CN109115220A - A method of for parking system path planning - Google Patents
A method of for parking system path planning Download PDFInfo
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- CN109115220A CN109115220A CN201810857003.5A CN201810857003A CN109115220A CN 109115220 A CN109115220 A CN 109115220A CN 201810857003 A CN201810857003 A CN 201810857003A CN 109115220 A CN109115220 A CN 109115220A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3407—Route searching; Route guidance specially adapted for specific applications
- G01C21/3415—Dynamic re-routing, e.g. recalculating the route when the user deviates from calculated route or after detecting real-time traffic data or accidents
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Abstract
The invention belongs to path planning fields, and in particular to a method of for parking system path planning.Specific step is as follows: judging which region target point B and starting point A are belonging respectively to, the node in other regions in addition to target point B and the region starting point A is put into taboo list.It is selected from set U apart from shortest node k, the i.e. the smallest node k of weight, and node k is added in set S, node k is removed from set U；Utilize the distance of each node in node k update set U to starting point A.Continue to repeat step, until selecting all the points in set U.The shortest distance of each node-to-node A is put into set S, and set U is sky, and the shortest distance of node B Yu node A are selected from set S.The time that the present invention calculates is smaller, and original unrelated node shield can can't be reduced the active domain of route searching by increased taboo list.
Description
Technical field
The invention belongs to path planning fields, and in particular to a method of for parking system path planning.
Background technique
Path planning refers in the given working space full of barrier, according to the beginning and end of task, finds one
The optimal running track of vehicle.Under different scenes, the criterion of optimal path is different, including path criterion, time criterion,
At these standards etc..In AGV (automated guided vehicle) system, path planning can be divided into single AGV path planning and more paths AGV
Two kinds of planning.Wherein, single AGV path planning is not consider the collision problem between AGV in the scene for having an AGV,
Optimal path is found for AGV.More AGV path plannings are then different from single AGV path planning, while having more AGV in limited work
Make to run in environment, therefore more AGV path plannings are more complicated in the presence of collision problems, path planning problems such as turning and deadlocks.
The unmanned parking system of AGV is as shown, be by Warehouse Management System (WMS), scheduling system (SS) and AGV system
(AGVS) three parts form.Wherein, WMS and AGVS requires to carry out communication connection with SS (based on ICP/IP protocol).Scheduling system
System is divided into display module and scheduler module.The main task of display module is to visualize task, and the reality of display scheduling system
When state and data.The main task of scheduler module is to calculate AGV walking path by algorithm, generates system task or business is appointed
It is engaged in and carries out task distribution；Meanwhile data communication is carried out with WMS and AGVS and completes task operating.
Summary of the invention
Existing more AGV paths planning methods there are aiming at the problem that, the invention proposes a kind of more AGV appoint path planning sides
Method, it is the problems of current the purpose is to solve.Improved dijkstra's algorithm effectively reduces the node of route searching
Number, the good results are evident on search shortest path.
Technical scheme is as follows:
A method of for parking system path planning, steps are as follows:
Step 1: object module is established
Known to the activity duration for accessing AGV；Parking lot is divided into four regions with two main roads of right-angled intersection, every is divided
It is no more than two AGV on area's main road, the case where being not in three AGV while meeting；The straight line of AGV is exercised, turning driving is equal
For at the uniform velocity；It is single track two-way mode, i.e. section direction bidirectional passing that AGV, which exercises section, even if opposite punching occurs in two AGV
It is prominent to block；
Turning time TturnIt is the turning time of AGV number of turns in the process of moving × each；
Tturn=N × tturn (1.1)
Keep straight on time TstraightThe time for the route segment that be AGV pass by before encountering conflict, i.e. current path distance/
The average speed of AGV traveling；
Tstraight=dbefore/vs (1.2)
Traffic time TtrafficRefer to that two AGV, to the time of the task of completion, are divided into two kinds of situations from the problem that clashes:
1) when two AGV met locks after death, two AGV are withouted waiting for；
Ttraffic1=dnew/vs+N×tturn (1.3)
2) catch up with and surpass conflict when two AGV encounter, according to time window, after following AGV to need that front AGV is waited to turn after
It is continuous to advance according to original plan；
Ttraffic2=dnew/vs+(N+1)×tturn (1.4)
Wherein, N is the number of AGV turning, dbeforeIt is current driving distance of the AGV before traffic problems occur, dnewIt is
The distance of the new route of generation, vsIt is the average speed of AGV traveling；
When task is sent to AGV, initial path is calculated according to initial position and destination, obtains path planning
Two stage mathematical model is respectively as follows:
1) two AGV do not clash problem, required time model:
Ttotal1=N × tturn+d/vs (1.5)
2) two AGV clash problem, required time model:
Ttotal2=Nbefore×tturn+dbefore/vs+Ttraffic (1.6)
Wherein, NbeforeIt is AGV number of turns before traffic problems occur；D be path obtained by no traffic problems situation away from
From；
Step 2: improved dijkstra's algorithm
Improved dijkstra's algorithm, the specific steps are as follows:
(1) parking lot is divided into four regions, and note point B is target point, and note point A is starting point, according to node region attribute
Zone, judges which region target point B and starting point A are belonging respectively to, by its in addition to target point B and the region starting point A
The node in his region is put into taboo list TabuList；
The node for having found out shortest path and corresponding shortest path length are put into set S；Shortest path is not found out
The distance of the node of diameter and the node to starting point A are put into set U；
Node definition with node A direct neighbor is nodes X, initializes nodes X and is used as weight at a distance from node A, just
When the beginning, set S only includes starting point A；Set U includes other nodes in addition to starting point A；
(2) it is selected from set U apart from shortest node k, the i.e. the smallest node k of weight, and node k is added to set
In S, node k is removed from set U；
(3) distance of each node in node k update set U to starting point A is utilized；
(4) continue to repeat step (2) to step (3), until selecting all the points in set U；
(5) shortest distance of each node-to-node A is put into set S, and set U is sky, selects node from set S
The shortest distance of B and node A, as dbefore、dnewOr one in d；
Step 3: the shortest distance of node B and node A that step 2 is obtained substitute into formula Ttotal1=N × tturn+d/
vsOr Ttotal2=Nbefore×tturn+dbefore/vs+Ttraffic, wherein TtrafficFor Ttraffic1 or TtrafficOne in 2, meter
Calculation obtains optimal time.
Beneficial effects of the present invention: the search time of classical dijkstra's algorithm is longer, improved dijkstra's algorithm meter
The time of calculation is smaller, the half of average out to dijkstra's algorithm, and increased taboo list can be by original no artis screen
It covers, the active domain of route searching can't be reduced.Although the search result in path is identical, the search time in path but not phase
Together.
Detailed description of the invention
Fig. 1 is Intelligent parking lot system figure.
Fig. 2 is parking lot electronic map.
Fig. 3 is simplified electronic map.
Fig. 4 is the route map with weighted value.
Fig. 5 is to calculate time comparison diagram.
Specific embodiment
Detailed description of embodiments of the present invention with reference to the accompanying drawing.Discussed specific embodiment is only used for
Bright implementation of the invention, and do not limit the scope of the invention.
(1) as shown in figure 4, note point B is target point, note point A is starting point, according to its node region attribute zone, judgement
Which region target point and starting point are belonging respectively to, other Area Nodes are put into taboo list TabuList, remaining in figure
Node and the same district A, B.
(2) node A is added in S, because the distance of node A to node B is infinity, the distance to node C is 3,
Distance to node D is 4, and the distance to node E is infinity, and the distance to node F is infinity, and the distance to node G is
Infinity, i.e.,
S={ A (0) }, U={ B (∞), C (3), D (4), E (∞), F (∞), G (∞) }；
(3) because the distance of node A to node C is minimum, node C is added in S, at this point, distance of the point A to node B
For infinity, the distance to node D is 4, and the distance to node E is 13, and the distance to node F is 9, and the distance to node G is
Infinity, i.e. S={ A (0), C (3) }, U={ B (∞), D (4), E (13), F (9), G (∞) }；
(4) because the distance of node A to node D is minimum, node D is added in S, at this point, distance of the point A to node B
For infinity, the distance to node E is 13, and the distance to node F is 6, and the distance to node G is 12, i.e. S={ A (0), C
(3), (4) D }, U={ B (∞), E (13), F (6), G (12) }；
(5) because the distance of node A to node F is minimum, node F is added in S, at this point, distance of the point A to node B
It is 22, the distance to node E is 13, and the distance to node G is 12, i.e. S={ A (0), C (3), D (4), F (6) }, U={ B
(22),E(13),G(12)}；
(6) because the distance of node A to node G is minimum, node G is added in S, at this point, distance of the point A to node B
It is 22, the distance to node E is 13, i.e.,
S={ A (0), C (3), D (4), F (6), G (12) }, U={ B (22), E (13) }；
(7) because the distance of node A to node E is minimum, node E is added in S, at this point, distance of the point A to node B
I.e. S={ A (0), C (3), D (4), F (6), G (12), E (13) } for 22, U={ B (22) }；
(8) last only remaining node B, node B is added in S, at this time
S={ A (0), C (3), D (4), F (6), G (12), E (13), B (22) }, U={ }；
At this point, the shortest distance of starting point A to each node is just calculated:
A(0),C(3),D(4),F(6),G(12),E(13),B(22)。
Improved dijkstra's algorithm is compared with classical dijkstra's algorithm, and there are two main targets: calculating the time
Complexity and path time.The verifying electronic map of this experiment is as shown in Figure 2.
Based on the same map, we carry out calculating the 10 groups of starting points and target being randomly generated respectively using two kinds of algorithms
Point, as shown in following table 1.1:
Table 1.1
Wherein, after the map of improved dijkstra's algorithm is simplified, with first group of data instance, effective body of a map or chart is
1st and the 4th region obtains map such as Fig. 3.As can be seen from the figure effective search space of modified hydrothermal process is constant, cuts down
Extra invalid search range, and reduce the calculating of final step.Result figure 5 is obtained by emulation experiment, dotted line is
Modified hydrothermal process, solid line are hull algorithm.As seen from the figure, pathfinding of the improved dijkstra's algorithm than classical dijkstra's algorithm
Time is obviously improved.This method can effectively reduce the pathfinding time.
Claims (1)
1. a kind of method for parking system path planning, which is characterized in that steps are as follows:
Step 1: object module is established
Known to the activity duration for accessing AGV；Parking lot is divided into four regions, every subregion master with two main roads of right-angled intersection
Road is no more than two AGV, the case where being not in three AGV while meeting；The straight line of AGV is exercised, turning driving is even
Speed；It is single track two-way mode, i.e. section direction bidirectional passing that AGV, which exercises section, and opposite conflict occurs even if in two AGV
It will not block；
Turning time TturnIt is the turning time of AGV number of turns in the process of moving × each；
Tturn=N × tturn (1.1)
Keep straight on time TstraightIt is the time for the route segment that AGV passes by before encountering conflict, i.e. current path distance/AGV row
The average speed sailed；
Tstraight=dbefore/vs (1.2)
Traffic time TtrafficRefer to that two AGV, to the time of the task of completion, are divided into two kinds of situations from the problem that clashes:
1) when two AGV met locks after death, two AGV are withouted waiting for；
Ttraffic1=dnew/vs+N×tturn (1.3)
2) catch up with and surpass conflict when two AGV encounter, according to time window, follow AGV need to continue after waiting front AGV to turn by
Advance according to original plan；
Ttraffic2=dnew/vs+(N+1)×tturn (1.4)
Wherein, N is the number of AGV turning, dbeforeIt is current driving distance of the AGV before traffic problems occur, dnewIt is to generate
New route distance, vsIt is the average speed of AGV traveling；
When task is sent to AGV, initial path is calculated according to initial position and destination, obtains two ranks of path planning
The mathematical model of section is respectively as follows:
1) two AGV do not clash problem, required time model:
Ttotal1=N × tturn+d/vs (1.5)
2) two AGV clash problem, required time model:
Ttotal2=Nbefore×tturn+dbefore/vs+Ttraffic (1.6)
Wherein, NbeforeIt is AGV number of turns before traffic problems occur；D is the distance in path obtained by no traffic problems situation；
Step 2: improved dijkstra's algorithm
Improved dijkstra's algorithm, the specific steps are as follows:
(1) parking lot is divided into four regions, and note point B is target point, and note point A is that starting point is sentenced according to node region attribute zone
Which region disconnected target point B and starting point A is belonging respectively to, by other regions in addition to target point B and the region starting point A
Node is put into taboo list TabuList；
The node for having found out shortest path and corresponding shortest path length are put into set S；Shortest path is not found out
The distance of node and the node to starting point A are put into set U；
Node definition with node A direct neighbor is nodes X, initializes nodes X and is used as weight at a distance from node A, when initial,
Set S only includes starting point A；Set U includes other nodes in addition to starting point A；
(2) it is selected from set U apart from shortest node k, the i.e. the smallest node k of weight, and node k is added in set S,
Node k is removed from set U；
(3) distance of each node in node k update set U to starting point A is utilized；
(4) continue to repeat step (2) to step (3), until selecting all the points in set U；
(5) shortest distance of each node-to-node A is put into set S, set U be sky, selected from set S node B and
The shortest distance of node A, as dbefore、dnewOr one in d；
Step 3: the shortest distance of node B and node A that step 2 is obtained substitute into formula Ttotal1=N × tturn+d/vsOr
Ttotal2=Nbefore×tturn+dbefore/vs+Ttraffic, wherein TtrafficFor Ttraffic1 or TtrafficIt one in 2, calculates
To optimal time.
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| CN117521934A (en) * | 2023-12-07 | 2024-02-06 | 大连理工大学 | Customized bus route planning method considering carbon emission cost |
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| CN112213113B (en) * | 2020-09-02 | 2022-07-15 | 中国第一汽车股份有限公司 | Method for selecting and planning actual road test scene of intelligent driving mobile device |
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| CN117521934B (en) * | 2023-12-07 | 2024-05-07 | 大连理工大学 | Customized bus route planning method considering carbon emission cost |
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