CN104914862A - 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, be specifically related to the path planning algorithm of the based target direction constrain of indoor (as transport hub, megastore etc.) route guidance technology.
Background technology
At present, the shortest route problem based on indoor guiding has had a lot of ripe algorithm, and common path planning algorithm has dijkstra's algorithm, Floyd algorithm, heuristic search algorithm etc.
Dijkstra's algorithm is the most classical Shortest Path Searching Algorithm, is also a kind of algorithm relatively consuming time.It asks shortest path by weights incremental order, has the feature of simple and clear, and the result of algorithm search gained is also more accurate.On the other hand, Dijkstra, for the various sparse graph of input number of nodes, is calculate appointment 2 or space any two points is all the shortest path calculating whole sparse graph, has that efficiency is low, take up room large shortcoming.
Floyd algorithm is also known as Freud's algorithm, a kind of for finding in given weight path topological network shortest path algorithm between summit, its ultimate principle is dynamic programming, it is first converted into weight matrix path network, in weight matrix, then ask the shortest path of any two points, it has had very large improvement compared to dijkstra's algorithm, dense graph best results, the change of starting point and terminal is little on algorithm impact, effectively simple, efficiency is higher than dijkstra's algorithm, but also life period complexity is high, is not suitable for the shortcoming calculating mass data.
Blind search algorithm is belonged to the shortest path first that dijkstra's algorithm, Floyd algorithm are representative, although can shortest path be tried to achieve, but calculated amount is but very large, is applicable to the graph structure that nodes is less, and inapplicable for the graph structure that number of nodes is very huge.
Heuristic search algorithm is the searching algorithm of the knowledge based on specific field, in the process of search, algorithm not only considers the current cost of node, and take into account the estimate cost of expansion required for this node, search procedure is advanced towards most promising direction, and then accelerates whole computation process.Enlightening information spinner will be reflected on evaluation function, and in search procedure, the task of evaluation function is exactly estimate that node to be searched is in the possibility on optimal path, thus the node that first search possibility is larger, thus reach the object improving search speed.
Shortest path first based on heuristic search has local preferentially searching algorithm, best-first search algorithm, and common A
*algorithm, local preferentially searching algorithm is the simplest heuristic search algorithm, in the process of search, after certain node is expanded, that node of " optimum " will be further extended, and give up to fall father node and other the expansion child node of this child node.If search procedure continues always, owing to giving up many expansion child nodes, just likely real optimal node is all given up, so the optimal node in one section of process is not the optimal node of the overall situation, so the optimal path that the path arrived of this algorithm search is not necessarily real.
A
*algorithm is one of method important in heuristic search algorithm.It is a kind of preferably first search algorithm, in search procedure, does not give up to fall node, in the appraisal of each step all current node and before the assessment values of node compare and obtain one " optimal node ".Effectively can prevent the loss of " optimal node " like this, improve the accuracy of algorithm search result.On fairly simple map, its speed quickly, can find shortest path very soon.In the map of more complicated, due to when each step expansion child node, the all extendible child node of present node is all remained, along with the carrying out of search, need the number of nodes retained to get more and more, the quantity retaining node is too huge, causes search efficiency not high, committed memory resource is comparatively large, so inapplicable in the search that some requirement of real-times are high.
Summary of the invention
The object of the invention is to overcome the deficiencies in the prior art, be provided in path search process, only retain present node and destination node extended node in the same direction, decrease the number of nodes in the state space of the expanding node of present node, reduce the search scale of algorithm, improve the path planning algorithm of the based target direction constrain of the search efficiency of algorithm.
The object of the invention is to be achieved through the following technical solutions: the path planning algorithm of based target direction constrain, its principle is: in path search process, only retain present node and destination node extended node in the same direction, and these extended nodes are added the state space of extended node, each extended node in state space is assessed, obtain the minimum extended node of evaluation function value as next present node, duplicate paths is searched for, until the extended node that in state space, evaluation function value is minimum is destination node, obtain optimal path.
Further, in described step S1, the defining method of state space is: set present node as S, the extended node of present node S is N number of, line present node S and destination node D, do this projection of N number of extended node on line direction respectively, choose the node that projection to drop on above line upwards just and add state space.
Further, the concrete operation method of the determination of described state space is: set the coordinate of present node S as (x
1, y
1, z
1), the coordinate of its extended node X is (x
2, y
2, z
2), the coordinate of destination node D is (x
3, y
3, z
3), line SX, SD respectively, then
|SX|
2＝(x
1-x
2)
2+(y
1-y
2)
2+(z
1-z
2)
2
|SD|
2＝(x
1-x
3)
2+(y
1-y
3)
2+(z
1-z
3)
2
|XD|
2＝(x
2-x
3)
2+(y
2-y
3)
2+(z
2-z
3)
2
Judge the size of cosXSD value:
(1) if cosXSD < 0, then extended node X is projected on the reverse extending line of directed line segment SD on line SD, then give up this extended node X;
(2) if cosXSD >=0, then the projection of extended node X on line SD is just on directed line segment SD, retains the extended node X meeting this condition, and all extended node X-shapeds is become the state space of the extended node of present node S.
Further, the concrete methods of realizing of described step S2 is: for the assessment of each extended node, adopts the heuristic evaluation function of this extended node to calculate, f'(x) function definition is:
f′(x)＝g(x)+h′(x)
In formula, f'(x) be the evaluation function of f (x), wherein f (x) is the actual cost value arriving destination node D from present node S, and g (x) is the actual cost value from present node S to extended node X; H'(x) be heuristic function, it is the estimation function of h (x), wherein h (x) is the minimum cost value from extended node X to the reality of destination node D, h'(x) be less than the minimum cost of extended node X to the reality of destination node D;
Utilize above-mentioned evaluation function f'(x) weigh the significance level of all extended nodes in state space, the value of the evaluation function of extended node is less, this extended node is more important to pathfinding, therefore finally namely choose the minimum extended node of evaluation function value, it can be used as the present node that next step is to be expanded.
Particularly, the concrete operation method of described path planning algorithm comprises the following steps:
Open, close table of step 1, generation sky, puts into open table by start node;
Step 2, judge that whether open table is empty, if open table is for empty, then represents and do not find path, unsuccessfully exit, otherwise, perform step 3;
Step 3, from open table find out head node as present node, and by its from open table remove, stored in close table in;
Step 4, judge whether this node is destination node, if so, then head node is terminal, judges whether it exists father node; If there is father node, then in close table, find the father node of this node, traversal close table is until start node, and find optimal path, algorithm terminates; If there is not father node, then algorithm terminates; If this node is not destination node, perform step 5;
Step 5, judge whether this head node is extended node, if then carry out step 6, if not then return step 2;
Step 6, this head node to be expanded, find its extended node, this head node of line and destination node, do the projection of extended node on line direction, select the extended node be projected on line to form set V;
Extended node in step 7, traversal set V, if extended node is neither in open table, again not in close table, this extended node is added in open table, and calculate the evaluation function of this extended node, the head node expanding these extended nodes is defined as the father node of its extended node;
If step 8 extended node is in open table, then originally there is an evaluation function and a father node in this extended node in open table, original evaluation function size during the evaluation function of this extended node of comparison and open show, if the evaluation function of extended node is less than original evaluation function in open table, just upgrade this extended node open table in evaluation function and father node, if be not less than, inoperation; If extended node in close table, does not then process, continue to judge other extended node;
Step 9, the order increased progressively according to evaluation function value, sort to all nodes in open table, return step 3.
Further, first head node in described step 3 in open table is start node, and each head node is the node that in open table, evaluation function value is minimum later.
The invention has the beneficial effects as follows: compared with existing algorithm, the present invention is in path search process, do not retain all extended nodes of present node, only retain present node and destination node extended node in the same direction, decrease the number of nodes in the state space of the expanding node of present node, reduce the search scale of algorithm, decrease taking of memory source, improve the search efficiency of algorithm, be applicable to the route searching of various scene, be specially adapted to the route searching of the high scene of requirement of real-time.
Accompanying drawing explanation
Fig. 1 is route searching schematic diagram of the present invention;
Fig. 2 is the algorithm flow chart of specific embodiments of the invention.
Embodiment
Technical scheme of the present invention is further illustrated below in conjunction with accompanying drawing.
The path planning algorithm of based target direction constrain, the path planning algorithm of based target direction constrain, its algorithm principle is: in path search process, only retain present node (start node is first present node) and destination node extended node in the same direction, and these extended nodes are added the state space of extended node, each extended node in state space is assessed, obtain the minimum extended node of evaluation function value as next present node (node as present node be not at state space suffered), duplicate paths is searched for, until the extended node that in state space, evaluation function value is minimum is destination node, obtain optimal path.
According to geometry, between 2, air line distance is the shortest, so when carrying out path planning to given 2 in road network topology, the line direction from start node to destination node, substantially represent the roughly trend of shortest path.That is, final shortest path is in the both sides of two node lines substantially, and usually in its vicinity, so carry out target direction constraint to the scope of algorithm search in line both sides, namely time to each point spread, connect the line of present node and destination node, to extended node the projecting on line direction of present node, if the projection of extended node drops on line just, retain this extended node: set present node as S, the extended node of present node S is N number of, line present node S and destination node D, do this projection of N number of extended node on line direction respectively, choose the node that projection to drop on above line upwards just and add state space.As shown in Figure 1, such as when present node S, its extendible node has six 1,2,3,6,7,8.Line present node S and destination node D, then this six projections of node on line direction are done respectively, then the projection of node 6,7,8 drops on line just, and the projection of node 1,2,3 has dropped on the reverse extending line of line, chooses projection and just drops on node on line.Concrete operation method is: set the coordinate of present node S as (x
1, y
1, z
1), the coordinate of its extended node X is (x
2, y
2, z
2), the coordinate of destination node D is (x
3, y
3, z
3), line SX, SD respectively, then
|SX|
2＝(x
1-x
2)
2+(y
1-y
2)
2+(z
1-z
2)
2
|SD|
2＝(x
1-x
3)
2+(y
1-y
3)
2+(z
1-z
3)
2
|XD|
2＝(x
2-x
3)
2+(y
2-y
3)
2+(z
2-z
3)
2
Judge the size of cosXSD value:
(1) if cosXSD < 0, then extended node X is projected on the reverse extending line of directed line segment SD on line SD, then give up this extended node X;
(2) if cosXSD >=0, then the projection of extended node X on line SD is just on directed line segment SD, retains the extended node X meeting this condition, and all extended node X-shapeds is become the state space of the extended node of present node S.
Further, the concrete methods of realizing of described step S2 is: for the assessment of each extended node, adopts the heuristic evaluation function of this extended node to calculate, f'(x) function definition is:
f′(x)＝g(x)+h′(x)
In formula, f'(x) be the evaluation function of f (x), wherein f (x) is the actual cost value arriving destination node D from present node S, and g (x) is the actual cost value from present node S to extended node X; H'(x) be heuristic function, it is the estimation function of h (x), wherein h (x) is the minimum cost value from extended node X to the reality of destination node D, h'(x) be less than the minimum cost of extended node X to destination node D;
Utilize above-mentioned evaluation function f'(x) weigh the significance level of all extended nodes in state space, the value of the evaluation function of extended node is less, this extended node is more important to pathfinding, therefore finally namely choose the minimum extended node of evaluation function value, it can be used as the present node that next step is to be expanded.
The present invention is when specifically operating, and in search procedure, be provided with two table: open show and close table, open table is not for preserving all generation by the node investigated, and close table is for recording by the node investigated.Description according to above: assess each extended node in state space, obtains the minimum extended node of evaluation function value as next present node.Therefore need the node reset according to evaluation function in open table when executable operations, like this, each step in circulation selects the minimum node of evaluation function value, puts into close table.For the node of each expansion, if find to there is same node (namely this node is also the previous extended node treating expanding node) in open table simultaneously, just compare the size of two node evaluation functions, as expanded the evaluation function of existing node before the new node cost obtained is greater than, then abandon expanding the new node obtained, otherwise just replace original node with new node, its flow process as shown in Figure 2.The concrete operation method of described path planning algorithm comprises the following steps:
Open, close table of step 1, generation sky, puts into open table by start node;
Step 2, judge that whether open table is empty, if open table is for empty, then represents and do not find path, unsuccessfully exit, otherwise, perform step 3;
Step 3, from open table find out head node as present node, and by its from open table remove, stored in close table in;
Step 4, judge whether this node is destination node, if so, then head node is terminal, judges whether it exists father node; If there is father node, then in close table, find the father node of this node, traversal close table is until start node, and find optimal path, algorithm terminates; If there is not father node, then algorithm terminates; If this node is not destination node, perform step 5;
Step 5, judge whether this head node is extended node, if then carry out step 6, if not then return step 2;
Step 6, this head node to be expanded, find its extended node, this head node of line and destination node, do the projection of extended node on line direction, select the extended node be projected on line to form set V;
Extended node in step 7, traversal set V, if extended node is neither in open table, again not in close table, this extended node is added in open table, and calculate the evaluation function of this extended node, the head node expanding these extended nodes is defined as the father node of its extended node;
If step 8 extended node is in open table, then originally there is an evaluation function and a father node in this extended node in open table, original evaluation function size during the evaluation function of this extended node of comparison and open show, if the evaluation function of extended node is less than original evaluation function in open table, just upgrade this extended node open table in evaluation function and father node, if be not less than, inoperation; If extended node in close table, does not then process, continue to judge other extended node;
Step 9, the order increased progressively according to evaluation function value, sort to all nodes in open table, return step 3.
Further, first head node in described step 3 in open table is start node, and each head node is the node that in open table, evaluation function value is minimum later.
Those of ordinary skill in the art will appreciate that, embodiment described here is to help reader understanding's principle of the present invention, should be understood to that protection scope of the present invention is not limited to so special statement and embodiment.Those of ordinary skill in the art can make various other various concrete distortion and combination of not departing from essence of the present invention according to these technology enlightenment disclosed by the invention, and these distortion and combination are still in protection scope of the present invention.
Claims (6)
1. the path planning algorithm of based target direction constrain, it is characterized in that, in path search process, only retain present node and destination node extended node in the same direction, and these extended nodes are added the state space of extended node, each extended node in state space is assessed, obtain the minimum extended node of evaluation function value as next present node, duplicate paths is searched for, until the extended node that in state space, evaluation function value is minimum is destination node, obtain optimal path.
2. the path planning algorithm of based target direction constrain according to claim 1, it is characterized in that, in described step S1, the defining method of state space is: set present node as S, the extended node of present node S is N number of, line present node S and destination node D, do this projection of N number of extended node on line direction respectively, choose the node that projection to drop on above line upwards just and add state space.
3. the path planning algorithm of based target direction constrain according to claim 2, is characterized in that, the concrete operation method of the determination of described state space is: set the coordinate of present node S as (x
1, y
1, z
1), the coordinate of its extended node X is (x
2, y
2, z
2), the coordinate of destination node D is (x
3, y
3, z
3), line SX, SD respectively, then
|SX|
2＝(x
1-x
2)
2+(y
1-y
2)
2+(z
1-z
2)
2
|SD|
2＝(x
1-x
3)
2+(y
1-y
3)
2+(z
1-z
3)
2
|XD|
2＝(x
2-x
3)
2+(y
2-y
3)
2+(z
2-z
3)
2
Judge the size of cos XSD value:
(1) if cos XSD < 0, then extended node X is projected on the reverse extending line of directed line segment SD on line SD, then give up this extended node X;
(2) if cos XSD >=0, then the projection of extended node X on line SD is just on directed line segment SD, retains the extended node X meeting this condition, and all extended node X-shapeds is become the state space of the extended node of present node S.
4. the path planning algorithm of based target direction constrain according to claim 3, it is characterized in that, the concrete methods of realizing of described step S2 is: for the assessment of each extended node, the heuristic evaluation function of this extended node is adopted to calculate, f'(x) function definition is:
f′(x)＝g(x)+h′(x)
In formula, f'(x) be the evaluation function of f (x), wherein f (x) is the actual cost value arriving destination node D from present node S, and g (x) is the actual cost value from present node S to extended node X; H'(x) be heuristic function, it is the estimation function of h (x), wherein h (x) is the minimum cost value from extended node X to the reality of destination node D, h'(x) be less than the minimum cost of extended node X to destination node D;
Utilize above-mentioned evaluation function f'(x) weigh the significance level of all extended nodes in state space, the value of the evaluation function of extended node is less, this extended node is more important to pathfinding, therefore finally namely choose the minimum extended node of evaluation function value, it can be used as the present node that next step is to be expanded.
5. according to the path planning algorithm of the based target direction constrain in Claims 1 to 4 described in any one, it is characterized in that, the concrete operation method of described path planning algorithm comprises the following steps:
Open, close table of step 1, generation sky, puts into open table by start node;
Step 2, judge that whether open table is empty, if open table is for empty, then represents and do not find path, unsuccessfully exit, otherwise, perform step 3;
Step 3, from open table find out head node as present node, and by its from open table remove, stored in close table in;
Step 4, judge whether this node is destination node, if so, then head node is terminal, judges whether it exists father node; If there is father node, then in close table, find the father node of this node, traversal close table is until start node, and find optimal path, algorithm terminates; If there is not father node, then algorithm terminates; If this node is not destination node, perform step 5;
Step 5, judge whether this head node is extended node, if then carry out step 6, if not then return step 2;
Step 6, this head node to be expanded, find its extended node, this head node of line and destination node, do the projection of extended node on line direction, select the extended node be projected on line to form set V;
Extended node in step 7, traversal set V, if extended node is neither in open table, again not in close table, this extended node is added in open table, and calculate the evaluation function of this extended node, the head node expanding these extended nodes is defined as the father node of its extended node;
If step 8 extended node is in open table, then originally there is an evaluation function and a father node in this extended node in open table, original evaluation function size during the evaluation function of this extended node of comparison and open show, if the evaluation function of extended node is less than original evaluation function in open table, just upgrade this extended node open table in evaluation function and father node, if be not less than, inoperation; If extended node in close table, does not then process, continue to judge other extended node;
Step 9, the order increased progressively according to evaluation function value, sort to all nodes in open table, return step 3.
6. the path planning algorithm of based target direction constrain according to claim 5, is characterized in that, first head node in described step 3 in open table is start node, and each head node is the node that in open table, evaluation function value is minimum later.
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| CN110769372A (en) * | 2019-07-23 | 2020-02-07 | 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 |
| CN111158366A (en) * | 2019-12-31 | 2020-05-15 | 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 |
| CN111369066A (en) * | 2020-03-09 | 2020-07-03 | 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 |
| 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 |
| CN111272187A (en) * | 2020-03-24 | 2020-06-12 | 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 |
| CN114779772A (en) * | 2022-04-13 | 2022-07-22 | 泉州装备制造研究所 | Path planning method and device fusing global and local algorithms |
| 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 |
| CN114884825A (en) * | 2022-05-17 | 2022-08-09 | 中国电信股份有限公司 | Network planning method and device and electronic equipment |
| 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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