CN113919543A - AGV dispatching path optimization method based on 5G Internet of things - Google Patents

AGV dispatching path optimization method based on 5G Internet of things Download PDF

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CN113919543A
CN113919543A CN202110991694.XA CN202110991694A CN113919543A CN 113919543 A CN113919543 A CN 113919543A CN 202110991694 A CN202110991694 A CN 202110991694A CN 113919543 A CN113919543 A CN 113919543A
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陈建勋
吴清财
李�一
赖岳华
刘宇航
苗圃
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Abstract

The invention discloses an AGV dispatching path optimization method based on a 5G Internet of things, which comprises the following steps that firstly, an MES system generates a carrying task according to a production work order and issues the carrying task to a WMS system; the method comprises the steps that a WMS system receives a task instruction, generates an AGV carrying task and then constructs a mathematical model of a search state of AGV dispatching path planning; the AGV control system solves the scheduling scheme according to a heuristic artificial intelligence algorithm and outputs an AGV path planning optimization scheme. Aiming at the constraint problems of the conventional AGV dispatching system in the aspects of dispatching quantity, data processing and algorithm optimization, the characteristics of large bandwidth, low time delay and multiple connections of the 5G Internet of things are applied, and the AGV path planning, real-time dispatching and position tracking are more flexible by combining the AGV management and control system with an artificial intelligence algorithm, so that the transportation efficiency and stability are improved, and the application of 5G enabled intelligent manufacturing is realized.

Description

AGV dispatching path optimization method based on 5G Internet of things
Technical Field
The invention relates to the field of Automatic Guided Vehicle (AGV) production scheduling of a 5G factory, in particular to an AGV scheduling path optimization method based on an artificial intelligence algorithm and based on a 5G Internet of things.
Background
The data volume generated, collected and processed by equipment in an intelligent factory is more and more, and a 4G network is difficult to meet the requirement of intellectualization; the WIFI communication mode has the problems of weak anti-interference performance, low transmission rate, connection interruption during switching and the like in an outdoor scene, cannot completely cover a wide factory environment outside a workshop, cannot effectively position and monitor logistics vehicles outside the workshop in real time, and is easy to cause the problems of untimely receiving of scheduling tasks, low transportation efficiency and the like when the traditional manual carrying equipment carries out logistics carrying tasks in the outdoor scene.
For a plurality of types, small batches and a plurality of batches of discrete flexible production operation modes, each process of a workpiece can have different machine selections, so that the AGV has a plurality of routes, the workpiece transfer time is different, the processing time of different machine equipment is inconsistent, different combinations have different results, and great challenges are brought to the actual path planning of the AGV.
For an intelligent manufacturing workshop, an AGV scheduling strategy determines whether the workshop can run efficiently to a great extent, the AGV scheduling problem belongs to an NP-hard problem, and the complexity of the AGV scheduling problem grows exponentially along with the scale of the problem. Zhang Bo et al studied to reduce the number of turns of the AGV based on a-x algorithm to improve the efficiency of AGV use. Bilgemit et al studied the travel path conflicts of AGV based on the time window method. The Yangzhao et al researches a self-adaptive multi-target genetic-differential evolution algorithm by constructing a multi-target scheduling optimization model of a job shop. However, the methods still have some defects, the actual production system has the situations of equipment sudden failure, production plan change and the like, some researches do not consider the conflict problem of the AGVs in the workshop scheduling process, and for the problem of intelligent and dynamic workshop scheduling in the discrete industry, the scheduling process is complex, and the AGV scheduling strategy and the path optimization algorithm in the manufacturing process need to be further determined.
The 5G network has the characteristic of high reliability, has better anti-interference capability and stability, and is favorable for realizing the dynamic dispatching and transportation of the AGV in a factory. In addition, based on the characteristic of low time delay of the 5G network, the position and the running state of the AGV are better fed back to the AGV control system in real time, and the optimal path planning and the real-time scheduling are carried out by combining an artificial intelligence algorithm, so that the logistics transportation in a factory is more efficient and smooth, the delivery beat of product materials is improved, the labor cost is saved, the production waiting time is reduced, the productivity is favorably improved, and the output value is enlarged.
Disclosure of Invention
The invention aims to provide an AGV dispatching path optimization method based on a 5G Internet of things, aiming at the defects in the prior art, and particularly aiming at the problems that an AGV dispatching strategy is unreasonable in the actual production process, the transportation efficiency is low, an AGV cannot be effectively positioned and monitored in real time outside a workshop and the like due to the fact that a certain scientific guidance basis is lacked in the AGV dynamic dispatching of a flexible job workshop in the prior art.
The technical solution for realizing the purpose of the invention is as follows: an AGV dispatching path optimization method based on a 5G Internet of things comprises the following steps:
step 1, the MES system generates a carrying task according to a production work order and issues the carrying task to the WMS system;
step 2, the WMS system receives a task instruction and generates an AGV carrying task;
step 3, constructing a mathematical model of a search state of the AGV dispatching path planning according to the production task information and the material calling information of the on-site material calling system;
step 4, the AGV management and control system solves according to a heuristic artificial intelligence algorithm based on the position of the AGV, the position of the initial station and the position of the target station, and optimally plans the traveling route of the AGV;
and 5, judging whether the production task of the upper system is updated, if so, returning to execute the step 3, and otherwise, outputting the optimal AGV scheduling strategy scheme.
An AGV dispatching path optimizing system based on a 5G Internet of things comprises the following modules:
the work order generation and issuing module comprises: the system is used for generating a carrying task according to the production work order and generating an AGV carrying task;
an AGV dispatching path planning model construction module: the mathematical model is used for constructing a search state of the AGV dispatching path planning;
a path planning module: and solving and outputting the optimal AGV scheduling strategy scheme according to a heuristic algorithm.
Compared with the prior art, the invention has the remarkable advantages that:
(1) based on the characteristics of high reliability, low time delay, strong anti-interference capability and strong stability of a 5G network, the invention realizes the dynamic path planning and scheduling of the cooperative operation of a plurality of AGV in a factory, and feeds back the position and the running state of the AGV to an AGV control system in real time;
(2) according to the method, on the basis of describing the AGV scheduling problem of the flexible job shop, a mathematical model of a path optimization searching state in the AGV scheduling process is established, a heuristic artificial intelligence algorithm is adopted to search a state with the minimum estimated value in a queue, a solution strategy is given according to the conflict type, the real-time AGV scheduling optimization schemes with good performance inside and outside the shop can be effectively obtained, the transportation efficiency of the whole logistics is improved, the production waiting time is reduced, and the logistics transportation in a factory area is more efficient and smooth;
(3) the method is integrated with a high-grade scheduling and storage system in an upper system, considers the abnormal conditions of equipment, materials and the like in production, realizes the AGV dynamic scheduling in the production process, reduces unnecessary resource waste in the process while obtaining a better optimization effect, reduces the production cost and improves the comprehensive competitiveness of manufacturing enterprises.
The invention is further illustrated by the accompanying drawings and the detailed description.
Drawings
Fig. 1 is a functional architecture diagram of an AGV dispatch path optimization method based on a 5G internet of things.
FIG. 2 is a flowchart of an AGV dispatching path optimizing method based on the 5G Internet of things.
FIG. 3 is a flowchart illustrating the optimization of a heuristic artificial intelligence algorithm according to the present invention.
Detailed Description
An AGV dispatching path optimization method based on a 5G Internet of things comprises the following steps:
step 1, an MES system (a manufacturing execution system) generates a carrying task according to a production work order and issues the carrying task to a WMS system (a warehousing management system);
step 2, a WMS system (a warehousing management system) receives a task instruction and generates an AGV carrying task;
step 3, constructing a mathematical model of a search state of the AGV dispatching path planning according to the production task information and the material calling information of the on-site material calling system;
because the electronic map formed between the actual workshop production line equipment and the warehouse is irregular in shape, and layout fields and resource equipment can be restricted and limited, the AGV dispatching path optimization problem is complex, objects involved in the logistics dispatching process are simplified and abstracted correspondingly, and the following model assumptions are made according to the actual production operation characteristics of the workshop:
the mathematical model, its preconditions (constraints) include:
(1) at any moment, each AGV only carries the same material, and each machine only processes one workpiece;
(2) the running speed of the AGV trolley under the load or no load is not influenced, and the AGV trolley returns to an appointed stopping point to wait after completing a task;
(3) each station is provided with a cache area for storing workpieces to be processed and processed;
(4) the processing equipment resource conflict is dynamically scheduled and monitored in state by a workshop manufacturing execution system MES through high-level scheduling, and an AGV control system is responsible for receiving the data;
(5) the AGV trolley can stop at any station in a planned route of a workshop to carry out loading or unloading operation, the loading/unloading time of the AGV trolley at each station is known, and the distance between each station is known;
(6) the AGV trolley route is a bidirectional single channel, and only one AGV trolley is allowed to pass through each node and each road section at the same time;
(7) considering collision and conflict problems between AGVs;
(8) setting a site exclusive queue and a shared queue, scheduling the AGV to preferentially enter the site exclusive queue by the AGV management and control system, and entering the shared queue to queue if the exclusive queue is full, wherein the shared queue supports to be shared by a plurality of sites;
(9) when the AGV enters the shared queue, if the site exclusive queue is found to have a vacancy, the AGV management and control system can automatically schedule the AGV to directly go to the site exclusive queue for queuing.
The mathematical model is as follows:
Figure BDA0003232563400000041
wherein, tkiIndicates the time t from the k AGV receiving the upper system task to the beginning of executionkjIndicates the time taken for the kth AGV to complete the task, fkThe situation that the kth AGV receives a task shows the expectation from the current point to the terminal point, and Q is a priority queue used for maintaining all search states;
a represents the current point at which the AGV is located, dir represents the direction in which the AGV is facing, tiIndicates the time, t, at which the AGV reaches point ajRepresenting the maximum time to travel from point a.
Step 4, the AGV management and control system solves the AGV according to a heuristic artificial intelligence algorithm based on the position of the AGV, the position of the initial station and the position of the target station, and performs optimal planning on the traveling route of the AGV, and the method specifically comprises the following steps:
step 4-1, constructing an AGV driving map and boundary conditions, and using M to represent a point set and an edge set of the AGV driving map:
M＝(P,L)
the method comprises the following steps that P represents a point set of positioning points of the AGV trolley, L represents an edge set and represents connectivity between the points, and for any one edge L belongs to the L, if two ends of any one edge L are connected with a point P and a point r, L is (P, r); setting a starting point o and an end point e of an AGV path and a current time tpre；
Step 4-2, constructing a mathematical model of a search state:
S＝(a，dir，ti，tj)
where a represents the current point at which the AGV is located, dir represents the direction in which the AGV is facing, and t represents the distance between the AGV and the current pointiIndicates the time, t, at which the AGV reaches point ajRepresenting the maximum time to drive off point a;
when the AGV is at tiTo tjThe time period moves from point a to point b along direction dir through edge l, and at this time, the state transition needs to be considered, and the mathematical model for constructing the new search state is as follows:
S′＝(b，dir′,ti+tc+tc′,tj+tc)
wherein, tcTime spent for AGV from a to b, tc'is the time for the AGV to go from the original orientation dir to the new orientation dir' after reaching point a;
4-3, defining an edge l in a known conflict set, and if two AGV trolleys are completely overlapped in the same direction and completely or partially overlapped in the opposite direction at the same time, collision can occur;
Figure BDA0003232563400000051
l ═ p, r), construct the set of conflict edges:
conf (L) { L '| L' ═ x, y) ∈ L, x ═ p, or x ═ r; y ═ p or y ═ r }
Step 4-4, enumerating all possible directions dir, and adding an initial search state into an empty priority queue Q:
S＝(o,dir,tpre,+∞)
wherein o is the starting point of AGV driving, dir is the direction of AGV driving, tpreIs the current time;
when the AGV is at tiTo tjWhen the moving path from the point a to the point b along the direction dir in the time period is a conflict path, adopting an artificial intelligence algorithm-based adjustment strategy to obtain a new path driving state:
S′＝(a,dir′,ti′,tj′)
wherein dir 'is the new direction of AGV, t'i＝ti+tc+tc′，t′j＝tj+tc，tcIndicates the time the AGV spends from a to b, tc'is the time for the AGV to go from the original orientation dir to the new orientation dir' after reaching point a;
if the condition is satisfied
ti≥t′iAnd t isj≤t′j
Obtaining a new path strategy through algorithm-based adjustment, otherwise, adopting a speed-based adjustment strategy, namely keeping an original path driving strategy of the AGV;
and 4-5, based on the mathematic model for avoiding conflict and deadlock, the optimized target of the AGV path planning is the total time for completing all AGV vehicle operations of the work order scheduling task, the state with the minimum estimated value in the queue is searched according to a heuristic algorithm to serve as a taking-out condition, and the mathematic model for constructing the AGV scheduling path optimization is as follows:
Figure BDA0003232563400000061
in the formula, tkiIndicates the time t from the k AGV receiving the upper system task to the beginning of executionkjIndicates the time taken for the k-th AGV to complete the task，fkThe situation that the kth AGV receives a task shows the expectation from the current point to the terminal point, and Q is a priority queue used for maintaining all search states;
Figure BDA0003232563400000062
step 4-6, judging whether the work order scheduling task queue is empty, if the work order scheduling task queue is empty and the state with the minimum evaluation value is not taken out, finishing the algorithm, indicating that no legal path exists between the starting point o and the end point e, and otherwise, turning to step 4-7;
and 4-7, judging whether a is equal to e, if so, indicating that an optimized path is found, ending the algorithm and outputting the optimal AGV scheduling strategy scheme, otherwise, returning to the step 4-5.
The constraint conditions for optimally planning the travel route of the AGV are as follows:
(1) the number of AGV vehicles in the scheduling task is not more than the total number of vehicles in the factory,
Figure BDA0003232563400000063
(2) there are no oncoming vehicles on the travel path p to r, and the number of vehicles on the path p to r should not be greater than the maximum number of vehicles that the path can accommodate:
Figure BDA0003232563400000064
wherein l is a selected path length, lAGVThe length of the AGV when loaded;
wherein xkiAnd ykjDefined by the following constraints;
Figure BDA0003232563400000065
Figure BDA0003232563400000066
furthermore, the AGV management and control system comprises a service arrangement system, a control system and a scheduling system;
the service arrangement system is used for receiving the upper system task instruction and then automatically generating an AGV carrying task according to warehouse in and out information of the warehouse system, production information of a workshop of a factory building, real-time position and cargo carrying of the AGV and other information;
the control system is used for communicating with the AGV in real time through a 5G wireless network of the AGV working area, acquiring information such as the position and the running state of the AGV, acquiring running state information of field equipment and monitoring the running condition of the AGV;
the dispatching system is used for receiving a production dispatching instruction and dispatching the AGV through the electronic map to realize the optimization and coordination of paths from a warehouse to a factory building and between stations, and the AGV vehicles are dispatched and arranged through an intelligent algorithm by combining specific tasks of the factory based on the principles of time priority, path priority and task balance.
And 5, judging whether the production task of the upper system is updated, if so, returning to execute the step 3, and otherwise, outputting the optimal AGV scheduling strategy scheme.
Furthermore, AGV management and control system and upper system (manufacturing execution system MES and warehouse management system WMS), AGV hardware equipment pass through 5G communication connection, and 5G wireless thing networking adopts 5G basic station, 5G to insert module and wireless router and realizes that 5G network is to indoor and outdoor full coverage.
An AGV dispatching path optimizing system based on a 5G Internet of things comprises the following modules:
the work order generation and issuing module comprises: the system is used for generating a carrying task according to the production work order and generating an AGV carrying task;
an AGV dispatching path planning model construction module: the mathematical model is used for constructing a search state of the AGV dispatching path planning;
a path planning module: and solving and outputting the optimal AGV scheduling strategy scheme according to a heuristic algorithm.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
step 1, the MES system generates a carrying task according to a production work order and issues the carrying task to the WMS system;
step 2, the WMS system receives a task instruction and generates an AGV carrying task;
step 3, constructing a mathematical model of a search state of the AGV dispatching path planning according to the production task information and the material calling information of the on-site material calling system;
step 4, the AGV management and control system solves according to a heuristic artificial intelligence algorithm based on the position of the AGV, the position of the initial station and the position of the target station, and optimally plans the traveling route of the AGV;
and 5, judging whether the production task of the upper system is updated, if so, returning to execute the step 3, and otherwise, outputting the optimal AGV scheduling strategy scheme.
A computer-storable medium having stored thereon a computer program which, when executed by a processor, performs the steps of:
step 1, the MES system generates a carrying task according to a production work order and issues the carrying task to the WMS system;
step 2, the WMS system receives a task instruction and generates an AGV carrying task;
step 3, constructing a mathematical model of a search state of the AGV dispatching path planning according to the production task information and the material calling information of the on-site material calling system;
step 4, the AGV management and control system solves according to a heuristic artificial intelligence algorithm based on the position of the AGV, the position of the initial station and the position of the target station, and optimally plans the traveling route of the AGV;
and 5, judging whether the production task of the upper system is updated, if so, returning to execute the step 3, and otherwise, outputting the optimal AGV scheduling strategy scheme.
The present invention will be further described with reference to the following examples and the accompanying drawings.
Examples
With reference to fig. 1 and fig. 2, an AGV dispatch path optimization method based on a 5G internet of things includes the following steps:
step 1, an MES system (a manufacturing execution system) generates a carrying task according to a production work order and issues the carrying task to a WMS system (a warehousing management system);
step 2, a WMS system (a warehousing management system) receives a task instruction and generates an AGV carrying task;
step 3, constructing a mathematical model of a search state of the AGV dispatching path planning according to the production task information and the material calling information of the on-site material calling system;
because the electronic map formed between the actual workshop production line equipment and the warehouse is irregular in shape, and layout fields and resource equipment can be restricted and limited, the AGV dispatching path optimization problem is complex, objects involved in the logistics dispatching process are simplified and abstracted correspondingly, and the following model assumptions are made according to the actual production operation characteristics of the workshop:
the mathematical model, its preconditions (constraints) include:
(1) at any moment, each AGV only carries the same material, and each machine only processes one workpiece;
(2) the running speed of the AGV trolley under the load or no load is not influenced, and the AGV trolley returns to an appointed stopping point to wait after completing a task;
(3) each station is provided with a cache area for storing workpieces to be processed and processed;
(4) the processing equipment resource conflict is dynamically scheduled and monitored in state by a workshop manufacturing execution system MES through high-level scheduling, and an AGV control system is responsible for receiving the data;
(5) the AGV trolley can stop at any station in a planned route of a workshop to carry out loading or unloading operation, the loading/unloading time of the AGV trolley at each station is known, and the distance between each station is known;
(6) the AGV trolley route is a bidirectional single channel, and only one AGV trolley is allowed to pass through each node and each road section at the same time;
(7) considering collision and conflict problems between AGVs;
(8) setting a site exclusive queue and a shared queue, scheduling the AGV to preferentially enter the site exclusive queue by the AGV management and control system, and entering the shared queue to queue if the exclusive queue is full, wherein the shared queue supports to be shared by a plurality of sites;
(9) when the AGV enters the shared queue, if the site exclusive queue is found to have a vacancy, the AGV management and control system can automatically schedule the AGV to directly go to the site exclusive queue for queuing.
The mathematical model is as follows:
Figure BDA0003232563400000091
wherein, tkiIndicates the time t from the k AGV receiving the upper system task to the beginning of executionkjIndicates the time taken for the kth AGV to complete the task, fkThe situation that the kth AGV receives a task shows the expectation from the current point to the terminal point, and Q is a priority queue used for maintaining all search states;
a represents the current point at which the AGV is located, dir represents the direction in which the AGV is facing, tiIndicates the time, t, at which the AGV reaches point ajRepresenting the maximum time to travel from point a.
Step 4, with reference to fig. 3, the AGV management and control system solves the AGV based on the position of the AGV, the position of the start station, and the position of the target station according to a heuristic artificial intelligence algorithm, and optimally plans the traveling route of the AGV, specifically including the following steps:
step 4-1, constructing an AGV driving map and boundary conditions, and using M to represent a point set and an edge set of the AGV driving map:
M＝(P,L)
the method comprises the following steps that P represents a point set of positioning points of the AGV trolley, L represents an edge set and represents connectivity between the points, and for any one edge L belongs to the L, if two ends of any one edge L are connected with a point P and a point r, L is (P, r); setting a starting point o and an end point e of an AGV path and a current time tpre；
Step 4-2, constructing a mathematical model of a search state:
S＝(a,dir,ti,tj)
where a represents the current point at which the AGV is located, dir represents the direction in which the AGV is facing, and t represents the distance between the AGV and the current pointiIndicates the time, t, at which the AGV reaches point ajIndicating the maximum distance from point aA large time;
when the AGV is at tiTo tjThe time period moves from point a to point b along direction dir through edge l, and at this time, the state transition needs to be considered, and the mathematical model for constructing the new search state is as follows:
S′＝(b,dir′,ti+tc+tc′，tj+tc)
wherein, tcTime spent for AGV from a to b, tc'is the time for the AGV to go from the original orientation dir to the new orientation dir' after reaching point a;
4-3, defining an edge l in a known conflict set, and if two AGV trolleys are completely overlapped in the same direction and completely or partially overlapped in the opposite direction at the same time, collision can occur;
Figure BDA0003232563400000102
l ═ p, r), construct the set of conflict edges:
conf (L) { L '| L' ═ x, y) ∈ L, x ═ p, or x ═ r; y ═ p or y ═ r }
Step 4-4, enumerating all possible directions dir, and adding an initial search state into an empty priority queue Q:
S＝(o,dir,tpre,+∞)
wherein o is the starting point of AGV driving, dir is the direction of AGV driving, tpreIs the current time;
when the AGV is at tiTo tjWhen the moving path from the point a to the point b along the direction dir in the time period is a conflict path, adopting an artificial intelligence algorithm-based adjustment strategy to obtain a new path driving state:
S′＝(a,dir′,t′i,t′j)
wherein dir 'is the new direction of AGV, t'i＝ti+tc+tc′，t′j＝tj+tc，tcIndicates the time the AGV spends from a to b, tc'is the time for the AGV to go from the original orientation dir to the new orientation dir' after reaching point a;
if the condition is satisfied
ti≥t′iAnd t isj≤t′j
Obtaining a new path strategy through algorithm-based adjustment, otherwise, adopting a speed-based adjustment strategy, namely keeping an original path driving strategy of the AGV;
and 4-5, based on the mathematic model for avoiding conflict and deadlock, the optimized target of the AGV path planning is the total time for completing all AGV vehicle operations of the work order scheduling task, the state with the minimum estimated value in the queue is searched according to a heuristic algorithm to serve as a taking-out condition, and the mathematic model for constructing the AGV scheduling path optimization is as follows:
Figure BDA0003232563400000101
in the formula, tkiIndicates the time t from the k AGV receiving the upper system task to the beginning of executionkjIndicates the time taken for the kth AGV to complete the task, fkThe situation that the kth AGV receives a task shows the expectation from the current point to the terminal point, and Q is a priority queue used for maintaining all search states;
Figure BDA0003232563400000111
step 4-6, judging whether the work order scheduling task queue is empty, if the work order scheduling task queue is empty and the state with the minimum evaluation value is not taken out, finishing the algorithm, indicating that no legal path exists between the starting point o and the end point e, and otherwise, turning to step 4-7;
and 4-7, judging whether a is equal to e, if so, indicating that an optimized path is found, ending the algorithm and outputting the optimal AGV scheduling strategy scheme, otherwise, returning to the step 4-5.
The constraint conditions for optimally planning the travel route of the AGV are as follows:
(1) the number of AGV vehicles in the scheduling task is not more than the total number of vehicles in the factory,
Figure BDA0003232563400000112
(2) there are no oncoming vehicles on the travel path p to r, and the number of vehicles on the path p to r should not be greater than the maximum number of vehicles that the path can accommodate:
Figure BDA0003232563400000113
wherein l is a selected path length, lAGVThe length of the AGV when loaded;
wherein xkiAnd ykjDefined by the following constraints;
Figure BDA0003232563400000114
Figure BDA0003232563400000115
furthermore, the AGV management and control system comprises a service arrangement system, a control system and a scheduling system;
the service arrangement system is used for receiving the upper system task instruction and then automatically generating an AGV carrying task according to warehouse in and out information of the warehouse system, production information of a workshop of a factory building, real-time position and cargo carrying of the AGV and other information;
the control system is used for communicating with the AGV in real time through a 5G wireless network of the AGV working area, acquiring information such as the position and the running state of the AGV, acquiring running state information of field equipment and monitoring the running condition of the AGV;
the dispatching system is used for receiving a production dispatching instruction and dispatching the AGV through the electronic map to realize the optimization and coordination of paths from a warehouse to a factory building and between stations, and the AGV vehicles are dispatched and arranged through an intelligent algorithm by combining specific tasks of the factory based on the principles of time priority, path priority and task balance.
And 5, judging whether the production task of the upper system is updated, if so, returning to execute the step 3, and otherwise, outputting the optimal AGV scheduling strategy scheme.
Furthermore, AGV management and control system and upper system (manufacturing execution system MES and warehouse management system WMS), AGV hardware equipment pass through 5G communication connection, and 5G wireless thing networking adopts 5G basic station, 5G to insert module and wireless router and realizes that 5G network is to indoor and outdoor full coverage.
The method provided by the invention applies the characteristics of large bandwidth, low time delay and multiple connections of the 5G Internet of things, and the AGV path planning, real-time scheduling and position tracking are more flexible by combining the AGV management and control system and the artificial intelligence algorithm, so that the transportation efficiency and stability are improved, and the application of 5G enabled intelligent manufacturing is realized.
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (9)

1. An AGV dispatching path optimization method based on a 5G Internet of things is characterized by comprising the following steps:
step 1, the MES system generates a carrying task according to a production work order and issues the carrying task to the WMS system;
step 2, the WMS system receives a task instruction and generates an AGV carrying task;
step 3, constructing a mathematical model of a search state of the AGV dispatching path planning according to the production task information and the material calling information of the on-site material calling system;
step 4, the AGV management and control system solves according to a heuristic artificial intelligence algorithm based on the position of the AGV, the position of the initial station and the position of the target station, and optimally plans the traveling route of the AGV;
and 5, judging whether the production task of the upper system is updated, if so, returning to execute the step 3, and otherwise, outputting the optimal AGV scheduling strategy scheme.
2. The AGV dispatching path optimizing method based on 5G internet of things according to claim 1, wherein the mathematical model of the searching state of the AGV dispatching path planning in the step 3 includes the following preconditions:
(1) at any moment, each AGV only carries the same material, and each machine only processes one workpiece;
(2) the running speed of the AGV trolley under the load or no load is not influenced, and the AGV trolley returns to an appointed stopping point to wait after completing a task;
(3) each station is provided with a cache area for storing workpieces to be processed and processed;
(4) the processing equipment resource conflict is dynamically scheduled and monitored in state by a workshop manufacturing execution system MES through high-level scheduling, and an AGV control system is responsible for receiving the data;
(5) the AGV trolley can stop at any station in a planned route of a workshop to carry out loading or unloading operation, the loading/unloading time of the AGV trolley at each station is known, and the distance between each station is known;
(6) the AGV trolley route is a bidirectional single channel, and only one AGV trolley is allowed to pass through each node and each road section at the same time;
(7) considering collision and conflict problems between AGVs;
(8) setting a site exclusive queue and a shared queue, scheduling the AGV to preferentially enter the site exclusive queue by the AGV management and control system, and entering the shared queue to queue if the exclusive queue is full, wherein the shared queue supports to be shared by a plurality of sites;
(9) when the AGV enters the shared queue, if the site exclusive queue is found to have a vacancy, the AGV management and control system can automatically schedule the AGV to directly go to the site exclusive queue for queuing.
3. The AGV dispatching path optimizing method based on the 5G Internet of things according to claim 2, wherein the mathematical model in the step 3 is as follows:
Figure FDA0003232563390000021
wherein, tkiIndicates the time t from the k AGV receiving the upper system task to the beginning of executionkjIndicates the time taken for the kth AGV to complete the task, fkThe situation that the kth AGV receives a task shows the expectation from the current point to the terminal point, and Q is a priority queue used for maintaining all search states;
a represents the current point at which the AGV is located, dir represents the direction in which the AGV is facing, tiIndicates the time, t, at which the AGV reaches point ajRepresenting the maximum time to travel from point a.
4. The AGV dispatching path optimizing method based on the 5G Internet of things according to claim 2, wherein the optimal planning of the travel route of the AGV in the step 4 specifically comprises the following steps:
step 4-1, constructing an AGV driving map and boundary conditions, and using M to represent a point set and an edge set of the AGV driving map:
M＝(P，L)
the method comprises the following steps that P represents a point set of positioning points of the AGV trolley, L represents an edge set and represents connectivity between the points, and for any one edge L belongs to the L, if two ends of any one edge L are connected with a point P and a point r, L is (P, r); setting a starting point o and an end point e of an AGV path and a current time tpre；
Step 4-2, constructing a mathematical model of a search state:
S＝(a，dir，ti，tj)
where a represents the current point at which the AGV is located, dir represents the direction in which the AGV is facing, and t represents the distance between the AGV and the current pointiIndicates the time, t, at which the AGV reaches point ajRepresenting the maximum time to drive off point a;
when the AGV is at tiTo tjThe time period moves from point a to point b along direction dir through edge l, and at this time, the state transition needs to be considered, and the mathematical model for constructing the new search state is as follows:
s′＝(b，dir′，ti+tc+tc′，tj+tc)
wherein, tcTime spent from a to b for AGV，tc'is the time for the AGV to go from the original orientation dir to the new orientation dir' after reaching point a;
4-3, defining an edge l in a known conflict set, and if two AGV trolleys are completely overlapped in the same direction and completely or partially overlapped in the opposite direction at the same time, collision can occur;
Figure FDA0003232563390000031
constructing a protruding edge set:
conf (L) { L '| L' ═ x, y) ∈ L, x ═ p, or x ═ r; y ═ p or y ═ r }
Step 4-4, enumerating all possible directions dir, and adding an initial search state into an empty priority queue Q:
S＝(o，dir，tpre，+∞)
wherein o is the starting point of AGV driving, dir is the direction of AGV driving, tpreIs the current time;
when the AGV is at tiTo tjWhen the moving path from the point a to the point b along the direction dir in the time period is a conflict path, adopting an artificial intelligence algorithm-based adjustment strategy to obtain a new path driving state:
S′＝(a，dir′，t′i，t′j)
wherein dir 'is the new direction of AGV, t'i＝ti+tc+tc′，t′j＝tj+tc，tcIndicates the time the AGV spends from a to b, tc'is the time for the AGV to go from the original orientation dir to the new orientation dir' after reaching point a;
if the condition is satisfied
ti≥t′iAnd t isj≤t′j
Obtaining a new path strategy through algorithm-based adjustment, otherwise, adopting a speed-based adjustment strategy, namely keeping an original path driving strategy of the AGV;
and 4-5, based on the mathematic model for avoiding conflict and deadlock, the optimized target of the AGV path planning is the total time for completing all AGV vehicle operations of the work order scheduling task, the state with the minimum estimated value in the queue is searched according to a heuristic algorithm to serve as a taking-out condition, and the mathematic model for constructing the AGV scheduling path optimization is as follows:
Figure FDA0003232563390000032
in the formula, tkiIndicates the time t from the k AGV receiving the upper system task to the beginning of executionkiIndicates the time taken for the kth AGV to complete the task, fkThe situation that the kth AGV receives a task shows the expectation from the current point to the terminal point, and Q is a priority queue used for maintaining all search states;
Figure FDA0003232563390000033
step 4-6, judging whether the work order scheduling task queue is empty, if the work order scheduling task queue is empty and the state with the minimum evaluation value is not taken out, finishing the algorithm, indicating that no legal path exists between the starting point o and the end point e, and otherwise, turning to step 4-7;
and 4-7, judging whether a is equal to e, if so, indicating that an optimized path is found, ending the algorithm and outputting the optimal AGV scheduling strategy scheme, otherwise, returning to the step 4-5.
5. The AGV dispatching path optimizing method based on the 5G Internet of things according to claim 4, wherein the constraint conditions for optimally planning the traveling route of the AGV are as follows:
(1) the number of AGV vehicles in the scheduling task is not more than the total number of vehicles in the factory,
Figure FDA0003232563390000041
(2) there are no oncoming vehicles on the travel path p to r, and the number of vehicles on the path p to r should not be greater than the maximum number of vehicles that the path can accommodate:
Figure FDA0003232563390000042
wherein l is a selected path length, lAGVThe length of the AGV when loaded;
wherein xkiAnd YkjDefined by the following constraints;
Figure FDA0003232563390000043
Figure FDA0003232563390000044
6. the AGV dispatching path optimizing method based on the 5G Internet of things according to claim 1, wherein the AGV management and control system is in 5G communication connection with an upper system and AGV hardware equipment, and a 5G wireless Internet of things network adopts a 5G base station, a 5G access module and a wireless router to realize indoor and outdoor full coverage of the 5G network.
7. The AGV dispatching path optimizing system based on the 5G Internet of things is characterized by comprising the following modules:
the work order generation and issuing module comprises: the system is used for generating a carrying task according to the production work order and generating an AGV carrying task;
an AGV dispatching path planning model construction module: the mathematical model is used for constructing a search state of the AGV dispatching path planning;
a path planning module: and solving and outputting the optimal AGV scheduling strategy scheme according to a heuristic algorithm.
8. A computer arrangement comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method as claimed in any one of claims 1-6 are implemented by the processor when executing the computer program.
9. A computer-storable medium having a computer program stored thereon, wherein the computer program is adapted to carry out the steps of the method according to any one of the claims 1-6 when executed by a processor.
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