CN110515380A - Shortest path planning method based on turning weight constraints - Google Patents
Shortest path planning method based on turning weight constraints Download PDFInfo
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Abstract
The present invention provides a kind of shortest path planning methods based on turning weight constraints.This method comprises: step 1, calculating the distance between source point v and other all nodes, the distance between source point v and other all nodes are stored in node range data table；Step 2 chooses one to source point v apart from shortest node k from U, and k is added in S；Step 3, using node k as intermediate node, recalculate each node in U based on turning weight and update storage the distance between the source point v recalculated and other all nodes in node range data table to the distance of source point v；Step 4 repeats step 2 and 3, is added in S until by terminal w, obtains the shortest path of source point v to terminal w.The present invention turns to crossing and carries out weight punishment while effectively calculating shortest path, and the shortest path obtained can avoid turning on the basis of link length is shorter as far as possible, and AGV guide car process is helped fast and effectively to complete task.
Description
Technical field
The present invention relates to Path Planning Technique field more particularly to a kind of shortest path plannings based on turning weight constraints
Method.
Background technique
AGV (Automated Guided Vehicle, automated guided vehicle) refers to magnetically or optically to be waited certainly equipped with electricity
Dynamic guiding device is controlled by computer, wheel type mobile is characterized and can be along the transport of defined guide path automatic running
Vehicle.In the intelligent automation services application such as the service of intelligent parking lot or wisdom storage, AGV is transport main force,
Omni-directional is even more to solve the problems such as vehicle turning difficulty is big, realizes 360 ° of any direction travelings.
Shortest path planning of the AGV under wisdom scene in addition to needing to follow the basic traffic rules of domestic current, including but
It is not limited to left rudder driving, right travel, straight trip etc. is given precedence in turning, it is also necessary to meet road conditions and the rule of related parking in scene:
1, other normal use lanes are two-way lane in addition to Emergency Vehicle Lane, and vehicle travels on the right side；
2, AGV vehicle travel speed is consistent, and standby mode is taken when meeting obstacle in front, and it is super must not to occupy opposite lane
Vehicle；
3, traveling straightway can have more AGV in the same direction or Facing Movement, meet AGV turn when driving into parking stall, need to occupy
Opposite lane, other AGV are waited, are successively passed through after turning.
Best traveling road is planned by mathematical algorithm (such as Floyd-Warshall algorithm and dijkstra's algorithm) at present
The case where diameter all fully considers 1,2 two o'clocks of above-mentioned rule, turns for omnidirectional AGV and occupies two-way traffic, other vehicles need to wait for does not have
Have in view of in shortest route-planning algorithm, having seriously affected the whole AGV parking speed of service.
Intelligent parking system or the path planning of wisdom warehousing system it is intended that vehicle provide given start node to
Surely the shortest path of node is terminated, the method for studying shortest path is mostly based on graph theory, makees using road key point and parking stall
Corresponding adjacency matrix is constructed using information such as link length or crowdings as side right weight for figure node, and is solved with algorithm
Adjacency matrix obtains start node to the shortest path for terminating node.Shortest path first includes that heuristic search, cam are basic
Method, Dynamic Programming and neural network etc., currently, shortest path first in the prior art includes: Floyd algorithm,
Bellman-ford algorithm, SPFA algorithm and dijkstra's algorithm.
The thought that Dynamic Programming is utilized for figure G, the Floyd algorithm that n node and m side form, it is most short to solve multi-source
Cost path problem, time complexity are O (n3), and space complexity is O (n2), are not suitable for mass data.Bellman-
Ford algorithm is based on Dynamic Programming and seeks signal source shortest path, it can be determined that whether there is or not negative power circuit, time complexity is O (n*m), empty
Between complexity be O (m), more scheme closely suitable for the relationship on sparse graph and side.SPFA algorithm is in Bellman-ford algorithm
On the basis of plus queue optimization, reduce the loose operations of redundancy, improve efficiency.Dijstra algorithm is based on greed and calculates
Method thought solves the problems, such as the most short cost path in single source, and time complexity and space complexity are all O (n2), are suitable for dense graph.
Because negative weight is not present in wisdom scenario path, and dijkstra's algorithm adapts to the variation of network topology, and performance is more stable,
Therefore it is often applied in Actual path planning.
Dijkstra shortest path first is widely used, and is mainly characterized by centered on start node expanding layer by layer outward
It dissipates, until terminating node.It is two groups: one groups is what shortest path had determined that all node set V are divided to by dijkstra's algorithm
Node S；Another group of node V-S not yet to determine shortest path, and will be in V-S according to shortest path length incremental order
Node is added in S one by one, and in adition process, the shortest path length for being always to maintain each node of source point v to S is not more than from source point
The shortest path length of any node in v to V-S.Dijkstra's algorithm is using road prong and parking stall as figure node, in road
In the setting of right of way value, using image radio-frequency technique measurement road real-time traffic flow amount information, and combining road length is as side right
Planning path realization is quickly parked.
It is in the prior art propose it is a kind of using Dijsktra algorithm calculate parking path optimization algorithm include: that will enter
The path in place and the path for being driven out to place model respectively, acquire the shortest distance with dijkstra's algorithm and sum.On parking road
On diameter planning problem, road node and parking stall node are distinguished, using hierarchical searching method, path search process,
Each process solved using Dijsktra, global more excellent solution is obtained.For road of more AGV under intelligent automation scene
Multiple AGV are carried out path planning using Dijkstar algorithm by diameter planning and scheduling problem, and it is more to update solution by time window
AGV scheduling problem.
The shortcomings that above-mentioned optimization algorithm in the prior art that parking path is calculated using Dijsktra algorithm are as follows: applied field
Scape is usually that warehouse logistics are matched or large parking lot artificially stops task, therefore the weight of algorithm usually only considers link length.
And for the AGV homing device of intelligent parking lot, opposite lane can be occupied when crossing is turned, if therefore shortest path
As a result comprising excessive turning, the waiting or crossing blocking of other AGV vehicles is may cause, vehicle access delay lag is caused.
Summary of the invention
It is existing to overcome the embodiment provides a kind of shortest path planning method based on turning weight constraints
The shortcomings that technology.
To achieve the goals above, this invention takes following technical solutions.
A kind of shortest path planning method based on turning weight constraints, comprising:
Step 1 sets start node in the path planning region comprising multiple nodes as source point v, and terminating node is end
Point w, when initial, setting set of paths S only includes source point v, includes calculating in candidate node combination U containing other nodes in addition to v
The distance between source point v and other all nodes are stored in node distance by the distance between source point v and other all nodes out
In tables of data；
Step 2, according to the range information stored in the node range data table, one is chosen from U to source point v distance
Shortest node k k is added in S；
Step 3, using node k as intermediate node, the distance that each node in U arrives source point v, general are recalculated based on turning weight
The distance between source point v and other all nodes for recalculating are updated storage in node range data table；
Step 4 repeats above-mentioned steps 2 and 3, is added in S until by terminal w, and process terminates, will be all in S
Node is sequentially connected according to the time sequencing for being added to S, obtains the shortest path of source point v to terminal w.
Preferably, before the step 1 further include:
The initial data stored in database includes parking stall, charging pile and entrance, the location information of road and is stopped
The exit angle information of parking stall, the initial data is stored in the database with the coordinate form of website, according to all websites
Coordinate constructs treatment of the earth figure, using website as node, generation figure node and correctly connects side, establishes a connection, according to
The length when coordinate of two leafs in connection in treatment of the earth figure calculates, the length on the side is as side right weight.
Preferably, the method further include:
When establishing treatment of the earth figure, corresponding node is constructed according to entrance, charge position and the attribute on parking stall, is every
One section of road generates corresponding starting point node and goal node, by every road and established entrance, charge position and parking
Position node is judged one by one, is judged as intersection point when being less than certain threshold value, since the start node of road, if after
The distance between the node and starting section in face are less than certain threshold value, then delete these nodes, otherwise retain these nodes；Again to protect
The node stayed is that start node starts to judge next time.
Preferably, the distance between source point v and other all nodes are calculated in the step 1, comprising:
If node u is that source point v goes out side abutment points in U, side v → u has side right value<v, u>, which is source point v
The distance between node u Dv→uIf node u be not source point v go out side abutment points, side right value<v, u>for infinity, as
The distance between source point v and node u Dv→uFor infinity.
Preferably, in the step 3 using node k as intermediate node, based on turning weight recalculate each node in U
To the distance of source point v, comprising:
To tie k as intermediate node, node u in U is chosen, calculates and passes through distance D of the intermediate node k from v to uv→k→u, first
Judgement by intermediate node k in path v → k → u from v to u with the presence or absence of turning, if while v → k and while k → u between folder
Foot degree of being not zero, then judge in path v → k → u by intermediate node k from v to u exist turning, then by intermediate node k from
The distance of v to u: Dv→k→u=<v, k>+<k, u>+2 × trunweight, the trunweight are the turning weight of setting；
If while v → k and while k → u between pinch be zero degree, judge path v by intermediate node k from v to u →
There is no turnings in k → u, then pass through distance of the intermediate node k from v to u are as follows: Dv→k→u=<v, k>+<k, u>；
By the calculated distance D by intermediate node k from v to uv→k→uWith store in node range data table slave v to
The distance D of uv→uIt is compared, if Dv→k→u≤Dv→u, then the distance of v to the u stored in node range data table is updated to
Dv→k→u；If Dv→k→u> Dv→u, then the distance of v to the u stored in node range data table is constant.
Preferably, the method is applied to the automated guided vehicle in intelligent parking system or wisdom warehousing system
The path planning of AGV.
As can be seen from the technical scheme provided by the above-mentioned embodiment of the present invention, the method for the embodiment of the present invention is effectively being counted
While calculating shortest path, in conjunction with actual conditions, turns to crossing and carry out weight punishment, the shortest path obtained according to new weight
Diameter can avoid turning on the basis of link length is shorter as far as possible, avoid causing other of crossing and intersection AGV vehicle
Be lined up and wait, help AGV guide car process fast and effectively complete task.
The additional aspect of the present invention and advantage will be set forth in part in the description, these will become from the following description
Obviously, or practice through the invention is recognized.
Detailed description of the invention
In order to illustrate the technical solution of the embodiments of the present invention more clearly, required use in being described below to embodiment
Attached drawing be briefly described, it should be apparent that, drawings in the following description are only some embodiments of the invention, for this
For the those of ordinary skill of field, without creative efforts, it can also be obtained according to these attached drawings others
Attached drawing.
Fig. 1 is a kind of omni-directional wheel AGV shortest path planning method based on weight constraints provided in an embodiment of the present invention
Realization principle schematic diagram；
Fig. 2 is provided in an embodiment of the present invention a kind of to solve known start node and termination simultaneously using dijkstra's algorithm
The process flow diagram of shortest-path method when node；
Fig. 3 is provided in an embodiment of the present invention a kind of to solve known simultaneously rise using the dijkstra's algorithm based on turning weight
The process flow diagram of shortest-path method when beginning node and termination node；
Fig. 4 is a kind of omni-directional wheel AGV shortest path planning method based on weight constraints provided in an embodiment of the present invention
Application implementation schematic diagram.
Specific embodiment
Embodiments of the present invention are described below in detail, the example of the embodiment is shown in the accompanying drawings, wherein from beginning
Same or similar element or element with the same or similar functions are indicated to same or similar label eventually.Below by ginseng
The embodiment for examining attached drawing description is exemplary, and for explaining only the invention, and is not construed as limiting the claims.
Those skilled in the art of the present technique are appreciated that unless expressly stated, singular " one " used herein, " one
It is a ", " described " and "the" may also comprise plural form.It is to be further understood that being arranged used in specification of the invention
Diction " comprising " refer to that there are the feature, integer, step, operation, element and/or component, but it is not excluded that in the presence of or addition
Other one or more features, integer, step, operation, element, component and/or their group.It should be understood that when we claim member
Part is " connected " or when " coupled " to another element, it can be directly connected or coupled to other elements, or there may also be
Intermediary element.In addition, " connection " used herein or " coupling " may include being wirelessly connected or coupling.Wording used herein
"and/or" includes one or more associated any cells for listing item and all combinations.
Those skilled in the art of the present technique are appreciated that unless otherwise defined, all terms used herein (including technology art
Language and scientific term) there is meaning identical with the general understanding of those of ordinary skill in fields of the present invention.Should also
Understand, those terms such as defined in the general dictionary, which should be understood that, to be had and the meaning in the context of the prior art
The consistent meaning of justice, and unless defined as here, it will not be explained in an idealized or overly formal meaning.
In order to facilitate understanding of embodiments of the present invention, it is done by taking several specific embodiments as an example below in conjunction with attached drawing further
Explanation, and each embodiment does not constitute the restriction to the embodiment of the present invention.
To solve this problem, the present invention is directed to the shortest path planning based on AGV system, proposes to Dijstra algorithm
It improves, considers that the time delay that vehicle turning may cause obtains when being included in new node toward the fixed node set of shortest path every time
Take whether pinch of its forerunner while subsequent between side is zero degree, and judges whether two sides belong to same path.If no
It is to encounter turning, then adds additional weight punishment when being included in node.
It is the service providing locations point such as wisdom parking lot or wisdom storage the present embodiments relate to path planning system is arrived
Match and optimal path computation service, the system are only concerned path computing, is not related to carriage guide process, specific carriage guide mistake
Journey is completed by AGV traveling guidance system.The system allows to select and call out according to dispatch command and AGV state using data server
Wake up specific AGV, establishes stable data connection, and planning path forms routing information table, and AGV reads specify information and path letter
Breath prolongs routing information automatic Pilot AGV to first object point using self poisoning navigation system, executes scheduled function, update
Status information.Data server plans source point optimal path to terminal again and updates routing information table, and AGV is according to updated
Routing information automatic Pilot to terminal and completes corresponding scheduler task；That is AGV is carried out twice when executing an assignment instructions
Self-navigation, automatic Pilot, automatic loading and unloading process.For this purpose, AGV shortest route-planning algorithm need to provide it is more efficient, practical
Real-time amendment routing information table (global path planning and local paths planning), auxiliary complete AGV corresponding task is rapidly completed.
A kind of realization of omni-directional wheel AGV shortest path planning method based on weight constraints provided in an embodiment of the present invention is former
Schematic diagram is managed as shown in Figure 1, including structure chart building and two treatment processes of path planning.Wherein structure chart building process includes
Construct treatment of the earth figure (graph) and target position (parking stall AGV) distribution: the initial data stored in database includes parking
Position, charging pile and entrance, the location information of road and the exit angle information on parking stall etc..Initial data is all in database
It is stored with the coordinate form of website, treatment of the earth figure is constructed according to the coordinate of all websites, using website as node, it is suitable to generate
Figure node and side is correctly connected, established a connection, this structure chart is that subsequent path calculates and the basis of traveling guidance.Root
According to the coordinate of two leafs of the side connection in structure chart, the length on side can be calculated, the length on the side as side right weight,
The side right weight is also the distance between two leafs of side connection.
Database and starting and terminal point information to be checked as input, are exported the sequence that origin-to-destination is passed through by system
Node set (i.e. path).Initial map is AutoCAD drafting, generates electronic map by third-party application software Mapinfo.
When establishing parking lot structure figure, corresponding point is constructed according to entrance, charge position and the attribute on parking stall, is each section of road
Generate corresponding starting point node and goal node, by every road and established entrance, charge position and parking stall node by
One is judged, is judged as intersection point when being less than certain threshold value, further needs exist for considering the intersection point between Lu Yulu.
In actual scene, because each section of road gathering is closer, unnecessary calculating often will cause.In order to reduce
The complexity of algorithm deletes the redundant node of road.The node of road is only considered at this time, other are put because being all key point,
It needs to save.Since start node, if subsequent node and its distance are less than certain threshold value, these nodes are deleted, otherwise
Retain these nodes.Then, and using the node of reservation as start node start to judge next time.Then side is initialized, is stopped
Vehicle, entrance, charging pile and all node set of relationship.The other all nodes of traversal, In are required for each node
Under conditions of meeting certain distance, its abutment points is found with this and saves relevant information.In addition, Lu Yulu is also to have syntople
, for each road, all nodes on present road are found, coordinate sorts from small to large.For straight line
On node, syntople if it exists, it is also desirable to connect.Certainly it also has some isolated points to exist, they should be
Parking stall, entrance, charging pile.Nearest node is found for these isolated points to be allocated.
When there is inter-related task arrival, by the optimal idle target position of breadth-first strategy selection, task distribution is carried out.Path rule
Streaking journey is to increase turning weight design based on dijkstra's algorithm, while being applied to following two task: 1. solve simultaneously
Shortest route problem when knowing start node and terminating node；2. the overall situation for solving the shortest path in figure between all nodes is most short
Routing problem.
The superiority and inferiority of the paths planning method of AGV plays a crucial role the efficiency of entire AGV shutdown system,
And the core of AGV path planning is exactly the shortest route problem of data structure and algorithm field.Dijkstra's algorithm thought are as follows: set
D=(V, E) is non-negative weight graph, and V represents node set in figure, and E represents the line set in figure containing weight.By whole node set V
It is divided into two groups, first group is the node set for having found out shortest path, indicated with S (only one source point in S when initial, after
As soon as a shortest path is often acquired, by the termination node join in the path into set S)；It is second group to be determined most short for remaining
The node set in path, is indicated with U.The U node gathered successively is added to S collection one by one by the increasing order of shortest path length
In conjunction, the shortest path length that constraint condition is to maintain each node in from source point v to S is not more than any node in from source point v to U
Shortest path length.The termination condition of algorithm is that set U is empty set, i.e. the node of set U is all added in set S.
It is provided in an embodiment of the present invention a kind of to solve known start node simultaneously using dijkstra's algorithm and terminate node
When shortest-path method process flow as shown in Fig. 2, include following processing step:
1: the start node in the path planning region comprising multiple nodes is set as source point v, termination node is terminal w,
When initial, setting set of paths S only includes source point v, includes containing other nodes in addition to v, i.e. S=in candidate node combination U
{ v }, the distance of v are ds=0.U includes other nodes in addition to v, it may be assumed that U={ remaining node other than v }, if in v and U
Node u has a side, i.e. u is that v goes out side abutment points, then side v → u has side right value<v, u>, the side right value be source point v and node u it
Between distance Dv→uIf u be not v go out side abutment points, side right value<v, u>for infinity, the distance between source point v and node u
Dv→uFor infinity.
The distance between source point v and other all nodes are stored in node range data table.
2: according to the range information stored in node range data table, one is chosen from U to source point v apart from shortest knot
Point k k is added in S, the selected shortest distance Dv→kIt is exactly side right value<v, k>.
3: it is the intermediate node newly considered with k, recalculates each node in U and choose node u in U to the distance of source point v,
It calculates and passes through distance D of the intermediate node k from v to uv→k→u=<v, k>+<k, u>；
Then, by the calculated distance D by intermediate node k from v to uv→k→uWith store in node range data table
Distance D from v to uv→uIt is compared, if Dv→k→u≤Dv→u, then by the distance of v to the u stored in node range data table
It is updated to Dv→k→u；If Dv→k→u> Dv→u, then the distance of v to the u stored in node range data table is constant.
4: repeating step (2) and (3), until the terminal w for terminating node will be used as to be added in S, process terminates, will
All nodes in S are sequentially connected according to the time sequencing for being added to S, obtain the shortest path of source point v to terminal w.
Dijkstra's algorithm based on turning weight occupies two-way lane when having fully considered AGV turning, leads to other AGV
The case where waiting, according to father node in passage path to the relationship on next node side to determine whether there is turning, if it does,
Then opposite side weight addition turning weight；Otherwise, side right value is constant continues to run subsequent arithmetic.Turning weight turnweight's is big
The small fixed value for setting, unit is length unit.It for example is 10.
It is provided in an embodiment of the present invention a kind of to solve known starting simultaneously using the dijkstra's algorithm based on turning weight
The process flow of shortest-path method when node and termination node is as shown in figure 3, include following processing step:
1: the start node in the path planning region comprising multiple nodes is set as source point v, termination node is terminal w,
When initial, setting set of paths S only includes source point v, includes containing other nodes in addition to v, i.e. S=in candidate node combination U
{ v }, the distance of v are ds=0.U includes other nodes in addition to v, it may be assumed that U={ remaining node other than v }, if in v and U
Node u has a side, i.e. u is that v goes out side abutment points, then side v → u has side right value<v, u>, the side right value be source point v and node u it
Between distance Dv→uIf u be not v go out side abutment points, side right value<v, u>for infinity, the distance between source point v and node u
Dv→uFor infinity.
The distance between source point v and other all nodes are stored in node range data table.
2: according to the range information stored in node range data table, one is chosen from U to source point v apart from shortest knot
Point k k is added in S, the selected shortest distance Dv→kIt is exactly side right value<v, k>.
3, it is the intermediate node newly considered with k, recalculates each node in U and choose node u in U to the distance of source point v,
It calculates and passes through distance D of the intermediate node k from v to uv→k→u, first determine whether in path v → k → u by intermediate node k from v to u
With the presence or absence of turning, if while v → k and while k → u between pinch degree of being not zero, judge by intermediate node k from v to u
There is turning in path v → k → u, then passes through distance of the intermediate node k from v to u: Dv→k→u=<v, k>+<k, u>+2 ×
turnweight；
If while v → k and while k → u between pinch be zero degree, judge path v by intermediate node k from v to u →
There is no turnings in k → u, then pass through distance of the intermediate node k from v to u are as follows: Dv→k→u=<v, k>+<k, u>；
Then, by the calculated distance D by intermediate node k from v to uv→k→uWith store in node range data table
Distance D from v to uv→uIt is compared, if Dv→k→u≤Dv→u, then by the distance of v to the u stored in node range data table
It is updated to Dv→k→u；If Dv→k→u> Dv→u, then the distance of v to the u stored in node range data table is constant.
4, step (2) and (3) are repeated, until the terminal w for terminating node will be used as to be added in S, process terminates, will be in S
All nodes be sequentially connected according to the time sequencing for being added to S, obtain the shortest path of source point v to terminal w.
Embodiment two:
Fig. 4 is a kind of omni-directional wheel AGV shortest path planning method based on weight constraints provided in an embodiment of the present invention
Application implementation schematic diagram, as shown in figure 4,1. and giving side right weight according to link length to being 8. figure node.AGV will be from starting
1. node is travelled to node is terminated 8., seek shortest path.
According to common Dijstra algorithm, obtain outbound path 1 be 1. -2. -4. -5. -6. -8., path length 45.Path 2
For 1. -3. -7. -8., path length 63.Final choice path 1.
The improved Dijsktra algorithm proposed according to the present invention, path 1 be 1. -2. -4. -5. -6. -8., turn by 4
It is curved, so final path length is 45+4*10=85；Path 2 be 1. -3. -7. -8., turn by 2, final path length
Degree is 63+2*10=83, final choice path 2.
In the scheduling of more paths AGV, the time that path 2 averagely uses is shorter than path 1, and scheme is more excellent.
In conclusion the embodiment of the invention provides a kind of shortest path planning side omni-directional wheel AGV based on weight constraints
Method is related to the applications and shortest path first design etc. such as wisdom parking, wisdom storage fields, can be realized the intelligent of science and stop
Parking lot service and wisdom Warehouse Service complete the real-time shortest path selection of omnidirectional AGV in related application scene.
The present invention, in conjunction with actual conditions, turns to crossing and carries out weight punishment, root while effectively calculating shortest path
The shortest path obtained according to new weight can avoid turning on the basis of link length is shorter as far as possible, avoid causing crossing and
Other AGV vehicle queues of intersection and waiting help AGV guide car process fast and effectively to complete task.
Those of ordinary skill in the art will appreciate that: attached drawing is the schematic diagram of one embodiment, module in attached drawing or
Process is not necessarily implemented necessary to the present invention.
As seen through the above description of the embodiments, those skilled in the art can be understood that the present invention can
It realizes by means of software and necessary general hardware platform.Based on this understanding, technical solution of the present invention essence
On in other words the part that contributes to existing technology can be embodied in the form of software products, the computer software product
It can store in storage medium, such as ROM/RAM, magnetic disk, CD, including some instructions are used so that a computer equipment
(can be personal computer, server or the network equipment etc.) executes the certain of each embodiment or embodiment of the invention
Method described in part.
All the embodiments in this specification are described in a progressive manner, same and similar portion between each embodiment
Dividing may refer to each other, and each embodiment focuses on the differences from other embodiments.Especially for device or
For system embodiment, since it is substantially similar to the method embodiment, so describing fairly simple, related place is referring to method
The part of embodiment illustrates.Apparatus and system embodiment described above is only schematical, wherein the conduct
The unit of separate part description may or may not be physically separated, component shown as a unit can be or
Person may not be physical unit, it can and it is in one place, or may be distributed over multiple network units.It can root
According to actual need that some or all of the modules therein is selected to achieve the purpose of the solution of this embodiment.Ordinary skill
Personnel can understand and implement without creative efforts.
The foregoing is only a preferred embodiment of the present invention, but scope of protection of the present invention is not limited thereto,
In the technical scope disclosed by the present invention, any changes or substitutions that can be easily thought of by anyone skilled in the art,
It should be covered by the protection scope of the present invention.Therefore, protection scope of the present invention should be with scope of protection of the claims
Subject to.
Claims (6)
1. a kind of shortest path planning method based on turning weight constraints characterized by comprising
Step 1 sets start node in the path planning region comprising multiple nodes as source point v, and termination node is terminal w,
When initial, setting set of paths S only includes source point v, includes calculating source containing other nodes in addition to v in candidate node combination U
The distance between source point v and other all nodes are stored in node range data by the distance between point v and other all nodes
In table；
Step 2, according to the range information stored in the node range data table, chosen from U one most short to source point v distance
Node k, k be added S in；
Step 3, using node k as intermediate node, based on turning weight recalculate each node in U, will again to the distance of source point v
The distance between the source point v of calculating and other all nodes are updated storage in node range data table；
Step 4 repeats above-mentioned steps 2 and 3, is added in S until by terminal w, and process terminates, by all nodes in S
It is sequentially connected according to the time sequencing for being added to S, obtains the shortest path of source point v to terminal w.
2. the method according to claim 1, wherein before the step 1 further include:
The initial data stored in database includes parking stall, charging pile and entrance, the location information of road and parking stall
Exit angle information, the initial data is stored in the database with the coordinate form of website, according to the coordinate of all websites
Treatment of the earth figure is constructed, using website as node, generation figure node and side is correctly connected, establishes a connection, according to place
The length when coordinate of two leafs in connection in structure chart calculates, the length on the side is as side right weight.
3. according to the method described in claim 2, it is characterized in that, the method further include:
When establishing treatment of the earth figure, corresponding node is constructed according to entrance, charge position and the attribute on parking stall, is each section
Road generates corresponding starting point node and goal node, and every road and established entrance, charge position and parking stall are tied
Point is judged one by one, is judged as intersection point when being less than certain threshold value, since the start node of road, if subsequent
The distance between node and starting section are less than certain threshold value, then delete these nodes, otherwise retain these nodes；Again with reservation
Node is that start node starts to judge next time.
4. according to the method described in claim 3, it is characterized in that, the source point v that calculates in the step 1 owns with other
The distance between node, comprising:
If node u is that source point v goes out side abutment points in U, side v → u has side right value<v, u>, which is source point v and knot
The distance between point u Dv→uIf node u be not source point v go out side abutment points, side right value<v, u>for infinity, as source point v
The distance between node u Dv→uFor infinity.
5. according to the method described in claim 4, it is characterized in that, in the step 3 using node k as intermediate node, base
Each node is recalculated in U to the distance of source point v in turning weight, comprising:
To tie k as intermediate node, node u in U is chosen, calculates and passes through distance D of the intermediate node k from v to uv→k→u, first determine whether
By intermediate node k in path v → k → u from v to u with the presence or absence of turning, if while v → k and while k → u between pinch not
For zero degree, then judge there is turning in path v → k → u by intermediate node k from v to u, then by intermediate node k from v to u
Distance: Dv→k→u=<v, k>+<k, u>+2 × trunweight, the trunweight are the turning weight of setting；
If while v → k and while k → u between pinch be zero degree, judge path v → k → u by intermediate node k from v to u
In there is no turning, then pass through distance of the intermediate node k from v to u are as follows: Dv→k→u=<v, k>+<k, u>；
By the calculated distance D by intermediate node k from v to uv→k→uWith slave v to the u's that is stored in node range data table
Distance Dv→uIt is compared, if Dv→k→u≤Dv→u, then the distance of v to the u stored in node range data table is updated to
Dv→k→u；If Dv→k→u> Dv→u, then the distance of v to the u stored in node range data table is constant.
6. method according to any one of claims 1 to 5, which is characterized in that the method is applied to intelligent parking system
The path planning of system or the automated guided vehicle AGV in wisdom warehousing system.
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