CN110503260A - A kind of AGV dispatching method based on active path planning - Google Patents
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Abstract
The present invention discloses a kind of AGV dispatching method based on active path planning, belong to Optimum Scheduling Technology field, the strategy initializes the work-yard AGV, the scheduling of different accuracy can be achieved, and initial work map, practical map can be simulated, can solve the problems, such as more unit multitasks under any scene；The adjacency matrix and distance matrix established based on Freud's algorithm, scheduling is optimized to the avoidance situation of AGV selection course and AGV in the process of implementation, realize the execution route of single step Dynamic Programming AGV, shorten AGV traveling time, working efficiency is improved, industrial automation is produced to the conversion of the direction of flexible production mode and is played an important role.
Description
Technical field
The present invention relates to Optimum Scheduling Technology field more particularly to a kind of AGV dispatching methods based on active path planning.
Background technique
With the development of automation, more stringent requirements are proposed to warehousing management, logistics transportation for manufacturing industry, and AGV relies on it
Plurality of advantages becomes the key equipment in the field, plays a part of can not be substituted in automatic transportation.AGV trolley is that realization is soft
Property manufacture system pith, and AGV dispatches core technology as AGV design, is always domestic and international manufacturing industry, automation
The research emphasis and difficult point in field.Therefore, the research for the problems such as carrying out the dispatching method to more AGV, path planning, to the field
There is important meaning.
For multitask system under complex scene AGV dispatching method since there are path conflict, resource utilization be not high
The problems such as, cause dynamic environment the case where deadlock occur.At this stage in terms of for path conflict problem, there is scholar to propose one kind
The strategy adjusted based on speed reconciliation geometric path；There are also scholars with AGV transport vehicle quantity, task satisfaction, traveling total distance
For target, Model for Multi-Objective Optimization is established, and designs hybrid genetic algorithm and is solved；In terms of being lined up scene, there is scholar to draw
Enter system call model, selecting queue length and waiting time is optimizing index, devises a kind of scheduling strategy；In path planning
Aspect has scholar to propose that a kind of bounded rationality self-organizing method carries out the path planning of more AGV, and this method can effectively solve to appoint
The local competition problem for resource of being engaged in.Although the above method can effectively solve the problems, such as that path conflict and part AGV utilization rate be not high,
But complicated model need to be established, calculating cycle is long, and may be improper due to parameter setting by the calculated optimal policy of model,
Lead to the only locally optimal solution that model is cooked up.
Summary of the invention
In view of the above shortcomings of the prior art, a kind of AGV dispatching method based on active path planning is provided.
The technical solution adopted by the present invention is that a kind of AGV dispatching method based on active path planning, including walk as follows
It is rapid:
Step 1: the work-yard AGV is initialized, work-yard is divided into the rectangular mesh with certain length and width,
The crosspoint of grid lines is known as node, and wherein the length and width of grid are adjustable, to realize the scheduling of different accuracy；
Step 2: initial work map determines AGV working node, and it is 1 that all nodes, which are numbered, in map,
2 ..., n determine that AGV quantity is m, and it is 1,2 that it, which is numbered, ..., m；
Step 3: calculating the shortest distance of any two working node by Freud's algorithm, generate corresponding adjacent
Matrix and distance matrix；
Step 4: task list is ranked up by the priority of task, and the task of highest priority is current task.It is in office
It is engaged in obtaining the starting point of current task in list, passes through idle AGV nearest from task starting point in distance matrix job search place
This task is executed, process is as shown in Figure 1；
Step 4.1: the initialization position AGV, wherein trolley engaged position is occupied node, remaining point is unoccupied section
Point；
Step 4.2: the starting point of current task is obtained in task list；
Step 4.3: judging whether available free AGV, if waiting idle AGV without if；
Step 4.4: if available free AGV, judging whether there is multiple free time AGV；
Step 4.5: if currently only 1 free time AGV, selects the AGV to execute task；
Step 4.6: if current idle AGV quantity is greater than 1, the distance matrix established by Freud's algorithm is counted
Calculate the distance of institute available free AGV arrival task starting point；
Step 4.7: selection calculates the nearest idle AGV of distance and executes this task；
Step 4.8: if being greater than 1 apart from nearest idle AGV quantity, selecting to number the smallest AGV and go execution task.
Step 5: Dynamic Programming AGV executes route, avoids occupied node, the AGV of execution task is made to can reach task
Starting point, process are as shown in Figure 2；
Rapid 5.1: selecting one once unbeaten node or to pass by time interval most apart from last time from adjacent node
Long node；
Step 5.1.1: the current location AGV is labeled as 1, remaining adjacent working node is labeled as 0；
Step 5.1.2: when AGV selects mobile, adjacent working node is all subtracted 1, then will be gone next
Node be changed to 1 again；
Step 5.1.3: the longest node of the time interval i.e. the smallest node of numerical value that selects to pass by apart from last time is next
The node walked；
Step 5.1.4: if there are many point for meeting condition, by adjacency matrix described in claim 1 and apart from square
Battle array calculates AGV and reaches the distance of adjacent unoccupied node and the sum of the distance of adjacent node arrival task starting point；
Step 5.1.5: the selection shortest node of sum of the distance is walked, at this time by the node to be walked in next step while selection
Labeled as having occupied.
Step 5.2: whether the working node that judgment step 5.1 obtains is occupied by AGV, if occupied by AGV, judgement is accounted for
Whether AGV has in execution task；
Step 5.3: if the AGV occupied is in idle condition, selecting one in unappropriated reachable point at random
A working node allows idle AGV to go to execute, if all nodes are all unreachable, are randomly assigned a unappropriated point and allow sky
Not busy AGV goes to execute, while the operated adjacent node motion that the current AGV selection step 5.1 for executing task obtains；
Step 5.4: if the AGV occupied is in running order, the current AGV selection one for executing task is apart from close phase
Neighbors is mobile；
Step 5.5: if the working node that step 5.1 obtains is not occupied by AGV, selecting the working node mobile.
Step 5.6: repeating the execution route that step 5.1 realizes single step Dynamic Programming AGV to step 5.5, Zhi Daobu
Rapid 5.1 obtained adjacent nodes are task starting point, stop circulation.
Step 6: after task starting point picking, Dynamic Programming executes route to AGV again, avoids occupied node, reaches
Task terminal；
Wherein, task terminal is considered as the task starting point in step 5, reaches task terminal Dynamic Programming AGV from picking point and holds
The process of walking along the street line is consistent with the process in step 5；
Step 7: updating task list, new work task cancels the task for having executed completion；
Step 8: repeating step 4 to step 7, until task list is sky.
The beneficial effects of adopting the technical scheme are that
1. paying the utmost attention to idle AGV when selecting AGV, guarantee that the frequency of use of each AGV is stablized；
2. during avoidance, route or travel by vehicle is just thought about before vehicle sets out, avoid in the process of running in order to
Hide other vehicles and changes route.
3. can simulate to practical map, the tune under different accuracy can also be realized by adjusting the length and width of grid
Degree strategy；
4. it is not limited only to the transportation problem in factory, but to the pervasive of more unit multitask problems under any scene
The scheme of property, in terms of can applying to more wide in life.
Detailed description of the invention
Fig. 1 is that AGV of the present invention selects flow chart；
Fig. 2 is AGV path planning process figure of the present invention；
Fig. 3 is gridding component in work-yard in the embodiment of the present invention；
Fig. 4 is initial work map in the embodiment of the present invention, and figure is numbered to working node；
Fig. 5 is the working node location drawing where AGV before dispatching in the embodiment of the present invention；
Fig. 6 is the working node location drawing where AGV after dispatching in the embodiment of the present invention.
Specific embodiment
With reference to the accompanying drawings and examples, specific embodiments of the present invention will be described in further detail.Implement below
Example is not intended to limit the scope of the invention for illustrating the present invention.
The present embodiment regard the region that size is 1600px*800px as work-yard, and the quantity of AGV is 4 in the place.
The method of the present embodiment is as described below:
Step 1: the work-yard that size is 1600px*800px being initialized, work-yard is divided into 100px*
The rectangular mesh of 100px, as shown in figure 3, the crosspoint of grid lines is known as node, wherein the length and width of grid are adjustable, to realize not
With the scheduling of precision；
Step 2: initial work map determines AGV working node, and it is 1 that all nodes, which are numbered, in map,
2 ..., 22, as shown in figure 4, determining that AGV quantity is 4, it is 1,2,3,4 that it, which is numbered,；
Step 3: calculating the shortest distance of any two working node by Freud's algorithm, generate corresponding adjacent
Matrix and distance matrix；
Step 4: task list is ranked up by the priority of task, and the task of highest priority is current task, task
List is as shown in table 1；
The task list that table 1 sorts by priority
| Priority | Task starting point | Task terminal |
| 1 | 3 | 22 |
| 2 | 4 | 18 |
| 3 | 14 | 20 |
| 4 | 13 | 11 |
In task list obtain current task starting point, by distance matrix job search place most from task starting point
Close idle AGV executes this task, and process is as shown in Figure 1；
Step 4.1: the initialization position AGV, the position of 4 AGV is as shown in figure 5, wherein trolley engaged position is occupied
Node, remaining point are unoccupied node；
Step 4.2: the starting point of current task is obtained in task list；
Step 4.3: judging whether available free AGV, if waiting idle AGV without if；
Step 4.4: if available free AGV, judging whether there is multiple free time AGV；
Step 4.5: if currently only 1 free time AGV, selects the AGV to execute task；
Step 4.6: if current idle AGV quantity is greater than 1, the distance matrix established by Freud's algorithm is counted
Calculate the distance of institute available free AGV arrival task starting point；
Step 4.7: selection calculates the nearest idle AGV of distance and executes this task；
Step 4.8: if being greater than 1 apart from nearest idle AGV quantity, selecting to number the smallest AGV and go execution task.
Step 5: Dynamic Programming AGV executes route, avoids occupied node, the AGV of execution task is made to can reach task
Starting point, process are as shown in Figure 2；
Rapid 5.1: selecting one once unbeaten node or to pass by time interval most apart from last time from adjacent node
Long node；
Step 5.1.1: the current location AGV is labeled as 1, remaining adjacent working node is labeled as 0；
Step 5.1.2: when AGV selects mobile, adjacent working node is all subtracted 1, then will be gone next
Node be changed to 1 again；
Step 5.1.3: the longest node of the time interval i.e. the smallest node of numerical value that selects to pass by apart from last time is next
The node walked；
Step 5.1.4: if there are many point for meeting condition, by adjacency matrix described in claim 1 and apart from square
Battle array calculates AGV and reaches the distance of adjacent unoccupied node and the sum of the distance of adjacent node arrival task starting point；
Step 5.1.5: the selection shortest node of sum of the distance is walked, at this time by the node to be walked in next step while selection
Labeled as having occupied.
Step 5.2: whether the working node that judgment step 5.1 obtains is occupied by AGV, if occupied by AGV, judgement is accounted for
Whether AGV has in execution task；
Step 5.3: if the AGV occupied is in idle condition, selecting one in unappropriated reachable point at random
A working node allows idle AGV to go to execute, if all nodes are all unreachable, are randomly assigned a unappropriated point and allow sky
Not busy AGV goes to execute, while the operated adjacent node motion that the current AGV selection step 5.1 for executing task obtains；
Step 5.4: if the AGV occupied is in running order, the current AGV selection one for executing task is apart from close phase
Neighbors is mobile；
Step 5.5: if the working node that step 5.1 obtains is not occupied by AGV, selecting the working node mobile.
Step 5.6: repeating the execution route that step 5.1 realizes single step Dynamic Programming AGV to step 5.5, Zhi Daobu
Rapid 5.1 obtained adjacent nodes are task starting point, stop circulation.
Step 6: after task starting point picking, Dynamic Programming executes route to AGV again, avoids occupied node, reaches
Task terminal；
Wherein, task terminal is considered as the task starting point in step 5, reaches task terminal Dynamic Programming AGV from picking point and holds
The process of walking along the street line is consistent with the process in step 5；
As shown in fig. 6, selecting to number task of being 1 for 1 AGV execution priority, from start node 1 to task starting point 3
After picking, toward 22 delivery of task terminal, during task is carrying out；Select to number task of being 3 for 3 AGV execution priority, from
After start node 15 arrives 14 picking of task node, toward 20 delivery of task terminal, during task is carrying out.
Step 7: updating task list, new work task cancels the task for having executed completion；
Step 8: repeating step 4 to step 7, until task list is sky.
Claims (7)
1. a kind of AGV dispatching method based on active path planning, it is characterised in that include the following steps:
Step 1: the work-yard AGV being initialized, work-yard is divided into the rectangular mesh with certain length and width, grid
The crosspoint of line is known as node, and wherein the length and width of grid are adjustable, to realize the scheduling of different accuracy；
Step 2: initial work map determines AGV working node, and it is 1,2 that all nodes, which are numbered, in map ...,
N determines that AGV quantity is m, and it is 1,2 that it, which is numbered, ..., m；
Step 3: calculating the shortest distance of any two working node by Freud's algorithm, generate corresponding adjacency matrix
And distance matrix；
Step 4: in task list obtain current task starting point, by distance matrix job search place from task starting point
Nearest idle AGV executes this task；
Step 5: Dynamic Programming AGV executes route, avoids occupied node, the AGV of execution task is made to can reach task starting point；
Step 6: after task starting point picking, Dynamic Programming executes route to AGV again, avoids occupied node, reaches task
Terminal；
Step 7: updating task list, new work task cancels the task for having executed completion；
Step 8: repeating step 4 to step 7, until task list is sky.
2. a kind of AGV dispatching method based on active path planning according to claim 1, it is characterised in that: the step
Task list is ranked up by the priority of task in rapid 4, and the task of highest priority is current task.
3. a kind of AGV dispatching method based on active path planning according to claim 1, it is characterised in that: the step
Rapid 4 process is as follows:
Step 4.1: the initialization position AGV, wherein trolley engaged position is occupied node, remaining point is unoccupied node；
Step 4.2: the starting point of current task is obtained in task list；
Step 4.3: judging whether available free AGV, if waiting idle AGV without if；
Step 4.4: if available free AGV, judging whether there is multiple free time AGV；
Step 4.5: if currently only 1 free time AGV, selects the AGV to execute task；
Step 4.6: if current idle AGV quantity is greater than 1, the distance matrix established by Freud's algorithm is calculated
Available free AGV reach the distance of task starting point；
Step 4.7: selection calculates the nearest idle AGV of distance and executes this task；
Step 4.8: if being greater than 1 apart from nearest idle AGV quantity, selecting to number the smallest AGV and go execution task.
4. a kind of AGV dispatching method based on active path planning according to claim 1, it is characterised in that: the step
Process in rapid 5 is as follows:
Step 5.1: selecting one once unbeaten node or to pass by time interval longest apart from last time from adjacent node
Node；
Step 5.2: whether the working node that judgment step 5.1 obtains is occupied by AGV, if occupied by AGV, judges occupancy
Whether AGV has in execution task；
Step 5.3: if the AGV occupied is in idle condition, selecting a work in unappropriated reachable point at random
It allows idle AGV to go to execute as node, if all nodes are all unreachable, are randomly assigned a unappropriated point and allow the free time
AGV goes to execute, while the operated adjacent node motion that the current AGV selection step 5.1 for executing task obtains；
Step 5.4: if the AGV occupied is in running order, the current AGV selection one for executing task is apart from close adjacent segments
Point movement；
Step 5.5: if the working node that step 5.1 obtains is not occupied by AGV, selecting the working node mobile；
Step 5.6: the execution route that step 5.1 realizes single step Dynamic Programming AGV to step 5.5 is repeated, until step 5.1
Obtained adjacent node is task starting point, stops circulation.
5. a kind of AGV dispatching method based on active path planning according to claim 4, it is characterised in that: the step
Rapid 5.1 process is as follows:
Step 5.1.1: the current location AGV is labeled as 1, remaining adjacent working node is labeled as 0；
Step 5.1.2: when AGV selects mobile, adjacent working node is all subtracted 1, then by next section that will be gone
Point is changed to 1 again；
Step 5.1.3: the longest node of the time interval i.e. the smallest node of numerical value that selects to pass by apart from last time it is next walk
Node；
Step 5.1.4: if there are many point for meeting condition, pass through adjacency matrix described in claim 1 and distance matrix meter
It calculates AGV and reaches the distance of adjacent unoccupied node and the sum of the distance of adjacent node arrival task starting point；
Step 5.1.5: the selection shortest node of sum of the distance is walked, at this time by the vertex ticks to be walked in next step while selection
To have occupied.
6. a kind of AGV dispatching method based on active path planning according to claim 1, it is characterised in that: the step
Task terminal in rapid 6 is considered as the task starting point in step 5, reaches task terminal Dynamic Programming AGV from picking point and executes route
Process it is consistent with the process in step 5 described in claim 1.
7. a kind of AGV dispatching method based on active path planning according to claim 4, it is characterised in that: the step
If the AGV occupied is in running order in rapid 5.4, and the occupied working node is that current the executing task AGV of the task rises
Point then currently executes the AGV selection one of task after close adjacent node movement, comes back to task starting point.
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| CN111888774A (en) * | 2020-07-03 | 2020-11-06 | 深圳怡丰自动化科技有限公司 | Cabin control method and device and storage medium |
| CN112061657A (en) * | 2020-09-11 | 2020-12-11 | 灵动科技（北京）有限公司 | Method and device for guiding a robot to transport goods in a warehouse |
| CN113110330B (en) * | 2021-04-15 | 2022-11-22 | 青岛港国际股份有限公司 | AGV dynamic scheduling management method based on global optimal matching |
| CN113110330A (en) * | 2021-04-15 | 2021-07-13 | 青岛港国际股份有限公司 | AGV dynamic scheduling management method based on global optimal matching |
| CN113743739A (en) * | 2021-08-11 | 2021-12-03 | 青岛港国际股份有限公司 | AGV scheduling method based on mixed integer programming and combined optimization algorithm |
| CN113743739B (en) * | 2021-08-11 | 2024-02-13 | 青岛港国际股份有限公司 | AGV scheduling method based on mixed integer programming and combined optimization algorithm |
| CN113934217A (en) * | 2021-12-15 | 2022-01-14 | 南京绛门信息科技股份有限公司 | Intelligent scheduling processing system based on 5G |
| CN113934217B (en) * | 2021-12-15 | 2022-02-25 | 南京绛门信息科技股份有限公司 | Intelligent scheduling processing system based on 5G |
| CN114580728A (en) * | 2022-02-28 | 2022-06-03 | 北京京东乾石科技有限公司 | Elevator dispatching method and device, storage medium and electronic equipment |
| CN115049096A (en) * | 2022-03-31 | 2022-09-13 | 日日顺供应链科技股份有限公司 | Warehouse operation efficiency improving method and system |
| CN117035372A (en) * | 2023-10-09 | 2023-11-10 | 成都思越智能装备股份有限公司 | OHT scheduling processing method and device |
| CN117035372B (en) * | 2023-10-09 | 2023-12-22 | 成都思越智能装备股份有限公司 | OHT scheduling processing method and device |
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