wherein, to some transport stations that wait that transport time is longer, if do not have the AGV to transport its goods for a long time, then can be waiting to when certain time, arrange the AGV to transport its goods, reduce to wait that transport station latency is longer, specifically include the following steps:
acquiring waiting time of a station to be transported and a preset waiting time threshold;
judging whether the waiting time of the station to be transported is larger than a waiting time threshold value or not;
if the waiting time of the station to be transported is larger than the waiting time threshold, planning a transport route diagram according to the position of the station to be transported, and carrying out cargo transport treatment.
In the implementation, through the processing of the stations to be transported, the conditions that the waiting time of the stations to be transported is long and the actual construction period is influenced are reduced.
Based on the same inventive concept, the embodiment of the application also discloses an intelligent terminal. An intelligent terminal comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the computer program is executed by the processor to realize the enterprise assessment method based on road transportation dynamic data.
Based on the same inventive concept, the embodiment of the application also discloses a computer readable storage medium, wherein at least one instruction, at least one section of program, code set or instruction set is stored in the storage medium, and the at least one instruction, the at least one section of program, the code set or the instruction set can be loaded and executed by a processor to realize the enterprise assessment method based on the road transportation dynamic data provided by the embodiment of the method.
It should be understood that references herein to "a plurality" are to two or more. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship.
Those of ordinary skill in the art will appreciate that all or part of the steps implementing the above embodiments may be implemented by hardware, or may be implemented by a program for instructing relevant hardware, and the program may be stored in a computer readable storage medium, where the above mentioned storage medium includes, for example: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing description of the preferred embodiments is merely exemplary in nature and is in no way intended to limit the invention, since it is intended that all modifications, equivalents, improvements, etc. that fall within the spirit and scope of the invention.
Claims (10)
1. An AGV multi-target point autonomous navigation method is characterized by comprising the following steps:
the method comprises the steps of obtaining the position and production information of each station in a production line, wherein the production information comprises production progress and goods information;
determining stations to be transported with transportation requirements according to the production progress of each station;
constructing a priority value judging index library, and determining a priority value judging index aiming at cargo information;
acquiring priority value judging data of the station according to the priority value judging index, and generating a priority value judging data matrix, wherein the priority value judging data matrix comprises a plurality of priority value judging sub-data;
generating a transportation priority calculation model according to the priority judgment indexes, and calculating the priority of the station to be transported according to the transportation priority calculation model and the priority judgment data matrix, wherein the transportation priority calculation model is provided with a corresponding weight ratio for each priority judgment sub-data;
sequencing the transportation sequence of the stations to be transported according to the calculated priority value of the stations to be transported, and generating a transportation route diagram according to the final transportation sequence.
2. The method for autonomous navigation of multiple target points of an AGV according to claim 1, wherein calculating the priority of the station to be transported according to the transport priority calculation model and the priority determination data matrix specifically comprises:
obtaining a priority value judging data matrix, calculating the score corresponding to each priority value judging sub-data through a transportation priority value calculating model, and calculating the priority value of the station to be transported according to the weight ratio corresponding to each priority value judging sub-data:
specifically, regarding the weight ratio corresponding to the priority value judging sub-data as a coefficient, generating a corresponding coefficient matrix according to the distribution of the priority value judging sub-data in the priority value judging data matrix, wherein the coefficient matrix is Y, the priority value judging data matrix is X, and calculating according to the following formula;
X·Y T ＝(x 1 ,x 2 ,x 3 ,...,x n )·(λ 1 ,λ 2 ,λ 3 ,...,λ n ) T ，λ 1 +λ 2 +λ 3 +...+λ n ＝1；
wherein x is a score corresponding to the priority value determination sub-data, and λ is a coefficient corresponding to the priority value determination sub-data.
3. The method of claim 1 wherein said priority decision sub-data includes cargo quantity data, cargo weight data and station position data.
4. The method for autonomous navigation of multiple target points of an AGV according to claim 1, wherein before calculating the priority value of the station to be transported according to the transport priority value calculation model and the priority value determination data matrix, the method comprises:
acquiring transportation mode information of a station to be transported;
judging whether the goods at the station to be transported are transported integrally or not according to the transportation mode information;
if the goods to be transported at the station are transported in whole, the quantity data of the goods is 1.
5. The method of autonomous navigation of an AGV of claim 1, further comprising:
acquiring the actual loading quantity and the actual unloading quantity of the goods;
judging whether the actual loading quantity is consistent with the actual unloading quantity;
if the actual loading quantity is inconsistent with the actual unloading quantity, which represents the condition that the goods fall in the transportation process, acquiring an actual transportation route diagram and other AGV transportation route diagrams;
judging the overlap ratio of the actual transport line diagram and the transport line diagrams of other AGVs, and selecting the AGV corresponding to the transport line diagram with the highest overlap ratio;
and generating a cargo detection task, and synchronizing the actual transportation route diagram and the cargo detection task to the AGV corresponding to the transportation route diagram with the highest overlap ratio.
6. The method of claim 5, further comprising determining whether the AGV receiving the cargo detection task satisfies a condition for performing the cargo detection task, and further comprising the steps of:
acquiring cargo volume data and cargo weight data of the dropped cargo;
acquiring actual installation conditions, and judging whether loading conditions are met according to cargo volume data, cargo weight data and the actual installation conditions, wherein the actual installation conditions comprise residual loadable space data and residual bearing capacity, and the met loading conditions comprise meeting space conditions and meeting bearing conditions;
if the cargo volume data is not greater than the remaining installable space data, then the space condition is satisfied;
if the weight data of the goods is not more than the residual bearing capacity, the bearing condition is met;
judging whether the space condition and the bearing condition are met at the same time;
if the space condition and the bearing condition are met at the same time, executing a cargo detection task;
if the space condition and the bearing condition are not met at the same time, the cargo detection task is not executed, and the road obstacle avoidance treatment is carried out.
7. The method of autonomous navigation of an AGV of claim 1, further comprising:
acquiring waiting time of a station to be transported and a preset waiting time threshold;
judging whether the waiting time of the station to be transported is larger than a waiting time threshold value or not;
if the waiting time of the station to be transported is larger than the waiting time threshold, planning a transport route diagram according to the position of the station to be transported, and carrying out cargo transport treatment.
8. The AGV multi-target point autonomous navigation system is characterized by comprising a data acquisition module (1), a data analysis module (2), an index storage module (3), a priority value calculation module (4) and a route planning module (5);
the data acquisition module (1) is used for acquiring the position and production information of each station in the production line;
the data analysis module (2) is used for determining stations to be transported with transportation requirements according to production information of each station;
the index storage module (3) is used for storing index items for judging station transportation priority values;
the priority value calculation module (4) is used for constructing a transportation priority value calculation model according to the stored index items and calculating the priority value of the station to be transported;
the route planning module (5) is used for carrying out route planning on the station to be transported with the largest priority value according to the priority value of the station to be transported.
9. An intelligent terminal, comprising a memory, a processor and a computer program stored in the memory and operable on the processor, wherein the computer program when executed by the processor implements an AGV multi-target autonomous navigation method according to any one of claims 1 to 7.
10. A computer readable storage medium comprising a readable storage medium and a computer program stored for execution on said readable storage medium, said computer program loaded and executed by a processor to implement an AGV multi-target autonomous navigation method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310361339.3A CN116562481B (en) | 2023-04-06 | 2023-04-06 | AGV multi-target point autonomous navigation method, system, terminal and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310361339.3A CN116562481B (en) | 2023-04-06 | 2023-04-06 | AGV multi-target point autonomous navigation method, system, terminal and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116562481A true CN116562481A (en) | 2023-08-08 |
| CN116562481B CN116562481B (en) | 2023-12-15 |
Family
ID=87492321
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310361339.3A Active CN116562481B (en) | 2023-04-06 | 2023-04-06 | AGV multi-target point autonomous navigation method, system, terminal and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116562481B (en) |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR19990058363A (en) * | 1997-12-30 | 1999-07-15 | 오상수 | Path search method and apparatus for vehicle navigation system |
| CN105675002A (en) * | 2016-01-27 | 2016-06-15 | 闫凯 | Multi-waypoint navigation route planning method and system |
| US20190114564A1 (en) * | 2017-10-18 | 2019-04-18 | United Parcel Service Of America, Inc. | Enriched Logistics System for Unmanned Vehicle Delivery of Parcels |
| CN109974711A (en) * | 2019-04-12 | 2019-07-05 | 重庆渝博创智能装备研究院有限公司 | A kind of AGV multiple target point autonomous navigation method towards wisdom factory |
| CN111105084A (en) * | 2019-12-10 | 2020-05-05 | 珠海格力电器股份有限公司 | Logistics information processing method, device and equipment and computer storage medium |
| CN113031603A (en) * | 2021-03-05 | 2021-06-25 | 湖南大学 | Task priority based collaborative path planning method for multiple logistics robots |
| WO2021254415A1 (en) * | 2020-06-18 | 2021-12-23 | 北京卫星制造厂有限公司 | Time window-based agv intelligent scheduling method |
| CN114331257A (en) * | 2021-12-06 | 2022-04-12 | 上海东普信息科技有限公司 | Logistics transportation loading management method, device, equipment and storage medium |
| CN114707820A (en) * | 2022-03-17 | 2022-07-05 | 高斯机器人(深圳)有限公司 | Cargo transportation method and device, terminal equipment and readable storage medium |
| CN114895690A (en) * | 2022-06-28 | 2022-08-12 | 济南大学 | Robot path planning method and system based on dynamic weighting and thermodynamic diagram algorithm |
| CN115860613A (en) * | 2023-02-28 | 2023-03-28 | 南京邮电大学 | Part load and goods matching and vehicle scheduling method considering reservation mechanism |
| CN115879603A (en) * | 2022-11-17 | 2023-03-31 | 武汉大学 | Multi-target-point-oriented multi-unmanned aerial vehicle cooperative data acquisition method and device |
-
2023
- 2023-04-06 CN CN202310361339.3A patent/CN116562481B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR19990058363A (en) * | 1997-12-30 | 1999-07-15 | 오상수 | Path search method and apparatus for vehicle navigation system |
| CN105675002A (en) * | 2016-01-27 | 2016-06-15 | 闫凯 | Multi-waypoint navigation route planning method and system |
| US20190114564A1 (en) * | 2017-10-18 | 2019-04-18 | United Parcel Service Of America, Inc. | Enriched Logistics System for Unmanned Vehicle Delivery of Parcels |
| CN109974711A (en) * | 2019-04-12 | 2019-07-05 | 重庆渝博创智能装备研究院有限公司 | A kind of AGV multiple target point autonomous navigation method towards wisdom factory |
| CN111105084A (en) * | 2019-12-10 | 2020-05-05 | 珠海格力电器股份有限公司 | Logistics information processing method, device and equipment and computer storage medium |
| WO2021254415A1 (en) * | 2020-06-18 | 2021-12-23 | 北京卫星制造厂有限公司 | Time window-based agv intelligent scheduling method |
| CN113031603A (en) * | 2021-03-05 | 2021-06-25 | 湖南大学 | Task priority based collaborative path planning method for multiple logistics robots |
| CN114331257A (en) * | 2021-12-06 | 2022-04-12 | 上海东普信息科技有限公司 | Logistics transportation loading management method, device, equipment and storage medium |
| CN114707820A (en) * | 2022-03-17 | 2022-07-05 | 高斯机器人(深圳)有限公司 | Cargo transportation method and device, terminal equipment and readable storage medium |
| CN114895690A (en) * | 2022-06-28 | 2022-08-12 | 济南大学 | Robot path planning method and system based on dynamic weighting and thermodynamic diagram algorithm |
| CN115879603A (en) * | 2022-11-17 | 2023-03-31 | 武汉大学 | Multi-target-point-oriented multi-unmanned aerial vehicle cooperative data acquisition method and device |
| CN115860613A (en) * | 2023-02-28 | 2023-03-28 | 南京邮电大学 | Part load and goods matching and vehicle scheduling method considering reservation mechanism |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116562481B (en) | 2023-12-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10846625B2 (en) | Autonomous condensing of pallets of items in a warehouse | |
| WO2019154445A2 (en) | Warehouse entry/exit control method for shelf array, and transportation system | |
| CN111796596B (en) | Automatic warehouse picking method and device, computer equipment and storage medium | |
| CN114415610B (en) | Scheduling method and device for robot, electronic equipment and storage medium | |
| CN111724015B (en) | Method, device, equipment and storage medium for switching transport tasks | |
| CN111860919A (en) | Method, device, medium and electronic equipment for determining storage strategy of library shelf | |
| WO2022228132A1 (en) | Logistics order task processing method and apparatus, electronic device, and computer medium | |
| CN112396362A (en) | Method and device for determining driving destination and storage medium | |
| CN114047769B (en) | Control method and device of unmanned carrier | |
| CN112193953B (en) | Elevator resource scheduling method and device | |
| US20240004403A1 (en) | Battery charge state based mission assignment for autonomous mobile robots | |
| CN116562481B (en) | AGV multi-target point autonomous navigation method, system, terminal and storage medium | |
| CN112415953A (en) | Elevator dispatching method and device | |
| WO2023226756A1 (en) | Device scheduling method, electronic device, and storage medium | |
| CN113534787A (en) | AGV scheduling method and device, electronic equipment and readable storage medium | |
| WO2022027357A1 (en) | Goods picking method and system in unmanned environment, and computer readable storage medium | |
| CN112462754B (en) | Automatic AGV control system | |
| CN114548880A (en) | Logistics planning method, equipment, device and storage medium | |
| CN117273606B (en) | Unmanned carrier scheduling method and system based on intelligent warehouse | |
| US20240094742A1 (en) | Method and apparatus for controlling logistics robot | |
| CN117196263B (en) | Cargo scheduling method, device, equipment and computer readable storage medium | |
| US20240227183A9 (en) | Local Idle Time Utilization in Centrally Controlled Mobile Robots | |
| CN112462755B (en) | AGV control system | |
| US20230415996A1 (en) | Warehouse work control system and warehouse work control method | |
| CN114066340A (en) | Method, device, equipment and medium for adjusting position of stock container |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |