CN104914862B - Path planning algorithm based on target direction constraint - Google Patents
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Abstract
The invention discloses a path planning algorithm based on a target direction constraint. In a path searching process, a current node and expandable nodes in the same direction as a target node are only saved, the expandable nodes are added into a state space of the expandable nodes, each expandable node in the state space is evaluated, the obtained expandable node an evaluation function value of which is minimum is taken as a next current node, path searching is repeated until the expandable node an evaluation function value of which is minimum in the state space is the target node, and then an optimal path is obtained. In the path searching process, the current node and the expandable nodes in the same direction as the target node are only saved, the number of the nodes in the state space of the expandable nodes of the current node is reduced, searching scale of the algorithm is reduced, occupancy of a memory resource is reduced, searching efficiency of the algorithm is improved, and the path planning algorithm is suitable for path searching of various scenes having a high real-time requirement.
Description
Technical field
The invention belongs to indoor guidance technology field, and in particular to lead in indoor (such as transport hub, megastore) path
Draw the path planning algorithm constrained based on target direction of technology.
Background technology
At present, there is the algorithm of many maturations based on the shortest route problem of indoor guiding, common path planning is calculated
Method has dijkstra's algorithm, Floyd algorithms, heuristic search algorithm etc..
Dijkstra's algorithm is most classical Shortest Path Searching Algorithm, is also a kind of relatively time-consuming algorithm.It is by power
Value incremental order seeks shortest path, and the characteristics of with simple and clear, the result obtained by algorithm search is also relatively more accurate.From the opposing party
Face sees, Dijkstra various for input number of nodes sparse graph, either calculates and specifies or space any two points at 2 points all
It is the shortest path for calculating whole sparse graph, has the shortcomings that efficiency is low, it is big to take up room.
Floyd algorithms, also known as Freud's algorithm, are a kind of for finding in given weight path topological network between summit
Shortest path algorithm, its general principle is Dynamic Programming, and it is first converted into weight matrix path network, then in weight matrix
The shortest path of any two points is sought, it has very big improvement, dense graph best results, starting point compared to dijkstra's algorithm
Change with terminal affects little to algorithm, and simple effective, efficiency is higher than dijkstra's algorithm, but there is also time complexity
Height, is not suitable for calculating the shortcoming of mass data.
Shortest path first with dijkstra's algorithm, Floyd algorithms as representative belongs to blind search algorithm, although can
Shortest path is tried to achieve, but amount of calculation is but very big, it is adaptable to the less graph structure of nodes, and it is very huge for number of nodes
Big graph structure is not but applied to.
Heuristic search algorithm is the searching algorithm based on the knowledge of specific field, and during search, algorithm is not only
Consider the current cost of node, and take into account and extend estimate cost required for the node, make search procedure towards most having
Desired direction is advanced, and then accelerates whole calculating process.Enlightening information is mainly reflected on evaluation function, in search procedure
The task of middle evaluation function is exactly the possibility for estimating node to be searched on optimal path, so as to first search possibility ratio
Larger node, so as to reach the purpose for improving search speed.
Local preferentially searching algorithm, best-first search algorithm are had based on the shortest path first of heuristic search, and it is common
A*Algorithm, preferentially searching algorithm is simplest heuristic search algorithm for local, during search, when certain node quilt
After extension, that node of " optimum " will be further extended, and give up to fall the father node of this child node and other expansions
Exhibition child node.If search procedure continues always, due to give up many extension child nodes, it is possible to it is real most
Happy festival time point is all given up, so the optimal node during a section is not global optimal node, so this algorithm is searched
Rope to path be not necessarily real optimal path.
A*Algorithm is one of method important in heuristic search algorithm.It is a kind of best first search algorithm, is being searched
During rope, do not give up to fall node, in the appraisal of each step all current node and before node assessment values ratio
Relatively obtain one " optimal node ".The loss of " optimal node " can be so effectively prevented, algorithm search result is improve
Accuracy.Its speed is very fast on fairly simple map, can quickly find shortest path.In more complicated map
In, due to when each step extends child node, all extendible child node of present node all being remained, with search
Carrying out, need retain number of nodes it is more and more, retain node quantity it is excessively huge, cause search efficiency not high, account for
It is larger with memory source, so in the high search of some requirement of real-time and not applying to.
The content of the invention
It is an object of the invention to overcome the deficiencies in the prior art, there is provided in path search process, only retain and work as prosthomere
Extended node of the point with destination node in the same direction, the section in the state space of the expanding node for reducing present node
Point quantity, reduces the search scale of algorithm, improves the path planning constrained based on target direction of the search efficiency of algorithm
Algorithm.
The purpose of the present invention is achieved through the following technical solutions：The path planning constrained based on target direction is calculated
Method, its principle is：In path search process, only retain present node and destination node expansible section in the same direction
Point, and these extended nodes are added into the state space of extended node, to each extended node in state space
It is estimated, obtains the minimum extended node of evaluation function value as next present node, duplicate paths search, Zhi Daozhuan
The minimum extended node of evaluation function value is destination node in state space, obtains optimal path.
Further, the determination method of state space is in described step S1：If present node is S, present node S's
For N number of, line present node S and destination node D makees respectively N number of extended node on line direction to extended node
Projection, chooses the node addition state space that projection falls above line upwards just.
Further, the concrete operation method of the determination of described state space is：If the coordinate of present node S is (x1,
y1, z1), the coordinate of its extended node X is (x2, y2, z2), the coordinate of destination node D is (x3, y3, z3), difference line SX,
SD, then
|SX|2=(x1-x2)2+(y1-y2)2+(z1-z2)2
|SD|2=(x1-x3)2+(y1-y3)2+(z1-z3)2
|XD|2=(x2-x3)2+(y2-y3)2+(z2-z3)2
Judge the size of cosXSD values：
(1) if cosXSD ＜ 0, the reverse extendings that be projected in directed line segment SD of the extended node X on line SD
On line, then give up extended node X；
(2) if cosXSD >=0, projections of the extended node X on line SD on directed line segment SD, retains just
Meet the extended node X of the condition, and by all of extended node X-shaped into the extended node of present node S state
Space.
Further, the concrete methods of realizing of described step S2 is：For the assessment of each extended node, adopting should
The heuristic evaluation function of extended node is calculated, f'(x) function definition is：
F'(x)=g (x)+h'(x)
In formula, f'(x) be f (x) evaluation function, wherein f (x) is the actual generation that destination node D is reached from present node S
Value, g (x) is the actual cost value from present node S to extended node X；H'(x) it is heuristic function, is the estimation of h (x)
Function, wherein h (x) are the actual minimum cost values from extended node X to destination node D, h'(x) it is less than extended node
The actual minimum cost of X to destination node D；
Using above-mentioned evaluation function f'(x) to weigh state space in all extended nodes significance level, it is expansible
The value of the evaluation function of node is less, and the extended node is more important for pathfinding, therefore finally chooses evaluation function
The minimum extended node of value, as next step present node to be extended.
Specifically, the concrete operation method of described path planning algorithm is comprised the following steps：
Step 1, open, close table for generating sky, start node is put in open tables；
Step 2, judge that whether open tables are empty, it is no if open tables are sky, then it represents that do not find path, unsuccessfully exit
Then, execution step 3；
Step 3, from open tables head node is found out as present node, and it is removed from open tables, be stored in close
In table；
Step 4, judge whether the node is destination node, if it is, head node is terminal, judge that it whether there is
Father node；If there is father node, the father node of the node is found in close tables, travels through close tables until start node,
Optimal path is found, algorithm terminates；If there is no father node, algorithm terminates；Perform if the node is not destination node
Step 5；
Step 5, judge whether the head node is extended node, if then carrying out step 6, if not then return to step 2；
Step 6, the head node is extended, finds its extended node, the line head node and destination node, work can
Projection of the expanding node on line direction, selection is projected in the extended node on line and constitutes set V；
Extended node in step 7, traversal set V, if extended node is neither in open tables, and does not exist
In close tables, the extended node is added in open tables, and calculate the evaluation function of the extended node, this will be expanded
The head node of a little extended nodes is defined as the father node of its extended node；
If step 8, extended node are in open tables, the extended node has had one originally in open tables
Individual evaluation function and a father node, compare original evaluation function in the evaluation function and open tables of the extended node big
It is little, if the evaluation function of extended node just updates the extended node and exists less than original evaluation function in open tables
Evaluation function and father node in open tables, does not operate if being not less than；If extended node does not do in close tables
Process, continuation judges other extended nodes；
Step 9, according to evaluation function value be incremented by order, to open tables in all nodes be ranked up, return to step
3。
Further, first head node in described step 3 in open tables is start node, later each cephalomere
Point is the minimum node of evaluation function value in open tables.
The invention has the beneficial effects as follows：Compared with existing algorithm, the present invention does not retain current in path search process
All extended nodes of node, only retain present node and destination node extended node in the same direction, reduce
Number of nodes in the state space of the expanding node of present node, reduces the search scale of algorithm, reduces memory source
Occupancy, improve the search efficiency of algorithm, it is adaptable to the route searching of various scenes, be particularly well-suited to requirement of real-time high
The route searching of scene.
Description of the drawings
Fig. 1 is the route searching schematic diagram of the invention of the present invention；
Fig. 2 is the algorithm flow chart of the specific embodiment of the present invention.
Specific embodiment
Technical scheme is further illustrated below in conjunction with the accompanying drawings.
Based on the path planning algorithm that target direction is constrained, its algorithm principle is：In path search process, only retain and work as
Front nodal point (start node is first present node) and destination node extended node in the same direction, and can by these
Expanding node adds the state space of extended node, and each extended node in state space is estimated, and obtains
The minimum extended node of evaluation function value (has not been at state sky as next present node as the node of present node
Between suffer), duplicate paths search, the minimum extended node of evaluation function value is destination node in state space, is obtained
Optimal path.
It can be seen from geometry, air line distance is most short between 2 points, so clicking through to given two in road network topology
During row path planning, the line direction from start node to destination node substantially represent the substantially trend of shortest path.
That is, final shortest path is substantially in the both sides of two node lines, and generally in its vicinity, so to algorithm search
Scope carry out target direction constraint in line both sides, i.e., when to each point spread, connection present node and target
The line of node, extended node the projecting on line direction to present node, if the projection of extended node is lucky
Fall on line, retain the extended node：If present node is S, the extended node of present node S is N number of, and line is current
Node S and destination node D, makees respectively the projection of N number of extended node on line direction, chooses projection and falls just in line
Top node upwards adds state space.As shown in figure 1, such as in present node S, its extendible node has six
1、2、3、6、7、8.Line present node S and destination node D, then makees respectively the projection of six nodes on line direction,
The then projection of node 6,7,8 falls on line just, and the projection of node 1,2,3 falls on the reverse extending line of line, choosing
Take and project the node on line that falls just.Concrete operation method is：If the coordinate of present node S is (x1, y1, z1), it can expand
The coordinate of exhibition nodes X is (x2, y2, z2), the coordinate of destination node D is (x3, y3, z3), difference line SX, SD, then
|SX|2=(x1-x2)2+(y1-y2)2+(z1-z2)2
|SD|2=(x1-x3)2+(y1-y3)2+(z1-z3)2
|XD|2=(x2-x3)2+(y2-y3)2+(z2-z3)2
Judge the size of cosXSD values：
(1) if cosXSD ＜ 0, the reverse extendings that be projected in directed line segment SD of the extended node X on line SD
On line, then give up extended node X；
(2) if cosXSD >=0, projections of the extended node X on line SD on directed line segment SD, retains just
Meet the extended node X of the condition, and by all of extended node X-shaped into the extended node of present node S state
Space.
Further, the concrete methods of realizing of described step S2 is：For the assessment of each extended node, adopting should
The heuristic evaluation function of extended node is calculated, f'(x) function definition is：
F'(x)=g (x)+h'(x)
In formula, f'(x) be f (x) evaluation function, wherein f (x) is the actual generation that destination node D is reached from present node S
Value, g (x) is the actual cost value from present node S to extended node X；H'(x) it is heuristic function, is the estimation of h (x)
Function, wherein h (x) are the actual minimum cost values from extended node X to destination node D, h'(x) it is less than extended node
Minimum costs of the X to destination node D；
Using above-mentioned evaluation function f'(x) to weigh state space in all extended nodes significance level, it is expansible
The value of the evaluation function of node is less, and the extended node is more important for pathfinding, therefore finally chooses evaluation function
The minimum extended node of value, as next step present node to be extended.
The present invention is provided with two tables when specifically being operated in search procedure：Open tables and close tables, open
Table is used for the node for preserving all generation and not investigated, and close tables are used for the node that record had been investigated.According to front
The description in face：Each extended node in state space is estimated, the minimum expansible section of evaluation function value is obtained
Point is used as next present node.Therefore need to reset the node in open tables according to evaluation function when operation is performed, so,
Each step in circulation selects the minimum node of evaluation function value, in being put into close tables.For the node of each extension, if
It was found that there is same node (i.e. this node is while be also the previous extended node for treating expanding node) in open tables, just
Relatively the size of two node evaluation functions, such as extends the appraisal letter of the new node cost existing node more than before for obtaining
Number, then abandon extending the new node for obtaining, and otherwise just with the node that new node replacement is original, its flow process is as shown in Figure 2.Described
The concrete operation method of path planning algorithm is comprised the following steps：
Step 1, open, close table for generating sky, start node is put in open tables；
Step 2, judge that whether open tables are empty, it is no if open tables are sky, then it represents that do not find path, unsuccessfully exit
Then, execution step 3；
Step 3, from open tables head node is found out as present node, and it is removed from open tables, be stored in close
In table；
Step 4, judge whether the node is destination node, if it is, head node is terminal, judge that it whether there is
Father node；If there is father node, the father node of the node is found in close tables, travels through close tables until start node,
Optimal path is found, algorithm terminates；If there is no father node, algorithm terminates；Perform if the node is not destination node
Step 5；
Step 5, judge whether the head node is extended node, if then carrying out step 6, if not then return to step 2；
Step 6, the head node is extended, finds its extended node, the line head node and destination node, work can
Projection of the expanding node on line direction, selection is projected in the extended node on line and constitutes set V；
Extended node in step 7, traversal set V, if extended node is neither in open tables, and does not exist
In close tables, the extended node is added in open tables, and calculate the evaluation function of the extended node, this will be expanded
The head node of a little extended nodes is defined as the father node of its extended node；
If step 8, extended node are in open tables, the extended node has had one originally in open tables
Individual evaluation function and a father node, compare original evaluation function in the evaluation function and open tables of the extended node big
It is little, if the evaluation function of extended node just updates the extended node and exists less than original evaluation function in open tables
Evaluation function and father node in open tables, does not operate if being not less than；If extended node does not do in close tables
Process, continuation judges other extended nodes；
Step 9, according to evaluation function value be incremented by order, to open tables in all nodes be ranked up, return to step
3。
Further, first head node in described step 3 in open tables is start node, later each cephalomere
Point is the minimum node of evaluation function value in open tables.
One of ordinary skill in the art will be appreciated that embodiment described here is to aid in reader and understands this
Bright principle, it should be understood that protection scope of the present invention is not limited to such especially statement and embodiment.This area
It is each that those of ordinary skill can make various other without departing from essence of the invention according to these technologies enlightenment disclosed by the invention
Plant concrete deformation and combine, these deformations and combination are still within the scope of the present invention.
Claims (4)
1. the path planning algorithm for being constrained based on target direction, it is characterised in that in path search process, is only retained and works as prosthomere
Extended node of the point with destination node in the same direction, and add the state of extended node empty these extended nodes
Between, each extended node in state space is estimated, obtain the minimum extended node conduct of evaluation function value
Next present node, duplicate paths search, the minimum extended node of evaluation function value is target section in state space
Point, obtains optimal path；
The determination method of the state space is：If present node is S, the extended node of present node S is N number of, and line is worked as
Front nodal point S and destination node D, makees respectively the projection of N number of extended node on line direction, chooses projection and falls just even
Line top node upwards adds state space；
For the assessment of each extended node, calculated using the heuristic evaluation function of the extended node, f'(x) letter
Counting definition is：
F'(x)=g (x)+h'(x)
In formula, f'(x) be f (x) evaluation function, wherein f (x) is the actual cost that destination node D is reached from present node S
Value, g (x) is the actual cost value from present node S to extended node X；H'(x) it is heuristic function, is the estimation letter of h (x)
Number, wherein h (x) is the actual minimum cost value from extended node X to destination node D, h'(x) it is less than extended node X
To the minimum cost of destination node D；
Using above-mentioned evaluation function f'(x) to weigh state space in all extended nodes significance level, extended node
Evaluation function value it is less, the extended node is more important for pathfinding, therefore final chooses evaluation function value most
Little extended node, as next step present node to be extended.
2. it is according to claim 1 based on target direction constrain path planning algorithm, it is characterised in that described state
The concrete operation method of the determination in space is：If the coordinate of present node S is (x1, y1, z1), the coordinate of its extended node X is
(x2, y2, z2), the coordinate of destination node D is (x3, y3, z3), difference line SX, SD, then
|SX|2=(x1-x2)2+(y1-y2)2+(z1-z2)2
|SD|2=(x1-x3)2+(y1-y3)2+(z1-z3)2
|XD|2=(x2-x3)2+(y2-y3)2+(z2-z3)2
Judge the size of cosXSD values：
(1) if cosXSD ＜ 0, extended node X being projected on the reverse extending line of directed line segment SD on line SD,
Then give up extended node X；
(2) if cosXSD >=0, just on directed line segment SD, reservation meets for projections of the extended node X on line SD
The extended node X of the condition, and by all of extended node X-shaped into the extended node of present node S state space.
3. it is according to claim 1 and 2 based on target direction constrain path planning algorithm, it is characterised in that it is described
The concrete operation method of path planning algorithm is comprised the following steps：
Step 1, open, close table for generating sky, start node is put in open tables；
Step 2, judge that whether open tables are empty, if open tables are sky, then it represents that do not find path, unsuccessfully exit, otherwise, hold
Row step 3；
Step 3, from open tables head node is found out as present node, and it is removed from open tables, be stored in close tables
In；
Step 4, judge whether the node is destination node, if it is, head node is terminal, judge that it whether there is father's section
Point；If there is father node, the father node of the node is found in close tables, traversal close tables find until start node
Optimal path, algorithm terminates；If there is no father node, algorithm terminates；The execution step if the node is not destination node
5；
Step 5, judge whether the head node is extended node, if then carrying out step 6, if not then return to step 2；
Step 6, the head node is extended, finds its extended node, the line head node and destination node, make expansible
Projection of the node on line direction, selection is projected in the extended node on line and constitutes set V；
Extended node in step 7, traversal set V, if extended node is neither in open tables, and not in close tables
In, the extended node is added in open tables, and the evaluation function of the extended node is calculated, these will be expanded expansible
The head node of node is defined as the father node of its extended node；
If step 8, extended node are in open tables, the extended node has had one and has estimated originally in open tables
Valency function and a father node, compare original evaluation function size in the evaluation function and open tables of the extended node, such as
The evaluation function of fruit extended node just updates the extended node in open tables less than original evaluation function in open tables
Evaluation function and father node, if not less than if do not operate；If extended node is not processed in close tables, continue
Judge other extended nodes；
Step 9, according to evaluation function value be incremented by order, to open tables in all nodes be ranked up, return to step 3.
4. it is according to claim 3 based on target direction constrain path planning algorithm, it is characterised in that described step
First head node in 3 in open tables is start node, and later each head node is in open tables evaluation function value most
Little node.
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| CN106780762A (en) * | 2016-12-20 | 2017-05-31 | 浙江工业大学 | 3 D complex scene is based on the path planning optimization method of bounding volume hierarchy (BVH) collision detection |
| CN106708049B (en) * | 2016-12-30 | 2018-02-06 | 北京理工大学 | A kind of paths planning method of the lower movable body of multistation relay navigation |
| CN108268971B (en) * | 2017-12-06 | 2021-12-07 | 腾讯科技（深圳）有限公司 | Path searching method, device, processor and electronic device |
| CN109255467A (en) * | 2018-07-27 | 2019-01-22 | 四川大学 | A kind of A* pathfinding optimization method of Virtual reality |
| CN110186462B (en) * | 2019-07-23 | 2019-12-20 | 恒大智慧充电科技有限公司 | Cloud platform, navigation method, computer equipment and computer-readable storage medium |
| CN110567477A (en) * | 2019-09-27 | 2019-12-13 | 五邑大学 | Path planning method and device based on improved A-x algorithm and robot |
| CN111158366B (en) * | 2019-12-31 | 2021-11-05 | 湖南大学 | Path planning method based on graph search and geometric curve fusion |
| CN111174798A (en) * | 2020-01-17 | 2020-05-19 | 长安大学 | Foot type robot path planning method |
| CN111369066B (en) * | 2020-03-09 | 2022-06-24 | 广东南方数码科技股份有限公司 | Path planning method and device, electronic equipment and readable storage medium |
| CN111272187B (en) * | 2020-03-24 | 2021-10-19 | 山东师范大学 | Optimal driving path planning method and system based on improved A-star algorithm |
| CN113494926A (en) * | 2021-09-06 | 2021-10-12 | 深圳慧拓无限科技有限公司 | Path finding method, device and equipment |
| CN114779772B (en) * | 2022-04-13 | 2023-08-08 | 泉州装备制造研究所 | Path planning method and device integrating global algorithm and local algorithm |
| CN114884825B (en) * | 2022-05-17 | 2024-04-23 | 中国电信股份有限公司 | Network planning method and device and electronic equipment |
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