CN110887491A - Aircraft route planning method - Google Patents
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Abstract
The application belongs to the technical field of aircraft route planning, and particularly relates to an aircraft route planning method, which comprises the following steps: firstly, discretizing a situation space to obtain a plurality of discrete points; generating a heuristic factor matrix of each discrete point; taking the discrete point at the planning starting point as a current planning point; selecting discrete points meeting safe flight conditions of the airplane in the range of the current planning points to form a set of next planning points; step five, judging whether the set is empty; step six, if the set is not empty, selecting a next planning point from the set to update the current planning point based on the heuristic factor matrix and the pheromone matrix thereof; judging whether the current planning point is a discrete point at the planning end point or not, if not, returning to the fourth step; if yes, searching to obtain and store the planned route; and seventhly, if the set is empty, searching to obtain and store the planned route.
Description
Technical Field
The application belongs to the technical field of aircraft route planning, and particularly relates to an aircraft route planning method.
Background
The aircraft executes the flight task, and in order to avoid the threat of the threat source, the flight route of the aircraft needs to be planned in advance, so that various scattered threat sources can be effectively avoided.
At present, the flight route of the airplane is planned based on an A-star algorithm, and the technical scheme has the following defects:
1) the solution space is huge in a three-dimensional situation environment by adopting a full-connection mode, the number of pre-generated auxiliary route points is large, and the timeliness requirement of the aircraft route planning is difficult to meet;
2) the air route planned based on the method can only avoid static threat sources, and cannot effectively avoid dynamic threat sources.
The present application is made in view of the above-mentioned drawbacks of the prior art.
Disclosure of Invention
It is an object of the present application to provide a method of aircraft route planning that overcomes or mitigates at least one of the disadvantages of the prior art.
The technical scheme of the application is as follows:
an aircraft routing method comprising:
firstly, discretizing a situation space to obtain a plurality of discrete points;
generating a heuristic factor matrix of each discrete point;
taking the discrete point at the planning starting point as a current planning point;
selecting discrete points meeting safe flight conditions of the airplane in the range of the current planning points to form a set of next planning points;
step five, judging whether the set is empty;
step six, if the set is not empty, selecting a next planning point from the set to update the current planning point based on the heuristic factor matrix and the pheromone matrix thereof; judging whether the current planning point is a discrete point at the planning end point or not, if not, returning to the fourth step; if yes, searching to obtain and store the planned route;
and seventhly, if the set is empty, searching to obtain and store the planned route.
According to at least one embodiment of the present application, in the step one, the distance between each adjacent discrete point in the obtained discrete points is not less than the minimum airplane distance of the airplane.
According to at least one embodiment of the application, in the second step, in the generated heuristic factor matrix, the heuristic factor of the discrete point at the planning end point is 1, the heuristic factor of the discrete point within the range of the threat source is 0, and the heuristic factors of the remaining discrete points are reciprocal of the distance between the discrete points and the planning end point.
According to at least one embodiment of the present application, in step four, each next planning point in the set is located within one or more set step ranges of the current planning point, wherein the set step is not less than the minimum aircraft distance of the aircraft.
In accordance with at least one embodiment of the present application, in step four, each next planning point in the set satisfies the following condition:
the connection to the current planning point does not cross the threat source area;
the connecting line with the current planning point meets the requirement of the maximum climbing rate of the airplane;
the connecting line with the current planning point meets the requirement of the maximum glide rate of the airplane;
and the turning angle formed by the current planning point and the last planning point of the current planning point is not less than the minimum turning angle of the airplane.
According to at least one embodiment of the present application, in step six, a next planning point is selected from the set based on the heuristic factor matrix and the pheromone matrix thereof and updated to be the current planning point, specifically:
determining the probability of each next planning point in the set being selected based on the heuristic factor matrix and the pheromones thereof;
and based on the probability of each next planning point in the set being selected, randomly selecting one next planning point in the set by using a random algorithm to update the next planning point into the current planning point.
According to at least one embodiment of the present application, in step six, the probability of each next planning point in the set being selected is determined based on the heuristic factor matrix and its pheromones, specifically:
p＝tauAlpha+EtaBeta(ii) a Wherein,
p comprises the probability of each next planning point being selected;
tau is a pheromone matrix;
alpha is the pheromone importance level;
eta is a heuristic factor matrix;
the heuristic factor importance degree Beta.
In accordance with at least one embodiment of the present application, in step six, the random algorithm comprises a roulette algorithm.
At least one embodiment according to the present application further comprises:
step eight, judging whether the maximum iteration times is reached, if not, updating the pheromone matrix, and returning to the step three; and if so, taking the lowest cost in the stored planned routes as the final planned route.
According to at least one embodiment of the present application, in step eight, the updating the pheromone matrix specifically includes:
tau is the updated pheromone matrix;
rho is the pheromone volatilization coefficient;
tau0is the pheromone matrix before updating;
q is the pheromone addition factor;
lk is the cost of planning the route.
Drawings
FIG. 1 is a flow chart of a method for planning an aircraft route according to an embodiment of the present disclosure;
fig. 2 is a schematic diagram of distribution of a planning start point, a planning end point, and a discrete point in a situation space provided in the embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant application and are not limiting of the application. It should be noted that, for convenience of description, only the portions related to the present application are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
It should be noted that in the description of the present application, the terms of direction or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, which are merely for convenience of description, and do not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present application. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
Furthermore, it should be noted that, in the description of the present application, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as being fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present application can be understood by those skilled in the art as the case may be.
The present application is described in further detail below with reference to fig. 1-2.
An aircraft routing method comprising:
firstly, discretizing a situation space to obtain a plurality of discrete points;
generating a heuristic factor matrix of each discrete point;
taking the discrete point at the planning starting point as a current planning point;
selecting discrete points meeting safe flight conditions of the airplane in the range of the current planning points to form a set of next planning points;
step five, judging whether the set is empty;
step six, if the set is not empty, selecting a next planning point from the set to update the current planning point based on the heuristic factor matrix and the pheromone matrix thereof; judging whether the current planning point is a discrete point at the planning end point or not, if not, returning to the fourth step; if yes, searching to obtain and store the planned route;
and seventhly, if the set is empty, searching to obtain and store the planned route.
For the aircraft route planning method disclosed in the above embodiment, those skilled in the art can understand that the aircraft route planning is realized based on the TSP, the solution of the planned route is completed by adopting a heuristic random algorithm, any auxiliary route point does not need to be generated in advance, the convergence rate is high, the timeliness requirement can be met, in addition, parameters required to be set by the heuristic algorithm can be determined in a self-adaptive manner, manual intervention is reduced, and the inheritance and multiplexing requirements are met.
In some optional embodiments, in the step one, the distance between each adjacent discrete point in the obtained discrete points is not less than the minimum airplane distance of the airplane.
In some optional embodiments, in the generated heuristic factor matrix, the heuristic factor of the discrete point at the planning end point is 1, the heuristic factor of the discrete point within the range of the threat source is 0, and the heuristic factors of the remaining discrete points are the reciprocal of the distance between the discrete points and the planning end point.
For the aircraft route planning method disclosed in the above embodiment, those skilled in the art can understand that in the generated heuristic factor matrix, the heuristic factor of the discrete points in the range of the threat source is 0, that is, the discrete points in the range of the threat source are filtered to a certain extent, and do not participate in the solution of the route planning, so that the planned route can effectively avoid the threat source.
In some optional embodiments, in the second step, the threat sources include a static threat source and a dynamic threat source, so that the obtained planned route can effectively avoid the static threat source and the dynamic threat source at the same time.
In some optional embodiments, in step four, each next planning point in the set is located within one or more set step sizes of the current planning point, where the set step size is not less than the minimum aircraft distance of the aircraft.
In some optional embodiments, in step four, each next planning point in the set satisfies the following condition:
the connecting line with the current planning point does not pass through the threat source area so as to avoid the obtained planning route from passing through the threat source;
the connecting line of the current planning point and the current planning point meets the requirement of the maximum climbing rate of the airplane, the connecting line of the current planning point and the current planning point meets the requirement of the maximum gliding rate of the airplane, and the turning angle formed by the current planning point and the last planning point of the current planning point is not less than the minimum turning angle of the airplane, so that the obtained planned route can meet the constraint of the self performance of the airplane flight.
In some optional embodiments, in step six, a next planning point is selected from the self-set based on the heuristic factor matrix and the pheromone matrix thereof and updated to be the current planning point, specifically:
determining the probability of each next planning point in the set being selected based on the heuristic factor matrix and the pheromones thereof;
and based on the probability of each next planning point in the set being selected, randomly selecting one next planning point in the set by using a random algorithm to update the next planning point into the current planning point.
In some optional embodiments, in step six, the probability of selecting each next planning point in the set is determined based on the heuristic factor matrix and the pheromone thereof, specifically:
p＝tauAlpha+EtaBeta(ii) a Wherein,
p comprises the probability of each next planning point being selected;
tau is a pheromone matrix;
alpha is the pheromone importance level;
eta is a heuristic factor matrix;
the heuristic factor importance degree Beta.
In some alternative embodiments, in step six, the random algorithm comprises a roulette algorithm.
In some optional embodiments, further comprising:
step eight, judging whether the maximum iteration times is reached, if not, updating the pheromone matrix, and returning to the step three; and if so, taking the lowest cost in the stored planned routes as the final planned route.
For the aircraft route planning method disclosed in the above embodiment, those skilled in the art can understand that the planned route is the final planned route by controlling the maximum iteration number and selecting the minimum cost in the planned routes, so as to realize optimization of the planned route.
In some optional embodiments, in step eight, when calculating the planned route cost, the minimum flight distance, the maximum climb rate, the maximum glide rate, and the minimum turning angle of the aircraft are fully considered.
In some optional embodiments, the planned route cost is represented by defining route penalty, including security penalty and performance constraint penalty.
In some optional embodiments, in step eight, the updating the pheromone matrix specifically includes:
tau is the updated pheromone matrix;
rho is the pheromone volatilization coefficient;
tau0is the pheromone matrix before updating;
q is the pheromone addition factor;
lk is the cost of planning the route.
For the aircraft route planning method disclosed in the above embodiment, those skilled in the art can understand that the pheromone updating principle in the basic ant colony algorithm is changed, and the solution accuracy of the planned route can be improved.
In some optional embodiments, further comprising:
and step nine, carrying out route smoothing treatment on the finally planned route.
In some optional embodiments, in step nine, the route smoothing process includes:
smoothing from a planning starting point to a planning end point;
and smoothing from the planning end point to the planning start point.
So far, the technical solutions of the present application have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present application is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the present application, and the technical scheme after the changes or substitutions will fall into the protection scope of the present application.
Claims (10)
1. An aircraft route planning method, comprising:
firstly, discretizing a situation space to obtain a plurality of discrete points;
generating a heuristic factor matrix of each discrete point;
taking the discrete point at the planning starting point as a current planning point;
selecting discrete points meeting safe flight conditions of the airplane in the range of the current planning points to form a set of next planning points;
step five, judging whether the set is empty;
step six, if the set is not empty, selecting a next planning point from the set to update the current planning point based on the heuristic factor matrix and the pheromone matrix thereof; judging whether the current planning point is a discrete point at the planning end point or not, if not, returning to the fourth step; if yes, searching to obtain and store the planned route;
and seventhly, if the set is empty, searching to obtain and store the planned route.
2. An aircraft routing method according to claim 1,
in the first step, the distance between each adjacent discrete point in the obtained discrete points is not less than the minimum airplane distance of the airplane.
3. An aircraft routing method according to claim 1,
in the second step, in the generated heuristic factor matrix, the heuristic factor of the discrete point at the planning end point is 1, the heuristic factor of the discrete point in the threat source range is 0, and the heuristic factors of the other discrete points are reciprocal of the distance between the heuristic factor and the planning end point.
4. An aircraft routing method according to claim 1,
in the fourth step, each next planning point in the set is located in one or more set step ranges of the current planning point, wherein the set step is not less than the minimum airplane distance of the airplane.
5. An aircraft routing method according to claim 1,
in the fourth step, each next planning point in the set meets the following conditions:
the connection to the current planning point does not cross the threat source area;
the connecting line with the current planning point meets the requirement of the maximum climbing rate of the airplane;
the connecting line with the current planning point meets the requirement of the maximum glide rate of the airplane;
and the turning angle formed by the current planning point and the last planning point of the current planning point is not less than the minimum turning angle of the airplane.
6. An aircraft routing method according to claim 1,
in the sixth step, a next planning point is selected from the self-set based on the heuristic factor matrix and the pheromone matrix thereof and updated to be the current planning point, which specifically comprises the following steps:
determining the probability of each next planning point in the set being selected based on the heuristic factor matrix and the pheromones thereof;
and based on the probability of each next planning point in the set being selected, randomly selecting one next planning point in the set by using a random algorithm to update the next planning point into the current planning point.
7. An aircraft routing method according to claim 6,
in the sixth step, the probability of selecting each next planning point in the set is determined based on the heuristic factor matrix and the pheromone thereof, which specifically comprises the following steps:
p＝tauAlpha+EtaBeta(ii) a Wherein,
p comprises the probability of each next planning point being selected;
tau is a pheromone matrix;
alpha is the pheromone importance level;
eta is a heuristic factor matrix;
the heuristic factor importance degree Beta.
8. An aircraft routing method according to claim 6,
in the sixth step, the random algorithm comprises a roulette algorithm.
9. An aircraft routing method according to claim 1,
further comprising:
step eight, judging whether the maximum iteration times is reached, if not, updating the pheromone matrix, and returning to the step three; and if so, taking the lowest cost in the stored planned routes as the final planned route.
10. An aircraft routing method according to claim 9,
in the step eight, updating the pheromone matrix specifically comprises:
tau is the updated pheromone matrix;
rho is the pheromone volatilization coefficient;
tau0is the pheromone matrix before updating;
q is the pheromone addition factor;
lk is the cost of planning the route.
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| CN116430906A (en) * | 2023-06-13 | 2023-07-14 | 西安羚控电子科技有限公司 | Unmanned aerial vehicle dynamic obstacle avoidance method, system, equipment and medium based on bump translation |
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