CN111272187A - Optimal driving path planning method and system based on improved A-star algorithm - Google Patents
Optimal driving path planning method and system based on improved A-star algorithm Download PDFInfo
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- G—PHYSICS
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
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Abstract
The present disclosure discloses an optimal driving path planning method and system based on an improved a-x algorithm, including: acquiring a starting node and a target node of a path to be planned; based on the initial node and the target node of the path to be planned, obtaining an optimal driving path by using an improved A-x algorithm; the improved A-algorithm is used for improving the valuation function of the A-algorithm; the improved valuation function is: the product of the valuation function and the direction parameter of the conventional a-algorithm. Starting from the evaluation function of the A-algorithm, the A-algorithm is improved by using the straight line and the direction parameters, a shorter travel path is planned for the driver, the travel time of the user is saved, and the travel efficiency is improved.
Description
Technical Field
The disclosure relates to the technical field of traffic route guidance in intelligent traffic, and in particular relates to an optimal driving route planning method and system based on an improved a-x algorithm.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
The price of the automobile is continuously reduced, the number of the automobiles is continuously increased, and the GPS navigation system of the automobile is continuously developed and advanced. Compared with developed countries abroad, the time for using the automobile GPS navigation equipment is shorter in China, although the automobile GPS navigation equipment is developed quickly, great convenience is brought to the transportation of China and the traveling of people.
However, the inventor finds that the technical content of the automobile GPS navigation equipment in China is generally low, the problems of low positioning precision, incomplete path planning algorithm and the like exist, and better navigation service cannot be brought to users.
Disclosure of Invention
In order to solve the defects of the prior art, the present disclosure provides an optimal driving path planning method and system based on an improved a-x algorithm;
in a first aspect, the present disclosure provides an optimal driving path planning method based on an improved a-x algorithm;
an optimal driving path planning method based on an improved A-algorithm comprises the following steps:
acquiring a starting node and a target node of a path to be planned;
based on the initial node and the target node of the path to be planned, obtaining an optimal driving path by using an improved A-x algorithm; the improved A-algorithm is used for improving the valuation function of the A-algorithm; the improved valuation function is: the product of the valuation function and the direction parameter of the conventional a-algorithm.
In a second aspect, the present disclosure provides an optimal driving path planning system based on an improved a-x algorithm;
an optimal driving path planning system based on an improved A-algorithm comprises:
an acquisition module configured to: acquiring a starting node and a target node of a path to be planned;
a path planning module configured to: based on the initial node and the target node of the path to be planned, obtaining an optimal driving path by using an improved A-x algorithm; the improved A-algorithm is used for improving the valuation function of the A-algorithm; the improved valuation function is: the product of the valuation function and the direction parameter of the conventional a-algorithm.
In a third aspect, the present disclosure also provides an electronic device comprising a memory and a processor, and computer instructions stored on the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method of the first aspect is performed.
In a fourth aspect, the present disclosure also provides a computer-readable storage medium for storing computer instructions which, when executed by a processor, perform the method of the first aspect.
Compared with the prior art, the beneficial effect of this disclosure is:
the invention provides an improved A-x algorithm for designing an optimal travel path of a driver, aiming at solving the problems that the optimal path cannot be designed by the A-x algorithm, and the searching range is large and the searching efficiency is high. Starting from the evaluation function of the A-algorithm, the A-algorithm is improved by using the straight line and the direction parameters, a shorter travel path is planned for the driver, the travel time of the user is saved, and the travel efficiency is improved.
The optimal path of the vehicle on the static road is planned by adopting the A-star algorithm, so that the utilization rate of the road can be improved, and energy conservation and emission reduction are realized. And the A-x algorithm is improved under the background of the shortest driving time, so that the foundation can be laid for the realization of path planning on an automobile GPS navigation system.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
FIG. 1 is a flow chart of the method of the first embodiment.
Fig. 2 is a schematic view of a terminal road section according to the first embodiment;
fig. 3 is a road condition simulated by the a-algorithm according to the first embodiment;
FIG. 4 is a schematic illustration of a portion of a road segment of the first embodiment;
FIG. 5 is a road network of a city portion according to the first embodiment;
FIG. 6 is a first embodiment of a map conversion to a directed graph mode;
fig. 7 shows the path planning trajectory of the a-algorithm according to the first embodiment.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiment provides an optimal driving path planning method based on an improved A-x algorithm;
an optimal driving path planning method based on an improved a-x algorithm, the flow of which is shown in fig. 1, includes:
acquiring a starting node and a target node of a path to be planned;
based on the initial node and the target node of the path to be planned, obtaining an optimal driving path by using an improved A-x algorithm; the improved A-algorithm is used for improving the valuation function of the A-algorithm; the improved valuation function is: the product of the valuation function and the direction parameter of the conventional a-algorithm.
As one or more embodiments, the optimal driving path is obtained by using an improved a-algorithm based on the initial node and the target node of the path to be planned; the method comprises the following specific steps:
s200: constructing an OPEN list and a CLOSE list;
s201: putting the starting node S into an OPEN list and enabling the CLOSE list to be empty;
s202: remove the start node S from the OPEN list, put the start node S into the CLOSE columnAll adjacent nodes O of S are numbered in the tableiPutting the obtained product into an OPEN list, wherein the value range of i is 1 to n; n represents the total number of all adjacent nodes of S;
s203: if the OPEN list is empty, the algorithm is ended, and if not, all the adjacent nodes O are judgediWhether a target node exists in the node; the value range of i is 1 to n;
s204: if all the adjacent nodes OiThere is a target node, an adjacent node O to be the target nodeiPut into the CLOSE list and the algorithm ends.
As one or more embodiments, the step S204 further includes, after:
if all the adjacent nodes OiAll nodes are not target nodes, and each adjacent node O is judgediWhether or not there is a next hop neighbor node mij(ii) a j ranges from 1 to p; p denotes the current connecting node OiThe total number of next hop neighbor nodes;
s205: if there is no next-hop neighbor node mijThe algorithm ends if there is a neighboring node mijThen, the following process is performed:
s2051: if there are several next-hop neighbor nodes mijIf all the nodes are target nodes, calculating the adjacent node mijCorresponding each node OiAnd selecting the node O having the smallest value of the first evaluation functioniAnd marked as S; returning to S202;
s2052: if next hop is adjacent to node mijIf not, calculating the adjacent node mijCorresponding each node OiAnd selecting the node O having the smallest second evaluation function valueiAnd marked as S; returning to S202.
The evaluation function of the conventional a-algorithm is the first evaluation function.
Further, the first valuation function is:
F(n)1＝G(n)+H(n)；
wherein H (n) is the linear distance from the current node to the target node, F (n)1Representing the first estimateThe cost function, G (n), is the actual distance between the current node and the starting node.
Further, the second valuation function is:
F(n)2＝B(n)*(G(n)+H(n))；
wherein H (n) is the linear distance from the current node to the target node, B (n) is a direction parameter, and theta is the included angle of the adjacent paths and has 0 DEG<Theta is less than or equal to 90 degrees. The included angle of the adjacent paths refers to an included angle between a connecting line from the current node to the next hop node and a connecting line from the current node to the previous hop node; g (n) is the actual distance between the current node and the starting node, F (n)2Representing a second valuation function.
After the second evaluation function is introduced, the searched path angle is thinned to be less than 90 degrees, the path searching space is enlarged, and after the first evaluation function is introduced, the optimal path searching effect can be improved.
The efficiency of searching for a path can be improved by using the first and second valuation functions at S2501 and S2502.
The beneficial effects of the above technical scheme are: in the disclosure, two factors of a straight line and a direction parameter are effectively combined; the search space is enlarged, and meanwhile, the efficiency of searching the optimal path is improved; the method is suitable for the situation that the number of selectable paths in the road network is large.
Further, the working principle of the first valuation function is: and the included angle between the next hop section selected by each node and the connecting line between the current node and the terminal is less than 90 degrees.
Further, the second valuation function works on the principle that the criterion for selecting the next-hop node is: the included angle between the connecting line between the current node and the next hop node and the connecting line between the current node and the previous hop node is larger than 90 degrees.
When the next hop node of the child node n is the target node, the second valuation function does not apply, as shown in fig. 2.
Compared with the prior art, the method has the following innovation points:
two factors of a straight line and a direction parameter are effectively combined; the evaluation function is improved, and the direction parameter is designed as
Compared with the prior art, the invention has the following remarkable advantages that the path searching range is enlarged, and the searching efficiency is improved; the method is suitable for the situation that the number of selectable paths in the road network is large; as shown in fig. 3 and 4, when the adjacent paths in the routing network alternate between being greater than 90 and being less than 90 during the path search process, the size of the direction parameter can be flexibly changed according to the situation. And in non-traffic rush hours, a shorter travel path can be planned than the original A-x algorithm.
In the research, a part of road sections in a certain city are selected for path planning, and as shown in fig. 5, the shortest path length from the point L to the point a is obtained. The path planning problem is converted into a solution problem of a directed graph, as shown in fig. 6:
the relevant GPS data is shown in tables 1 and 2:
TABLE 1 GPS data
TABLE 2 GPS data
In addition, ∠ ALK is 44 °, ∠ ALJ is 51 °, ∠ 0AKD is 62 °, ∠ 1AKI is 37 °, ∠ AJI is 39 °, ∠ AJH is 51 °, ∠ AIC is 44 °, ∠ AIG is 59 °, ∠ AHG is 22 °, ∠ AHF is 70 °, ∠ AGB is 35 °, and ∠ AGE is 73 °.
H(A)＝0km，H(B)＝0.54km，H(C)＝1.33km，H(D)＝2.19km，H(E)＝1.58km，H(F)＝2.22km，H(G)＝1.62km，H(H)＝2.27km，H(I)＝1.96km，H(J)＝2.64km，H(K)＝2.56km，H(L)＝3.24km。
According to the evaluation function f (n) ═ g (n) + h (n) of the a-algorithm, the shortest path from the start node L to the target node a is planned by using the above data, and the calculation process is briefly introduced as follows:
(1) taking L as an initial node, calculating K, J f (n), f (K) ═ g (K) + h (K) ═ 3.28km, f (j) ═ g (j) + h (j) ═ 3.3km, f (K) < f (j), and selecting point K as the next node;
(2) calculating D, I f (n), f (d) ═ g (d) + h (d) ═ 4.52km, f (I) ═ g (I) + h (I) ═ 3.35km, f (I) < f (d), and selecting point I as the next node;
(3) calculating C, G f (n), f (c) ═ G (c) + h (c) ═ 4.16km, f (G) ═ G (G) + h (G) ═ 3.85km, f (G) < f (c), and selecting point G as the next node;
(4) calculating B, E f (n), f (B) ═ g (B) + h (B) ═ 4.3km, f (e) ═ g (e) + h (e) ═ 4.39km, f (B) < f (e), and selecting point B as the next node;
(5) and selecting the point A as the next node, namely the target node, and finishing the algorithm.
The path planned by the A-x algorithm is L-K-I-G-B-A, and the distance is 4.3 km.
According to the improved A-x algorithm, the shortest path from the starting node L to the target node A is planned by using the data, and the calculation process is simply introduced as follows:
(1) with L as the starting node, its neighbor K, J being neither the target node nor its next node, a valuation function F (n) is selected2(f (n) of calculation K, J ═ b (n) ((g) (n)) + h (n))2Value, F (K)2＝B(K)*(G(K)+H(K))＝2.154km，F(J)2＝B(J)*(G(J)+H(J))＝2.387km，F(K)<F, (J), selecting the K point as the next node;
(2) k's neighbor node D, I is neither the target node nor its next node, selects a valuation function F (n)2(f (n) of calculation D, I ═ b (n) ((g) (n)) + h (n))2Value, F (D)2＝B(D)*(G(D)+H(D))＝3.687km，F(I)2＝B(I)*(G(I)+H(I))＝1.952km，F(I)<F, (D), selecting the point I as the next node;
(3) i's neighbor node C, G neitherIs a target node and the next node is not the target node, selects a valuation function F (n)2(f (n) of calculation C, G ═ b (n) ((g) (n)) + h (n))2Value, F (C)2＝B(C)*(G(C)+H(C))＝2.732km，F(G)2＝B(G)*(G(G)+H(G))＝3.049km，F(C)<F (G), selecting the point C as the next node;
(4) c, selecting a next node as a next node by using an evaluation function f (n) ═ g (n) + h (n);
(5) the adjacent node A of B is the target node, and the algorithm is finished.
The improved A-x algorithm plans a path of L-K-I-C-B-A and a distance of 4.16 km.
And (3) analyzing an experimental result:
from the above calculation, the path planned by the original a-x algorithm is L-K-I-G-B-a, and the distance is 4.3 km; the improved A-x algorithm plans a path of L-K-I-C-B-A and a distance of 4.16 km. The path length is reduced by 0.14km, which shows that the improved algorithm has certain advantages and can plan a shorter travel path for the driver. The trajectory is shown in fig. 7.
In a second embodiment, the present embodiment provides an optimal driving path planning system based on an improved a-x algorithm;
an optimal driving path planning system based on an improved A-algorithm comprises:
an acquisition module configured to: acquiring a starting node and a target node of a path to be planned;
a path planning module configured to: based on the initial node and the target node of the path to be planned, obtaining an optimal driving path by using an improved A-x algorithm; the improved A-algorithm is used for improving the valuation function of the A-algorithm; the improved valuation function is: the product of the valuation function and the direction parameter of the conventional a-algorithm.
In a third embodiment, the present embodiment further provides an electronic device, which includes a memory, a processor, and computer instructions stored in the memory and executed on the processor, where the computer instructions, when executed by the processor, implement the method in the first embodiment.
In a fourth embodiment, the present embodiment further provides a computer-readable storage medium for storing computer instructions, and the computer instructions, when executed by a processor, implement the method of the first embodiment.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Claims (10)
1. An optimal driving path planning method based on an improved A-algorithm is characterized by comprising the following steps:
acquiring a starting node and a target node of a path to be planned;
based on the initial node and the target node of the path to be planned, obtaining an optimal driving path by using an improved A-x algorithm; the improved A-algorithm is used for improving the valuation function of the A-algorithm; the improved valuation function is: the product of the valuation function and the direction parameter of the conventional a-algorithm.
2. The method as claimed in claim 1, wherein the optimal driving path is obtained by using a modified a-algorithm based on the initial node and the target node of the path to be planned; the method comprises the following specific steps:
s200: constructing an OPEN list and a CLOSE list;
s201: putting the starting node S into an OPEN list and enabling the CLOSE list to be empty;
s202: remove the start node S from the OPEN list, place the start node S in the CLOSE list and prefix the number N, place all the neighbor nodes O of SiPutting the obtained product into an OPEN list, wherein the value range of i is 1 to n; n represents the total number of all adjacent nodes of S;
s203: if the OPEN list is empty, the algorithm is ended, and if not, all the adjacent nodes O are judgediWhether a target node exists in the node; the value range of i is 1 to n;
s204: if it isAll adjacent nodes OiThere is a target node, an adjacent node O to be the target nodeiPut into the CLOSE list and the algorithm ends.
3. The method as claimed in claim 2, wherein the step S204 is followed by further comprising:
if all the adjacent nodes OiAll nodes are not target nodes, and each adjacent node O is judgediWhether or not there is a next hop neighbor node mij(ii) a j ranges from 1 to p; p denotes the current connecting node OiThe total number of next hop neighbor nodes;
s205: if there is no next-hop neighbor node mijThe algorithm ends if there is a neighboring node mijThen, the following process is performed:
s2051: if there are several next-hop neighbor nodes mijIf all the nodes are target nodes, calculating the adjacent node mijCorresponding each node OiAnd selecting the node O having the smallest value of the first evaluation functioniAnd marked as S; returning to S202;
s2052: if next hop is adjacent to node mijIf not, calculating the adjacent node mijCorresponding each node OiAnd selecting the node O having the smallest second evaluation function valueiAnd marked as S; returning to S202.
4. The method of claim 3, wherein the first valuation function is:
F(n)1＝G(n)+H(n)；
wherein H (n) is the linear distance from the current node to the target node, F (n)1Representing a first valuation function, g (n) being the actual distance between the current node and the starting node.
5. The method of claim 3, wherein the second valuation function is:
F(n)2＝B(n)*(G(n)+H(n))；
wherein H (n) is the linear distance from the current node to the target node, B (n) is a direction parameter, and theta is the included angle of the adjacent paths and has 0 DEG<Theta is less than or equal to 90 degrees; the included angle of the adjacent paths refers to an included angle between a connecting line from the current node to the next hop node and a connecting line from the current node to the previous hop node; g (n) is the actual distance between the current node and the starting node, F (n)2Representing a second valuation function.
6. A method as claimed in claim 3, wherein the first valuation function operates on the principle of: and the included angle between the next hop section selected by each node and the connecting line between the current node and the terminal is less than 90 degrees.
7. A method as claimed in claim 3, wherein the second valuation function operates on the principle that the criterion for selecting the next hop node is: the included angle between the connecting line between the current node and the next hop node and the connecting line between the current node and the previous hop node is larger than 90 degrees.
8. An optimal driving path planning system based on an improved A-algorithm is characterized by comprising the following components:
an acquisition module configured to: acquiring a starting node and a target node of a path to be planned;
a path planning module configured to: based on the initial node and the target node of the path to be planned, obtaining an optimal driving path by using an improved A-x algorithm; the improved A-algorithm is used for improving the valuation function of the A-algorithm; the improved valuation function is: the product of the valuation function and the direction parameter of the conventional a-algorithm.
9. An electronic device comprising a memory and a processor and computer instructions stored on the memory and executable on the processor, the computer instructions when executed by the processor performing the method of any of claims 1-7.
10. A computer-readable storage medium storing computer instructions which, when executed by a processor, perform the method of any one of claims 1 to 7.
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